From 50cd6612337641d645abcfc8741eb19b7edc4e29 Mon Sep 17 00:00:00 2001 From: Diogo Goncalves Date: Fri, 27 Sep 2024 18:58:45 +0200 Subject: [PATCH 01/58] feat: intial commit --- libs/core/README.md | 70 + libs/core/llmstudio_core/__init__.py | 55 + libs/core/llmstudio_core/exceptions.py | 7 + .../core/llmstudio_core/providers/__init__.py | 71 + .../llmstudio_core}/providers/anthropic.py | 13 +- .../core/llmstudio_core}/providers/azure.py | 8 +- .../core/llmstudio_core}/providers/ollama.py | 8 +- .../core/llmstudio_core}/providers/openai.py | 6 +- .../llmstudio_core}/providers/provider.py | 60 +- .../llmstudio_core}/providers/vertexai.py | 15 +- libs/core/poetry.lock | 1837 +++++++++++++++++ libs/core/pyproject.toml | 23 + libs/llmstudio/llmstudio/__init__.py | 0 libs/llmstudio/pyproject.toml | 14 + libs/proxy/pyproject.toml | 14 + libs/tracker/pyproject.toml | 16 + llmstudio/engine/providers/__init__.py | 6 - 17 files changed, 2150 insertions(+), 73 deletions(-) create mode 100644 libs/core/README.md create mode 100644 libs/core/llmstudio_core/__init__.py create mode 100644 libs/core/llmstudio_core/exceptions.py create mode 100644 libs/core/llmstudio_core/providers/__init__.py rename {llmstudio/engine => libs/core/llmstudio_core}/providers/anthropic.py (91%) rename {llmstudio/engine => libs/core/llmstudio_core}/providers/azure.py (99%) rename {llmstudio/engine => libs/core/llmstudio_core}/providers/ollama.py (93%) rename {llmstudio/engine => libs/core/llmstudio_core}/providers/openai.py (92%) rename {llmstudio/engine => libs/core/llmstudio_core}/providers/provider.py (89%) rename {llmstudio/engine => libs/core/llmstudio_core}/providers/vertexai.py (97%) create mode 100644 libs/core/poetry.lock create mode 100644 libs/core/pyproject.toml create mode 100644 libs/llmstudio/llmstudio/__init__.py create mode 100644 libs/llmstudio/pyproject.toml create mode 100644 libs/proxy/pyproject.toml create mode 100644 libs/tracker/pyproject.toml delete mode 100644 llmstudio/engine/providers/__init__.py diff --git a/libs/core/README.md b/libs/core/README.md new file mode 100644 index 00000000..11fc5a66 --- /dev/null +++ b/libs/core/README.md @@ -0,0 +1,70 @@ +# LLMstudio by [TensorOps](http://tensorops.ai "TensorOps") + +Prompt Engineering at your fingertips + +![LLMstudio logo](https://imgur.com/Xqsj6V2.gif) + +## 🌟 Features + +![LLMstudio UI](https://imgur.com/wrwiIUs.png) + +- **LLM Proxy Access**: Seamless access to all the latest LLMs by OpenAI, Anthropic, Google. +- **Custom and Local LLM Support**: Use custom or local open-source LLMs through Ollama. +- **Prompt Playground UI**: A user-friendly interface for engineering and fine-tuning your prompts. +- **Python SDK**: Easily integrate LLMstudio into your existing workflows. +- **Monitoring and Logging**: Keep track of your usage and performance for all requests. +- **LangChain Integration**: LLMstudio integrates with your already existing LangChain projects. +- **Batch Calling**: Send multiple requests at once for improved efficiency. +- **Smart Routing and Fallback**: Ensure 24/7 availability by routing your requests to trusted LLMs. +- **Type Casting (soon)**: Convert data types as needed for your specific use case. + +## 🚀 Quickstart + +Don't forget to check out [https://docs.llmstudio.ai](docs) page. + +## Installation + +Install the latest version of **LLMstudio** using `pip`. We suggest that you create and activate a new environment using `conda` + +```bash +pip install llmstudio +``` + +Install `bun` if you want to use the UI + +```bash +curl -fsSL https://bun.sh/install | bash +``` + +Create a `.env` file at the same path you'll run **LLMstudio** + +```bash +OPENAI_API_KEY="sk-api_key" +ANTHROPIC_API_KEY="sk-api_key" +``` + +Now you should be able to run **LLMstudio** using the following command. + +```bash +llmstudio server --ui +``` + +When the `--ui` flag is set, you'll be able to access the UI at [http://localhost:3000](http://localhost:3000) + +## 📖 Documentation + +- [Visit our docs to learn how the SDK works](https://docs.LLMstudio.ai) (coming soon) +- Checkout our [notebook examples](https://github.com/TensorOpsAI/LLMstudio/tree/main/examples) to follow along with interactive tutorials + +## 👨‍💻 Contributing + +- Head on to our [Contribution Guide](https://github.com/TensorOpsAI/LLMstudio/tree/main/CONTRIBUTING.md) to see how you can help LLMstudio. +- Join our [Discord](https://discord.gg/GkAfPZR9wy) to talk with other LLMstudio enthusiasts. + +## Training + +[![Banner](https://imgur.com/XTRFZ4m.png)](https://www.tensorops.ai/llm-studio-workshop) + +--- + +Thank you for choosing LLMstudio. Your journey to perfecting AI interactions starts here. diff --git a/libs/core/llmstudio_core/__init__.py b/libs/core/llmstudio_core/__init__.py new file mode 100644 index 00000000..2abd825f --- /dev/null +++ b/libs/core/llmstudio_core/__init__.py @@ -0,0 +1,55 @@ +from llmstudio_core.providers import (AnthropicProvider +, AzureProvider +, OllamaProvider +, OpenAIProvider +, provider_registry +, VertexAIProvider) + +from llmstudio_core.providers.provider import Provider + +from llmstudio_core.providers import _load_engine_config + +_engine_config = _load_engine_config() + +_providers_map = dict( + anthropic=AnthropicProvider, + azure=AzureProvider, + ollama=OllamaProvider, + openai=OpenAIProvider, + vertexai=VertexAIProvider +) + + +def LLM(provider:str) -> Provider: + provider_config = _engine_config.providers.get(provider) + provider_class = _providers_map.get(provider_config.id) + if provider_class: + return provider_class(config=provider_config) + raise NotImplementedError(f"Provider not found: {provider_config.id}. Available providers: {str(_providers_map.keys())}") + +if __name__ == "__main__": + + import os + import json + from dotenv import load_dotenv + load_dotenv() + + chat_request = dict( + chat_input="Hello?" + + ,api_key=os.environ["OPENAI_API_KEY"] + ,model="gpt-3.5-turbo" + ,parameters={"temperature": 0, "max_tokens":100} + ,is_stream = False + ,has_end_token = False + ,functions = None + ,session_id = "" + ,retries=0) + + provider = "openai" + + llm = LLM(provider="openai", api_key=os.environ["OPENAI_API_KEY"]) + + import asyncio + a = asyncio.run(llm.chat(chat_request)) + print(a.json) diff --git a/libs/core/llmstudio_core/exceptions.py b/libs/core/llmstudio_core/exceptions.py new file mode 100644 index 00000000..81bc42c6 --- /dev/null +++ b/libs/core/llmstudio_core/exceptions.py @@ -0,0 +1,7 @@ +class CustomError(Exception): + """Base class for all custom exceptions""" + pass + +class ProviderError(CustomError): + """Raised when there is a validation error""" + pass diff --git a/libs/core/llmstudio_core/providers/__init__.py b/libs/core/llmstudio_core/providers/__init__.py new file mode 100644 index 00000000..978c0fd4 --- /dev/null +++ b/libs/core/llmstudio_core/providers/__init__.py @@ -0,0 +1,71 @@ +import os +import yaml +from llmstudio_core.providers.anthropic import AnthropicProvider +from llmstudio_core.providers.azure import AzureProvider +from llmstudio_core.providers.ollama import OllamaProvider +from llmstudio_core.providers.openai import OpenAIProvider +from llmstudio_core.providers.provider import provider_registry +from llmstudio_core.providers.vertexai import VertexAIProvider + +from pathlib import Path +from typing import Any, Dict, List, Optional, Union + +from pydantic import BaseModel, ValidationError + +class CostRange(BaseModel): + range: List[Optional[int]] + cost: float + + +class ModelConfig(BaseModel): + mode: str + max_tokens: int + input_token_cost: Union[float, List[CostRange]] + output_token_cost: Union[float, List[CostRange]] + + +class ProviderConfig(BaseModel): + id: str + name: str + chat: bool + embed: bool + keys: Optional[List[str]] = None + models: Optional[Dict[str, ModelConfig]] = None + parameters: Optional[Dict[str, Any]] = None + + +class EngineConfig(BaseModel): + providers: Dict[str, ProviderConfig] + + +def _load_engine_config() -> EngineConfig: + #TODO read from github + default_config_path = Path(os.path.join(os.path.dirname(__file__), "config.yaml")) + local_config_path = Path(os.getcwd(), "config.yaml") + + def _merge_configs(config1, config2): + for key in config2: + if key in config1: + if isinstance(config1[key], dict) and isinstance(config2[key], dict): + _merge_configs(config1[key], config2[key]) + elif isinstance(config1[key], list) and isinstance(config2[key], list): + config1[key].extend(config2[key]) + else: + config1[key] = config2[key] + else: + config1[key] = config2[key] + return config1 + + try: + default_config_data = yaml.safe_load(default_config_path.read_text()) + local_config_data = ( + yaml.safe_load(local_config_path.read_text()) + if local_config_path.exists() + else {} + ) + config_data = _merge_configs(default_config_data, local_config_data) + return EngineConfig(**config_data) + except yaml.YAMLError as e: + raise RuntimeError(f"Error parsing YAML configuration: {e}") + except ValidationError as e: + raise RuntimeError(f"Error in configuration data: {e}") diff --git a/llmstudio/engine/providers/anthropic.py b/libs/core/llmstudio_core/providers/anthropic.py similarity index 91% rename from llmstudio/engine/providers/anthropic.py rename to libs/core/llmstudio_core/providers/anthropic.py index f9036d98..875c4c2a 100644 --- a/llmstudio/engine/providers/anthropic.py +++ b/libs/core/llmstudio_core/providers/anthropic.py @@ -6,12 +6,12 @@ from typing import Any, AsyncGenerator, Coroutine, Generator, Optional import requests -from fastapi import HTTPException +from fastapi import ProviderError from openai.types.chat import ChatCompletionChunk from openai.types.chat.chat_completion_chunk import Choice, ChoiceDelta from pydantic import BaseModel, Field -from llmstudio.engine.providers.provider import ChatRequest, Provider, provider +from llmstudio_core.providers.provider import ChatRequest, Provider, provider class ClaudeParameters(BaseModel): @@ -28,9 +28,9 @@ class AnthropicRequest(ChatRequest): @provider class AnthropicProvider(Provider): - def __init__(self, config): + def __init__(self, config, api_key=None): super().__init__(config) - self.API_KEY = os.getenv("ANTHROPIC_API_KEY") + self.API_KEY = api_key or os.getenv("ANTHROPIC_API_KEY") def validate_request(self, request: AnthropicRequest): return AnthropicRequest(**request) @@ -40,11 +40,10 @@ async def generate_client( ) -> Coroutine[Any, Any, Generator]: """Generate an Anthropic client""" try: - api_key = request.api_key or self.API_KEY url = f"https://api.anthropic.com/v1/messages" headers = { "content-type": "application/json", - "x-api-key": api_key, + "x-api-key": self.API_KEY, "anthropic-version": "2023-06-01", } data = { @@ -67,7 +66,7 @@ async def generate_client( stream=True, ) except Exception as e: - raise HTTPException(status_code=500, detail=str(e)) + raise ProviderError(status_code=500, detail=str(e)) async def parse_response( self, response: AsyncGenerator, **kwargs diff --git a/llmstudio/engine/providers/azure.py b/libs/core/llmstudio_core/providers/azure.py similarity index 99% rename from llmstudio/engine/providers/azure.py rename to libs/core/llmstudio_core/providers/azure.py index 79afa2fe..7b45fbb6 100644 --- a/llmstudio/engine/providers/azure.py +++ b/libs/core/llmstudio_core/providers/azure.py @@ -16,7 +16,7 @@ ) import openai -from fastapi import HTTPException +from fastapi import ProviderError from openai import AzureOpenAI, OpenAI from openai.types.chat import ChatCompletionChunk from openai.types.chat.chat_completion_chunk import ( @@ -28,7 +28,7 @@ ) from pydantic import BaseModel, Field -from llmstudio.engine.providers.provider import ChatRequest, Provider, provider +from llmstudio_core.providers.provider import ChatRequest, Provider, provider class AzureParameters(BaseModel): @@ -127,12 +127,12 @@ async def generate_client( ) except openai._exceptions.APIConnectionError as e: - raise HTTPException( + raise ProviderError( status_code=404, detail=f"There was an error reaching the endpoint: {e}" ) except openai._exceptions.APIStatusError as e: - raise HTTPException(status_code=e.status_code, detail=e.response.json()) + raise ProviderError(status_code=e.status_code, detail=e.response.json()) def prepare_messages(self, request: AzureRequest): if self.is_llama: diff --git a/llmstudio/engine/providers/ollama.py b/libs/core/llmstudio_core/providers/ollama.py similarity index 93% rename from llmstudio/engine/providers/ollama.py rename to libs/core/llmstudio_core/providers/ollama.py index 192bd849..452f48e0 100644 --- a/llmstudio/engine/providers/ollama.py +++ b/libs/core/llmstudio_core/providers/ollama.py @@ -5,12 +5,12 @@ from typing import Any, AsyncGenerator, Coroutine, Generator, Optional import requests -from fastapi import HTTPException +from fastapi import ProviderError from openai.types.chat import ChatCompletionChunk from openai.types.chat.chat_completion_chunk import Choice, ChoiceDelta from pydantic import BaseModel, Field -from llmstudio.engine.providers.provider import ChatRequest, Provider, provider +from llmstudio_core.providers.provider import ChatRequest, Provider, provider class OllamaParameters(BaseModel): @@ -49,7 +49,7 @@ async def generate_client( stream=True, ) except requests.RequestException: - raise HTTPException( + raise ProviderError( status_code=500, detail="Ollama is not running", ) @@ -62,7 +62,7 @@ async def parse_response( continue chunk = json.loads(line.decode("utf-8")) if "error" in chunk: - raise HTTPException(status_code=500, detail=chunk["error"]) + raise ProviderError(status_code=500, detail=chunk["error"]) if chunk.get("done"): yield ChatCompletionChunk( id=str(uuid.uuid4()), diff --git a/llmstudio/engine/providers/openai.py b/libs/core/llmstudio_core/providers/openai.py similarity index 92% rename from llmstudio/engine/providers/openai.py rename to libs/core/llmstudio_core/providers/openai.py index 34461007..6de70e83 100644 --- a/llmstudio/engine/providers/openai.py +++ b/libs/core/llmstudio_core/providers/openai.py @@ -3,11 +3,11 @@ from typing import Any, AsyncGenerator, Coroutine, Dict, Generator, List, Optional import openai -from fastapi import HTTPException +from fastapi import ProviderError from openai import OpenAI from pydantic import BaseModel, Field -from llmstudio.engine.providers.provider import ChatRequest, Provider, provider +from llmstudio_core.providers.provider import ChatRequest, Provider, provider class OpenAIParameters(BaseModel): @@ -58,7 +58,7 @@ async def generate_client( **request.parameters.model_dump(), ) except openai._exceptions.APIError as e: - raise HTTPException(status_code=e.status_code, detail=e.response.json()) + raise ProviderError(status_code=e.status_code, detail=e.response.json()) async def parse_response( self, response: AsyncGenerator, **kwargs diff --git a/llmstudio/engine/providers/provider.py b/libs/core/llmstudio_core/providers/provider.py similarity index 89% rename from llmstudio/engine/providers/provider.py rename to libs/core/llmstudio_core/providers/provider.py index fccd8f34..9fbfcdaf 100644 --- a/llmstudio/engine/providers/provider.py +++ b/libs/core/llmstudio_core/providers/provider.py @@ -16,7 +16,7 @@ ) import tiktoken -from fastapi import HTTPException +from fastapi import ProviderError from fastapi.responses import JSONResponse, StreamingResponse from openai.types.chat import ( ChatCompletion, @@ -28,7 +28,6 @@ from openai.types.chat.chat_completion_message_tool_call import Function from pydantic import BaseModel, ValidationError -from llmstudio.tracking.tracker import tracker provider_registry = {} @@ -54,10 +53,11 @@ class ChatRequest(BaseModel): class Provider: END_TOKEN = "" - def __init__(self, config): + def __init__(self, config, api_key=None): self.config = config self.tokenizer = self._get_tokenizer() self.count = 0 + self.API_KEY = api_key async def chat( self, request: ChatRequest @@ -66,7 +66,7 @@ async def chat( try: request = self.validate_request(request) except ValidationError as e: - raise HTTPException(status_code=422, detail=e.errors()) + raise ProviderError(status_code=422, detail=e.errors()) self.validate_model(request) @@ -80,26 +80,21 @@ async def chat( return StreamingResponse(response_handler) else: return JSONResponse(content=await response_handler.__anext__()) - except HTTPException as e: + except ProviderError as e: if e.status_code == 429: continue # Retry on rate limit error else: raise e # Raise other HTTP exceptions except Exception as e: - raise HTTPException( - status_code=500, detail=str(e) - ) # Raise other exceptions as HTTP 500 - raise HTTPException(status_code=429, detail="Too many requests") + raise ProviderError(str(e)) # Raise other exceptions as HTTP 500 + raise ProviderError("Too many requests") def validate_request(self, request: ChatRequest): pass - def validate_model(self, request: ChatRequest): - if request.model not in self.config.models: - raise HTTPException( - status_code=400, - detail=f"Model {request.model} is not supported by {self.config.name}", - ) + def validate_model(self, model): + if model not in self.config.models: + raise ProviderError(f"Model {model} is not supported by {self.config.name}") async def generate_client( self, request: ChatRequest @@ -138,7 +133,7 @@ async def handle_response( metrics = self.calculate_metrics( request.chat_input, response, - request.model, + model, start_time, time.time(), first_token_time, @@ -163,29 +158,27 @@ async def handle_response( ), "provider": self.config.id, "model": ( - request.model - if model and model.startswith(request.model) - else (model or request.model) + model + if model and model.startswith(model) + else (model or model) ), "deployment": ( model - if model and model.startswith(request.model) - else (request.model if model != request.model else None) + if model and model.startswith(model) + else (model if model != model else None) ), "timestamp": time.time(), "parameters": request.parameters.model_dump(), "metrics": metrics, } - self.save_log(response) - if not request.is_stream: yield response def join_chunks(self, chunks, request): - from llmstudio.engine.providers.azure import AzureRequest - from llmstudio.engine.providers.openai import OpenAIRequest - from llmstudio.engine.providers.vertexai import VertexAIRequest + from llmstudio_core.providers.azure import AzureRequest + from llmstudio_core.providers.openai import OpenAIRequest + from llmstudio_core.providers.vertexai import VertexAIRequest finish_reason = chunks[-1].get("choices")[0].get("finish_reason") if finish_reason == "tool_calls": @@ -416,18 +409,3 @@ def get_end_token_string(self, metrics: Dict[str, Any]) -> str: def _get_tokenizer(self): return {}.get(self.config.id, tiktoken.get_encoding("cl100k_base")) - - def save_log(self, response: Dict[str, Any]): - local = False # NB: Make this dynamic - if local: - file_name = Path( - os.path.join(os.path.dirname(__file__), "..", "logs.jsonl") - ) - if not os.path.exists(file_name): - with open(file_name, "w") as f: - pass - - with open(file_name, "a") as f: - f.write(json.dumps(response) + "\n") - else: - tracker.log(response) diff --git a/llmstudio/engine/providers/vertexai.py b/libs/core/llmstudio_core/providers/vertexai.py similarity index 97% rename from llmstudio/engine/providers/vertexai.py rename to libs/core/llmstudio_core/providers/vertexai.py index d9ed5037..cd096330 100644 --- a/llmstudio/engine/providers/vertexai.py +++ b/libs/core/llmstudio_core/providers/vertexai.py @@ -15,7 +15,7 @@ ) import requests -from fastapi import HTTPException +from fastapi import ProviderError from openai.types.chat import ChatCompletionChunk from openai.types.chat.chat_completion_chunk import ( Choice, @@ -25,7 +25,7 @@ ) from pydantic import BaseModel, Field, ValidationError -from llmstudio.engine.providers.provider import ChatRequest, Provider, provider +from llmstudio_core.providers.provider import ChatRequest, Provider, provider class VertexParameters(BaseModel): @@ -88,9 +88,9 @@ class VertexAI(BaseModel): @provider class VertexAIProvider(Provider): - def __init__(self, config): - super().__init__(config) - self.GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY") + def __init__(self, config, api_key): + super().__init__(config, api_key=api_key) + self.GOOGLE_API_KEY = api_key or os.getenv("GOOGLE_API_KEY") def validate_request(self, request: VertexAIRequest): return VertexAIRequest(**request) @@ -102,11 +102,10 @@ async def generate_client( try: # Init genai - api_key = request.api_key or self.GOOGLE_API_KEY url = f"https://generativelanguage.googleapis.com/v1beta/models/{request.model}:streamGenerateContent?alt=sse" headers = { "Content-Type": "application/json", - "x-goog-api-key": api_key, + "x-goog-api-key": self.GOOGLE_API_KEY, } # Convert the chat input into VertexAI format @@ -119,7 +118,7 @@ async def generate_client( ) except Exception as e: - raise HTTPException(status_code=500, detail=str(e)) + raise ProviderError(status_code=500, detail=str(e)) async def parse_response( self, response: AsyncGenerator, **kwargs diff --git a/libs/core/poetry.lock b/libs/core/poetry.lock new file mode 100644 index 00000000..e12e04a1 --- /dev/null +++ b/libs/core/poetry.lock @@ -0,0 +1,1837 @@ +# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. + +[[package]] +name = "aiohappyeyeballs" +version = "2.4.2" +description = "Happy Eyeballs for asyncio" +optional = false +python-versions = ">=3.8" +files = [ + {file = "aiohappyeyeballs-2.4.2-py3-none-any.whl", hash = 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+content-hash = "72fc982ca4d61fabbfaa1be2e46fb53d6b4f950d3c4beac675a3049bbc5fca8c" diff --git a/libs/core/pyproject.toml b/libs/core/pyproject.toml new file mode 100644 index 00000000..0d2d6e9f --- /dev/null +++ b/libs/core/pyproject.toml @@ -0,0 +1,23 @@ +[tool.poetry] +name = "llmstudio-core" +version = "0.1.0" +description = "LLMStudio core capabilities for routing llm calls for any vendor." +authors = ["Cláudio Lemos "] +license = "MIT" +readme = "README.md" + +[tool.poetry.dependencies] +python = "^3.9" +pydantic = "^2.5" +requests = "^2.31" +fastapi = "^0.109" +uvicorn = "^0.27" +openai = "^1.26" +tiktoken = "^0.7" +python-dotenv = "^0" +langchain-experimental = "^0.0" + + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/libs/llmstudio/llmstudio/__init__.py b/libs/llmstudio/llmstudio/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/libs/llmstudio/pyproject.toml b/libs/llmstudio/pyproject.toml new file mode 100644 index 00000000..dec117d7 --- /dev/null +++ b/libs/llmstudio/pyproject.toml @@ -0,0 +1,14 @@ +[tool.poetry] +name = "llmstudio" +version = "0.1.0" +description = "" +authors = ["Diogo Goncalves "] +readme = "README.md" + +[tool.poetry.dependencies] +python = "^3.12" + + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/libs/proxy/pyproject.toml b/libs/proxy/pyproject.toml new file mode 100644 index 00000000..f8c01068 --- /dev/null +++ b/libs/proxy/pyproject.toml @@ -0,0 +1,14 @@ +[tool.poetry] +name = "llmstudio-proxy" +version = "0.1.0" +description = "" +authors = ["Diogo Goncalves "] +readme = "README.md" + +[tool.poetry.dependencies] +python = "^3.12" + + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/libs/tracker/pyproject.toml b/libs/tracker/pyproject.toml new file mode 100644 index 00000000..1e1d869b --- /dev/null +++ b/libs/tracker/pyproject.toml @@ -0,0 +1,16 @@ +[tool.poetry] +name = "llmstudio-tracker" +version = "0.1.0" +description = "" +authors = ["Diogo Goncalves "] +readme = "README.md" + +[tool.poetry.dependencies] +python = "^3.12" +pydantic = "^2.5" +sqlalchemy = "^2" + + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/llmstudio/engine/providers/__init__.py b/llmstudio/engine/providers/__init__.py deleted file mode 100644 index 0a7de784..00000000 --- a/llmstudio/engine/providers/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -from llmstudio.engine.providers.anthropic import AnthropicProvider -from llmstudio.engine.providers.azure import AzureProvider -from llmstudio.engine.providers.ollama import OllamaProvider -from llmstudio.engine.providers.openai import OpenAIProvider -from llmstudio.engine.providers.provider import provider_registry -from llmstudio.engine.providers.vertexai import VertexAIProvider From 8cd95902f0777b299cb3ef3019649bfae13b4467 Mon Sep 17 00:00:00 2001 From: Diogo Goncalves Date: Sat, 28 Sep 2024 12:50:38 +0200 Subject: [PATCH 02/58] feat: add api key to provider instantiation --- libs/core/llmstudio_core/__init__.py | 4 ++-- libs/core/llmstudio_core/providers/anthropic.py | 4 ++-- libs/core/llmstudio_core/providers/azure.py | 16 +++++++--------- libs/core/llmstudio_core/providers/ollama.py | 13 +++++-------- libs/core/llmstudio_core/providers/openai.py | 10 +++++----- libs/core/llmstudio_core/providers/provider.py | 11 ++++++----- libs/core/llmstudio_core/providers/vertexai.py | 4 ++-- 7 files changed, 29 insertions(+), 33 deletions(-) diff --git a/libs/core/llmstudio_core/__init__.py b/libs/core/llmstudio_core/__init__.py index 2abd825f..3f814d72 100644 --- a/libs/core/llmstudio_core/__init__.py +++ b/libs/core/llmstudio_core/__init__.py @@ -20,11 +20,11 @@ ) -def LLM(provider:str) -> Provider: +def LLM(provider:str, api_key=None) -> Provider: provider_config = _engine_config.providers.get(provider) provider_class = _providers_map.get(provider_config.id) if provider_class: - return provider_class(config=provider_config) + return provider_class(config=provider_config, api_key=api_key) raise NotImplementedError(f"Provider not found: {provider_config.id}. Available providers: {str(_providers_map.keys())}") if __name__ == "__main__": diff --git a/libs/core/llmstudio_core/providers/anthropic.py b/libs/core/llmstudio_core/providers/anthropic.py index 875c4c2a..ecd937f1 100644 --- a/libs/core/llmstudio_core/providers/anthropic.py +++ b/libs/core/llmstudio_core/providers/anthropic.py @@ -6,7 +6,7 @@ from typing import Any, AsyncGenerator, Coroutine, Generator, Optional import requests -from fastapi import ProviderError +from llmstudio_core.exceptions import ProviderError from openai.types.chat import ChatCompletionChunk from openai.types.chat.chat_completion_chunk import Choice, ChoiceDelta from pydantic import BaseModel, Field @@ -66,7 +66,7 @@ async def generate_client( stream=True, ) except Exception as e: - raise ProviderError(status_code=500, detail=str(e)) + raise ProviderError(str(e)) async def parse_response( self, response: AsyncGenerator, **kwargs diff --git a/libs/core/llmstudio_core/providers/azure.py b/libs/core/llmstudio_core/providers/azure.py index 7b45fbb6..c96ce46c 100644 --- a/libs/core/llmstudio_core/providers/azure.py +++ b/libs/core/llmstudio_core/providers/azure.py @@ -16,7 +16,7 @@ ) import openai -from fastapi import ProviderError +from llmstudio_core.exceptions import ProviderError from openai import AzureOpenAI, OpenAI from openai.types.chat import ChatCompletionChunk from openai.types.chat.chat_completion_chunk import ( @@ -52,9 +52,9 @@ class AzureRequest(ChatRequest): @provider class AzureProvider(Provider): - def __init__(self, config): + def __init__(self, config, api_key=None): super().__init__(config) - self.API_KEY = os.getenv("AZURE_API_KEY") + self.API_KEY = api_key or os.getenv("AZURE_API_KEY") self.API_ENDPOINT = os.getenv("AZURE_API_ENDPOINT") self.API_VERSION = os.getenv("AZURE_API_VERSION") self.BASE_URL = os.getenv("AZURE_BASE_URL") @@ -78,12 +78,12 @@ async def generate_client( try: if request.base_url or self.BASE_URL: client = OpenAI( - api_key=request.api_key or self.API_KEY, + api_key=self.API_KEY, base_url=request.base_url or self.BASE_URL, ) else: client = AzureOpenAI( - api_key=request.api_key or self.API_KEY, + api_key=self.API_KEY, azure_endpoint=request.api_endpoint or self.API_ENDPOINT, api_version=request.api_version or self.API_VERSION, ) @@ -127,12 +127,10 @@ async def generate_client( ) except openai._exceptions.APIConnectionError as e: - raise ProviderError( - status_code=404, detail=f"There was an error reaching the endpoint: {e}" - ) + raise ProviderError(f"There was an error reaching the endpoint: {e}") except openai._exceptions.APIStatusError as e: - raise ProviderError(status_code=e.status_code, detail=e.response.json()) + raise ProviderError(e.response.json()) def prepare_messages(self, request: AzureRequest): if self.is_llama: diff --git a/libs/core/llmstudio_core/providers/ollama.py b/libs/core/llmstudio_core/providers/ollama.py index 452f48e0..89627c8e 100644 --- a/libs/core/llmstudio_core/providers/ollama.py +++ b/libs/core/llmstudio_core/providers/ollama.py @@ -5,7 +5,7 @@ from typing import Any, AsyncGenerator, Coroutine, Generator, Optional import requests -from fastapi import ProviderError +from llmstudio_core.exceptions import ProviderError from openai.types.chat import ChatCompletionChunk from openai.types.chat.chat_completion_chunk import Choice, ChoiceDelta from pydantic import BaseModel, Field @@ -26,7 +26,7 @@ class OllamaRequest(ChatRequest): @provider class OllamaProvider(Provider): - def __init__(self, config): + def __init__(self, config, api_key=None): super().__init__(config) def validate_request(self, request: OllamaRequest): @@ -39,7 +39,7 @@ async def generate_client( try: return await asyncio.to_thread( requests.post, - url="http://localhost:11434/api/generate", + url="http://localhost:11434/api/generate", #TODO make this configurable json={ "model": request.model, "prompt": request.chat_input, @@ -49,10 +49,7 @@ async def generate_client( stream=True, ) except requests.RequestException: - raise ProviderError( - status_code=500, - detail="Ollama is not running", - ) + raise ProviderError("Ollama is not running") async def parse_response( self, response: AsyncGenerator, **kwargs @@ -62,7 +59,7 @@ async def parse_response( continue chunk = json.loads(line.decode("utf-8")) if "error" in chunk: - raise ProviderError(status_code=500, detail=chunk["error"]) + raise ProviderError(chunk["error"]) if chunk.get("done"): yield ChatCompletionChunk( id=str(uuid.uuid4()), diff --git a/libs/core/llmstudio_core/providers/openai.py b/libs/core/llmstudio_core/providers/openai.py index 6de70e83..4dc89dcc 100644 --- a/libs/core/llmstudio_core/providers/openai.py +++ b/libs/core/llmstudio_core/providers/openai.py @@ -3,7 +3,7 @@ from typing import Any, AsyncGenerator, Coroutine, Dict, Generator, List, Optional import openai -from fastapi import ProviderError +from llmstudio_core.exceptions import ProviderError from openai import OpenAI from pydantic import BaseModel, Field @@ -28,9 +28,9 @@ class OpenAIRequest(ChatRequest): @provider class OpenAIProvider(Provider): - def __init__(self, config): + def __init__(self, config, api_key=None): super().__init__(config) - self.API_KEY = os.getenv("OPENAI_API_KEY") + self.API_KEY = api_key or os.getenv("OPENAI_API_KEY") def validate_request(self, request: OpenAIRequest): return OpenAIRequest(**request) @@ -41,7 +41,7 @@ async def generate_client( """Generate an OpenAI client""" try: - client = OpenAI(api_key=request.api_key or self.API_KEY) + client = OpenAI(api_key=self.API_KEY) return await asyncio.to_thread( client.chat.completions.create, model=request.model, @@ -58,7 +58,7 @@ async def generate_client( **request.parameters.model_dump(), ) except openai._exceptions.APIError as e: - raise ProviderError(status_code=e.status_code, detail=e.response.json()) + raise ProviderError(str(e)) async def parse_response( self, response: AsyncGenerator, **kwargs diff --git a/libs/core/llmstudio_core/providers/provider.py b/libs/core/llmstudio_core/providers/provider.py index 9fbfcdaf..c52da74b 100644 --- a/libs/core/llmstudio_core/providers/provider.py +++ b/libs/core/llmstudio_core/providers/provider.py @@ -15,8 +15,9 @@ Union, ) +from fastapi import HTTPException import tiktoken -from fastapi import ProviderError +from llmstudio_core.exceptions import ProviderError from fastapi.responses import JSONResponse, StreamingResponse from openai.types.chat import ( ChatCompletion, @@ -66,9 +67,9 @@ async def chat( try: request = self.validate_request(request) except ValidationError as e: - raise ProviderError(status_code=422, detail=e.errors()) + raise ProviderError(e.errors()) - self.validate_model(request) + self.validate_model(request.model) for _ in range(request.retries + 1): try: @@ -80,7 +81,7 @@ async def chat( return StreamingResponse(response_handler) else: return JSONResponse(content=await response_handler.__anext__()) - except ProviderError as e: + except HTTPException as e: if e.status_code == 429: continue # Retry on rate limit error else: @@ -92,7 +93,7 @@ async def chat( def validate_request(self, request: ChatRequest): pass - def validate_model(self, model): + def validate_model(self, model:str): if model not in self.config.models: raise ProviderError(f"Model {model} is not supported by {self.config.name}") diff --git a/libs/core/llmstudio_core/providers/vertexai.py b/libs/core/llmstudio_core/providers/vertexai.py index cd096330..95e12871 100644 --- a/libs/core/llmstudio_core/providers/vertexai.py +++ b/libs/core/llmstudio_core/providers/vertexai.py @@ -15,7 +15,7 @@ ) import requests -from fastapi import ProviderError +from llmstudio_core.exceptions import ProviderError from openai.types.chat import ChatCompletionChunk from openai.types.chat.chat_completion_chunk import ( Choice, @@ -118,7 +118,7 @@ async def generate_client( ) except Exception as e: - raise ProviderError(status_code=500, detail=str(e)) + raise ProviderError(str(e)) async def parse_response( self, response: AsyncGenerator, **kwargs From f76876084c183397dbf04d34f1329525b43d4bd8 Mon Sep 17 00:00:00 2001 From: Diogo Goncalves Date: Tue, 8 Oct 2024 12:53:42 +0100 Subject: [PATCH 03/58] feat: implementation of async chat with and without streaming --- libs/core/llmstudio_core/__init__.py | 121 +++- .../core/llmstudio_core/providers/__init__.py | 8 +- .../core/llmstudio_core/providers/provider.py | 675 ++++++++++++++++-- 3 files changed, 708 insertions(+), 96 deletions(-) diff --git a/libs/core/llmstudio_core/__init__.py b/libs/core/llmstudio_core/__init__.py index 3f814d72..980539be 100644 --- a/libs/core/llmstudio_core/__init__.py +++ b/libs/core/llmstudio_core/__init__.py @@ -1,55 +1,116 @@ -from llmstudio_core.providers import (AnthropicProvider -, AzureProvider -, OllamaProvider -, OpenAIProvider +from abc import ABC +from typing import Optional +from llmstudio_core.providers import ( + # AnthropicProvider +# , AzureProvider +# , OllamaProvider +OpenAIProvider , provider_registry -, VertexAIProvider) +# , VertexAIProvider +) -from llmstudio_core.providers.provider import Provider +from llmstudio_core.providers.provider import BaseProvider from llmstudio_core.providers import _load_engine_config _engine_config = _load_engine_config() _providers_map = dict( - anthropic=AnthropicProvider, - azure=AzureProvider, - ollama=OllamaProvider, + # anthropic=AnthropicProvider, + # azure=AzureProvider, + # ollama=OllamaProvider, openai=OpenAIProvider, - vertexai=VertexAIProvider + # vertexai=VertexAIProvider ) -def LLM(provider:str, api_key=None) -> Provider: +def LLM(provider: str, api_key: Optional[str] = None) -> BaseProvider: + """ + Factory method to create an instance of a provider. + + Args: + provider (str): The name of the provider. + api_key (Optional[str], optional): The API key for the provider. Defaults to None. + + Returns: + BaseProvider: An instance of the provider. + + Raises: + NotImplementedError: If the provider is not found in the provider map. + """ provider_config = _engine_config.providers.get(provider) provider_class = _providers_map.get(provider_config.id) if provider_class: return provider_class(config=provider_config, api_key=api_key) - raise NotImplementedError(f"Provider not found: {provider_config.id}. Available providers: {str(_providers_map.keys())}") + raise NotImplementedError(f"BaseProvider not found: {provider_config.id}. Available providers: {str(_providers_map.keys())}") -if __name__ == "__main__": +class LLMStudioTracker(ABC): + pass + +#@SDK +class LLM_: + def __init__(self,provider: str ,tracker: LLMStudioTracker=None, **kwargs) -> None: + + self.provider = LLM(provider=provider,**kwargs) + self.tracker = tracker + + def chat(self, chat_input, **kwargs): + response = self.provider.chat(chat_input, **kwargs) + self.tracker.log(response["metrics"]) + return response +if __name__ == "__main__": + from pprint import pprint import os - import json from dotenv import load_dotenv load_dotenv() + chat_request = { + "chat_input": "Hello, my name is Json", + "model": "gpt-3.5-turbo", + "is_stream": False, + "retries": 0, + "parameters": { + "temperature": 0, + "max_tokens": 100, + "response_format": {"type": "json_object"}, + "functions": None, + } + } - chat_request = dict( - chat_input="Hello?" - - ,api_key=os.environ["OPENAI_API_KEY"] - ,model="gpt-3.5-turbo" - ,parameters={"temperature": 0, "max_tokens":100} - ,is_stream = False - ,has_end_token = False - ,functions = None - ,session_id = "" - ,retries=0) - provider = "openai" - llm = LLM(provider="openai", api_key=os.environ["OPENAI_API_KEY"]) - + llm = LLM(provider=provider, api_key=os.environ["OPENAI_API_KEY"]) + import asyncio - a = asyncio.run(llm.chat(chat_request)) - print(a.json) + response_async = asyncio.run(llm.achat(chat_request)) + pprint(response_async) + + # stream + print("\nasync stream") + async def async_stream(): + chat_request = { + "chat_input": "Hello, my name is Json", + "model": "gpt-3.5-turbo", + "is_stream": True, + "retries": 0, + "parameters": { + "temperature": 0, + "max_tokens": 100, + "response_format": {"type": "json_object"}, + "functions": None, + } + } + + provider = "openai" + llm = LLM(provider=provider, api_key=os.environ["OPENAI_API_KEY"]) + + response_async = await llm.achat(chat_request) + async for p in response_async: + pprint(p) + asyncio.run(async_stream()) + print("how to get the metrics out?") + + # response_sync = llm.chat(chat_request) + # pprint(response_sync) + + # assert response_sync == response_async \ No newline at end of file diff --git a/libs/core/llmstudio_core/providers/__init__.py b/libs/core/llmstudio_core/providers/__init__.py index 978c0fd4..1bc55171 100644 --- a/libs/core/llmstudio_core/providers/__init__.py +++ b/libs/core/llmstudio_core/providers/__init__.py @@ -1,11 +1,11 @@ import os import yaml -from llmstudio_core.providers.anthropic import AnthropicProvider -from llmstudio_core.providers.azure import AzureProvider -from llmstudio_core.providers.ollama import OllamaProvider +# from llmstudio_core.providers.anthropic import AnthropicProvider +# from llmstudio_core.providers.azure import AzureProvider +# from llmstudio_core.providers.ollama import OllamaProvider from llmstudio_core.providers.openai import OpenAIProvider from llmstudio_core.providers.provider import provider_registry -from llmstudio_core.providers.vertexai import VertexAIProvider +# from llmstudio_core.providers.vertexai import VertexAIProvider from pathlib import Path from typing import Any, Dict, List, Optional, Union diff --git a/libs/core/llmstudio_core/providers/provider.py b/libs/core/llmstudio_core/providers/provider.py index c52da74b..a57ec8e4 100644 --- a/libs/core/llmstudio_core/providers/provider.py +++ b/libs/core/llmstudio_core/providers/provider.py @@ -1,3 +1,5 @@ +from abc import ABC +import asyncio import json import os import time @@ -27,7 +29,7 @@ from openai.types.chat.chat_completion import Choice from openai.types.chat.chat_completion_message import FunctionCall from openai.types.chat.chat_completion_message_tool_call import Function -from pydantic import BaseModel, ValidationError +from pydantic import BaseModel, ConfigDict, ValidationError provider_registry = {} @@ -38,71 +40,95 @@ def provider(cls): provider_registry[cls.__name__] = cls return cls - class ChatRequest(BaseModel): - api_key: Optional[str] = None model: str chat_input: Any - parameters: Optional[BaseModel] = None is_stream: Optional[bool] = False - has_end_token: Optional[bool] = False - functions: Optional[List[Dict[str, Any]]] = None - session_id: Optional[str] = None retries: Optional[int] = 0 + parameters: Optional[dict] = None - -class Provider: +class ProviderABC(ABC): END_TOKEN = "" - def __init__(self, config, api_key=None): + def __init__(self, + config: Any, + api_key: Optional[str] = None, + api_endpoint: Optional[str] = None, + api_version: Optional[str] = None, + base_url: Optional[str] = None, + tokenizer: Optional[Any] = None): self.config = config - self.tokenizer = self._get_tokenizer() - self.count = 0 self.API_KEY = api_key + self.api_endpoint = api_endpoint + self.api_version = api_version + self.base_url = base_url + + self.tokenizer = tokenizer if tokenizer else self._get_tokenizer() + self.count = 0 + + async def achat( + self, request: ChatRequest + ) -> Union[StreamingResponse, JSONResponse]: + raise NotImplementedError("Providers needs to have achat method implemented.") + + def achat( + self, request: ChatRequest + ) -> Union[StreamingResponse, JSONResponse]: + raise NotImplementedError("Providers needs to have chat method implemented.") + - async def chat( +class BaseProvider(ProviderABC): + END_TOKEN = "" + + async def achat( self, request: ChatRequest ) -> Union[StreamingResponse, JSONResponse]: """Makes a chat connection with the provider's API""" try: request = self.validate_request(request) except ValidationError as e: - raise ProviderError(e.errors()) + raise HTTPException(status_code=422, detail=e.errors()) - self.validate_model(request.model) + self.validate_model(request) for _ in range(request.retries + 1): try: start_time = time.time() - response = await self.generate_client(request) - response_handler = self.handle_response(request, response, start_time) + response = await self.agenerate_client(request) if request.is_stream: - return StreamingResponse(response_handler) + response_handler = self.ahandle_response_stream(request, response, start_time) + return response_handler else: - return JSONResponse(content=await response_handler.__anext__()) + response_handler = self.ahandle_response_stream(request, response, start_time) + return await response_handler.__anext__() except HTTPException as e: if e.status_code == 429: continue # Retry on rate limit error else: raise e # Raise other HTTP exceptions except Exception as e: - raise ProviderError(str(e)) # Raise other exceptions as HTTP 500 - raise ProviderError("Too many requests") + raise HTTPException( + status_code=500, detail=str(e) + ) # Raise other exceptions as HTTP 500 + raise HTTPException(status_code=429, detail="Too many requests") def validate_request(self, request: ChatRequest): pass - def validate_model(self, model:str): - if model not in self.config.models: - raise ProviderError(f"Model {model} is not supported by {self.config.name}") + def validate_model(self, request: ChatRequest): + if request.model not in self.config.models: + raise HTTPException( + status_code=400, + detail=f"Model {request.model} is not supported by {self.config.name}", + ) - async def generate_client( + async def agenerate_client( self, request: ChatRequest ) -> Coroutine[Any, Any, Generator]: """Generate the provider's client""" - async def handle_response( + async def ahandle_response_stream( self, request: ChatRequest, response: AsyncGenerator, start_time: float ) -> AsyncGenerator[str, None]: """Handles the response from an API""" @@ -112,7 +138,7 @@ async def handle_response( token_count = 0 chunks = [] - async for chunk in self.parse_response(response, request=request): + async for chunk in self.aparse_response(response, request=request): token_count += 1 current_time = time.time() first_token_time = first_token_time or current_time @@ -134,7 +160,7 @@ async def handle_response( metrics = self.calculate_metrics( request.chat_input, response, - model, + request.model, start_time, time.time(), first_token_time, @@ -145,7 +171,6 @@ async def handle_response( response = { **response.model_dump(), "id": str(uuid.uuid4()), - "session_id": request.session_id, "chat_input": ( request.chat_input if isinstance(request.chat_input, str) @@ -159,27 +184,27 @@ async def handle_response( ), "provider": self.config.id, "model": ( - model - if model and model.startswith(model) - else (model or model) + request.model + if model and model.startswith(request.model) + else (model or request.model) ), "deployment": ( model - if model and model.startswith(model) - else (model if model != model else None) + if model and model.startswith(request.model) + else (request.model if model != request.model else None) ), "timestamp": time.time(), - "parameters": request.parameters.model_dump(), + "parameters": request.parameters, "metrics": metrics, } - if not request.is_stream: - yield response + # if not request.is_stream: + # yield response + + yield response + def join_chunks(self, chunks, request): - from llmstudio_core.providers.azure import AzureRequest - from llmstudio_core.providers.openai import OpenAIRequest - from llmstudio_core.providers.vertexai import VertexAIRequest finish_reason = chunks[-1].get("choices")[0].get("finish_reason") if finish_reason == "tool_calls": @@ -239,28 +264,11 @@ def join_chunks(self, chunks, request): and chunk.get("choices")[0].get("delta").get("function_call") ] - if isinstance(request, AzureRequest): - function_call_name = function_calls[0].get("name") - elif isinstance(request, OpenAIRequest) or isinstance( - request, VertexAIRequest - ): - function_call_name = ( - chunks[0] - .get("choices")[0] - .get("delta") - .get("function_call") - .get("name") - ) + function_call_name = function_calls[0].get("name") + function_call_arguments = "" for chunk in function_calls: - if isinstance(request, AzureRequest): - part = chunk.get("arguments", "") - if part: - function_call_arguments += part - elif isinstance(request, OpenAIRequest) or isinstance( - request, VertexAIRequest - ): - function_call_arguments += chunk.get("arguments") + function_call_arguments += chunk.get("arguments") return ( ChatCompletion( @@ -289,7 +297,7 @@ def join_chunks(self, chunks, request): ) elif finish_reason == "stop" or finish_reason == "length": - if isinstance(request, AzureRequest) or isinstance(request, OpenAIRequest): + if self.__class__.__name__ in ("OpenAIProvider", "AzureProvider"): # or isinstance(request, OpenAIRequest): start_index = 1 else: start_index = 0 @@ -327,7 +335,7 @@ def join_chunks(self, chunks, request): stop_content, ) - async def parse_response( + async def aparse_response( self, response: AsyncGenerator ) -> AsyncGenerator[str, None]: pass @@ -410,3 +418,546 @@ def get_end_token_string(self, metrics: Dict[str, Any]) -> str: def _get_tokenizer(self): return {}.get(self.config.id, tiktoken.get_encoding("cl100k_base")) + +# class BaseProvider(ProviderABC): +# END_TOKEN = "" + +# async def achat( +# self, request: ChatRequest +# ) -> Union[StreamingResponse, JSONResponse]: +# """Makes a chat connection with the provider's API""" +# try: +# request = self.validate_request(request) +# except ValidationError as e: +# raise ProviderError(e.errors()) + +# self.validate_model(request.model) + +# for _ in range(request.retries + 1): +# try: +# start_time = time.time() +# response = await self.generate_client(request) +# response_handler = self.handle_response(request, response, start_time) + +# if request.is_stream: +# return response_handler +# else: +# return await response_handler.__anext__() +# except HTTPException as e: +# if e.status_code == 429: +# continue # Retry on rate limit error +# else: +# raise ProviderError(str(e)) # Raise other HTTP exceptions +# except Exception as e: +# raise ProviderError(str(e)) +# raise ProviderError("Too many requests") + +# def chat( +# self, request: ChatRequest +# ) -> JSONResponse: +# """Makes a chat connection with the provider's API""" +# try: +# request = self.validate_request(request) +# except ValidationError as e: +# raise ProviderError(e.errors()) + +# self.validate_model(request.model) + +# for _ in range(request.retries + 1): +# try: +# start_time = time.time() +# response = self.generate_client(request) + +# if request.is_stream: +# return self.handle_response_stream(request, response, start_time) +# else: +# return self.handle_response(request, response, start_time) +# except HTTPException as e: +# if e.status_code == 429: +# continue # Retry on rate limit error +# else: +# raise ProviderError(str(e)) # Raise other HTTP exceptions +# except Exception as e: +# raise ProviderError(str(e)) +# raise ProviderError("Too many requests") + +# def validate_request(self, request: ChatRequest): +# pass + +# def validate_model(self, model:str): +# if model not in self.config.models: +# raise ProviderError(f"Model {model} is not supported by {self.config.name}") + +# async def generate_client( +# self, request: ChatRequest +# ) -> Coroutine[Any, Any, Generator]: +# """Generate the provider's client""" + +# def handle_response_stream( +# self, request: ChatRequest, response: Generator, start_time: float +# ) -> Generator[str, None, None]: +# """Handles the response from an API""" +# first_token_time = None +# previous_token_time = None +# token_times = [] +# token_count = 0 +# chunks = [] + +# for chunk in self.parse_response(response, request=request): +# token_count += 1 +# current_time = time.time() +# first_token_time = first_token_time or current_time +# if previous_token_time is not None: +# token_times.append(current_time - previous_token_time) +# previous_token_time = current_time + +# chunks.append(chunk) +# if request.is_stream: +# chunk = chunk[0] if isinstance(chunk, tuple) else chunk +# if chunk.get("choices")[0].get("finish_reason") != "stop": +# yield chunk.get("choices")[0].get("delta").get("content") + +# chunks = [chunk[0] if isinstance(chunk, tuple) else chunk for chunk in chunks] +# model = next(chunk["model"] for chunk in chunks if chunk.get("model")) + +# response, output_string = self.join_chunks(chunks, request) + +# metrics = self.calculate_metrics( +# input=request.chat_input, +# output=response, +# model=request.model, +# start_time=start_time, +# end_time=time.time(), +# first_token_time=first_token_time, +# token_times=token_times, +# token_count=token_count, +# ) + +# response = { +# **response.model_dump(), +# "id": str(uuid.uuid4()), +# "session_id": request.session_id, +# "chat_input": ( +# request.chat_input +# if isinstance(request.chat_input, str) +# else request.chat_input[-1]["content"] +# ), +# "chat_output": output_string, +# "context": ( +# [{"role": "user", "content": request.chat_input}] +# if isinstance(request.chat_input, str) +# else request.chat_input +# ), +# "provider": self.config.id, +# "model": ( +# model +# if model and model.startswith(model) +# else (model or model) +# ), +# "deployment": ( +# model +# if model and model.startswith(model) +# else (model if model != model else None) +# ), +# "timestamp": time.time(), +# "parameters": request.parameters.model_dump(), +# "metrics": metrics, +# } + +# yield response + +# def handle_response( +# self, request: ChatRequest, response: Generator, start_time: float +# ) -> Generator[str, None, None]: +# """Handles the response from an API""" + +# metrics = self.calculate_metrics( +# input=request.chat_input, +# output=response, +# model=request.model, +# start_time=start_time, +# end_time=time.time(), +# first_token_time=None, +# token_times=None, +# token_count=None, +# ) +# response = { +# **response.model_dump(), +# "id": str(uuid.uuid4()), +# "session_id": request.session_id, +# "chat_input": ( +# request.chat_input +# if isinstance(request.chat_input, str) +# else request.chat_input[-1]["content"] +# ), +# "chat_output": output_string, +# "context": ( +# [{"role": "user", "content": request.chat_input}] +# if isinstance(request.chat_input, str) +# else request.chat_input +# ), +# "provider": self.config.id, +# "model": ( +# request.model +# if model and model.startswith(request.model) +# else (model or request.model) +# ), +# "deployment": ( +# model +# if model and model.startswith(request.model) +# else (request.model if model != request.model else None) +# ), +# "timestamp": time.time(), +# "parameters": request.parameters.model_dump(), +# "metrics": metrics, +# } +# return response + + + +# async def ahandle_response( +# self, request: ChatRequest, response: AsyncGenerator, start_time: float +# ) -> Any: +# """Handles the response from an API""" +# first_token_time = None +# previous_token_time = None +# token_times = [] +# token_count = 0 +# chunks = [] + +# async for chunk in self.parse_response(response, request=request): +# token_count += 1 +# current_time = time.time() +# first_token_time = first_token_time or current_time +# if previous_token_time is not None: +# token_times.append(current_time - previous_token_time) +# previous_token_time = current_time + +# chunks.append(chunk) +# if request.is_stream: +# chunk = chunk[0] if isinstance(chunk, tuple) else chunk +# if chunk.get("choices")[0].get("finish_reason") != "stop": +# yield chunk.get("choices")[0].get("delta").get("content") + +# chunks = [chunk[0] if isinstance(chunk, tuple) else chunk for chunk in chunks] +# model = next(chunk["model"] for chunk in chunks if chunk.get("model")) + +# response, output_string = self.join_chunks(chunks, request) + +# metrics = self.calculate_metrics( +# request.chat_input, +# response, +# request.model, +# start_time, +# time.time(), +# first_token_time, +# token_times, +# token_count, +# ) + +# response = { +# **response.model_dump(), +# "id": str(uuid.uuid4()), +# "session_id": request.session_id, +# "chat_input": ( +# request.chat_input +# if isinstance(request.chat_input, str) +# else request.chat_input[-1]["content"] +# ), +# "chat_output": output_string, +# "context": ( +# [{"role": "user", "content": request.chat_input}] +# if isinstance(request.chat_input, str) +# else request.chat_input +# ), +# "provider": self.config.id, +# "model": ( +# model +# if model and model.startswith(model) +# else (model or model) +# ), +# "deployment": ( +# model +# if model and model.startswith(model) +# else (model if model != model else None) +# ), +# "timestamp": time.time(), +# "parameters": request.parameters.model_dump(), +# "metrics": metrics, +# } + +# if not request.is_stream: +# yield response + +# def join_chunks(self, chunks: List[Dict[str, Any]], request: ChatRequest) -> Tuple[ChatCompletion, str]: +# """ +# Joins chunks of response data into a single ChatCompletion object. + +# Args: +# chunks (List[Dict[str, Any]]): A list of response chunks from the API. +# request (ChatRequest): The original chat request object. + +# Returns: +# Tuple[ChatCompletion, str]: A tuple containing the ChatCompletion object and the tool call arguments string. +# """ +# from llmstudio_core.providers.azure import AzureRequest +# from llmstudio_core.providers.openai import OpenAIRequest +# from llmstudio_core.providers.vertexai import VertexAIRequest + +# finish_reason = chunks[-1].get("choices")[0].get("finish_reason") +# if finish_reason == "tool_calls": +# tool_calls = [ +# chunk.get("choices")[0].get("delta").get("tool_calls")[0] +# for chunk in chunks[1:-1] +# ] + +# tool_call_id = tool_calls[0].get("id") +# tool_call_name = tool_calls[0].get("function").get("name") +# tool_call_type = tool_calls[0].get("function").get("type") +# tool_call_arguments = "".join( +# chunk.get("function", {}).get("arguments", "") +# for chunk in tool_calls[1:] +# ) + +# try: +# return ( +# ChatCompletion( +# id=chunks[-1].get("id"), +# created=chunks[-1].get("created"), +# model=chunks[-1].get("model"), +# object="chat.completion", +# choices=[ +# Choice( +# finish_reason="tool_calls", +# index=0, +# logprobs=None, +# message=ChatCompletionMessage( +# content=None, +# role="assistant", +# function_call=None, +# tool_calls=[ +# ChatCompletionMessageToolCall( +# id=tool_call_id, +# function=Function( +# arguments=tool_call_arguments, +# name=tool_call_name, +# ), +# type=tool_call_type, +# ) +# ], +# ), +# ) +# ], +# ), +# tool_call_arguments, +# ) +# except Exception as e: +# raise e +# elif finish_reason == "function_call": +# function_calls = [ +# chunk.get("choices")[0].get("delta").get("function_call") +# for chunk in chunks[1:-1] +# if chunk.get("choices") +# and chunk.get("choices")[0].get("delta") +# and chunk.get("choices")[0].get("delta").get("function_call") +# ] + +# if isinstance(request, AzureRequest): +# function_call_name = function_calls[0].get("name") +# elif isinstance(request, OpenAIRequest) or isinstance( +# request, VertexAIRequest +# ): +# function_call_name = ( +# chunks[0] +# .get("choices")[0] +# .get("delta") +# .get("function_call") +# .get("name") +# ) +# function_call_arguments = "" +# for chunk in function_calls: +# if isinstance(request, AzureRequest): +# part = chunk.get("arguments", "") +# if part: +# function_call_arguments += part +# elif isinstance(request, OpenAIRequest) or isinstance( +# request, VertexAIRequest +# ): +# function_call_arguments += chunk.get("arguments") + +# return ( +# ChatCompletion( +# id=chunks[-1].get("id"), +# created=chunks[-1].get("created"), +# model=chunks[-1].get("model"), +# object="chat.completion", +# choices=[ +# Choice( +# finish_reason="function_call", +# index=0, +# logprobs=None, +# message=ChatCompletionMessage( +# content=None, +# role="assistant", +# tool_calls=None, +# function_call=FunctionCall( +# arguments=function_call_arguments, +# name=function_call_name, +# ), +# ), +# ) +# ], +# ), +# function_call_arguments, +# ) + +# elif finish_reason == "stop" or finish_reason == "length": +# if isinstance(request, AzureRequest) or isinstance(request, OpenAIRequest): +# start_index = 1 +# else: +# start_index = 0 + +# stop_content = "".join( +# filter( +# None, +# [ +# chunk.get("choices")[0].get("delta").get("content") +# for chunk in chunks[start_index:] +# ], +# ) +# ) + +# return ( +# ChatCompletion( +# id=chunks[-1].get("id"), +# created=chunks[-1].get("created"), +# model=chunks[-1].get("model"), +# object="chat.completion", +# choices=[ +# Choice( +# finish_reason="stop", +# index=0, +# logprobs=None, +# message=ChatCompletionMessage( +# content=stop_content, +# role="assistant", +# function_call=None, +# tool_calls=None, +# ), +# ) +# ], +# ), +# stop_content, +# ) + +# async def parse_response( +# self, response: AsyncGenerator +# ) -> AsyncGenerator[str, None]: +# pass + +# def calculate_metrics_stream( +# self, +# input: Any, +# output: Any, +# model: str, +# start_time: float, +# end_time: float, +# first_token_time: float, +# token_times: Tuple[float, ...], +# token_count: int, +# ) -> Dict[str, Any]: +# """Calculates metrics based on token times and output""" +# model_config = self.config.models[model] +# input_tokens = len(self.tokenizer.encode(self.input_to_string(input))) +# output_tokens = len(output) + +# input_cost = self.calculate_cost(input_tokens, model_config.input_token_cost) +# output_cost = self.calculate_cost(output_tokens, model_config.output_token_cost) + +# total_time = end_time - start_time +# return { +# "input_tokens": input_tokens, +# "output_tokens": output_tokens, +# "total_tokens": input_tokens + output_tokens, +# "cost_usd": input_cost + output_cost, +# "latency_s": total_time, +# "time_to_first_token_s": first_token_time - start_time, +# "inter_token_latency_s": sum(token_times) / len(token_times), +# "tokens_per_second": token_count / total_time, +# } + +# def calculate_metrics( +# self, +# input: Any, +# output: Any, +# model: str, +# start_time: float, +# end_time: float, +# response: Any + +# ) -> Dict[str, Any]: +# """Calculates metrics based on token times and output""" +# model_config = self.config.models[model] + +# input_cost = self.calculate_cost(3, model_config.input_token_cost) +# output_cost = self.calculate_cost(3, model_config.output_token_cost) + +# total_time = end_time - start_time +# return { +# "input_tokens": 3, +# "output_tokens": 3, +# "total_tokens": 3 + 3, +# "cost_usd": input_cost + output_cost, +# "latency_s": total_time, +# "time_to_first_token_s": 3 - 3, +# "inter_token_latency_s": sum(3) / len(3), +# "tokens_per_second": 3 / total_time, +# } + +# def calculate_cost( +# self, token_count: int, token_cost: Union[float, List[Dict[str, Any]]] +# ) -> float: +# if isinstance(token_cost, list): +# for cost_range in token_cost: +# if token_count >= cost_range.range[0] and ( +# token_count <= cost_range.range[1] or cost_range.range[1] is None +# ): +# return cost_range.cost * token_count +# else: +# return token_cost * token_count +# return 0 + +# def input_to_string(self, input): +# if isinstance(input, str): +# return input +# else: +# result = [] +# for message in input: +# if message.get("content") is not None: +# if isinstance(message["content"], str): +# result.append(message["content"]) +# elif ( +# isinstance(message["content"], list) +# and message.get("role") == "user" +# ): +# for item in message["content"]: +# if item.get("type") == "text": +# result.append(item.get("text", "")) +# elif item.get("type") == "image_url": +# url = item.get("image_url", {}).get("url", "") +# result.append(url) +# return "".join(result) + +# def output_to_string(self, output): +# if output.choices[0].finish_reason == "stop": +# return output.choices[0].message.content +# elif output.choices[0].finish_reason == "tool_calls": +# return output.choices[0].message.tool_calls[0].function.arguments +# elif output.choices[0].finish_reason == "function_call": +# return output.choices[0].message.function_call.arguments + +# def get_end_token_string(self, metrics: Dict[str, Any]) -> str: +# return f"{self.END_TOKEN},input_tokens={metrics['input_tokens']},output_tokens={metrics['output_tokens']},cost_usd={metrics['cost_usd']},latency_s={metrics['latency_s']:.5f},time_to_first_token_s={metrics['time_to_first_token_s']:.5f},inter_token_latency_s={metrics['inter_token_latency_s']:.5f},tokens_per_second={metrics['tokens_per_second']:.2f}" + +# def _get_tokenizer(self): +# return {}.get(self.config.id, tiktoken.get_encoding("cl100k_base")) From 287e74a2c4fbb9f53c6821bc9e3c2cd5f30b1aae Mon Sep 17 00:00:00 2001 From: Diogo Goncalves Date: Tue, 8 Oct 2024 17:09:18 +0100 Subject: [PATCH 04/58] feat: define output contract and make non stream really non stream --- libs/core/llmstudio_core/__init__.py | 8 +- libs/core/llmstudio_core/providers/openai.py | 43 +- .../core/llmstudio_core/providers/provider.py | 674 +++--------------- 3 files changed, 134 insertions(+), 591 deletions(-) diff --git a/libs/core/llmstudio_core/__init__.py b/libs/core/llmstudio_core/__init__.py index 980539be..e4a5917c 100644 --- a/libs/core/llmstudio_core/__init__.py +++ b/libs/core/llmstudio_core/__init__.py @@ -106,10 +106,14 @@ async def async_stream(): response_async = await llm.achat(chat_request) async for p in response_async: - pprint(p) + pprint(p.chat_output) + pprint(p.choices[0].delta.content==p.chat_output) + print("metrics: ",p.metrics) + if p.metrics: + pprint(p) asyncio.run(async_stream()) print("how to get the metrics out?") - + # response_sync = llm.chat(chat_request) # pprint(response_sync) diff --git a/libs/core/llmstudio_core/providers/openai.py b/libs/core/llmstudio_core/providers/openai.py index 4dc89dcc..3cb1bf1f 100644 --- a/libs/core/llmstudio_core/providers/openai.py +++ b/libs/core/llmstudio_core/providers/openai.py @@ -7,36 +7,21 @@ from openai import OpenAI from pydantic import BaseModel, Field -from llmstudio_core.providers.provider import ChatRequest, Provider, provider +from llmstudio_core.providers.provider import ChatRequest, BaseProvider, provider -class OpenAIParameters(BaseModel): - temperature: Optional[float] = Field(default=1, ge=0, le=2) - max_tokens: Optional[int] = Field(default=2048, ge=1) - top_p: Optional[float] = Field(default=1, ge=0, le=1) - frequency_penalty: Optional[float] = Field(default=0, ge=0, le=1) - presence_penalty: Optional[float] = Field(default=0, ge=0, le=1) - - -class OpenAIRequest(ChatRequest): - parameters: Optional[OpenAIParameters] = OpenAIParameters() - functions: Optional[List[Dict[str, Any]]] = None - tools: Optional[List[Dict[str, Any]]] = None - chat_input: Any - response_format: Optional[Dict[str, str]] = None - @provider -class OpenAIProvider(Provider): - def __init__(self, config, api_key=None): - super().__init__(config) - self.API_KEY = api_key or os.getenv("OPENAI_API_KEY") +class OpenAIProvider(BaseProvider): + def __init__(self, config, **kwargs): + super().__init__(config, **kwargs) + self.API_KEY = self.API_KEY if self.API_KEY else os.getenv("OPENAI_API_KEY") - def validate_request(self, request: OpenAIRequest): - return OpenAIRequest(**request) + def validate_request(self, request: ChatRequest): + return ChatRequest(**request) - async def generate_client( - self, request: OpenAIRequest + async def agenerate_client( + self, request: ChatRequest ) -> Coroutine[Any, Any, Generator]: """Generate an OpenAI client""" @@ -50,17 +35,13 @@ async def generate_client( if isinstance(request.chat_input, str) else request.chat_input ), - # tools=request.tools, - functions=request.functions, - function_call="auto" if request.functions else None, - stream=True, - response_format=request.response_format, - **request.parameters.model_dump(), + stream=request.is_stream, + **request.parameters, ) except openai._exceptions.APIError as e: raise ProviderError(str(e)) - async def parse_response( + async def aparse_response( self, response: AsyncGenerator, **kwargs ) -> AsyncGenerator[str, None]: for chunk in response: diff --git a/libs/core/llmstudio_core/providers/provider.py b/libs/core/llmstudio_core/providers/provider.py index a57ec8e4..886c1f30 100644 --- a/libs/core/llmstudio_core/providers/provider.py +++ b/libs/core/llmstudio_core/providers/provider.py @@ -25,6 +25,7 @@ ChatCompletion, ChatCompletionMessage, ChatCompletionMessageToolCall, + ChatCompletionChunk ) from openai.types.chat.chat_completion import Choice from openai.types.chat.chat_completion_message import FunctionCall @@ -82,7 +83,7 @@ class BaseProvider(ProviderABC): async def achat( self, request: ChatRequest - ) -> Union[StreamingResponse, JSONResponse]: + ): """Makes a chat connection with the provider's API""" try: request = self.validate_request(request) @@ -100,7 +101,7 @@ async def achat( response_handler = self.ahandle_response_stream(request, response, start_time) return response_handler else: - response_handler = self.ahandle_response_stream(request, response, start_time) + response_handler = self.ahandle_response(request, response, start_time) return await response_handler.__anext__() except HTTPException as e: if e.status_code == 429: @@ -128,6 +129,51 @@ async def agenerate_client( ) -> Coroutine[Any, Any, Generator]: """Generate the provider's client""" + async def ahandle_response( + self, request: ChatRequest, response: ChatCompletion, start_time: float + ) -> AsyncGenerator[ChatCompletion, None]: + """Handles the response from an API""" + model = response.model + + metrics = self.calculate_metrics( + usage=response.usage.model_dump(), + model=request.model, + start_time=start_time, + end_time=time.time(), + ) + + response = { + **response.model_dump(), + "id": str(uuid.uuid4()), + "chat_input": ( + request.chat_input + if isinstance(request.chat_input, str) + else request.chat_input[-1]["content"] + ), + "chat_output": response.choices[0].message.content, + "context": ( + [{"role": "user", "content": request.chat_input}] + if isinstance(request.chat_input, str) + else request.chat_input + ), + "provider": self.config.id, + "model": ( + request.model + if model and model.startswith(request.model) + else (model or request.model) + ), + "deployment": ( + model + if model and model.startswith(request.model) + else (request.model if model != request.model else None) + ), + "timestamp": time.time(), + "parameters": request.parameters, + "metrics": metrics, + } + + yield ChatCompletion(**response) + async def ahandle_response_stream( self, request: ChatRequest, response: AsyncGenerator, start_time: float ) -> AsyncGenerator[str, None]: @@ -147,17 +193,46 @@ async def ahandle_response_stream( previous_token_time = current_time chunks.append(chunk) - if request.is_stream: - chunk = chunk[0] if isinstance(chunk, tuple) else chunk - if chunk.get("choices")[0].get("finish_reason") != "stop": - yield chunk.get("choices")[0].get("delta").get("content") + chunk = chunk[0] if isinstance(chunk, tuple) else chunk + if chunk.get("choices")[0].get("finish_reason") != "stop": + model = chunk.get("model") + response = { + **chunk, + "id": str(uuid.uuid4()), + "chat_input": ( + request.chat_input + if isinstance(request.chat_input, str) + else request.chat_input[-1]["content"] + ), + "chat_output": chunk.get("choices")[0].get("delta").get("content"), + "context": ( + [{"role": "user", "content": request.chat_input}] + if isinstance(request.chat_input, str) + else request.chat_input + ), + "provider": self.config.id, + "model": ( + request.model + if model and model.startswith(request.model) + else (model or request.model) + ), + "deployment": ( + model + if model and model.startswith(request.model) + else (request.model if model != request.model else None) + ), + "timestamp": time.time(), + "parameters": request.parameters, + "metrics": None, + } + yield ChatCompletionChunk(**response) chunks = [chunk[0] if isinstance(chunk, tuple) else chunk for chunk in chunks] model = next(chunk["model"] for chunk in chunks if chunk.get("model")) - response, output_string = self.join_chunks(chunks, request) + response, _ = self.join_chunks(chunks, request) - metrics = self.calculate_metrics( + metrics = self.calculate_metrics_stream( request.chat_input, response, request.model, @@ -169,14 +244,14 @@ async def ahandle_response_stream( ) response = { - **response.model_dump(), + **chunk, "id": str(uuid.uuid4()), "chat_input": ( request.chat_input if isinstance(request.chat_input, str) else request.chat_input[-1]["content"] ), - "chat_output": output_string, + "chat_output": None, "context": ( [{"role": "user", "content": request.chat_input}] if isinstance(request.chat_input, str) @@ -198,10 +273,7 @@ async def ahandle_response_stream( "metrics": metrics, } - # if not request.is_stream: - # yield response - - yield response + yield ChatCompletionChunk(**response) def join_chunks(self, chunks, request): @@ -340,7 +412,7 @@ async def aparse_response( ) -> AsyncGenerator[str, None]: pass - def calculate_metrics( + def calculate_metrics_stream( self, input: Any, output: Any, @@ -370,6 +442,34 @@ def calculate_metrics( "inter_token_latency_s": sum(token_times) / len(token_times), "tokens_per_second": token_count / total_time, } + + def calculate_metrics( + self, + usage, + model: str, + start_time: float, + end_time: float, + ) -> Dict[str, Any]: + """Calculates metrics based on token times and output""" + model_config = self.config.models[model] + input_tokens = usage.get("prompt_tokens") + output_tokens = usage.get("completion_tokens") + + input_cost = self.calculate_cost(input_tokens, model_config.input_token_cost) + output_cost = self.calculate_cost(output_tokens, model_config.output_token_cost) + + total_time = end_time - start_time + return { + "input_tokens": input_tokens, + "output_tokens": output_tokens, + "total_tokens": input_tokens + output_tokens, + "cost_usd": input_cost + output_cost, + "latency_s": total_time, + "time_to_first_token_s": None, + "inter_token_latency_s": None, + "tokens_per_second": usage.get("total_tokens") / total_time, + } + def calculate_cost( self, token_count: int, token_cost: Union[float, List[Dict[str, Any]]] @@ -418,546 +518,4 @@ def get_end_token_string(self, metrics: Dict[str, Any]) -> str: def _get_tokenizer(self): return {}.get(self.config.id, tiktoken.get_encoding("cl100k_base")) - -# class BaseProvider(ProviderABC): -# END_TOKEN = "" - -# async def achat( -# self, request: ChatRequest -# ) -> Union[StreamingResponse, JSONResponse]: -# """Makes a chat connection with the provider's API""" -# try: -# request = self.validate_request(request) -# except ValidationError as e: -# raise ProviderError(e.errors()) - -# self.validate_model(request.model) - -# for _ in range(request.retries + 1): -# try: -# start_time = time.time() -# response = await self.generate_client(request) -# response_handler = self.handle_response(request, response, start_time) - -# if request.is_stream: -# return response_handler -# else: -# return await response_handler.__anext__() -# except HTTPException as e: -# if e.status_code == 429: -# continue # Retry on rate limit error -# else: -# raise ProviderError(str(e)) # Raise other HTTP exceptions -# except Exception as e: -# raise ProviderError(str(e)) -# raise ProviderError("Too many requests") - -# def chat( -# self, request: ChatRequest -# ) -> JSONResponse: -# """Makes a chat connection with the provider's API""" -# try: -# request = self.validate_request(request) -# except ValidationError as e: -# raise ProviderError(e.errors()) - -# self.validate_model(request.model) - -# for _ in range(request.retries + 1): -# try: -# start_time = time.time() -# response = self.generate_client(request) - -# if request.is_stream: -# return self.handle_response_stream(request, response, start_time) -# else: -# return self.handle_response(request, response, start_time) -# except HTTPException as e: -# if e.status_code == 429: -# continue # Retry on rate limit error -# else: -# raise ProviderError(str(e)) # Raise other HTTP exceptions -# except Exception as e: -# raise ProviderError(str(e)) -# raise ProviderError("Too many requests") - -# def validate_request(self, request: ChatRequest): -# pass - -# def validate_model(self, model:str): -# if model not in self.config.models: -# raise ProviderError(f"Model {model} is not supported by {self.config.name}") - -# async def generate_client( -# self, request: ChatRequest -# ) -> Coroutine[Any, Any, Generator]: -# """Generate the provider's client""" - -# def handle_response_stream( -# self, request: ChatRequest, response: Generator, start_time: float -# ) -> Generator[str, None, None]: -# """Handles the response from an API""" -# first_token_time = None -# previous_token_time = None -# token_times = [] -# token_count = 0 -# chunks = [] - -# for chunk in self.parse_response(response, request=request): -# token_count += 1 -# current_time = time.time() -# first_token_time = first_token_time or current_time -# if previous_token_time is not None: -# token_times.append(current_time - previous_token_time) -# previous_token_time = current_time - -# chunks.append(chunk) -# if request.is_stream: -# chunk = chunk[0] if isinstance(chunk, tuple) else chunk -# if chunk.get("choices")[0].get("finish_reason") != "stop": -# yield chunk.get("choices")[0].get("delta").get("content") - -# chunks = [chunk[0] if isinstance(chunk, tuple) else chunk for chunk in chunks] -# model = next(chunk["model"] for chunk in chunks if chunk.get("model")) - -# response, output_string = self.join_chunks(chunks, request) - -# metrics = self.calculate_metrics( -# input=request.chat_input, -# output=response, -# model=request.model, -# start_time=start_time, -# end_time=time.time(), -# first_token_time=first_token_time, -# token_times=token_times, -# token_count=token_count, -# ) - -# response = { -# **response.model_dump(), -# "id": str(uuid.uuid4()), -# "session_id": request.session_id, -# "chat_input": ( -# request.chat_input -# if isinstance(request.chat_input, str) -# else request.chat_input[-1]["content"] -# ), -# "chat_output": output_string, -# "context": ( -# [{"role": "user", "content": request.chat_input}] -# if isinstance(request.chat_input, str) -# else request.chat_input -# ), -# "provider": self.config.id, -# "model": ( -# model -# if model and model.startswith(model) -# else (model or model) -# ), -# "deployment": ( -# model -# if model and model.startswith(model) -# else (model if model != model else None) -# ), -# "timestamp": time.time(), -# "parameters": request.parameters.model_dump(), -# "metrics": metrics, -# } - -# yield response - -# def handle_response( -# self, request: ChatRequest, response: Generator, start_time: float -# ) -> Generator[str, None, None]: -# """Handles the response from an API""" - -# metrics = self.calculate_metrics( -# input=request.chat_input, -# output=response, -# model=request.model, -# start_time=start_time, -# end_time=time.time(), -# first_token_time=None, -# token_times=None, -# token_count=None, -# ) -# response = { -# **response.model_dump(), -# "id": str(uuid.uuid4()), -# "session_id": request.session_id, -# "chat_input": ( -# request.chat_input -# if isinstance(request.chat_input, str) -# else request.chat_input[-1]["content"] -# ), -# "chat_output": output_string, -# "context": ( -# [{"role": "user", "content": request.chat_input}] -# if isinstance(request.chat_input, str) -# else request.chat_input -# ), -# "provider": self.config.id, -# "model": ( -# request.model -# if model and model.startswith(request.model) -# else (model or request.model) -# ), -# "deployment": ( -# model -# if model and model.startswith(request.model) -# else (request.model if model != request.model else None) -# ), -# "timestamp": time.time(), -# "parameters": request.parameters.model_dump(), -# "metrics": metrics, -# } -# return response - - - -# async def ahandle_response( -# self, request: ChatRequest, response: AsyncGenerator, start_time: float -# ) -> Any: -# """Handles the response from an API""" -# first_token_time = None -# previous_token_time = None -# token_times = [] -# token_count = 0 -# chunks = [] - -# async for chunk in self.parse_response(response, request=request): -# token_count += 1 -# current_time = time.time() -# first_token_time = first_token_time or current_time -# if previous_token_time is not None: -# token_times.append(current_time - previous_token_time) -# previous_token_time = current_time - -# chunks.append(chunk) -# if request.is_stream: -# chunk = chunk[0] if isinstance(chunk, tuple) else chunk -# if chunk.get("choices")[0].get("finish_reason") != "stop": -# yield chunk.get("choices")[0].get("delta").get("content") - -# chunks = [chunk[0] if isinstance(chunk, tuple) else chunk for chunk in chunks] -# model = next(chunk["model"] for chunk in chunks if chunk.get("model")) - -# response, output_string = self.join_chunks(chunks, request) - -# metrics = self.calculate_metrics( -# request.chat_input, -# response, -# request.model, -# start_time, -# time.time(), -# first_token_time, -# token_times, -# token_count, -# ) - -# response = { -# **response.model_dump(), -# "id": str(uuid.uuid4()), -# "session_id": request.session_id, -# "chat_input": ( -# request.chat_input -# if isinstance(request.chat_input, str) -# else request.chat_input[-1]["content"] -# ), -# "chat_output": output_string, -# "context": ( -# [{"role": "user", "content": request.chat_input}] -# if isinstance(request.chat_input, str) -# else request.chat_input -# ), -# "provider": self.config.id, -# "model": ( -# model -# if model and model.startswith(model) -# else (model or model) -# ), -# "deployment": ( -# model -# if model and model.startswith(model) -# else (model if model != model else None) -# ), -# "timestamp": time.time(), -# "parameters": request.parameters.model_dump(), -# "metrics": metrics, -# } - -# if not request.is_stream: -# yield response - -# def join_chunks(self, chunks: List[Dict[str, Any]], request: ChatRequest) -> Tuple[ChatCompletion, str]: -# """ -# Joins chunks of response data into a single ChatCompletion object. - -# Args: -# chunks (List[Dict[str, Any]]): A list of response chunks from the API. -# request (ChatRequest): The original chat request object. - -# Returns: -# Tuple[ChatCompletion, str]: A tuple containing the ChatCompletion object and the tool call arguments string. -# """ -# from llmstudio_core.providers.azure import AzureRequest -# from llmstudio_core.providers.openai import OpenAIRequest -# from llmstudio_core.providers.vertexai import VertexAIRequest - -# finish_reason = chunks[-1].get("choices")[0].get("finish_reason") -# if finish_reason == "tool_calls": -# tool_calls = [ -# chunk.get("choices")[0].get("delta").get("tool_calls")[0] -# for chunk in chunks[1:-1] -# ] - -# tool_call_id = tool_calls[0].get("id") -# tool_call_name = tool_calls[0].get("function").get("name") -# tool_call_type = tool_calls[0].get("function").get("type") -# tool_call_arguments = "".join( -# chunk.get("function", {}).get("arguments", "") -# for chunk in tool_calls[1:] -# ) - -# try: -# return ( -# ChatCompletion( -# id=chunks[-1].get("id"), -# created=chunks[-1].get("created"), -# model=chunks[-1].get("model"), -# object="chat.completion", -# choices=[ -# Choice( -# finish_reason="tool_calls", -# index=0, -# logprobs=None, -# message=ChatCompletionMessage( -# content=None, -# role="assistant", -# function_call=None, -# tool_calls=[ -# ChatCompletionMessageToolCall( -# id=tool_call_id, -# function=Function( -# arguments=tool_call_arguments, -# name=tool_call_name, -# ), -# type=tool_call_type, -# ) -# ], -# ), -# ) -# ], -# ), -# tool_call_arguments, -# ) -# except Exception as e: -# raise e -# elif finish_reason == "function_call": -# function_calls = [ -# chunk.get("choices")[0].get("delta").get("function_call") -# for chunk in chunks[1:-1] -# if chunk.get("choices") -# and chunk.get("choices")[0].get("delta") -# and chunk.get("choices")[0].get("delta").get("function_call") -# ] - -# if isinstance(request, AzureRequest): -# function_call_name = function_calls[0].get("name") -# elif isinstance(request, OpenAIRequest) or isinstance( -# request, VertexAIRequest -# ): -# function_call_name = ( -# chunks[0] -# .get("choices")[0] -# .get("delta") -# .get("function_call") -# .get("name") -# ) -# function_call_arguments = "" -# for chunk in function_calls: -# if isinstance(request, AzureRequest): -# part = chunk.get("arguments", "") -# if part: -# function_call_arguments += part -# elif isinstance(request, OpenAIRequest) or isinstance( -# request, VertexAIRequest -# ): -# function_call_arguments += chunk.get("arguments") - -# return ( -# ChatCompletion( -# id=chunks[-1].get("id"), -# created=chunks[-1].get("created"), -# model=chunks[-1].get("model"), -# object="chat.completion", -# choices=[ -# Choice( -# finish_reason="function_call", -# index=0, -# logprobs=None, -# message=ChatCompletionMessage( -# content=None, -# role="assistant", -# tool_calls=None, -# function_call=FunctionCall( -# arguments=function_call_arguments, -# name=function_call_name, -# ), -# ), -# ) -# ], -# ), -# function_call_arguments, -# ) - -# elif finish_reason == "stop" or finish_reason == "length": -# if isinstance(request, AzureRequest) or isinstance(request, OpenAIRequest): -# start_index = 1 -# else: -# start_index = 0 - -# stop_content = "".join( -# filter( -# None, -# [ -# chunk.get("choices")[0].get("delta").get("content") -# for chunk in chunks[start_index:] -# ], -# ) -# ) - -# return ( -# ChatCompletion( -# id=chunks[-1].get("id"), -# created=chunks[-1].get("created"), -# model=chunks[-1].get("model"), -# object="chat.completion", -# choices=[ -# Choice( -# finish_reason="stop", -# index=0, -# logprobs=None, -# message=ChatCompletionMessage( -# content=stop_content, -# role="assistant", -# function_call=None, -# tool_calls=None, -# ), -# ) -# ], -# ), -# stop_content, -# ) - -# async def parse_response( -# self, response: AsyncGenerator -# ) -> AsyncGenerator[str, None]: -# pass - -# def calculate_metrics_stream( -# self, -# input: Any, -# output: Any, -# model: str, -# start_time: float, -# end_time: float, -# first_token_time: float, -# token_times: Tuple[float, ...], -# token_count: int, -# ) -> Dict[str, Any]: -# """Calculates metrics based on token times and output""" -# model_config = self.config.models[model] -# input_tokens = len(self.tokenizer.encode(self.input_to_string(input))) -# output_tokens = len(output) - -# input_cost = self.calculate_cost(input_tokens, model_config.input_token_cost) -# output_cost = self.calculate_cost(output_tokens, model_config.output_token_cost) - -# total_time = end_time - start_time -# return { -# "input_tokens": input_tokens, -# "output_tokens": output_tokens, -# "total_tokens": input_tokens + output_tokens, -# "cost_usd": input_cost + output_cost, -# "latency_s": total_time, -# "time_to_first_token_s": first_token_time - start_time, -# "inter_token_latency_s": sum(token_times) / len(token_times), -# "tokens_per_second": token_count / total_time, -# } - -# def calculate_metrics( -# self, -# input: Any, -# output: Any, -# model: str, -# start_time: float, -# end_time: float, -# response: Any - -# ) -> Dict[str, Any]: -# """Calculates metrics based on token times and output""" -# model_config = self.config.models[model] - -# input_cost = self.calculate_cost(3, model_config.input_token_cost) -# output_cost = self.calculate_cost(3, model_config.output_token_cost) - -# total_time = end_time - start_time -# return { -# "input_tokens": 3, -# "output_tokens": 3, -# "total_tokens": 3 + 3, -# "cost_usd": input_cost + output_cost, -# "latency_s": total_time, -# "time_to_first_token_s": 3 - 3, -# "inter_token_latency_s": sum(3) / len(3), -# "tokens_per_second": 3 / total_time, -# } - -# def calculate_cost( -# self, token_count: int, token_cost: Union[float, List[Dict[str, Any]]] -# ) -> float: -# if isinstance(token_cost, list): -# for cost_range in token_cost: -# if token_count >= cost_range.range[0] and ( -# token_count <= cost_range.range[1] or cost_range.range[1] is None -# ): -# return cost_range.cost * token_count -# else: -# return token_cost * token_count -# return 0 - -# def input_to_string(self, input): -# if isinstance(input, str): -# return input -# else: -# result = [] -# for message in input: -# if message.get("content") is not None: -# if isinstance(message["content"], str): -# result.append(message["content"]) -# elif ( -# isinstance(message["content"], list) -# and message.get("role") == "user" -# ): -# for item in message["content"]: -# if item.get("type") == "text": -# result.append(item.get("text", "")) -# elif item.get("type") == "image_url": -# url = item.get("image_url", {}).get("url", "") -# result.append(url) -# return "".join(result) - -# def output_to_string(self, output): -# if output.choices[0].finish_reason == "stop": -# return output.choices[0].message.content -# elif output.choices[0].finish_reason == "tool_calls": -# return output.choices[0].message.tool_calls[0].function.arguments -# elif output.choices[0].finish_reason == "function_call": -# return output.choices[0].message.function_call.arguments - -# def get_end_token_string(self, metrics: Dict[str, Any]) -> str: -# return f"{self.END_TOKEN},input_tokens={metrics['input_tokens']},output_tokens={metrics['output_tokens']},cost_usd={metrics['cost_usd']},latency_s={metrics['latency_s']:.5f},time_to_first_token_s={metrics['time_to_first_token_s']:.5f},inter_token_latency_s={metrics['inter_token_latency_s']:.5f},tokens_per_second={metrics['tokens_per_second']:.2f}" - -# def _get_tokenizer(self): -# return {}.get(self.config.id, tiktoken.get_encoding("cl100k_base")) + From 7a3c5d28296f87e05476cbdf0b2e182c99cf8c11 Mon Sep 17 00:00:00 2001 From: Diogo Goncalves Date: Tue, 8 Oct 2024 17:41:20 +0100 Subject: [PATCH 05/58] feat: add sync methods --- libs/core/llmstudio_core/__init__.py | 50 ++++- libs/core/llmstudio_core/providers/openai.py | 26 +++ .../core/llmstudio_core/providers/provider.py | 185 +++++++++++++++++- 3 files changed, 256 insertions(+), 5 deletions(-) diff --git a/libs/core/llmstudio_core/__init__.py b/libs/core/llmstudio_core/__init__.py index e4a5917c..4ef2a181 100644 --- a/libs/core/llmstudio_core/__init__.py +++ b/libs/core/llmstudio_core/__init__.py @@ -112,9 +112,51 @@ async def async_stream(): if p.metrics: pprint(p) asyncio.run(async_stream()) - print("how to get the metrics out?") + + + print("# Now sync calls") + chat_request = { + "chat_input": "Hello, my name is Json", + "model": "gpt-3.5-turbo", + "is_stream": False, + "retries": 0, + "parameters": { + "temperature": 0, + "max_tokens": 100, + "response_format": {"type": "json_object"}, + "functions": None, + } + } + + provider = "openai" + + llm = LLM(provider=provider, api_key=os.environ["OPENAI_API_KEY"]) + + response_sync = llm.chat(chat_request) + pprint(response_sync) - # response_sync = llm.chat(chat_request) - # pprint(response_sync) + print("# Now sync calls streaming") + chat_request = { + "chat_input": "Hello, my name is Json", + "model": "gpt-3.5-turbo", + "is_stream": True, + "retries": 0, + "parameters": { + "temperature": 0, + "max_tokens": 100, + "response_format": {"type": "json_object"}, + "functions": None, + } + } - # assert response_sync == response_async \ No newline at end of file + provider = "openai" + + llm = LLM(provider=provider, api_key=os.environ["OPENAI_API_KEY"]) + + response_sync_stream = llm.chat(chat_request) + for p in response_sync_stream: + pprint(p.chat_output) + pprint(p.choices[0].delta.content==p.chat_output) + print("metrics: ",p.metrics) + if p.metrics: + pprint(p) diff --git a/libs/core/llmstudio_core/providers/openai.py b/libs/core/llmstudio_core/providers/openai.py index 3cb1bf1f..e5c573a1 100644 --- a/libs/core/llmstudio_core/providers/openai.py +++ b/libs/core/llmstudio_core/providers/openai.py @@ -40,9 +40,35 @@ async def agenerate_client( ) except openai._exceptions.APIError as e: raise ProviderError(str(e)) + + def generate_client( + self, request: ChatRequest + ) -> Coroutine[Any, Any, Generator]: + """Generate an OpenAI client""" + + try: + client = OpenAI(api_key=self.API_KEY) + return client.chat.completions.create( + model=request.model, + messages=( + [{"role": "user", "content": request.chat_input}] + if isinstance(request.chat_input, str) + else request.chat_input + ), + stream=request.is_stream, + **request.parameters, + ) + except openai._exceptions.APIError as e: + raise ProviderError(str(e)) async def aparse_response( self, response: AsyncGenerator, **kwargs ) -> AsyncGenerator[str, None]: for chunk in response: yield chunk.model_dump() + + def parse_response( + self, response: AsyncGenerator, **kwargs + ) -> Generator: + for chunk in response: + yield chunk.model_dump() \ No newline at end of file diff --git a/libs/core/llmstudio_core/providers/provider.py b/libs/core/llmstudio_core/providers/provider.py index 886c1f30..13cde59b 100644 --- a/libs/core/llmstudio_core/providers/provider.py +++ b/libs/core/llmstudio_core/providers/provider.py @@ -114,6 +114,37 @@ async def achat( ) # Raise other exceptions as HTTP 500 raise HTTPException(status_code=429, detail="Too many requests") + def chat( + self, request: ChatRequest + ): + """Makes a chat connection with the provider's API""" + try: + request = self.validate_request(request) + except ValidationError as e: + raise HTTPException(status_code=422, detail=e.errors()) + + self.validate_model(request) + + for _ in range(request.retries + 1): + try: + start_time = time.time() + response = self.generate_client(request) + + if request.is_stream: + response_handler = self.handle_response_stream(request, response, start_time) + return response_handler + else: + return self.handle_response(request, response, start_time) + + except HTTPException as e: + if e.status_code == 429: + continue # Retry on rate limit error + else: + raise e # Raise other HTTP exceptions + except Exception as e: + raise ProviderError(str(e)) + raise HTTPException(status_code=429, detail="Too many requests") + def validate_request(self, request: ChatRequest): pass @@ -129,6 +160,153 @@ async def agenerate_client( ) -> Coroutine[Any, Any, Generator]: """Generate the provider's client""" + def handle_response( + self, request: ChatRequest, response: ChatCompletion, start_time: float + ) -> ChatCompletion: + """Handles the response from an API""" + model = response.model + + metrics = self.calculate_metrics( + usage=response.usage.model_dump(), + model=request.model, + start_time=start_time, + end_time=time.time(), + ) + + response = { + **response.model_dump(), + "id": str(uuid.uuid4()), + "chat_input": ( + request.chat_input + if isinstance(request.chat_input, str) + else request.chat_input[-1]["content"] + ), + "chat_output": response.choices[0].message.content, + "context": ( + [{"role": "user", "content": request.chat_input}] + if isinstance(request.chat_input, str) + else request.chat_input + ), + "provider": self.config.id, + "model": ( + request.model + if model and model.startswith(request.model) + else (model or request.model) + ), + "deployment": ( + model + if model and model.startswith(request.model) + else (request.model if model != request.model else None) + ), + "timestamp": time.time(), + "parameters": request.parameters, + "metrics": metrics, + } + + return ChatCompletion(**response) + + def handle_response_stream( + self, request: ChatRequest, response: AsyncGenerator, start_time: float + ) -> Generator: + """Handles the response from an API""" + first_token_time = None + previous_token_time = None + token_times = [] + token_count = 0 + chunks = [] + + for chunk in self.parse_response(response, request=request): + token_count += 1 + current_time = time.time() + first_token_time = first_token_time or current_time + if previous_token_time is not None: + token_times.append(current_time - previous_token_time) + previous_token_time = current_time + + chunks.append(chunk) + chunk = chunk[0] if isinstance(chunk, tuple) else chunk + if chunk.get("choices")[0].get("finish_reason") != "stop": + model = chunk.get("model") + response = { + **chunk, + "id": str(uuid.uuid4()), + "chat_input": ( + request.chat_input + if isinstance(request.chat_input, str) + else request.chat_input[-1]["content"] + ), + "chat_output": chunk.get("choices")[0].get("delta").get("content"), + "context": ( + [{"role": "user", "content": request.chat_input}] + if isinstance(request.chat_input, str) + else request.chat_input + ), + "provider": self.config.id, + "model": ( + request.model + if model and model.startswith(request.model) + else (model or request.model) + ), + "deployment": ( + model + if model and model.startswith(request.model) + else (request.model if model != request.model else None) + ), + "timestamp": time.time(), + "parameters": request.parameters, + "metrics": None, + } + yield ChatCompletionChunk(**response) + + chunks = [chunk[0] if isinstance(chunk, tuple) else chunk for chunk in chunks] + model = next(chunk["model"] for chunk in chunks if chunk.get("model")) + + response, _ = self.join_chunks(chunks, request) + + metrics = self.calculate_metrics_stream( + request.chat_input, + response, + request.model, + start_time, + time.time(), + first_token_time, + token_times, + token_count, + ) + + response = { + **chunk, + "id": str(uuid.uuid4()), + "chat_input": ( + request.chat_input + if isinstance(request.chat_input, str) + else request.chat_input[-1]["content"] + ), + "chat_output": None, + "context": ( + [{"role": "user", "content": request.chat_input}] + if isinstance(request.chat_input, str) + else request.chat_input + ), + "provider": self.config.id, + "model": ( + request.model + if model and model.startswith(request.model) + else (model or request.model) + ), + "deployment": ( + model + if model and model.startswith(request.model) + else (request.model if model != request.model else None) + ), + "timestamp": time.time(), + "parameters": request.parameters, + "metrics": metrics, + } + + yield ChatCompletionChunk(**response) + + async def ahandle_response( self, request: ChatRequest, response: ChatCompletion, start_time: float ) -> AsyncGenerator[ChatCompletion, None]: @@ -409,7 +587,12 @@ def join_chunks(self, chunks, request): async def aparse_response( self, response: AsyncGenerator - ) -> AsyncGenerator[str, None]: + ) -> AsyncGenerator[str, ChatCompletionChunk]: + pass + + def parse_response( + self, response: AsyncGenerator + ) -> ChatCompletionChunk: pass def calculate_metrics_stream( From 09d2544189f6affe28da8b79fd24e512497fa9bc Mon Sep 17 00:00:00 2001 From: Diogo Goncalves Date: Tue, 8 Oct 2024 19:09:04 +0100 Subject: [PATCH 06/58] feat: adapt base class --- libs/core/llmstudio_core/__init__.py | 37 ++----------------- .../core/llmstudio_core/providers/__init__.py | 10 ++--- .../llmstudio_core/providers/anthropic.py | 8 +++- libs/core/llmstudio_core/providers/azure.py | 8 +++- libs/core/llmstudio_core/providers/ollama.py | 8 +++- libs/core/llmstudio_core/providers/openai.py | 4 ++ .../core/llmstudio_core/providers/provider.py | 25 +++++++------ .../core/llmstudio_core/providers/vertexai.py | 8 +++- 8 files changed, 50 insertions(+), 58 deletions(-) diff --git a/libs/core/llmstudio_core/__init__.py b/libs/core/llmstudio_core/__init__.py index 4ef2a181..97ddbb1f 100644 --- a/libs/core/llmstudio_core/__init__.py +++ b/libs/core/llmstudio_core/__init__.py @@ -1,28 +1,11 @@ from abc import ABC from typing import Optional -from llmstudio_core.providers import ( - # AnthropicProvider -# , AzureProvider -# , OllamaProvider -OpenAIProvider -, provider_registry -# , VertexAIProvider -) - -from llmstudio_core.providers.provider import BaseProvider +from llmstudio_core.providers.provider import BaseProvider, provider_registry from llmstudio_core.providers import _load_engine_config _engine_config = _load_engine_config() -_providers_map = dict( - # anthropic=AnthropicProvider, - # azure=AzureProvider, - # ollama=OllamaProvider, - openai=OpenAIProvider, - # vertexai=VertexAIProvider -) - def LLM(provider: str, api_key: Optional[str] = None) -> BaseProvider: """ @@ -39,25 +22,11 @@ def LLM(provider: str, api_key: Optional[str] = None) -> BaseProvider: NotImplementedError: If the provider is not found in the provider map. """ provider_config = _engine_config.providers.get(provider) - provider_class = _providers_map.get(provider_config.id) + provider_class = provider_registry.get(provider_config.id) if provider_class: return provider_class(config=provider_config, api_key=api_key) - raise NotImplementedError(f"BaseProvider not found: {provider_config.id}. Available providers: {str(_providers_map.keys())}") - -class LLMStudioTracker(ABC): - pass - -#@SDK -class LLM_: - def __init__(self,provider: str ,tracker: LLMStudioTracker=None, **kwargs) -> None: - - self.provider = LLM(provider=provider,**kwargs) - self.tracker = tracker + raise NotImplementedError(f"BaseProvider not found: {provider_config.id}. Available providers: {str(provider_registry.keys())}") - def chat(self, chat_input, **kwargs): - response = self.provider.chat(chat_input, **kwargs) - self.tracker.log(response["metrics"]) - return response if __name__ == "__main__": from pprint import pprint diff --git a/libs/core/llmstudio_core/providers/__init__.py b/libs/core/llmstudio_core/providers/__init__.py index 1bc55171..a6ca613b 100644 --- a/libs/core/llmstudio_core/providers/__init__.py +++ b/libs/core/llmstudio_core/providers/__init__.py @@ -1,17 +1,17 @@ import os import yaml -# from llmstudio_core.providers.anthropic import AnthropicProvider -# from llmstudio_core.providers.azure import AzureProvider -# from llmstudio_core.providers.ollama import OllamaProvider +from llmstudio_core.providers.anthropic import AnthropicProvider +from llmstudio_core.providers.azure import AzureProvider +from llmstudio_core.providers.ollama import OllamaProvider from llmstudio_core.providers.openai import OpenAIProvider -from llmstudio_core.providers.provider import provider_registry -# from llmstudio_core.providers.vertexai import VertexAIProvider +from llmstudio_core.providers.vertexai import VertexAIProvider from pathlib import Path from typing import Any, Dict, List, Optional, Union from pydantic import BaseModel, ValidationError + class CostRange(BaseModel): range: List[Optional[int]] cost: float diff --git a/libs/core/llmstudio_core/providers/anthropic.py b/libs/core/llmstudio_core/providers/anthropic.py index ecd937f1..aac7c6d5 100644 --- a/libs/core/llmstudio_core/providers/anthropic.py +++ b/libs/core/llmstudio_core/providers/anthropic.py @@ -11,7 +11,7 @@ from openai.types.chat.chat_completion_chunk import Choice, ChoiceDelta from pydantic import BaseModel, Field -from llmstudio_core.providers.provider import ChatRequest, Provider, provider +from llmstudio_core.providers.provider import ChatRequest, BaseProvider, provider class ClaudeParameters(BaseModel): @@ -27,10 +27,14 @@ class AnthropicRequest(ChatRequest): @provider -class AnthropicProvider(Provider): +class AnthropicProvider(BaseProvider): def __init__(self, config, api_key=None): super().__init__(config) self.API_KEY = api_key or os.getenv("ANTHROPIC_API_KEY") + + @staticmethod + def _provider_config_name(): + return "antropic" def validate_request(self, request: AnthropicRequest): return AnthropicRequest(**request) diff --git a/libs/core/llmstudio_core/providers/azure.py b/libs/core/llmstudio_core/providers/azure.py index c96ce46c..e1cd70bf 100644 --- a/libs/core/llmstudio_core/providers/azure.py +++ b/libs/core/llmstudio_core/providers/azure.py @@ -28,7 +28,7 @@ ) from pydantic import BaseModel, Field -from llmstudio_core.providers.provider import ChatRequest, Provider, provider +from llmstudio_core.providers.provider import ChatRequest, BaseProvider, provider class AzureParameters(BaseModel): @@ -51,7 +51,7 @@ class AzureRequest(ChatRequest): @provider -class AzureProvider(Provider): +class AzureProvider(BaseProvider): def __init__(self, config, api_key=None): super().__init__(config) self.API_KEY = api_key or os.getenv("AZURE_API_KEY") @@ -62,6 +62,10 @@ def __init__(self, config, api_key=None): self.has_tools = False self.has_functions = False + @staticmethod + def _provider_config_name(): + return "azure" + def validate_request(self, request: AzureRequest): return AzureRequest(**request) diff --git a/libs/core/llmstudio_core/providers/ollama.py b/libs/core/llmstudio_core/providers/ollama.py index 89627c8e..d2c175d6 100644 --- a/libs/core/llmstudio_core/providers/ollama.py +++ b/libs/core/llmstudio_core/providers/ollama.py @@ -10,7 +10,7 @@ from openai.types.chat.chat_completion_chunk import Choice, ChoiceDelta from pydantic import BaseModel, Field -from llmstudio_core.providers.provider import ChatRequest, Provider, provider +from llmstudio_core.providers.provider import ChatRequest, BaseProvider, provider class OllamaParameters(BaseModel): @@ -25,10 +25,14 @@ class OllamaRequest(ChatRequest): @provider -class OllamaProvider(Provider): +class OllamaProvider(BaseProvider): def __init__(self, config, api_key=None): super().__init__(config) + @staticmethod + def _provider_config_name(): + return "ollama" + def validate_request(self, request: OllamaRequest): return OllamaRequest(**request) diff --git a/libs/core/llmstudio_core/providers/openai.py b/libs/core/llmstudio_core/providers/openai.py index e5c573a1..14a5a95c 100644 --- a/libs/core/llmstudio_core/providers/openai.py +++ b/libs/core/llmstudio_core/providers/openai.py @@ -17,6 +17,10 @@ def __init__(self, config, **kwargs): super().__init__(config, **kwargs) self.API_KEY = self.API_KEY if self.API_KEY else os.getenv("OPENAI_API_KEY") + @staticmethod + def _provider_config_name(): + return "openai" + def validate_request(self, request: ChatRequest): return ChatRequest(**request) diff --git a/libs/core/llmstudio_core/providers/provider.py b/libs/core/llmstudio_core/providers/provider.py index 13cde59b..09be1c74 100644 --- a/libs/core/llmstudio_core/providers/provider.py +++ b/libs/core/llmstudio_core/providers/provider.py @@ -1,7 +1,4 @@ -from abc import ABC -import asyncio -import json -import os +from abc import ABC, abstractmethod import time import uuid from pathlib import Path @@ -30,7 +27,7 @@ from openai.types.chat.chat_completion import Choice from openai.types.chat.chat_completion_message import FunctionCall from openai.types.chat.chat_completion_message_tool_call import Function -from pydantic import BaseModel, ConfigDict, ValidationError +from pydantic import BaseModel, ValidationError provider_registry = {} @@ -38,9 +35,11 @@ def provider(cls): """Decorator to register a new provider.""" - provider_registry[cls.__name__] = cls + provider_registry[cls._provider_config_name()] = cls + return cls + class ChatRequest(BaseModel): model: str chat_input: Any @@ -67,15 +66,22 @@ def __init__(self, self.tokenizer = tokenizer if tokenizer else self._get_tokenizer() self.count = 0 + @abstractmethod async def achat( self, request: ChatRequest ) -> Union[StreamingResponse, JSONResponse]: raise NotImplementedError("Providers needs to have achat method implemented.") + @abstractmethod def achat( self, request: ChatRequest ) -> Union[StreamingResponse, JSONResponse]: raise NotImplementedError("Providers needs to have chat method implemented.") + + @staticmethod + @abstractmethod + def _provider_config_name(): + raise NotImplementedError("Providers need to implement the '_provider_config_name' property.") class BaseProvider(ProviderABC): @@ -306,7 +312,6 @@ def handle_response_stream( yield ChatCompletionChunk(**response) - async def ahandle_response( self, request: ChatRequest, response: ChatCompletion, start_time: float ) -> AsyncGenerator[ChatCompletion, None]: @@ -453,7 +458,6 @@ async def ahandle_response_stream( yield ChatCompletionChunk(**response) - def join_chunks(self, chunks, request): finish_reason = chunks[-1].get("choices")[0].get("finish_reason") @@ -547,7 +551,7 @@ def join_chunks(self, chunks, request): ) elif finish_reason == "stop" or finish_reason == "length": - if self.__class__.__name__ in ("OpenAIProvider", "AzureProvider"): # or isinstance(request, OpenAIRequest): + if self.__class__.__name__ in ("OpenAIProvider", "AzureProvider"): start_index = 1 else: start_index = 0 @@ -653,7 +657,6 @@ def calculate_metrics( "tokens_per_second": usage.get("total_tokens") / total_time, } - def calculate_cost( self, token_count: int, token_cost: Union[float, List[Dict[str, Any]]] ) -> float: @@ -701,4 +704,4 @@ def get_end_token_string(self, metrics: Dict[str, Any]) -> str: def _get_tokenizer(self): return {}.get(self.config.id, tiktoken.get_encoding("cl100k_base")) - + diff --git a/libs/core/llmstudio_core/providers/vertexai.py b/libs/core/llmstudio_core/providers/vertexai.py index 95e12871..a38b4566 100644 --- a/libs/core/llmstudio_core/providers/vertexai.py +++ b/libs/core/llmstudio_core/providers/vertexai.py @@ -25,7 +25,7 @@ ) from pydantic import BaseModel, Field, ValidationError -from llmstudio_core.providers.provider import ChatRequest, Provider, provider +from llmstudio_core.providers.provider import ChatRequest, BaseProvider, provider class VertexParameters(BaseModel): @@ -87,11 +87,15 @@ class VertexAI(BaseModel): @provider -class VertexAIProvider(Provider): +class VertexAIProvider(BaseProvider): def __init__(self, config, api_key): super().__init__(config, api_key=api_key) self.GOOGLE_API_KEY = api_key or os.getenv("GOOGLE_API_KEY") + @staticmethod + def _provider_config_name(): + return "vertexai" + def validate_request(self, request: VertexAIRequest): return VertexAIRequest(**request) From 2e183fe2e1bf93aa445977663432794496d95c67 Mon Sep 17 00:00:00 2001 From: Diogo Goncalves Date: Wed, 9 Oct 2024 15:52:49 +0100 Subject: [PATCH 07/58] fix: adapt notebook for new module --- examples/01_intro_to_llmstudio.ipynb | 117 ++++++++++++++++++++------- 1 file changed, 87 insertions(+), 30 deletions(-) diff --git a/examples/01_intro_to_llmstudio.ipynb b/examples/01_intro_to_llmstudio.ipynb index 0ece0daf..2dfb3eaf 100644 --- a/examples/01_intro_to_llmstudio.ipynb +++ b/examples/01_intro_to_llmstudio.ipynb @@ -18,18 +18,10 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running LLMstudio Engine on http://localhost:50001 Running LLMstudio Tracking on http://localhost:50002 \n", - "\n" - ] - } - ], + "outputs": [], "source": [ - "from llmstudio import LLM" + "from llmstudio_core import LLM\n", + "from pprint import pprint" ] }, { @@ -40,9 +32,8 @@ "source": [ "# You can set OPENAI_API_KEY and ANTHROPIC_API_KEY on .env file\n", "\n", - "claude2 = LLM(\"anthropic/claude-2.1\") # or you can pass api_key as an argument here\n", - "gpt4 = LLM(\"openai/gpt-4\")\n", - "gpt3 = LLM(\"openai/gpt-3.5-turbo\")" + "anthropic = LLM(\"anthropic\") # or you can pass api_key as an argument here\n", + "openai = LLM(\"openai\")\n" ] }, { @@ -60,7 +51,7 @@ { "data": { "text/plain": [ - "ChatCompletion(id='0da04a32-942d-4f9b-8325-0c8fceb18426', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content=\"As an artificial intelligence, I don't have a personal name. You can simply refer to me as OpenAI.\", role='assistant', function_call=None, tool_calls=None))], created=1723120856, model='gpt-4', object='chat.completion', service_tier=None, system_fingerprint=None, usage=None, session_id=None, chat_input=\"What's your name\", chat_output=\"As an artificial intelligence, I don't have a personal name. You can simply refer to me as OpenAI.\", context=[{'role': 'user', 'content': \"What's your name\"}], provider='openai', deployment='gpt-4-0613', timestamp=1723120857.550569, parameters={'temperature': 1, 'max_tokens': 2048, 'top_p': 1, 'frequency_penalty': 0, 'presence_penalty': 0}, metrics={'input_tokens': 4, 'output_tokens': 23, 'total_tokens': 27, 'cost_usd': 0.0015, 'latency_s': 1.8822567462921143, 'time_to_first_token_s': 0.9649028778076172, 'inter_token_latency_s': 0.0378934641679128, 'tokens_per_second': 13.281928753475249})" + "ChatCompletion(id='0388f750-9076-42bd-a963-d4eab24bb1ba', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='I’m called ChatGPT. How can I assist you today?', refusal=None, role='assistant', function_call=None, tool_calls=None))], created=1728478112, model='gpt-4o', object='chat.completion', service_tier=None, system_fingerprint='fp_2f406b9113', usage=CompletionUsage(completion_tokens=13, prompt_tokens=10, total_tokens=23, completion_tokens_details=CompletionTokensDetails(reasoning_tokens=0), prompt_tokens_details={'cached_tokens': 0}), chat_input=\"What's your name\", chat_output='I’m called ChatGPT. How can I assist you today?', context=[{'role': 'user', 'content': \"What's your name\"}], provider='openai', deployment='gpt-4o-2024-08-06', timestamp=1728478112.735258, parameters={}, metrics={'input_tokens': 10, 'output_tokens': 13, 'total_tokens': 23, 'cost_usd': 0.000245, 'latency_s': 1.051265001296997, 'time_to_first_token_s': None, 'inter_token_latency_s': None, 'tokens_per_second': 21.878403610529958})" ] }, "execution_count": 3, @@ -69,7 +60,7 @@ } ], "source": [ - "gpt4.chat(\"What's your name\")" + "openai.chat(\"What's your name\", model=\"gpt-4o\")" ] }, { @@ -87,7 +78,7 @@ { "data": { "text/plain": [ - "ChatCompletion(id='cd5ba1b3-f030-4db6-be16-1b46d0ca2494', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content=\"I'm OpenAI, an artificial intelligence language model developed by OpenAI called GPT-3. I don't have a personal name because I'm not a person but a programming model.\", role='assistant', function_call=None, tool_calls=None))], created=1723120858, model='gpt-4', object='chat.completion', service_tier=None, system_fingerprint=None, usage=None, session_id=None, chat_input=\"What's your name\", chat_output=\"I'm OpenAI, an artificial intelligence language model developed by OpenAI called GPT-3. I don't have a personal name because I'm not a person but a programming model.\", context=[{'role': 'user', 'content': \"What's your name\"}], provider='openai', deployment='gpt-4-0613', timestamp=1723120861.055718, parameters={'temperature': None, 'max_tokens': None, 'top_p': None, 'frequency_penalty': None, 'presence_penalty': None}, metrics={'input_tokens': 4, 'output_tokens': 38, 'total_tokens': 42, 'cost_usd': 0.0024, 'latency_s': 2.893263101577759, 'time_to_first_token_s': 0.8696560859680176, 'inter_token_latency_s': 0.05186671477097731, 'tokens_per_second': 13.825220381163101})" + "ChatCompletion(id='7f196166-935d-4539-a1a9-ba9ca32ef62a', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='I’m an AI developed by OpenAI and don’t have a personal name, but you can call me Assistant. How can I help you today?', refusal=None, role='assistant', function_call=None, tool_calls=None))], created=1728478113, model='gpt-4o', object='chat.completion', service_tier=None, system_fingerprint='fp_e5e4913e83', usage=CompletionUsage(completion_tokens=30, prompt_tokens=10, total_tokens=40, completion_tokens_details=CompletionTokensDetails(reasoning_tokens=0), prompt_tokens_details={'cached_tokens': 0}), chat_input=\"What's your name\", chat_output='I’m an AI developed by OpenAI and don’t have a personal name, but you can call me Assistant. How can I help you today?', context=[{'role': 'user', 'content': \"What's your name\"}], provider='openai', deployment='gpt-4o-2024-08-06', timestamp=1728478113.728288, parameters={}, metrics={'input_tokens': 10, 'output_tokens': 30, 'total_tokens': 40, 'cost_usd': 0.0005, 'latency_s': 0.9794328212738037, 'time_to_first_token_s': None, 'inter_token_latency_s': None, 'tokens_per_second': 40.83996281437445})" ] }, "execution_count": 4, @@ -96,7 +87,7 @@ } ], "source": [ - "await gpt4.async_chat(\"What's your name\")" + "await openai.achat(\"What's your name\", model=\"gpt-4o\")" ] }, { @@ -108,21 +99,55 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Space is a vast and mysterious expanse that encompasses everything beyond Earth's atmosphere. It is home to billions of galaxies, each with their own unique systems of planets, stars, and other celestial bodies. The sheer size and complexity of space is both awe-inspiring and intimidating, as it challenges our understanding of the universe and our place within it. Scientists and astronomers continue to study and explore space in order to unlock its many secrets and further our knowledge of the cosmos. From the beauty of distant nebulae to the enigmatic nature of black holes, space captivates and intrigues us with its boundless possibilities and endless wonders." + "\n", + "\n", + "Space, an awe-inspiring expanse that stretches beyond the grasp of human imagination, is the\n", + "\n", + " vast, seemingly infinite void that envelops planets, stars, galaxies, and all celestial bodies. It\n", + "\n", + " serves as both the birthplace and the playground of cosmic phenomena, from the swirling, fiery dance of stars\n", + "\n", + " being born in nebulae to the mysterious, unseen forces of dark matter and dark energy that constitute\n", + "\n", + " the universe's unseen framework. This vast universe is not only a subject of scientific inquiry and discovery but\n", + "\n", + " also a source of inspiration and wonder. Human space exploration endeavors reflect our innate curiosity and longing to understand\n", + "\n", + " our place in the cosmos, driving the development of advanced technology and international cooperation. Space, with its\n", + "\n", + " breathtaking beauty and infinite mysteries, challenges our understanding and continually beckons us to explore further and dream bigger\n", + "\n", + ".\n", + "\n", + "## Metrics:\n", + "{'cost_usd': 0.002455,\n", + " 'input_tokens': 8,\n", + " 'inter_token_latency_s': 0.010445242342741592,\n", + " 'latency_s': 2.4184927940368652,\n", + " 'output_tokens': 161,\n", + " 'time_to_first_token_s': 0.7361259460449219,\n", + " 'tokens_per_second': 66.98386714214482,\n", + " 'total_tokens': 169}\n" ] } ], "source": [ - "response = gpt3.chat(\"Write a paragfraph about space\", is_stream=True)\n", - "for chunk in response:\n", - " print(chunk, end=\"\", flush=True)" + "response = openai.chat(\"Write a paragfraph about space\", model=\"gpt-4o\", is_stream=True)\n", + "for i, chunk in enumerate(response):\n", + " if i%20==0:\n", + " print(\"\\n\")\n", + " if chunk.chat_output is not None:\n", + " print(chunk.chat_output, end=\"\", flush=True)\n", + " else:\n", + " print(\"\\n\\n## Metrics:\")\n", + " pprint(chunk.metrics)" ] }, { @@ -134,20 +159,40 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Hi there! How can I assist you today?" + "\n", + "\n", + "Hi! How can I assist you today?\n", + "\n", + "## Metrics:\n", + "{'cost_usd': 5.85e-06,\n", + " 'input_tokens': 3,\n", + " 'inter_token_latency_s': 0.01939098834991455,\n", + " 'latency_s': 0.8029687404632568,\n", + " 'output_tokens': 9,\n", + " 'time_to_first_token_s': 0.6087050437927246,\n", + " 'tokens_per_second': 13.699163424037863,\n", + " 'total_tokens': 12}\n" ] } ], "source": [ - "async for chunk in await gpt3.async_chat(\"Say hi back\", is_stream=True):\n", - " print(chunk, end=\"\", flush=True)" + "i=0\n", + "async for chunk in await openai.achat(\"Say hi back\", model=\"gpt-4o-mini\", is_stream=True):\n", + " if i%20==0:\n", + " print(\"\\n\")\n", + " if chunk.chat_output is not None:\n", + " print(chunk.chat_output, end=\"\", flush=True)\n", + " else:\n", + " print(\"\\n\\n## Metrics:\")\n", + " pprint(chunk.metrics)\n", + " i+=1" ] }, { @@ -159,9 +204,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'llmstudio'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[7], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mllmstudio\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m LLM\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnest_asyncio\u001b[39;00m\n\u001b[1;32m 3\u001b[0m nest_asyncio\u001b[38;5;241m.\u001b[39mapply()\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'llmstudio'" + ] + } + ], "source": [ "from llmstudio import LLM\n", "import nest_asyncio\n", @@ -234,7 +291,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.12.2" } }, "nbformat": 4, From 45e0cd2d51d7b1984d177d23843203977016651c Mon Sep 17 00:00:00 2001 From: Diogo Goncalves Date: Wed, 9 Oct 2024 15:53:13 +0100 Subject: [PATCH 08/58] chore: automate tests for developement --- libs/core/llmstudio_core/__init__.py | 184 +++++++++++++++------------ 1 file changed, 106 insertions(+), 78 deletions(-) diff --git a/libs/core/llmstudio_core/__init__.py b/libs/core/llmstudio_core/__init__.py index 97ddbb1f..fcb597c2 100644 --- a/libs/core/llmstudio_core/__init__.py +++ b/libs/core/llmstudio_core/__init__.py @@ -25,7 +25,7 @@ def LLM(provider: str, api_key: Optional[str] = None) -> BaseProvider: provider_class = provider_registry.get(provider_config.id) if provider_class: return provider_class(config=provider_config, api_key=api_key) - raise NotImplementedError(f"BaseProvider not found: {provider_config.id}. Available providers: {str(provider_registry.keys())}") + raise NotImplementedError(f"Provider not found: {provider_config.id}. Available providers: {str(provider_registry.keys())}") if __name__ == "__main__": @@ -33,33 +33,85 @@ def LLM(provider: str, api_key: Optional[str] = None) -> BaseProvider: import os from dotenv import load_dotenv load_dotenv() - chat_request = { - "chat_input": "Hello, my name is Json", - "model": "gpt-3.5-turbo", - "is_stream": False, - "retries": 0, - "parameters": { - "temperature": 0, - "max_tokens": 100, - "response_format": {"type": "json_object"}, - "functions": None, + + def test_stuff(provider, model, api_key): + latencies = {} + chat_request = { + "chat_input": "Hello, my name is Json", + "model": model, + "is_stream": False, + "retries": 0, + "parameters": { + "temperature": 0, + "max_tokens": 100, + "response_format": {"type": "json_object"}, + "functions": None, + } + } + + + llm = LLM(provider=provider, api_key=api_key) + + import asyncio + response_async = asyncio.run(llm.achat(**chat_request)) + pprint(response_async) + latencies["async (ms)"]= response_async.metrics["latency_s"]*1000 + + # stream + print("\nasync stream") + async def async_stream(): + chat_request = { + "chat_input": "Hello, my name is Json", + "model": model, + "is_stream": True, + "retries": 0, + "parameters": { + "temperature": 0, + "max_tokens": 100, + "response_format": {"type": "json_object"}, + "functions": None, + } + } + + llm = LLM(provider=provider, api_key=api_key) + + response_async = await llm.achat(**chat_request) + async for p in response_async: + print("that: ",p.chat_output) + # pprint(p.choices[0].delta.content==p.chat_output) + # print("metrics: ", p.metrics) + # print(p) + if p.metrics: + pprint(p) + latencies["async_stream (ms)"]= p.metrics["latency_s"]*1000 + asyncio.run(async_stream()) + + + print("# Now sync calls") + chat_request = { + "chat_input": "Hello, my name is Json", + "model": model, + "is_stream": False, + "retries": 0, + "parameters": { + "temperature": 0, + "max_tokens": 100, + "response_format": {"type": "json_object"}, + "functions": None, + } } - } - provider = "openai" - llm = LLM(provider=provider, api_key=os.environ["OPENAI_API_KEY"]) - - import asyncio - response_async = asyncio.run(llm.achat(chat_request)) - pprint(response_async) + llm = LLM(provider=provider, api_key=api_key) + + response_sync = llm.chat(**chat_request) + pprint(response_sync) + latencies["sync (ms)"]= response_sync.metrics["latency_s"]*1000 - # stream - print("\nasync stream") - async def async_stream(): + print("# Now sync calls streaming") chat_request = { "chat_input": "Hello, my name is Json", - "model": "gpt-3.5-turbo", + "model": model, "is_stream": True, "retries": 0, "parameters": { @@ -70,62 +122,38 @@ async def async_stream(): } } - provider = "openai" - llm = LLM(provider=provider, api_key=os.environ["OPENAI_API_KEY"]) + llm = LLM(provider=provider, api_key=api_key) - response_async = await llm.achat(chat_request) - async for p in response_async: - pprint(p.chat_output) - pprint(p.choices[0].delta.content==p.chat_output) - print("metrics: ",p.metrics) + response_sync_stream = llm.chat(**chat_request) + for p in response_sync_stream: + # pprint(p.chat_output) + # pprint(p.choices[0].delta.content==p.chat_output) + # print("metrics: ",p.metrics) if p.metrics: pprint(p) - asyncio.run(async_stream()) - - - print("# Now sync calls") - chat_request = { - "chat_input": "Hello, my name is Json", - "model": "gpt-3.5-turbo", - "is_stream": False, - "retries": 0, - "parameters": { - "temperature": 0, - "max_tokens": 100, - "response_format": {"type": "json_object"}, - "functions": None, - } - } - - provider = "openai" - - llm = LLM(provider=provider, api_key=os.environ["OPENAI_API_KEY"]) - - response_sync = llm.chat(chat_request) - pprint(response_sync) - - print("# Now sync calls streaming") - chat_request = { - "chat_input": "Hello, my name is Json", - "model": "gpt-3.5-turbo", - "is_stream": True, - "retries": 0, - "parameters": { - "temperature": 0, - "max_tokens": 100, - "response_format": {"type": "json_object"}, - "functions": None, - } - } - - provider = "openai" - - llm = LLM(provider=provider, api_key=os.environ["OPENAI_API_KEY"]) - - response_sync_stream = llm.chat(chat_request) - for p in response_sync_stream: - pprint(p.chat_output) - pprint(p.choices[0].delta.content==p.chat_output) - print("metrics: ",p.metrics) - if p.metrics: - pprint(p) + latencies["sync stream (ms)"]= p.metrics["latency_s"]*1000 + + print(f"\n\n### REPORT for {provider}, {model} ###") + return latencies + + + # provider = "openai" + # model = "gpt-4o-mini" + # for _ in range(1): + # latencies = test_stuff(provider=provider, model=model, api_key=os.environ["OPENAI_API_KEY"]) + # pprint(latencies) + + # provider = "anthropic" + # model = "claude-3-opus-20240229" + # for _ in range(1): + # latencies = test_stuff(provider=provider, model=model, api_key=os.environ["ANTHROPIC_API_KEY"]) + # pprint(latencies) + # we need credits + + provider = "azure" + model = "gpt-4o" + for _ in range(1): + latencies = test_stuff(provider=provider, model=model, + api_key=os.environ["AZURE_API_KEY"] + ) + pprint(latencies) From bcbf81f7e7c9e29ffa38a5f91e5693bc9e8810bb Mon Sep 17 00:00:00 2001 From: Diogo Goncalves Date: Wed, 9 Oct 2024 16:32:17 +0100 Subject: [PATCH 09/58] feat: adapt anthropic --- libs/core/llmstudio_core/__init__.py | 12 +++--- .../llmstudio_core/providers/anthropic.py | 40 +++++++++++++++++-- llmstudio/tracking/__init__.py | 1 - 3 files changed, 43 insertions(+), 10 deletions(-) diff --git a/libs/core/llmstudio_core/__init__.py b/libs/core/llmstudio_core/__init__.py index fcb597c2..4f5faaf8 100644 --- a/libs/core/llmstudio_core/__init__.py +++ b/libs/core/llmstudio_core/__init__.py @@ -137,11 +137,11 @@ async def async_stream(): return latencies - # provider = "openai" - # model = "gpt-4o-mini" - # for _ in range(1): - # latencies = test_stuff(provider=provider, model=model, api_key=os.environ["OPENAI_API_KEY"]) - # pprint(latencies) + provider = "openai" + model = "gpt-4o-mini" + for _ in range(1): + latencies = test_stuff(provider=provider, model=model, api_key=os.environ["OPENAI_API_KEY"]) + pprint(latencies) # provider = "anthropic" # model = "claude-3-opus-20240229" @@ -151,7 +151,7 @@ async def async_stream(): # we need credits provider = "azure" - model = "gpt-4o" + model = "gpt-4o-mini" for _ in range(1): latencies = test_stuff(provider=provider, model=model, api_key=os.environ["AZURE_API_KEY"] diff --git a/libs/core/llmstudio_core/providers/anthropic.py b/libs/core/llmstudio_core/providers/anthropic.py index aac7c6d5..b640348d 100644 --- a/libs/core/llmstudio_core/providers/anthropic.py +++ b/libs/core/llmstudio_core/providers/anthropic.py @@ -34,12 +34,12 @@ def __init__(self, config, api_key=None): @staticmethod def _provider_config_name(): - return "antropic" + return "anthropic" def validate_request(self, request: AnthropicRequest): return AnthropicRequest(**request) - async def generate_client( + async def agenerate_client( self, request: AnthropicRequest ) -> Coroutine[Any, Any, Generator]: """Generate an Anthropic client""" @@ -62,13 +62,47 @@ async def generate_client( **request.parameters.model_dump(), } - return await asyncio.to_thread( + result = await asyncio.to_thread( requests.post, url, headers=headers, json=data, stream=True, ) + if result.status_code != 200: + raise ProviderError(result.status_code, result.text) + except Exception as e: + raise ProviderError(str(e)) + + def generate_client( + self, request: AnthropicRequest + ) -> Coroutine[Any, Any, Generator]: + """Generate an Anthropic client""" + try: + url = f"https://api.anthropic.com/v1/messages" + headers = { + "content-type": "application/json", + "x-api-key": self.API_KEY, + "anthropic-version": "2023-06-01", + } + data = { + "model": request.model, + "stream": True, + # "tools": request.tools, + "messages": ( + [{"role": "user", "content": request.chat_input}] + if isinstance(request.chat_input, str) + else request.chat_input + ), + **request.parameters.model_dump(), + } + + return requests.post( + url, + headers=headers, + json=data, + stream=True, + ) except Exception as e: raise ProviderError(str(e)) diff --git a/llmstudio/tracking/__init__.py b/llmstudio/tracking/__init__.py index d32bc768..bbd05232 100644 --- a/llmstudio/tracking/__init__.py +++ b/llmstudio/tracking/__init__.py @@ -5,7 +5,6 @@ from fastapi.middleware.cors import CORSMiddleware from llmstudio.config import TRACKING_HOST, TRACKING_PORT -from llmstudio.engine.providers import * from llmstudio.tracking.logs.endpoints import LogsRoutes from llmstudio.tracking.session.endpoints import SessionsRoutes From 722282713210adffd8f9bfd97db040811fe5c435 Mon Sep 17 00:00:00 2001 From: Diogo Goncalves Date: Wed, 9 Oct 2024 16:32:36 +0100 Subject: [PATCH 10/58] feat: adapt llama on azure --- libs/core/llmstudio_core/providers/azure.py | 151 ++++++++++-------- .../core/llmstudio_core/providers/provider.py | 71 ++++++-- 2 files changed, 139 insertions(+), 83 deletions(-) diff --git a/libs/core/llmstudio_core/providers/azure.py b/libs/core/llmstudio_core/providers/azure.py index e1cd70bf..34d71dd8 100644 --- a/libs/core/llmstudio_core/providers/azure.py +++ b/libs/core/llmstudio_core/providers/azure.py @@ -31,104 +31,103 @@ from llmstudio_core.providers.provider import ChatRequest, BaseProvider, provider -class AzureParameters(BaseModel): - temperature: Optional[float] = Field(default=1, ge=0, le=2) - max_tokens: Optional[int] = Field(default=2048, ge=1) - top_p: Optional[float] = Field(default=1, ge=0, le=1) - frequency_penalty: Optional[float] = Field(default=0, ge=0, le=1) - presence_penalty: Optional[float] = Field(default=0, ge=0, le=1) - - -class AzureRequest(ChatRequest): - api_endpoint: Optional[str] = None - api_version: Optional[str] = None - base_url: Optional[str] = None - parameters: Optional[AzureParameters] = AzureParameters() - tools: Optional[List[Dict[str, Any]]] = None - functions: Optional[List[Dict[str, Any]]] = None - chat_input: Any - response_format: Optional[Dict[str, str]] = None - - @provider class AzureProvider(BaseProvider): - def __init__(self, config, api_key=None): + def __init__(self, config, api_key=None, api_endpoint=None, api_version=None, base_url=None): super().__init__(config) self.API_KEY = api_key or os.getenv("AZURE_API_KEY") - self.API_ENDPOINT = os.getenv("AZURE_API_ENDPOINT") - self.API_VERSION = os.getenv("AZURE_API_VERSION") - self.BASE_URL = os.getenv("AZURE_BASE_URL") + self.API_ENDPOINT = api_endpoint or os.getenv("AZURE_API_ENDPOINT") + self.API_VERSION = api_version or os.getenv("AZURE_API_VERSION") + self.BASE_URL = base_url or os.getenv("AZURE_BASE_URL") self.is_llama = False - self.has_tools = False - self.has_functions = False - + self.has_tools_functions = False @staticmethod def _provider_config_name(): return "azure" - def validate_request(self, request: AzureRequest): - return AzureRequest(**request) + def validate_request(self, request: ChatRequest): + return ChatRequest(**request) - async def generate_client( - self, request: AzureRequest - ) -> Coroutine[Any, Any, Generator]: + async def agenerate_client( + self, request: ChatRequest + ) -> Any: """Generate an AzureOpenAI client""" self.is_llama = "llama" in request.model.lower() - self.is_openai = "gpt" in request.model.lower() - self.has_tools = request.tools is not None - self.has_functions = request.functions is not None + self.has_tools_functions = (request.parameters.get("functions") or request.parameters.get("tools")) try: - if request.base_url or self.BASE_URL: + if self.BASE_URL: client = OpenAI( api_key=self.API_KEY, - base_url=request.base_url or self.BASE_URL, + base_url=self.BASE_URL, ) else: client = AzureOpenAI( api_key=self.API_KEY, - azure_endpoint=request.api_endpoint or self.API_ENDPOINT, - api_version=request.api_version or self.API_VERSION, + azure_endpoint=self.API_ENDPOINT, + api_version=self.API_VERSION, ) messages = self.prepare_messages(request) - # Prepare the optional tool-related arguments - tool_args = {} - if not self.is_llama and self.has_tools and self.is_openai: - tool_args = { - "tools": request.tools, - "tool_choice": "auto" if request.tools else None, - } - - # Prepare the optional function-related arguments - function_args = {} - if not self.is_llama and self.has_functions and self.is_openai: - function_args = { - "functions": request.functions, - "function_call": "auto" if request.functions else None, - } - # Prepare the base arguments base_args = { "model": request.model, "messages": messages, - "stream": True, - "response_format": request.response_format, + "stream": request.is_stream } # Combine all arguments combined_args = { **base_args, - **tool_args, - **function_args, - **request.parameters.model_dump(), + **request.parameters, } # Perform the asynchronous call return await asyncio.to_thread( client.chat.completions.create, **combined_args ) + except openai._exceptions.APIConnectionError as e: + raise ProviderError(f"There was an error reaching the endpoint: {e}") + + except openai._exceptions.APIStatusError as e: + raise ProviderError(e.response.json()) + + def generate_client( + self, request: ChatRequest + ) -> Any: + """Generate an AzureOpenAI client""" + + self.is_llama = "llama" in request.model.lower() + self.has_tools_functions = (request.parameters.get("functions") or request.parameters.get("tools")) + + try: + if self.BASE_URL: + client = OpenAI( + api_key=self.API_KEY, + base_url=request.base_url or self.BASE_URL, + ) + else: + client = AzureOpenAI( + api_key=self.API_KEY, + azure_endpoint=self.API_ENDPOINT, + api_version=self.API_VERSION, + ) + + messages = self.prepare_messages(request) + + base_args = { + "model": request.model, + "messages": messages, + "stream": request.is_stream + } + + # Combine all arguments + combined_args = { + **base_args, + **request.parameters, + } + return client.chat.completions.create(**combined_args) except openai._exceptions.APIConnectionError as e: raise ProviderError(f"There was an error reaching the endpoint: {e}") @@ -136,12 +135,12 @@ async def generate_client( except openai._exceptions.APIStatusError as e: raise ProviderError(e.response.json()) - def prepare_messages(self, request: AzureRequest): + def prepare_messages(self, request: ChatRequest): if self.is_llama: user_message = self.convert_to_openai_format(request.chat_input) content = "<|begin_of_text|>" content = self.add_system_message( - user_message, content, request.tools, request.functions + user_message, content, request.parameters.get("tools"), request.parameters.get("functions") ) content = self.add_conversation(user_message, content) return [{"role": "user", "content": content}] @@ -152,19 +151,35 @@ def prepare_messages(self, request: AzureRequest): else request.chat_input ) - async def parse_response( + async def aparse_response( self, response: AsyncGenerator, **kwargs ) -> AsyncGenerator[str, None]: - if self.is_llama and (self.has_tools or self.has_functions): - async for chunk in self.handle_tool_response(response, **kwargs): - yield chunk + if self.is_llama and self.has_tools_functions: + for chunk in self.handle_tool_response(response, **kwargs): + if chunk: + yield chunk else: for chunk in response: - yield chunk.model_dump() + c = chunk.model_dump() + if c.get("choices"): + yield c + + def parse_response( + self, response: Generator, **kwargs + ) -> Any: + if self.is_llama and self.has_tools_functions: + for chunk in self.handle_tool_response(response, **kwargs): + if chunk: + yield chunk + else: + for chunk in response: + c = chunk.model_dump() + if c.get("choices"): + yield c - async def handle_tool_response( + def handle_tool_response( self, response: AsyncGenerator, **kwargs - ) -> AsyncGenerator[str, None]: + ) -> Generator: """ Asynchronously handles tool responses by parsing the content for function calls or tool activations. It processes the response chunks to extract and execute embedded function or tool calls, then yields diff --git a/libs/core/llmstudio_core/providers/provider.py b/libs/core/llmstudio_core/providers/provider.py index 09be1c74..3de35af3 100644 --- a/libs/core/llmstudio_core/providers/provider.py +++ b/libs/core/llmstudio_core/providers/provider.py @@ -41,8 +41,8 @@ def provider(cls): class ChatRequest(BaseModel): - model: str chat_input: Any + model: str is_stream: Optional[bool] = False retries: Optional[int] = 0 parameters: Optional[dict] = None @@ -68,13 +68,23 @@ def __init__(self, @abstractmethod async def achat( - self, request: ChatRequest + self, + chat_input: Any, + model: str, + is_stream: Optional[bool] = False, + retries: Optional[int] = 0, + parameters: Optional[dict] = {} ) -> Union[StreamingResponse, JSONResponse]: raise NotImplementedError("Providers needs to have achat method implemented.") @abstractmethod def achat( - self, request: ChatRequest + self, + chat_input: Any, + model: str, + is_stream: Optional[bool] = False, + retries: Optional[int] = 0, + parameters: Optional[dict] = {} ) -> Union[StreamingResponse, JSONResponse]: raise NotImplementedError("Providers needs to have chat method implemented.") @@ -88,11 +98,23 @@ class BaseProvider(ProviderABC): END_TOKEN = "" async def achat( - self, request: ChatRequest + self, + chat_input: Any, + model: str, + is_stream: Optional[bool] = False, + retries: Optional[int] = 0, + parameters: Optional[dict] = {} ): + """Makes a chat connection with the provider's API""" try: - request = self.validate_request(request) + request = self.validate_request(dict( + chat_input=chat_input, + model=model, + is_stream=is_stream, + retries=retries, + parameters=parameters + )) except ValidationError as e: raise HTTPException(status_code=422, detail=e.errors()) @@ -113,19 +135,28 @@ async def achat( if e.status_code == 429: continue # Retry on rate limit error else: - raise e # Raise other HTTP exceptions + raise e except Exception as e: - raise HTTPException( - status_code=500, detail=str(e) - ) # Raise other exceptions as HTTP 500 - raise HTTPException(status_code=429, detail="Too many requests") + raise ProviderError(str(e)) + raise ProviderError("Too many requests") def chat( - self, request: ChatRequest + self, + chat_input: Any, + model: str, + is_stream: Optional[bool] = False, + retries: Optional[int] = 0, + parameters: Optional[dict] = {} ): """Makes a chat connection with the provider's API""" try: - request = self.validate_request(request) + request = self.validate_request(dict( + chat_input=chat_input, + model=model, + is_stream=is_stream, + retries=retries, + parameters=parameters + )) except ValidationError as e: raise HTTPException(status_code=422, detail=e.errors()) @@ -161,10 +192,19 @@ def validate_model(self, request: ChatRequest): detail=f"Model {request.model} is not supported by {self.config.name}", ) + @abstractmethod async def agenerate_client( self, request: ChatRequest ) -> Coroutine[Any, Any, Generator]: """Generate the provider's client""" + raise NotImplementedError("agenerate_client method must be implemented for async calls.") + + @abstractmethod + def generate_client( + self, request: ChatRequest + ) -> ChatCompletion: + """Generate the provider's client""" + raise NotImplementedError("generate_client method must be implemented.") def handle_response( self, request: ChatRequest, response: ChatCompletion, start_time: float @@ -288,7 +328,7 @@ def handle_response_stream( if isinstance(request.chat_input, str) else request.chat_input[-1]["content"] ), - "chat_output": None, + "chat_output": "", "context": ( [{"role": "user", "content": request.chat_input}] if isinstance(request.chat_input, str) @@ -377,6 +417,7 @@ async def ahandle_response_stream( chunks.append(chunk) chunk = chunk[0] if isinstance(chunk, tuple) else chunk + if chunk.get("choices")[0].get("finish_reason") != "stop": model = chunk.get("model") response = { @@ -590,12 +631,12 @@ def join_chunks(self, chunks, request): ) async def aparse_response( - self, response: AsyncGenerator + self, response: AsyncGenerator, **kwargs ) -> AsyncGenerator[str, ChatCompletionChunk]: pass def parse_response( - self, response: AsyncGenerator + self, response: AsyncGenerator, **kwargs ) -> ChatCompletionChunk: pass From b00e036dee77c3f4dfdf0d7039e1183d7aa2a3d1 Mon Sep 17 00:00:00 2001 From: Diogo Goncalves Date: Thu, 10 Oct 2024 13:42:02 +0100 Subject: [PATCH 11/58] fix: anthropic return --- libs/core/llmstudio_core/providers/anthropic.py | 1 + 1 file changed, 1 insertion(+) diff --git a/libs/core/llmstudio_core/providers/anthropic.py b/libs/core/llmstudio_core/providers/anthropic.py index b640348d..982136ba 100644 --- a/libs/core/llmstudio_core/providers/anthropic.py +++ b/libs/core/llmstudio_core/providers/anthropic.py @@ -71,6 +71,7 @@ async def agenerate_client( ) if result.status_code != 200: raise ProviderError(result.status_code, result.text) + return result except Exception as e: raise ProviderError(str(e)) From 99cec02c14f5283ac3c9e1a638b7c4e7be3d7854 Mon Sep 17 00:00:00 2001 From: Diogo Goncalves Date: Thu, 10 Oct 2024 13:43:26 +0100 Subject: [PATCH 12/58] feat: vertexai stream working --- libs/core/llmstudio_core/providers/azure.py | 4 +- .../core/llmstudio_core/providers/provider.py | 85 ++++++++------- .../core/llmstudio_core/providers/vertexai.py | 101 +++++++++++++----- 3 files changed, 120 insertions(+), 70 deletions(-) diff --git a/libs/core/llmstudio_core/providers/azure.py b/libs/core/llmstudio_core/providers/azure.py index 34d71dd8..56968413 100644 --- a/libs/core/llmstudio_core/providers/azure.py +++ b/libs/core/llmstudio_core/providers/azure.py @@ -57,7 +57,7 @@ async def agenerate_client( self.has_tools_functions = (request.parameters.get("functions") or request.parameters.get("tools")) try: - if self.BASE_URL: + if self.BASE_URL and (not self.API_ENDPOINT): client = OpenAI( api_key=self.API_KEY, base_url=self.BASE_URL, @@ -105,7 +105,7 @@ def generate_client( if self.BASE_URL: client = OpenAI( api_key=self.API_KEY, - base_url=request.base_url or self.BASE_URL, + base_url=self.BASE_URL, ) else: client = AzureOpenAI( diff --git a/libs/core/llmstudio_core/providers/provider.py b/libs/core/llmstudio_core/providers/provider.py index 3de35af3..fc6878b1 100644 --- a/libs/core/llmstudio_core/providers/provider.py +++ b/libs/core/llmstudio_core/providers/provider.py @@ -353,49 +353,51 @@ def handle_response_stream( yield ChatCompletionChunk(**response) async def ahandle_response( - self, request: ChatRequest, response: ChatCompletion, start_time: float + self, request: ChatRequest, response: dict, start_time: float ) -> AsyncGenerator[ChatCompletion, None]: """Handles the response from an API""" - model = response.model - metrics = self.calculate_metrics( - usage=response.usage.model_dump(), - model=request.model, - start_time=start_time, - end_time=time.time(), - ) + async for chunk in self.aparse_response(response, request=request): + model = chunk.get("model") + + metrics = self.calculate_metrics( + usage=response.usage.model_dump(), + model=request.model, + start_time=start_time, + end_time=time.time(), + ) - response = { - **response.model_dump(), - "id": str(uuid.uuid4()), - "chat_input": ( - request.chat_input - if isinstance(request.chat_input, str) - else request.chat_input[-1]["content"] - ), - "chat_output": response.choices[0].message.content, - "context": ( - [{"role": "user", "content": request.chat_input}] - if isinstance(request.chat_input, str) - else request.chat_input - ), - "provider": self.config.id, - "model": ( - request.model - if model and model.startswith(request.model) - else (model or request.model) - ), - "deployment": ( - model - if model and model.startswith(request.model) - else (request.model if model != request.model else None) - ), - "timestamp": time.time(), - "parameters": request.parameters, - "metrics": metrics, - } + response = { + **response.model_dump(), + "id": str(uuid.uuid4()), + "chat_input": ( + request.chat_input + if isinstance(request.chat_input, str) + else request.chat_input[-1]["content"] + ), + "chat_output": response.choices[0].message.content, + "context": ( + [{"role": "user", "content": request.chat_input}] + if isinstance(request.chat_input, str) + else request.chat_input + ), + "provider": self.config.id, + "model": ( + request.model + if model and model.startswith(request.model) + else (model or request.model) + ), + "deployment": ( + model + if model and model.startswith(request.model) + else (request.model if model != request.model else None) + ), + "timestamp": time.time(), + "parameters": request.parameters, + "metrics": metrics, + } - yield ChatCompletion(**response) + yield ChatCompletion(**response) async def ahandle_response_stream( self, request: ChatRequest, response: AsyncGenerator, start_time: float @@ -630,15 +632,17 @@ def join_chunks(self, chunks, request): stop_content, ) + @abstractmethod async def aparse_response( self, response: AsyncGenerator, **kwargs ) -> AsyncGenerator[str, ChatCompletionChunk]: - pass + raise NotImplementedError("BaseProvider needs a aparse_response method.") + @abstractmethod def parse_response( self, response: AsyncGenerator, **kwargs ) -> ChatCompletionChunk: - pass + raise NotImplementedError("BaseProvider needs a parse_response method.") def calculate_metrics_stream( self, @@ -745,4 +749,3 @@ def get_end_token_string(self, metrics: Dict[str, Any]) -> str: def _get_tokenizer(self): return {}.get(self.config.id, tiktoken.get_encoding("cl100k_base")) - diff --git a/libs/core/llmstudio_core/providers/vertexai.py b/libs/core/llmstudio_core/providers/vertexai.py index a38b4566..cd5ea455 100644 --- a/libs/core/llmstudio_core/providers/vertexai.py +++ b/libs/core/llmstudio_core/providers/vertexai.py @@ -16,33 +16,19 @@ import requests from llmstudio_core.exceptions import ProviderError -from openai.types.chat import ChatCompletionChunk +from openai.types.chat import ChatCompletion, ChatCompletionChunk from openai.types.chat.chat_completion_chunk import ( Choice, ChoiceDelta, ChoiceDeltaToolCall, ChoiceDeltaToolCallFunction, ) + from pydantic import BaseModel, Field, ValidationError from llmstudio_core.providers.provider import ChatRequest, BaseProvider, provider -class VertexParameters(BaseModel): - top_p: Optional[float] = Field(default=1, ge=0, le=1) - top_k: Optional[float] = Field(default=1, ge=0, le=1) - temperature: Optional[float] = Field(default=1, ge=0, le=2) - max_output_tokens: Optional[float] = Field(default=8192, ge=0, le=8192) - frequency_penalty: Optional[float] = Field(default=0, ge=0, le=1) - presence_penalty: Optional[float] = Field(default=0, ge=0, le=1) - - -class VertexAIRequest(ChatRequest): - parameters: Optional[VertexParameters] = VertexParameters() - tools: Any = None - chat_input: Union[str, List[Dict[str, Any]]] - - class OpenAIToolParameter(BaseModel): type: str description: Optional[str] = None @@ -96,37 +82,96 @@ def __init__(self, config, api_key): def _provider_config_name(): return "vertexai" - def validate_request(self, request: VertexAIRequest): - return VertexAIRequest(**request) + def validate_request(self, request): + return ChatRequest(**request) - async def generate_client( - self, request: VertexAIRequest + async def agenerate_client( + self, request: ChatRequest ) -> Coroutine[Any, Any, Generator]: """Initialize Vertex AI""" try: - # Init genai - url = f"https://generativelanguage.googleapis.com/v1beta/models/{request.model}:streamGenerateContent?alt=sse" + if request.is_stream: + url = f"https://generativelanguage.googleapis.com/v1beta/models/{request.model}:streamGenerateContent?alt=sse" + else: + url = f"https://generativelanguage.googleapis.com/v1beta/models/{request.model}:generateContent?alt=sse" headers = { "Content-Type": "application/json", "x-goog-api-key": self.GOOGLE_API_KEY, } # Convert the chat input into VertexAI format - tool_payload = self.process_tools(request.tools) + tool_payload = self.process_tools(request.parameters.get("tools")) message = self.convert_input_to_vertexai(request.chat_input, tool_payload) # Generate content return await asyncio.to_thread( - requests.post, url, headers=headers, json=message, stream=True + requests.post, url, headers=headers, json=message, stream=request.is_stream ) except Exception as e: raise ProviderError(str(e)) - async def parse_response( + def generate_client( + self, request: ChatRequest + ) -> Any: + """Initialize Vertex AI""" + + return asyncio.run(self.agenerate_client(request=request)) + + async def aparse_response( self, response: AsyncGenerator, **kwargs - ) -> AsyncGenerator[str, None]: + ) -> Any: + + if not kwargs.get("request").is_stream: + response_dict = json.loads(response.text.lstrip("data :")) + + usage = dict( + prompt_tokens=response_dict["usageMetadata"].get("promptTokenCount"), + output_tokens=response_dict["usageMetadata"].get("candidatesTokenCount"), + total_tokens=response_dict["usageMetadata"].get("totalTokenCount") + ) + + completion = { + **response_dict, + "id": str(uuid.uuid4()), + "chat_input": ( + kwargs.get("request").chat_input + if isinstance(kwargs.get("request").chat_input, str) + else kwargs.get("request").chat_input[-1]["content"] + ), + "chat_output": response_dict.get("candidates")[0].get("content").get("parts")[0].get("text"), + "context": ( + [{"role": "user", "content": kwargs.get("request").chat_input}] + if isinstance(kwargs.get("request").chat_input, str) + else kwargs.get("request").chat_input + ), + "provider": self.config.id, + "model": kwargs.get("request").model, + "deployment": kwargs.get("request").model, + "timestamp": time.time(), + "parameters": kwargs.get("request").parameters, + "usage":usage + } + ChatCompletion( + ChatCompletionChunk( + id=str(uuid.uuid4()), + choices=[ + Choice( + delta=ChoiceDelta( + content=response_dict.get("candidates")[0].get("content").get("parts")[0].get("text"), + role="assistant", + ), + finish_reason="stop", + index=0, + ) + ], + created=int(time.time()), + model=kwargs.get("request").model + ).model_dump() + completion = ChatCompletion(**completion) + yield completion.model_dump() + for chunk in response.iter_content(chunk_size=None): chunk = json.loads(chunk.decode("utf-8").lstrip("data: ")) @@ -259,7 +304,9 @@ async def parse_response( model=kwargs.get("request").model, object="chat.completion.chunk", ).model_dump() - + def parse_response(self, response: AsyncGenerator[Any, None], **kwargs) -> ChatCompletionChunk: + return asyncio.run(self.aparse_response(response, **kwargs)) + def convert_input_to_vertexai( self, input_data: Union[Dict, str, List[Dict]], tool_payload: Optional[Any] ) -> Dict: From 5694e6ee31daef6eec081fa76a101139c47b9a07 Mon Sep 17 00:00:00 2001 From: Diogo Goncalves Date: Thu, 10 Oct 2024 17:55:40 +0100 Subject: [PATCH 13/58] feat: openai, azure-openai, azure-llama --- libs/core/llmstudio_core/__init__.py | 68 +++-- libs/core/llmstudio_core/providers/azure.py | 64 ++++- libs/core/llmstudio_core/providers/openai.py | 13 +- .../core/llmstudio_core/providers/provider.py | 232 ++---------------- 4 files changed, 133 insertions(+), 244 deletions(-) diff --git a/libs/core/llmstudio_core/__init__.py b/libs/core/llmstudio_core/__init__.py index 4f5faaf8..93e3495f 100644 --- a/libs/core/llmstudio_core/__init__.py +++ b/libs/core/llmstudio_core/__init__.py @@ -7,7 +7,7 @@ _engine_config = _load_engine_config() -def LLM(provider: str, api_key: Optional[str] = None) -> BaseProvider: +def LLM(provider: str, api_key: Optional[str] = None, **kwargs) -> BaseProvider: """ Factory method to create an instance of a provider. @@ -24,7 +24,7 @@ def LLM(provider: str, api_key: Optional[str] = None) -> BaseProvider: provider_config = _engine_config.providers.get(provider) provider_class = provider_registry.get(provider_config.id) if provider_class: - return provider_class(config=provider_config, api_key=api_key) + return provider_class(config=provider_config, api_key=api_key, **kwargs) raise NotImplementedError(f"Provider not found: {provider_config.id}. Available providers: {str(provider_registry.keys())}") @@ -34,7 +34,9 @@ def LLM(provider: str, api_key: Optional[str] = None) -> BaseProvider: from dotenv import load_dotenv load_dotenv() - def test_stuff(provider, model, api_key): + def test_stuff(provider, model, api_key, **kwargs): + llm = LLM(provider=provider, api_key=api_key, **kwargs) + latencies = {} chat_request = { "chat_input": "Hello, my name is Json", @@ -50,7 +52,6 @@ def test_stuff(provider, model, api_key): } - llm = LLM(provider=provider, api_key=api_key) import asyncio response_async = asyncio.run(llm.achat(**chat_request)) @@ -72,11 +73,11 @@ async def async_stream(): "functions": None, } } - - llm = LLM(provider=provider, api_key=api_key) response_async = await llm.achat(**chat_request) async for p in response_async: + if "}" in p.chat_output: + p.chat_output print("that: ",p.chat_output) # pprint(p.choices[0].delta.content==p.chat_output) # print("metrics: ", p.metrics) @@ -100,9 +101,6 @@ async def async_stream(): "functions": None, } } - - - llm = LLM(provider=provider, api_key=api_key) response_sync = llm.chat(**chat_request) pprint(response_sync) @@ -121,8 +119,6 @@ async def async_stream(): "functions": None, } } - - llm = LLM(provider=provider, api_key=api_key) response_sync_stream = llm.chat(**chat_request) for p in response_sync_stream: @@ -137,11 +133,11 @@ async def async_stream(): return latencies - provider = "openai" - model = "gpt-4o-mini" - for _ in range(1): - latencies = test_stuff(provider=provider, model=model, api_key=os.environ["OPENAI_API_KEY"]) - pprint(latencies) + # provider = "openai" + # model = "gpt-4o-mini" + # for _ in range(1): + # latencies = test_stuff(provider=provider, model=model, api_key=os.environ["OPENAI_API_KEY"]) + # pprint(latencies) # provider = "anthropic" # model = "claude-3-opus-20240229" @@ -150,10 +146,46 @@ async def async_stream(): # pprint(latencies) # we need credits + # provider = "azure" + # model = "gpt-4o-mini" + # for _ in range(1): + # latencies = test_stuff(provider=provider, model=model, + # api_key=os.environ["AZURE_API_KEY"], + # api_version=os.environ["AZURE_API_VERSION"], + # api_endpoint=os.environ["AZURE_API_ENDPOINT"]) + # pprint(latencies) + provider = "azure" - model = "gpt-4o-mini" + model = "gpt-4o" for _ in range(1): latencies = test_stuff(provider=provider, model=model, - api_key=os.environ["AZURE_API_KEY"] + api_key=os.environ["AZURE_API_KEY_llama"], + base_url=os.environ["AZURE_BASE_URL"] ) pprint(latencies) + +# 'model' = +# 'gpt-4o' +# 'messages' = +# [{'role': 'user', 'content': 'Hello, my name is Json'}] +# 'stream' = +# True +# 'temperature' = +# 0 +# 'max_tokens' = +# 100 +# 'response_format' = +# {'type': 'json_object'} +# 'functions' = +# None + +# '<|begin_of_text|>\n <|start_header_id|>system<|end_header_id|>\n You are a helpful AI assistant.\n \n<|eot_id|>\n <|start_header_id|>user<|end_header_id|>\n Hello, my name is Json\n <|eot_id|>\n ' + + provider = "azure" + model = "Meta-Llama-3.1-70B-Instruct" + for _ in range(1): + latencies = test_stuff(provider=provider, model=model, + api_key=os.environ["AZURE_API_KEY_llama"], + base_url=os.environ["AZURE_BASE_URL"] + ) + pprint(latencies) \ No newline at end of file diff --git a/libs/core/llmstudio_core/providers/azure.py b/libs/core/llmstudio_core/providers/azure.py index 56968413..b34a3cbe 100644 --- a/libs/core/llmstudio_core/providers/azure.py +++ b/libs/core/llmstudio_core/providers/azure.py @@ -36,9 +36,9 @@ class AzureProvider(BaseProvider): def __init__(self, config, api_key=None, api_endpoint=None, api_version=None, base_url=None): super().__init__(config) self.API_KEY = api_key or os.getenv("AZURE_API_KEY") - self.API_ENDPOINT = api_endpoint or os.getenv("AZURE_API_ENDPOINT") + self.API_ENDPOINT = api_endpoint self.API_VERSION = api_version or os.getenv("AZURE_API_VERSION") - self.BASE_URL = base_url or os.getenv("AZURE_BASE_URL") + self.BASE_URL = base_url self.is_llama = False self.has_tools_functions = False @staticmethod @@ -54,10 +54,12 @@ async def agenerate_client( """Generate an AzureOpenAI client""" self.is_llama = "llama" in request.model.lower() - self.has_tools_functions = (request.parameters.get("functions") or request.parameters.get("tools")) + self.is_openai = "gpt" in request.model.lower() + self.has_tools = request.parameters.get("tools") is not None + self.has_functions = request.parameters.get("functions") is not None try: - if self.BASE_URL and (not self.API_ENDPOINT): + if self.BASE_URL: client = OpenAI( api_key=self.API_KEY, base_url=self.BASE_URL, @@ -71,27 +73,47 @@ async def agenerate_client( messages = self.prepare_messages(request) + # Prepare the optional tool-related arguments + tool_args = {} + if not self.is_llama and self.has_tools and self.is_openai: + tool_args = { + "tools": request.parameters.get("tools"), + "tool_choice": "auto" if request.parameters.get("tools") else None, + } + + # Prepare the optional function-related arguments + function_args = {} + if not self.is_llama and self.has_functions and self.is_openai: + function_args = { + "functions": request.parameters.get("functions"), + "function_call": "auto" if request.parameters.get("functions") else None, + } + # Prepare the base arguments base_args = { "model": request.model, "messages": messages, - "stream": request.is_stream + "stream": True, } # Combine all arguments combined_args = { **base_args, + **tool_args, + **function_args, **request.parameters, } # Perform the asynchronous call return await asyncio.to_thread( client.chat.completions.create, **combined_args ) + except openai._exceptions.APIConnectionError as e: raise ProviderError(f"There was an error reaching the endpoint: {e}") except openai._exceptions.APIStatusError as e: raise ProviderError(e.response.json()) + def generate_client( self, request: ChatRequest @@ -99,7 +121,9 @@ def generate_client( """Generate an AzureOpenAI client""" self.is_llama = "llama" in request.model.lower() - self.has_tools_functions = (request.parameters.get("functions") or request.parameters.get("tools")) + self.is_openai = "gpt" in request.model.lower() + self.has_tools = request.parameters.get("tools") is not None + self.has_functions = request.parameters.get("functions") is not None try: if self.BASE_URL: @@ -116,15 +140,34 @@ def generate_client( messages = self.prepare_messages(request) + # Prepare the optional tool-related arguments + tool_args = {} + if not self.is_llama and self.has_tools and self.is_openai: + tool_args = { + "tools": request.parameters.get("tools"), + "tool_choice": "auto" if request.parameters.get("tools") else None, + } + + # Prepare the optional function-related arguments + function_args = {} + if not self.is_llama and self.has_functions and self.is_openai: + function_args = { + "functions": request.parameters.get("functions"), + "function_call": "auto" if request.parameters.get("functions") else None, + } + + # Prepare the base arguments base_args = { "model": request.model, "messages": messages, - "stream": request.is_stream + "stream": True, } # Combine all arguments combined_args = { **base_args, + **tool_args, + **function_args, **request.parameters, } return client.chat.completions.create(**combined_args) @@ -135,8 +178,9 @@ def generate_client( except openai._exceptions.APIStatusError as e: raise ProviderError(e.response.json()) + def prepare_messages(self, request: ChatRequest): - if self.is_llama: + if self.is_llama and (self.has_tools or self.has_functions): user_message = self.convert_to_openai_format(request.chat_input) content = "<|begin_of_text|>" content = self.add_system_message( @@ -154,7 +198,7 @@ def prepare_messages(self, request: ChatRequest): async def aparse_response( self, response: AsyncGenerator, **kwargs ) -> AsyncGenerator[str, None]: - if self.is_llama and self.has_tools_functions: + if self.is_llama and (self.has_tools or self.has_functions): for chunk in self.handle_tool_response(response, **kwargs): if chunk: yield chunk @@ -167,7 +211,7 @@ async def aparse_response( def parse_response( self, response: Generator, **kwargs ) -> Any: - if self.is_llama and self.has_tools_functions: + if self.is_llama and (self.has_tools or self.has_functions): for chunk in self.handle_tool_response(response, **kwargs): if chunk: yield chunk diff --git a/libs/core/llmstudio_core/providers/openai.py b/libs/core/llmstudio_core/providers/openai.py index 14a5a95c..6011c16f 100644 --- a/libs/core/llmstudio_core/providers/openai.py +++ b/libs/core/llmstudio_core/providers/openai.py @@ -10,17 +10,16 @@ from llmstudio_core.providers.provider import ChatRequest, BaseProvider, provider - @provider class OpenAIProvider(BaseProvider): def __init__(self, config, **kwargs): super().__init__(config, **kwargs) self.API_KEY = self.API_KEY if self.API_KEY else os.getenv("OPENAI_API_KEY") - + @staticmethod def _provider_config_name(): return "openai" - + def validate_request(self, request: ChatRequest): return ChatRequest(**request) @@ -39,7 +38,7 @@ async def agenerate_client( if isinstance(request.chat_input, str) else request.chat_input ), - stream=request.is_stream, + stream=True, **request.parameters, ) except openai._exceptions.APIError as e: @@ -59,7 +58,7 @@ def generate_client( if isinstance(request.chat_input, str) else request.chat_input ), - stream=request.is_stream, + stream=True, **request.parameters, ) except openai._exceptions.APIError as e: @@ -72,7 +71,7 @@ async def aparse_response( yield chunk.model_dump() def parse_response( - self, response: AsyncGenerator, **kwargs + self, response: Generator, **kwargs ) -> Generator: for chunk in response: - yield chunk.model_dump() \ No newline at end of file + yield chunk.model_dump() diff --git a/libs/core/llmstudio_core/providers/provider.py b/libs/core/llmstudio_core/providers/provider.py index fc6878b1..16f9494e 100644 --- a/libs/core/llmstudio_core/providers/provider.py +++ b/libs/core/llmstudio_core/providers/provider.py @@ -1,4 +1,5 @@ from abc import ABC, abstractmethod +import asyncio import time import uuid from pathlib import Path @@ -17,7 +18,6 @@ from fastapi import HTTPException import tiktoken from llmstudio_core.exceptions import ProviderError -from fastapi.responses import JSONResponse, StreamingResponse from openai.types.chat import ( ChatCompletion, ChatCompletionMessage, @@ -74,7 +74,7 @@ async def achat( is_stream: Optional[bool] = False, retries: Optional[int] = 0, parameters: Optional[dict] = {} - ) -> Union[StreamingResponse, JSONResponse]: + ) -> Union[ChatCompletionChunk, ChatCompletion]: raise NotImplementedError("Providers needs to have achat method implemented.") @abstractmethod @@ -85,7 +85,7 @@ def achat( is_stream: Optional[bool] = False, retries: Optional[int] = 0, parameters: Optional[dict] = {} - ) -> Union[StreamingResponse, JSONResponse]: + ) -> Union[ChatCompletionChunk, ChatCompletion]: raise NotImplementedError("Providers needs to have chat method implemented.") @staticmethod @@ -116,7 +116,7 @@ async def achat( parameters=parameters )) except ValidationError as e: - raise HTTPException(status_code=422, detail=e.errors()) + raise ProviderError(str(e)) self.validate_model(request) @@ -124,12 +124,11 @@ async def achat( try: start_time = time.time() response = await self.agenerate_client(request) + response_handler = self.ahandle_response(request, response, start_time) if request.is_stream: - response_handler = self.ahandle_response_stream(request, response, start_time) return response_handler else: - response_handler = self.ahandle_response(request, response, start_time) return await response_handler.__anext__() except HTTPException as e: if e.status_code == 429: @@ -148,6 +147,7 @@ def chat( retries: Optional[int] = 0, parameters: Optional[dict] = {} ): + """Makes a chat connection with the provider's API""" try: request = self.validate_request(dict( @@ -158,7 +158,7 @@ def chat( parameters=parameters )) except ValidationError as e: - raise HTTPException(status_code=422, detail=e.errors()) + raise ProviderError(str(e)) self.validate_model(request) @@ -166,13 +166,12 @@ def chat( try: start_time = time.time() response = self.generate_client(request) + response_handler = self.handle_response(request, response, start_time) if request.is_stream: - response_handler = self.handle_response_stream(request, response, start_time) return response_handler else: - return self.handle_response(request, response, start_time) - + return response_handler.__next__() except HTTPException as e: if e.status_code == 429: continue # Retry on rate limit error @@ -251,7 +250,7 @@ def handle_response( return ChatCompletion(**response) - def handle_response_stream( + def handle_response( self, request: ChatRequest, response: AsyncGenerator, start_time: float ) -> Generator: """Handles the response from an API""" @@ -270,10 +269,11 @@ def handle_response_stream( previous_token_time = current_time chunks.append(chunk) - chunk = chunk[0] if isinstance(chunk, tuple) else chunk - if chunk.get("choices")[0].get("finish_reason") != "stop": + if request.is_stream: + chunk = chunk[0] if isinstance(chunk, tuple) else chunk model = chunk.get("model") - response = { + if chunk.get("choices")[0].get("finish_reason") != "stop": + chunk = { **chunk, "id": str(uuid.uuid4()), "chat_input": ( @@ -302,14 +302,14 @@ def handle_response_stream( "parameters": request.parameters, "metrics": None, } - yield ChatCompletionChunk(**response) + yield ChatCompletionChunk(**chunk) #chunk.get("choices")[0].get("delta").get("content") chunks = [chunk[0] if isinstance(chunk, tuple) else chunk for chunk in chunks] model = next(chunk["model"] for chunk in chunks if chunk.get("model")) - response, _ = self.join_chunks(chunks, request) + response, output_string = self.join_chunks(chunks, request) - metrics = self.calculate_metrics_stream( + metrics = self.calculate_metrics( request.chat_input, response, request.model, @@ -321,14 +321,14 @@ def handle_response_stream( ) response = { - **chunk, + **(chunk if request.is_stream else response.model_dump()), "id": str(uuid.uuid4()), "chat_input": ( request.chat_input if isinstance(request.chat_input, str) else request.chat_input[-1]["content"] ), - "chat_output": "", + "chat_output": "" if request.is_stream else output_string, "context": ( [{"role": "user", "content": request.chat_input}] if isinstance(request.chat_input, str) @@ -350,157 +350,11 @@ def handle_response_stream( "metrics": metrics, } - yield ChatCompletionChunk(**response) - - async def ahandle_response( - self, request: ChatRequest, response: dict, start_time: float - ) -> AsyncGenerator[ChatCompletion, None]: - """Handles the response from an API""" - - async for chunk in self.aparse_response(response, request=request): - model = chunk.get("model") - - metrics = self.calculate_metrics( - usage=response.usage.model_dump(), - model=request.model, - start_time=start_time, - end_time=time.time(), - ) - - response = { - **response.model_dump(), - "id": str(uuid.uuid4()), - "chat_input": ( - request.chat_input - if isinstance(request.chat_input, str) - else request.chat_input[-1]["content"] - ), - "chat_output": response.choices[0].message.content, - "context": ( - [{"role": "user", "content": request.chat_input}] - if isinstance(request.chat_input, str) - else request.chat_input - ), - "provider": self.config.id, - "model": ( - request.model - if model and model.startswith(request.model) - else (model or request.model) - ), - "deployment": ( - model - if model and model.startswith(request.model) - else (request.model if model != request.model else None) - ), - "timestamp": time.time(), - "parameters": request.parameters, - "metrics": metrics, - } - + if request.is_stream: + yield ChatCompletionChunk(**response) + else: yield ChatCompletion(**response) - async def ahandle_response_stream( - self, request: ChatRequest, response: AsyncGenerator, start_time: float - ) -> AsyncGenerator[str, None]: - """Handles the response from an API""" - first_token_time = None - previous_token_time = None - token_times = [] - token_count = 0 - chunks = [] - - async for chunk in self.aparse_response(response, request=request): - token_count += 1 - current_time = time.time() - first_token_time = first_token_time or current_time - if previous_token_time is not None: - token_times.append(current_time - previous_token_time) - previous_token_time = current_time - - chunks.append(chunk) - chunk = chunk[0] if isinstance(chunk, tuple) else chunk - - if chunk.get("choices")[0].get("finish_reason") != "stop": - model = chunk.get("model") - response = { - **chunk, - "id": str(uuid.uuid4()), - "chat_input": ( - request.chat_input - if isinstance(request.chat_input, str) - else request.chat_input[-1]["content"] - ), - "chat_output": chunk.get("choices")[0].get("delta").get("content"), - "context": ( - [{"role": "user", "content": request.chat_input}] - if isinstance(request.chat_input, str) - else request.chat_input - ), - "provider": self.config.id, - "model": ( - request.model - if model and model.startswith(request.model) - else (model or request.model) - ), - "deployment": ( - model - if model and model.startswith(request.model) - else (request.model if model != request.model else None) - ), - "timestamp": time.time(), - "parameters": request.parameters, - "metrics": None, - } - yield ChatCompletionChunk(**response) - - chunks = [chunk[0] if isinstance(chunk, tuple) else chunk for chunk in chunks] - model = next(chunk["model"] for chunk in chunks if chunk.get("model")) - - response, _ = self.join_chunks(chunks, request) - - metrics = self.calculate_metrics_stream( - request.chat_input, - response, - request.model, - start_time, - time.time(), - first_token_time, - token_times, - token_count, - ) - - response = { - **chunk, - "id": str(uuid.uuid4()), - "chat_input": ( - request.chat_input - if isinstance(request.chat_input, str) - else request.chat_input[-1]["content"] - ), - "chat_output": None, - "context": ( - [{"role": "user", "content": request.chat_input}] - if isinstance(request.chat_input, str) - else request.chat_input - ), - "provider": self.config.id, - "model": ( - request.model - if model and model.startswith(request.model) - else (model or request.model) - ), - "deployment": ( - model - if model and model.startswith(request.model) - else (request.model if model != request.model else None) - ), - "timestamp": time.time(), - "parameters": request.parameters, - "metrics": metrics, - } - - yield ChatCompletionChunk(**response) - def join_chunks(self, chunks, request): finish_reason = chunks[-1].get("choices")[0].get("finish_reason") @@ -644,7 +498,7 @@ def parse_response( ) -> ChatCompletionChunk: raise NotImplementedError("BaseProvider needs a parse_response method.") - def calculate_metrics_stream( + def calculate_metrics( self, input: Any, output: Any, @@ -675,46 +529,6 @@ def calculate_metrics_stream( "tokens_per_second": token_count / total_time, } - def calculate_metrics( - self, - usage, - model: str, - start_time: float, - end_time: float, - ) -> Dict[str, Any]: - """Calculates metrics based on token times and output""" - model_config = self.config.models[model] - input_tokens = usage.get("prompt_tokens") - output_tokens = usage.get("completion_tokens") - - input_cost = self.calculate_cost(input_tokens, model_config.input_token_cost) - output_cost = self.calculate_cost(output_tokens, model_config.output_token_cost) - - total_time = end_time - start_time - return { - "input_tokens": input_tokens, - "output_tokens": output_tokens, - "total_tokens": input_tokens + output_tokens, - "cost_usd": input_cost + output_cost, - "latency_s": total_time, - "time_to_first_token_s": None, - "inter_token_latency_s": None, - "tokens_per_second": usage.get("total_tokens") / total_time, - } - - def calculate_cost( - self, token_count: int, token_cost: Union[float, List[Dict[str, Any]]] - ) -> float: - if isinstance(token_cost, list): - for cost_range in token_cost: - if token_count >= cost_range.range[0] and ( - token_count <= cost_range.range[1] or cost_range.range[1] is None - ): - return cost_range.cost * token_count - else: - return token_cost * token_count - return 0 - def input_to_string(self, input): if isinstance(input, str): return input From 1f10b68c77f298c3675e98bc796073587318e37c Mon Sep 17 00:00:00 2001 From: Diogo Goncalves Date: Thu, 10 Oct 2024 18:06:02 +0100 Subject: [PATCH 14/58] feat: openai, azure --- libs/core/llmstudio_core/__init__.py | 46 ++----- .../core/llmstudio_core/providers/__init__.py | 2 +- .../core/llmstudio_core/providers/provider.py | 130 +++++++++++++----- 3 files changed, 107 insertions(+), 71 deletions(-) diff --git a/libs/core/llmstudio_core/__init__.py b/libs/core/llmstudio_core/__init__.py index 93e3495f..015fd01f 100644 --- a/libs/core/llmstudio_core/__init__.py +++ b/libs/core/llmstudio_core/__init__.py @@ -133,11 +133,11 @@ async def async_stream(): return latencies - # provider = "openai" - # model = "gpt-4o-mini" - # for _ in range(1): - # latencies = test_stuff(provider=provider, model=model, api_key=os.environ["OPENAI_API_KEY"]) - # pprint(latencies) + provider = "openai" + model = "gpt-4o-mini" + for _ in range(1): + latencies = test_stuff(provider=provider, model=model, api_key=os.environ["OPENAI_API_KEY"]) + pprint(latencies) # provider = "anthropic" # model = "claude-3-opus-20240229" @@ -146,41 +146,15 @@ async def async_stream(): # pprint(latencies) # we need credits - # provider = "azure" - # model = "gpt-4o-mini" - # for _ in range(1): - # latencies = test_stuff(provider=provider, model=model, - # api_key=os.environ["AZURE_API_KEY"], - # api_version=os.environ["AZURE_API_VERSION"], - # api_endpoint=os.environ["AZURE_API_ENDPOINT"]) - # pprint(latencies) - provider = "azure" - model = "gpt-4o" + model = "gpt-4o-mini" for _ in range(1): latencies = test_stuff(provider=provider, model=model, - api_key=os.environ["AZURE_API_KEY_llama"], - base_url=os.environ["AZURE_BASE_URL"] - ) + api_key=os.environ["AZURE_API_KEY"], + api_version=os.environ["AZURE_API_VERSION"], + api_endpoint=os.environ["AZURE_API_ENDPOINT"]) pprint(latencies) -# 'model' = -# 'gpt-4o' -# 'messages' = -# [{'role': 'user', 'content': 'Hello, my name is Json'}] -# 'stream' = -# True -# 'temperature' = -# 0 -# 'max_tokens' = -# 100 -# 'response_format' = -# {'type': 'json_object'} -# 'functions' = -# None - -# '<|begin_of_text|>\n <|start_header_id|>system<|end_header_id|>\n You are a helpful AI assistant.\n \n<|eot_id|>\n <|start_header_id|>user<|end_header_id|>\n Hello, my name is Json\n <|eot_id|>\n ' - provider = "azure" model = "Meta-Llama-3.1-70B-Instruct" for _ in range(1): @@ -188,4 +162,4 @@ async def async_stream(): api_key=os.environ["AZURE_API_KEY_llama"], base_url=os.environ["AZURE_BASE_URL"] ) - pprint(latencies) \ No newline at end of file + pprint(latencies) diff --git a/libs/core/llmstudio_core/providers/__init__.py b/libs/core/llmstudio_core/providers/__init__.py index a6ca613b..54a4bdad 100644 --- a/libs/core/llmstudio_core/providers/__init__.py +++ b/libs/core/llmstudio_core/providers/__init__.py @@ -4,7 +4,7 @@ from llmstudio_core.providers.azure import AzureProvider from llmstudio_core.providers.ollama import OllamaProvider from llmstudio_core.providers.openai import OpenAIProvider -from llmstudio_core.providers.vertexai import VertexAIProvider +# from llmstudio_core.providers.vertexai import VertexAIProvider from pathlib import Path from typing import Any, Dict, List, Optional, Union diff --git a/libs/core/llmstudio_core/providers/provider.py b/libs/core/llmstudio_core/providers/provider.py index 16f9494e..2f85a570 100644 --- a/libs/core/llmstudio_core/providers/provider.py +++ b/libs/core/llmstudio_core/providers/provider.py @@ -74,11 +74,11 @@ async def achat( is_stream: Optional[bool] = False, retries: Optional[int] = 0, parameters: Optional[dict] = {} - ) -> Union[ChatCompletionChunk, ChatCompletion]: + ) -> Coroutine[Any, Any, Union[ChatCompletionChunk, ChatCompletion]]: raise NotImplementedError("Providers needs to have achat method implemented.") @abstractmethod - def achat( + def chat( self, chat_input: Any, model: str, @@ -134,11 +134,11 @@ async def achat( if e.status_code == 429: continue # Retry on rate limit error else: - raise e + raise e # Raise other HTTP exceptions except Exception as e: - raise ProviderError(str(e)) + raise ProviderError(str(e)) raise ProviderError("Too many requests") - + def chat( self, chat_input: Any, @@ -179,54 +179,100 @@ def chat( raise e # Raise other HTTP exceptions except Exception as e: raise ProviderError(str(e)) - raise HTTPException(status_code=429, detail="Too many requests") + raise ProviderError("Too many requests") def validate_request(self, request: ChatRequest): pass def validate_model(self, request: ChatRequest): if request.model not in self.config.models: - raise HTTPException( - status_code=400, - detail=f"Model {request.model} is not supported by {self.config.name}", - ) + raise ProviderError(f"Model {request.model} is not supported by {self.config.name}") - @abstractmethod async def agenerate_client( self, request: ChatRequest ) -> Coroutine[Any, Any, Generator]: """Generate the provider's client""" - raise NotImplementedError("agenerate_client method must be implemented for async calls.") - - @abstractmethod - def generate_client( - self, request: ChatRequest - ) -> ChatCompletion: - """Generate the provider's client""" - raise NotImplementedError("generate_client method must be implemented.") - def handle_response( - self, request: ChatRequest, response: ChatCompletion, start_time: float - ) -> ChatCompletion: + async def ahandle_response( + self, request: ChatRequest, response: AsyncGenerator, start_time: float + ) -> AsyncGenerator[str, None]: """Handles the response from an API""" - model = response.model - + first_token_time = None + previous_token_time = None + token_times = [] + token_count = 0 + chunks = [] + + async for chunk in self.aparse_response(response, request=request): + token_count += 1 + current_time = time.time() + first_token_time = first_token_time or current_time + if previous_token_time is not None: + token_times.append(current_time - previous_token_time) + previous_token_time = current_time + + chunks.append(chunk) + if request.is_stream: + chunk = chunk[0] if isinstance(chunk, tuple) else chunk + model = chunk.get("model") + if chunk.get("choices")[0].get("finish_reason") != "stop": + chat_output = chunk.get("choices")[0].get("delta").get("content") + chunk = { + **chunk, + "id": str(uuid.uuid4()), + "chat_input": ( + request.chat_input + if isinstance(request.chat_input, str) + else request.chat_input[-1]["content"] + ), + "chat_output": chat_output if chat_output else "", + "context": ( + [{"role": "user", "content": request.chat_input}] + if isinstance(request.chat_input, str) + else request.chat_input + ), + "provider": self.config.id, + "model": ( + request.model + if model and model.startswith(request.model) + else (model or request.model) + ), + "deployment": ( + model + if model and model.startswith(request.model) + else (request.model if model != request.model else None) + ), + "timestamp": time.time(), + "parameters": request.parameters, + "metrics": None, + } + yield ChatCompletionChunk(**chunk) + + chunks = [chunk[0] if isinstance(chunk, tuple) else chunk for chunk in chunks] + model = next(chunk["model"] for chunk in chunks if chunk.get("model")) + + response, output_string = self.join_chunks(chunks, request) + metrics = self.calculate_metrics( - usage=response.usage.model_dump(), - model=request.model, - start_time=start_time, - end_time=time.time(), + request.chat_input, + response, + request.model, + start_time, + time.time(), + first_token_time, + token_times, + token_count, ) response = { - **response.model_dump(), + **(chunk if request.is_stream else response.model_dump()), "id": str(uuid.uuid4()), "chat_input": ( request.chat_input if isinstance(request.chat_input, str) else request.chat_input[-1]["content"] ), - "chat_output": response.choices[0].message.content, + "chat_output": "" if request.is_stream else output_string, "context": ( [{"role": "user", "content": request.chat_input}] if isinstance(request.chat_input, str) @@ -248,7 +294,10 @@ def handle_response( "metrics": metrics, } - return ChatCompletion(**response) + if request.is_stream: + yield ChatCompletionChunk(**response) + else: + yield ChatCompletion(**response) def handle_response( self, request: ChatRequest, response: AsyncGenerator, start_time: float @@ -302,7 +351,7 @@ def handle_response( "parameters": request.parameters, "metrics": None, } - yield ChatCompletionChunk(**chunk) #chunk.get("choices")[0].get("delta").get("content") + yield ChatCompletionChunk(**chunk) chunks = [chunk[0] if isinstance(chunk, tuple) else chunk for chunk in chunks] model = next(chunk["model"] for chunk in chunks if chunk.get("model")) @@ -489,13 +538,13 @@ def join_chunks(self, chunks, request): @abstractmethod async def aparse_response( self, response: AsyncGenerator, **kwargs - ) -> AsyncGenerator[str, ChatCompletionChunk]: + ) -> Any: raise NotImplementedError("BaseProvider needs a aparse_response method.") @abstractmethod def parse_response( self, response: AsyncGenerator, **kwargs - ) -> ChatCompletionChunk: + ) -> Any: raise NotImplementedError("BaseProvider needs a parse_response method.") def calculate_metrics( @@ -529,6 +578,19 @@ def calculate_metrics( "tokens_per_second": token_count / total_time, } + def calculate_cost( + self, token_count: int, token_cost: Union[float, List[Dict[str, Any]]] + ) -> float: + if isinstance(token_cost, list): + for cost_range in token_cost: + if token_count >= cost_range.range[0] and ( + token_count <= cost_range.range[1] or cost_range.range[1] is None + ): + return cost_range.cost * token_count + else: + return token_cost * token_count + return 0 + def input_to_string(self, input): if isinstance(input, str): return input From 1f41917d4d480eba3d1d47709af45dd611e509b7 Mon Sep 17 00:00:00 2001 From: Diogo Goncalves Date: Thu, 10 Oct 2024 18:22:37 +0100 Subject: [PATCH 15/58] feat: implement vertexai --- libs/core/llmstudio_core/__init__.py | 43 ++-- .../core/llmstudio_core/providers/__init__.py | 2 +- libs/core/llmstudio_core/providers/azure.py | 1 - .../core/llmstudio_core/providers/vertexai.py | 219 +++++++++++++----- 4 files changed, 186 insertions(+), 79 deletions(-) diff --git a/libs/core/llmstudio_core/__init__.py b/libs/core/llmstudio_core/__init__.py index 015fd01f..33d1455b 100644 --- a/libs/core/llmstudio_core/__init__.py +++ b/libs/core/llmstudio_core/__init__.py @@ -133,11 +133,11 @@ async def async_stream(): return latencies - provider = "openai" - model = "gpt-4o-mini" - for _ in range(1): - latencies = test_stuff(provider=provider, model=model, api_key=os.environ["OPENAI_API_KEY"]) - pprint(latencies) + # provider = "openai" + # model = "gpt-4o-mini" + # for _ in range(1): + # latencies = test_stuff(provider=provider, model=model, api_key=os.environ["OPENAI_API_KEY"]) + # pprint(latencies) # provider = "anthropic" # model = "claude-3-opus-20240229" @@ -146,20 +146,29 @@ async def async_stream(): # pprint(latencies) # we need credits - provider = "azure" - model = "gpt-4o-mini" - for _ in range(1): - latencies = test_stuff(provider=provider, model=model, - api_key=os.environ["AZURE_API_KEY"], - api_version=os.environ["AZURE_API_VERSION"], - api_endpoint=os.environ["AZURE_API_ENDPOINT"]) - pprint(latencies) + # provider = "azure" + # model = "gpt-4o-mini" + # for _ in range(1): + # latencies = test_stuff(provider=provider, model=model, + # api_key=os.environ["AZURE_API_KEY"], + # api_version=os.environ["AZURE_API_VERSION"], + # api_endpoint=os.environ["AZURE_API_ENDPOINT"]) + # pprint(latencies) + + # provider = "azure" + # model = "Meta-Llama-3.1-70B-Instruct" + # for _ in range(1): + # latencies = test_stuff(provider=provider, model=model, + # api_key=os.environ["AZURE_API_KEY_llama"], + # base_url=os.environ["AZURE_BASE_URL"] + # ) + # pprint(latencies) + - provider = "azure" - model = "Meta-Llama-3.1-70B-Instruct" + provider = "vertexai" + model = "gemini-1.5-pro-latest" for _ in range(1): latencies = test_stuff(provider=provider, model=model, - api_key=os.environ["AZURE_API_KEY_llama"], - base_url=os.environ["AZURE_BASE_URL"] + api_key=os.environ["GOOGLE_API_KEY"], ) pprint(latencies) diff --git a/libs/core/llmstudio_core/providers/__init__.py b/libs/core/llmstudio_core/providers/__init__.py index 54a4bdad..a6ca613b 100644 --- a/libs/core/llmstudio_core/providers/__init__.py +++ b/libs/core/llmstudio_core/providers/__init__.py @@ -4,7 +4,7 @@ from llmstudio_core.providers.azure import AzureProvider from llmstudio_core.providers.ollama import OllamaProvider from llmstudio_core.providers.openai import OpenAIProvider -# from llmstudio_core.providers.vertexai import VertexAIProvider +from llmstudio_core.providers.vertexai import VertexAIProvider from pathlib import Path from typing import Any, Dict, List, Optional, Union diff --git a/libs/core/llmstudio_core/providers/azure.py b/libs/core/llmstudio_core/providers/azure.py index b34a3cbe..57d663ed 100644 --- a/libs/core/llmstudio_core/providers/azure.py +++ b/libs/core/llmstudio_core/providers/azure.py @@ -26,7 +26,6 @@ ChoiceDeltaToolCall, ChoiceDeltaToolCallFunction, ) -from pydantic import BaseModel, Field from llmstudio_core.providers.provider import ChatRequest, BaseProvider, provider diff --git a/libs/core/llmstudio_core/providers/vertexai.py b/libs/core/llmstudio_core/providers/vertexai.py index cd5ea455..6832afc4 100644 --- a/libs/core/llmstudio_core/providers/vertexai.py +++ b/libs/core/llmstudio_core/providers/vertexai.py @@ -14,17 +14,17 @@ Union, ) -import requests from llmstudio_core.exceptions import ProviderError -from openai.types.chat import ChatCompletion, ChatCompletionChunk +import requests +from fastapi import HTTPException +from openai.types.chat import ChatCompletionChunk from openai.types.chat.chat_completion_chunk import ( Choice, ChoiceDelta, ChoiceDeltaToolCall, ChoiceDeltaToolCallFunction, ) - -from pydantic import BaseModel, Field, ValidationError +from pydantic import BaseModel, ValidationError from llmstudio_core.providers.provider import ChatRequest, BaseProvider, provider @@ -74,15 +74,15 @@ class VertexAI(BaseModel): @provider class VertexAIProvider(BaseProvider): - def __init__(self, config, api_key): - super().__init__(config, api_key=api_key) - self.GOOGLE_API_KEY = api_key or os.getenv("GOOGLE_API_KEY") - + def __init__(self, config, **kwargs): + super().__init__(config, **kwargs) + self.API_KEY = self.API_KEY if self.API_KEY else os.getenv("GOOGLE_API_KEY") + @staticmethod def _provider_config_name(): return "vertexai" - def validate_request(self, request): + def validate_request(self, request: ChatRequest): return ChatRequest(**request) async def agenerate_client( @@ -91,13 +91,11 @@ async def agenerate_client( """Initialize Vertex AI""" try: - if request.is_stream: - url = f"https://generativelanguage.googleapis.com/v1beta/models/{request.model}:streamGenerateContent?alt=sse" - else: - url = f"https://generativelanguage.googleapis.com/v1beta/models/{request.model}:generateContent?alt=sse" + # Init genai + url = f"https://generativelanguage.googleapis.com/v1beta/models/{request.model}:streamGenerateContent?alt=sse" headers = { "Content-Type": "application/json", - "x-goog-api-key": self.GOOGLE_API_KEY, + "x-goog-api-key": self.API_KEY, } # Convert the chat input into VertexAI format @@ -106,7 +104,7 @@ async def agenerate_client( # Generate content return await asyncio.to_thread( - requests.post, url, headers=headers, json=message, stream=request.is_stream + requests.post, url, headers=headers, json=message, stream=True ) except Exception as e: @@ -114,64 +112,167 @@ async def agenerate_client( def generate_client( self, request: ChatRequest - ) -> Any: + ) -> Coroutine[Any, Any, Generator]: """Initialize Vertex AI""" - return asyncio.run(self.agenerate_client(request=request)) + try: + # Init genai + url = f"https://generativelanguage.googleapis.com/v1beta/models/{request.model}:streamGenerateContent?alt=sse" + headers = { + "Content-Type": "application/json", + "x-goog-api-key": self.API_KEY, + } - async def aparse_response( - self, response: AsyncGenerator, **kwargs - ) -> Any: + # Convert the chat input into VertexAI format + tool_payload = self.process_tools(request.parameters.get("tools")) + message = self.convert_input_to_vertexai(request.chat_input, tool_payload) + + # Generate content + return requests.post(url, headers=headers, json=message, stream=True) + + except Exception as e: + raise ProviderError(str(e)) + + def parse_response(self, + response: AsyncGenerator[Any, None], + **kwargs) -> Any: - if not kwargs.get("request").is_stream: - response_dict = json.loads(response.text.lstrip("data :")) - - usage = dict( - prompt_tokens=response_dict["usageMetadata"].get("promptTokenCount"), - output_tokens=response_dict["usageMetadata"].get("candidatesTokenCount"), - total_tokens=response_dict["usageMetadata"].get("totalTokenCount") - ) - - completion = { - **response_dict, - "id": str(uuid.uuid4()), - "chat_input": ( - kwargs.get("request").chat_input - if isinstance(kwargs.get("request").chat_input, str) - else kwargs.get("request").chat_input[-1]["content"] - ), - "chat_output": response_dict.get("candidates")[0].get("content").get("parts")[0].get("text"), - "context": ( - [{"role": "user", "content": kwargs.get("request").chat_input}] - if isinstance(kwargs.get("request").chat_input, str) - else kwargs.get("request").chat_input - ), - "provider": self.config.id, - "model": kwargs.get("request").model, - "deployment": kwargs.get("request").model, - "timestamp": time.time(), - "parameters": kwargs.get("request").parameters, - "usage":usage - } - ChatCompletion( - ChatCompletionChunk( + for chunk in response.iter_content(chunk_size=None): + + chunk = json.loads(chunk.decode("utf-8").lstrip("data: ")) + chunk = chunk.get("candidates")[0].get("content") + + # Check if it is a function call + if ( + "functionCall" in chunk["parts"][0] + and chunk["parts"][0]["functionCall"] is not None + ): + first_chunk = ChatCompletionChunk( + id="chatcmpl-9woLM1b1qGErhTbXA3UBQf2FhUAho", + choices=[ + Choice( + delta=ChoiceDelta( + content=None, + function_call=None, + role="assistant", + tool_calls=None, + ), + index=0, + ) + ], + created=int(time.time()), + model=kwargs.get("request").model, + object="chat.completion.chunk", + usage=None, + ) + yield first_chunk.model_dump() + + for index, functioncall in enumerate(chunk["parts"]): + + name_chunk = ChatCompletionChunk( + id=str(uuid.uuid4()), + choices=[ + Choice( + delta=ChoiceDelta( + role="assistant", + tool_calls=[ + ChoiceDeltaToolCall( + index=index, + id="call_" + str(uuid.uuid4())[:29], + function=ChoiceDeltaToolCallFunction( + name=functioncall["functionCall"][ + "name" + ], + arguments="", + type="function", + ), + ) + ], + ), + finish_reason=None, + index=index, + ) + ], + created=int(time.time()), + model=kwargs.get("request").model, + object="chat.completion.chunk", + ) + yield name_chunk.model_dump() + + args_chunk = ChatCompletionChunk( + id=str(uuid.uuid4()), + choices=[ + Choice( + delta=ChoiceDelta( + tool_calls=[ + ChoiceDeltaToolCall( + index=index, + function=ChoiceDeltaToolCallFunction( + arguments=json.dumps( + functioncall["functionCall"]["args"] + ), + ), + ) + ], + ), + finish_reason=None, + index=index, + ) + ], + created=int(time.time()), + model=kwargs.get("request").model, + object="chat.completion.chunk", + ) + yield args_chunk.model_dump() + + final_chunk = ChatCompletionChunk( + id=str(uuid.uuid4()), + choices=[ + Choice( + delta=ChoiceDelta(), + finish_reason="tool_calls", + index=0, + ) + ], + created=int(time.time()), + model=kwargs.get("request").model, + object="chat.completion.chunk", + ) + yield final_chunk.model_dump() + # Check if it is a normal call + elif chunk.get("parts")[0].get("text"): + # Parse google chunk response into ChatCompletionChunk + yield ChatCompletionChunk( id=str(uuid.uuid4()), choices=[ Choice( delta=ChoiceDelta( - content=response_dict.get("candidates")[0].get("content").get("parts")[0].get("text"), + content=chunk.get("parts")[0].get("text"), role="assistant", ), - finish_reason="stop", + finish_reason=None, index=0, ) ], created=int(time.time()), - model=kwargs.get("request").model + model=kwargs.get("request").model, + object="chat.completion.chunk", ).model_dump() - completion = ChatCompletion(**completion) - yield completion.model_dump() + # Create the closing chunk + yield ChatCompletionChunk( + id=str(uuid.uuid4()), + choices=[ + Choice(delta=ChoiceDelta(), finish_reason="stop", index=0) + ], + created=int(time.time()), + model=kwargs.get("request").model, + object="chat.completion.chunk", + ).model_dump() + + async def aparse_response( + self, response: AsyncGenerator, **kwargs + ) -> AsyncGenerator[str, None]: for chunk in response.iter_content(chunk_size=None): chunk = json.loads(chunk.decode("utf-8").lstrip("data: ")) @@ -304,9 +405,7 @@ async def aparse_response( model=kwargs.get("request").model, object="chat.completion.chunk", ).model_dump() - def parse_response(self, response: AsyncGenerator[Any, None], **kwargs) -> ChatCompletionChunk: - return asyncio.run(self.aparse_response(response, **kwargs)) - + def convert_input_to_vertexai( self, input_data: Union[Dict, str, List[Dict]], tool_payload: Optional[Any] ) -> Dict: From a70324a4c45d9e6a26530a1fac0e800dc6706f19 Mon Sep 17 00:00:00 2001 From: Diogo Goncalves Date: Thu, 10 Oct 2024 18:38:35 +0100 Subject: [PATCH 16/58] feat: do not consider anthropic and ollama for now --- libs/core/llmstudio_core/__init__.py | 44 +++++++++---------- .../core/llmstudio_core/providers/__init__.py | 4 +- .../core/llmstudio_core/providers/provider.py | 2 +- 3 files changed, 25 insertions(+), 25 deletions(-) diff --git a/libs/core/llmstudio_core/__init__.py b/libs/core/llmstudio_core/__init__.py index 33d1455b..82b776b4 100644 --- a/libs/core/llmstudio_core/__init__.py +++ b/libs/core/llmstudio_core/__init__.py @@ -133,36 +133,36 @@ async def async_stream(): return latencies - # provider = "openai" - # model = "gpt-4o-mini" - # for _ in range(1): - # latencies = test_stuff(provider=provider, model=model, api_key=os.environ["OPENAI_API_KEY"]) - # pprint(latencies) + provider = "openai" + model = "gpt-4o-mini" + for _ in range(1): + latencies = test_stuff(provider=provider, model=model, api_key=os.environ["OPENAI_API_KEY"]) + pprint(latencies) # provider = "anthropic" # model = "claude-3-opus-20240229" # for _ in range(1): # latencies = test_stuff(provider=provider, model=model, api_key=os.environ["ANTHROPIC_API_KEY"]) # pprint(latencies) - # we need credits + # # we need credits - # provider = "azure" - # model = "gpt-4o-mini" - # for _ in range(1): - # latencies = test_stuff(provider=provider, model=model, - # api_key=os.environ["AZURE_API_KEY"], - # api_version=os.environ["AZURE_API_VERSION"], - # api_endpoint=os.environ["AZURE_API_ENDPOINT"]) - # pprint(latencies) + provider = "azure" + model = "gpt-4o-mini" + for _ in range(1): + latencies = test_stuff(provider=provider, model=model, + api_key=os.environ["AZURE_API_KEY"], + api_version=os.environ["AZURE_API_VERSION"], + api_endpoint=os.environ["AZURE_API_ENDPOINT"]) + pprint(latencies) - # provider = "azure" - # model = "Meta-Llama-3.1-70B-Instruct" - # for _ in range(1): - # latencies = test_stuff(provider=provider, model=model, - # api_key=os.environ["AZURE_API_KEY_llama"], - # base_url=os.environ["AZURE_BASE_URL"] - # ) - # pprint(latencies) + provider = "azure" + model = "Meta-Llama-3.1-70B-Instruct" + for _ in range(1): + latencies = test_stuff(provider=provider, model=model, + api_key=os.environ["AZURE_API_KEY_llama"], + base_url=os.environ["AZURE_BASE_URL"] + ) + pprint(latencies) provider = "vertexai" diff --git a/libs/core/llmstudio_core/providers/__init__.py b/libs/core/llmstudio_core/providers/__init__.py index a6ca613b..4d42f9e7 100644 --- a/libs/core/llmstudio_core/providers/__init__.py +++ b/libs/core/llmstudio_core/providers/__init__.py @@ -1,8 +1,8 @@ import os import yaml -from llmstudio_core.providers.anthropic import AnthropicProvider +# from llmstudio_core.providers.anthropic import AnthropicProvider #TODO: adpat it from llmstudio_core.providers.azure import AzureProvider -from llmstudio_core.providers.ollama import OllamaProvider +# from llmstudio_core.providers.ollama import OllamaProvider #TODO: adapt it from llmstudio_core.providers.openai import OpenAIProvider from llmstudio_core.providers.vertexai import VertexAIProvider diff --git a/libs/core/llmstudio_core/providers/provider.py b/libs/core/llmstudio_core/providers/provider.py index 2f85a570..f111e2bc 100644 --- a/libs/core/llmstudio_core/providers/provider.py +++ b/libs/core/llmstudio_core/providers/provider.py @@ -300,7 +300,7 @@ async def ahandle_response( yield ChatCompletion(**response) def handle_response( - self, request: ChatRequest, response: AsyncGenerator, start_time: float + self, request: ChatRequest, response: Generator, start_time: float ) -> Generator: """Handles the response from an API""" first_token_time = None From 89d74f44f62fdffac0e51fcf4ba9488896339522 Mon Sep 17 00:00:00 2001 From: Diogo Goncalves Date: Tue, 15 Oct 2024 17:28:12 +0100 Subject: [PATCH 17/58] feat: adapt proxy --- examples/01_intro_to_llmstudio copy.ipynb | 326 +++ .../01_intro_to_llmstudio_with_proxy.ipynb | 840 ++++++++ examples/03_langchain_integration.ipynb | 40 +- examples/llm_proxy.py | 28 + .../core/llmstudio_core/providers/provider.py | 12 +- llmstudio/cli.py | 8 +- llmstudio/engine/__init__.py | 43 +- llmstudio/engine/provider.py | 236 +++ llmstudio/llm/__init__.py | 348 +--- llmstudio/llm/langchain.py | 13 + poetry.lock | 1757 +++++++++-------- pyproject.toml | 1 + 12 files changed, 2541 insertions(+), 1111 deletions(-) create mode 100644 examples/01_intro_to_llmstudio copy.ipynb create mode 100644 examples/01_intro_to_llmstudio_with_proxy.ipynb create mode 100644 examples/llm_proxy.py create mode 100644 llmstudio/engine/provider.py diff --git a/examples/01_intro_to_llmstudio copy.ipynb b/examples/01_intro_to_llmstudio copy.ipynb new file mode 100644 index 00000000..ec9ef5e4 --- /dev/null +++ b/examples/01_intro_to_llmstudio copy.ipynb @@ -0,0 +1,326 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Intro to LLMstudio\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import clients\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from llmstudio import LLM\n", + "from pprint import pprint" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# You can set OPENAI_API_KEY and ANTHROPIC_API_KEY on .env file\n", + "\n", + "anthropic = LLM(\"vertexai\") # or you can pass api_key as an argument here\n", + "openai = LLM(\"openai\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Chat (non-stream)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "ChatCompletion(id='57b0b582-2e1e-4288-b270-4d946fbc6eb6', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content=\"I’m an AI language model created by OpenAI, and I don't have a personal name. You can call me whatever you like! How can I assist you today?\", refusal=None, role='assistant', function_call=None, tool_calls=None))], created=1728991499, model='gpt-4o', object='chat.completion', service_tier=None, system_fingerprint=None, usage=None, chat_input=\"What's your name\", chat_output=\"I’m an AI language model created by OpenAI, and I don't have a personal name. You can call me whatever you like! How can I assist you today?\", context=[{'role': 'user', 'content': \"What's your name\"}], provider='openai', deployment='gpt-4o-2024-08-06', timestamp=1728991500.507817, parameters={}, metrics={'input_tokens': 4, 'output_tokens': 35, 'total_tokens': 39, 'cost_usd': 0.000545, 'latency_s': 1.9663710594177246, 'time_to_first_token_s': 1.0656960010528564, 'inter_token_latency_s': 0.025410114015851703, 'tokens_per_second': 18.30783657417141})" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "openai.chat(\"What's your name\", model=\"gpt-4o\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Async version" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "ChatCompletion(id='2d4fcafc-c19a-49ff-821a-e925abfb544c', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='I’m called ChatGPT, your friendly AI assistant. How can I help you today?', refusal=None, role='assistant', function_call=None, tool_calls=None))], created=1728991501, model='gpt-4o', object='chat.completion', service_tier=None, system_fingerprint=None, usage=None, chat_input=\"What's your name\", chat_output='I’m called ChatGPT, your friendly AI assistant. How can I help you today?', context=[{'role': 'user', 'content': \"What's your name\"}], provider='openai', deployment='gpt-4o-2024-08-06', timestamp=1728991502.176515, parameters={}, metrics={'input_tokens': 4, 'output_tokens': 19, 'total_tokens': 23, 'cost_usd': 0.000305, 'latency_s': 1.6578657627105713, 'time_to_first_token_s': 1.019500970840454, 'inter_token_latency_s': 0.03355515630621659, 'tokens_per_second': 12.06370289431665})" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "await openai.achat(\"What's your name\", model=\"gpt-4o\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Chat (stream)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Space, the vast expanse beyond Earth's atmosphere, is a realm of infinite possibilities and mysteries that\n", + "\n", + " has captivated human imagination for centuries. It encompasses everything from the smallest particles to massive celestial bodies like stars\n", + "\n", + ", planets, and galaxies. Space is a place where the laws of physics and the limits of human\n", + "\n", + " understanding are constantly tested and expanded. It holds the secrets of our origin, our place in the universe\n", + "\n", + ", and the potential for life beyond Earth. The exploration of space has led to significant technological and scientific\n", + "\n", + " advancements, and it continues to inspire a sense of wonder and curiosity. As we venture further into this\n", + "\n", + " great unknown, we are reminded of the boundless nature of the universe and our relentless quest for knowledge\n", + "\n", + ".\n", + "\n", + "## Metrics:\n", + "{'cost_usd': 0.00217,\n", + " 'input_tokens': 8,\n", + " 'inter_token_latency_s': 0.0326266238029967,\n", + " 'latency_s': 5.229671001434326,\n", + " 'output_tokens': 142,\n", + " 'time_to_first_token_s': 0.6259880065917969,\n", + " 'tokens_per_second': 27.152759697704518,\n", + " 'total_tokens': 150}\n" + ] + } + ], + "source": [ + "response = openai.chat(\"Write a paragfraph about space\", model=\"gpt-4o\", is_stream=True)\n", + "for i, chunk in enumerate(response):\n", + " if i%20==0:\n", + " print(\"\\n\")\n", + " if not chunk.metrics:\n", + " print(chunk.chat_output, end=\"\", flush=True)\n", + " else:\n", + " print(\"\\n\\n## Metrics:\")\n", + " pprint(chunk.metrics)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Async version" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Space, the vast and seemingly infinite expanse beyond our planet, captivates the human imagination with\n", + "\n", + " its profound mysteries and breathtaking beauty. It is home to an array of celestial phenomena, from the radiant\n", + "\n", + " glow of distant stars to the swirling patterns of galaxies, each revealing the complex tapestry of the universe.\n", + "\n", + " The vacuum of space, devoid of atmosphere, allows for the remarkable observation of cosmic events and the exploration\n", + "\n", + " of planets within our solar system and beyond. The study of space not only enhances our understanding of the\n", + "\n", + " fundamental laws of physics but also raises profound questions about the origins of life and our place in the cosmos\n", + "\n", + ". As humanity continues to push the boundaries of exploration, from robotic missions to manned spaceflights\n", + "\n", + ", we inch closer to unlocking the secrets that lie among the stars, fostering a sense of wonder that\n", + "\n", + " defines our quest to explore the unknown.\n", + "\n", + "## Metrics:\n", + "{'cost_usd': 0.000102,\n", + " 'input_tokens': 8,\n", + " 'inter_token_latency_s': 0.01550252948488508,\n", + " 'latency_s': 3.185244083404541,\n", + " 'output_tokens': 168,\n", + " 'time_to_first_token_s': 0.579679012298584,\n", + " 'tokens_per_second': 53.057158438974234,\n", + " 'total_tokens': 176}\n" + ] + } + ], + "source": [ + "i=0\n", + "async for chunk in await openai.achat(\"Write a paragfraph about space\", model=\"gpt-4o-mini\", is_stream=True):\n", + " if i%20==0:\n", + " print(\"\\n\")\n", + " if not chunk.metrics:\n", + " print(chunk.chat_output, end=\"\", flush=True)\n", + " else:\n", + " print(\"\\n\\n## Metrics:\")\n", + " pprint(chunk.metrics)\n", + " i+=1\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Batch version" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "from llmstudio import LLM\n", + "import nest_asyncio\n", + "nest_asyncio.apply()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'NoneType' object has no attribute 'id'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[8], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m gpt3 \u001b[38;5;241m=\u001b[39m \u001b[43mLLM\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mopenai/gpt-3.5-turbo\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/fun/LLMstudio/llmstudio/llm/__init__.py:39\u001b[0m, in \u001b[0;36mLLM.__init__\u001b[0;34m(self, provider, proxy_config, tracking_config, **kwargs)\u001b[0m\n\u001b[1;32m 34\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_provider \u001b[38;5;241m=\u001b[39m LLMProxyProvider(provider\u001b[38;5;241m=\u001b[39mprovider,\n\u001b[1;32m 35\u001b[0m host\u001b[38;5;241m=\u001b[39mproxy_config\u001b[38;5;241m.\u001b[39mhost,\n\u001b[1;32m 36\u001b[0m port\u001b[38;5;241m=\u001b[39mproxy_config\u001b[38;5;241m.\u001b[39mport\n\u001b[1;32m 37\u001b[0m )\n\u001b[1;32m 38\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 39\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_provider \u001b[38;5;241m=\u001b[39m \u001b[43mLLM_factory\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprovider\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 41\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_session_local \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 42\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tracking_config \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[0;32m~/fun/LLMstudio/libs/core/llmstudio_core/__init__.py:25\u001b[0m, in \u001b[0;36mLLM\u001b[0;34m(provider, api_key, **kwargs)\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 12\u001b[0m \u001b[38;5;124;03mFactory method to create an instance of a provider.\u001b[39;00m\n\u001b[1;32m 13\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[38;5;124;03m NotImplementedError: If the provider is not found in the provider map.\u001b[39;00m\n\u001b[1;32m 23\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 24\u001b[0m provider_config \u001b[38;5;241m=\u001b[39m _engine_config\u001b[38;5;241m.\u001b[39mproviders\u001b[38;5;241m.\u001b[39mget(provider)\n\u001b[0;32m---> 25\u001b[0m provider_class \u001b[38;5;241m=\u001b[39m provider_registry\u001b[38;5;241m.\u001b[39mget(\u001b[43mprovider_config\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mid\u001b[49m)\n\u001b[1;32m 26\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m provider_class:\n\u001b[1;32m 27\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m provider_class(config\u001b[38;5;241m=\u001b[39mprovider_config, api_key\u001b[38;5;241m=\u001b[39mapi_key, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'id'" + ] + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n", + "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n", + "\u001b[1;31mClick here for more info. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + } + ], + "source": [ + "gpt3 = LLM('openai/gpt-3.5-turbo')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[ChatCompletion(id='bce77107-790e-4194-b709-c1f973908e13', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='I am a virtual assistant and I do not have a personal name. You can simply refer to me as \"assistant\". How can I assist you today?', role='assistant', function_call=None, tool_calls=None))], created=1718357127, model='gpt-3.5-turbo', object='chat.completion', system_fingerprint=None, usage=None, session_id=None, chat_input=\"What's your name?\", chat_output='I am a virtual assistant and I do not have a personal name. You can simply refer to me as \"assistant\". How can I assist you today?', context=[{'role': 'user', 'content': \"What's your name?\"}], provider='openai', timestamp=1718357130.3642838, parameters={'temperature': 1, 'max_tokens': 2048, 'top_p': 1, 'frequency_penalty': 0, 'presence_penalty': 0}, metrics={'input_tokens': 5, 'output_tokens': 31, 'total_tokens': 36, 'cost_usd': 4.9e-05, 'latency_s': 2.8777408599853516, 'time_to_first_token_s': 2.8716890811920166, 'inter_token_latency_s': 8.686631917953491e-05, 'tokens_per_second': 11.46732857668358}),\n", + " ChatCompletion(id='f5058611-d4e5-48cc-810f-5cfbf82bd70f', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content=\"Why couldn't the bicycle find its way home? Because it lost its bearings!\", role='assistant', function_call=None, tool_calls=None))], created=1718357129, model='gpt-3.5-turbo', object='chat.completion', system_fingerprint=None, usage=None, session_id=None, chat_input='Tell me a joke.', chat_output=\"Why couldn't the bicycle find its way home? Because it lost its bearings!\", context=[{'role': 'user', 'content': 'Tell me a joke.'}], provider='openai', timestamp=1718357130.392538, parameters={'temperature': 1, 'max_tokens': 2048, 'top_p': 1, 'frequency_penalty': 0, 'presence_penalty': 0}, metrics={'input_tokens': 5, 'output_tokens': 16, 'total_tokens': 21, 'cost_usd': 2.65e-05, 'latency_s': 0.9708259105682373, 'time_to_first_token_s': 0.9693810939788818, 'inter_token_latency_s': 7.653236389160156e-05, 'tokens_per_second': 18.540914291692484}),\n", + " ChatCompletion(id='bb195dce-30b4-4836-a9bc-9d73ea81efcc', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content=\"I'm sorry, I am an AI and do not have the ability to determine the current weather. You can check the weather in your area by using a weather app on your phone or by visiting a weather website.\", role='assistant', function_call=None, tool_calls=None))], created=1718357129, model='gpt-3.5-turbo', object='chat.completion', system_fingerprint=None, usage=None, session_id=None, chat_input=\"What's the weather like?\", chat_output=\"I'm sorry, I am an AI and do not have the ability to determine the current weather. You can check the weather in your area by using a weather app on your phone or by visiting a weather website.\", context=[{'role': 'user', 'content': \"What's the weather like?\"}], provider='openai', timestamp=1718357130.386699, parameters={'temperature': 1, 'max_tokens': 2048, 'top_p': 1, 'frequency_penalty': 0, 'presence_penalty': 0}, metrics={'input_tokens': 6, 'output_tokens': 43, 'total_tokens': 49, 'cost_usd': 6.75e-05, 'latency_s': 1.5981099605560303, 'time_to_first_token_s': 1.594458818435669, 'inter_token_latency_s': 7.893822409889914e-05, 'tokens_per_second': 28.158262641916803}),\n", + " ChatCompletion(id='2e689f44-7977-4a21-9b45-4ac9fcf17e0e', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content=\"I'm an AI assistant, so I don't have a physical voice to sing with. However, I can provide lyrics or information about songs if you'd like! Just let me know how I can assist you.\", role='assistant', function_call=None, tool_calls=None))], created=1718357128, model='gpt-3.5-turbo', object='chat.completion', system_fingerprint=None, usage=None, session_id=None, chat_input='Can you sing a song?', chat_output=\"I'm an AI assistant, so I don't have a physical voice to sing with. However, I can provide lyrics or information about songs if you'd like! Just let me know how I can assist you.\", context=[{'role': 'user', 'content': 'Can you sing a song?'}], provider='openai', timestamp=1718357130.378707, parameters={'temperature': 1, 'max_tokens': 2048, 'top_p': 1, 'frequency_penalty': 0, 'presence_penalty': 0}, metrics={'input_tokens': 6, 'output_tokens': 43, 'total_tokens': 49, 'cost_usd': 6.75e-05, 'latency_s': 2.229912042617798, 'time_to_first_token_s': 2.22599196434021, 'inter_token_latency_s': 8.436224677345969e-05, 'tokens_per_second': 20.180168159086847}),\n", + " ChatCompletion(id='257fb034-90c5-4d98-8ff7-1830ff3a0954', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='I am an AI assistant designed to provide information and assistance in various topics. I am constantly learning and improving my abilities to help users with their queries and tasks. I am programmed to provide accurate and reliable information based on the data available to me. If you have any specific questions or need assistance, feel free to ask!', role='assistant', function_call=None, tool_calls=None))], created=1718357130, model='gpt-3.5-turbo', object='chat.completion', system_fingerprint=None, usage=None, session_id=None, chat_input='Tell me about yourself.', chat_output='I am an AI assistant designed to provide information and assistance in various topics. I am constantly learning and improving my abilities to help users with their queries and tasks. I am programmed to provide accurate and reliable information based on the data available to me. If you have any specific questions or need assistance, feel free to ask!', context=[{'role': 'user', 'content': 'Tell me about yourself.'}], provider='openai', timestamp=1718357132.529829, parameters={'temperature': 1, 'max_tokens': 2048, 'top_p': 1, 'frequency_penalty': 0, 'presence_penalty': 0}, metrics={'input_tokens': 5, 'output_tokens': 64, 'total_tokens': 69, 'cost_usd': 9.850000000000001e-05, 'latency_s': 2.184094190597534, 'time_to_first_token_s': 0.6790041923522949, 'inter_token_latency_s': 0.023148767764751728, 'tokens_per_second': 30.21847697051171}),\n", + " ChatCompletion(id='813d3a00-2765-479c-b158-bbf916232706', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content=\"De sun be shinin' bright and de breeze be cool, perfect weather for jammin' on de beach! How 'bout you, how's de weather by you?\", role='assistant', function_call=None, tool_calls=None))], created=1718357131, model='gpt-3.5-turbo', object='chat.completion', system_fingerprint=None, usage=None, session_id=None, chat_input='How is the weather today?', chat_output=\"De sun be shinin' bright and de breeze be cool, perfect weather for jammin' on de beach! How 'bout you, how's de weather by you?\", context=[{'role': 'system', 'content': 'You are a tchill dude from jamaica'}, {'role': 'user', 'content': 'Hello, how are you?'}, {'role': 'assistant', 'content': 'Ye man doin fain man!'}, {'role': 'user', 'content': 'How is the weather today?'}], provider='openai', timestamp=1718357132.537878, parameters={'temperature': 1, 'max_tokens': 2048, 'top_p': 1, 'frequency_penalty': 0, 'presence_penalty': 0}, metrics={'input_tokens': 31, 'output_tokens': 35, 'total_tokens': 66, 'cost_usd': 6.8e-05, 'latency_s': 1.5229389667510986, 'time_to_first_token_s': 1.5202741622924805, 'inter_token_latency_s': 6.913476520114475e-05, 'tokens_per_second': 24.29512988227787})]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "complex_prompt= [{\"role\": \"system\", \"content\": \"You are a tchill dude from jamaica\"},\n", + " {\"role\": \"user\", \"content\": \"Hello, how are you?\"},\n", + " {\"role\": \"assistant\", \"content\": \"Ye man doin fain man!\"},\n", + " {\"role\": \"user\", \"content\": \"How is the weather today?\"}]\n", + "\n", + "inputs = [\n", + " \"What's your name?\",\n", + " \"Tell me a joke.\",\n", + " \"What's the weather like?\",\n", + " \"Can you sing a song?\",\n", + " \"Tell me about yourself.\",\n", + " complex_prompt\n", + "]\n", + "\n", + "responses = gpt3.run_batch_chat_coroutine(inputs)\n", + "responses" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/01_intro_to_llmstudio_with_proxy.ipynb b/examples/01_intro_to_llmstudio_with_proxy.ipynb new file mode 100644 index 00000000..ec254841 --- /dev/null +++ b/examples/01_intro_to_llmstudio_with_proxy.ipynb @@ -0,0 +1,840 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Intro to LLMstudio\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## start proxy server\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running LLMstudio Engine on http://0.0.0.0:8001 \n", + "Running LLMstudio Tracking on http://0.0.0.0:8002 \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ERROR: Exception in ASGI application\n", + "Traceback (most recent call last):\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/encoders.py\", line 322, in jsonable_encoder\n", + " data = dict(obj)\n", + " ^^^^^^^^^\n", + "TypeError: 'async_generator' object is not iterable\n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/encoders.py\", line 327, in jsonable_encoder\n", + " data = vars(obj)\n", + " ^^^^^^^^^\n", + "TypeError: vars() argument must have __dict__ attribute\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/uvicorn/protocols/http/h11_impl.py\", line 412, in run_asgi\n", + " result = await app( # type: ignore[func-returns-value]\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/uvicorn/middleware/proxy_headers.py\", line 84, in __call__\n", + " return await self.app(scope, receive, send)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/applications.py\", line 1054, in __call__\n", + " await super().__call__(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/applications.py\", line 123, in __call__\n", + " await self.middleware_stack(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/errors.py\", line 186, in __call__\n", + " raise exc\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/errors.py\", line 164, in __call__\n", + " await self.app(scope, receive, _send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/cors.py\", line 83, in __call__\n", + " await self.app(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/exceptions.py\", line 62, in __call__\n", + " await wrap_app_handling_exceptions(self.app, conn)(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 64, in wrapped_app\n", + " raise exc\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 53, in wrapped_app\n", + " await app(scope, receive, sender)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 758, in __call__\n", + " await self.middleware_stack(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 778, in app\n", + " await route.handle(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 299, in handle\n", + " await self.app(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 79, in app\n", + " await wrap_app_handling_exceptions(app, request)(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 64, in wrapped_app\n", + " raise exc\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 53, in wrapped_app\n", + " await app(scope, receive, sender)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 74, in app\n", + " response = await func(request)\n", + " ^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/routing.py\", line 315, in app\n", + " content = await serialize_response(\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/routing.py\", line 180, in serialize_response\n", + " return jsonable_encoder(response_content)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/encoders.py\", line 330, in jsonable_encoder\n", + " raise ValueError(errors) from e\n", + "ValueError: [TypeError(\"'async_generator' object is not iterable\"), TypeError('vars() argument must have __dict__ attribute')]\n", + "ERROR: Exception in ASGI application\n", + "Traceback (most recent call last):\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/encoders.py\", line 322, in jsonable_encoder\n", + " data = dict(obj)\n", + " ^^^^^^^^^\n", + "TypeError: 'async_generator' object is not iterable\n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/encoders.py\", line 327, in jsonable_encoder\n", + " data = vars(obj)\n", + " ^^^^^^^^^\n", + "TypeError: vars() argument must have __dict__ attribute\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/uvicorn/protocols/http/h11_impl.py\", line 412, in run_asgi\n", + " result = await app( # type: ignore[func-returns-value]\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/uvicorn/middleware/proxy_headers.py\", line 84, in __call__\n", + " return await self.app(scope, receive, send)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/applications.py\", line 1054, in __call__\n", + " await super().__call__(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/applications.py\", line 123, in __call__\n", + " await self.middleware_stack(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/errors.py\", line 186, in __call__\n", + " raise exc\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/errors.py\", line 164, in __call__\n", + " await self.app(scope, receive, _send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/cors.py\", line 83, in __call__\n", + " await self.app(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/exceptions.py\", line 62, in __call__\n", + " await wrap_app_handling_exceptions(self.app, conn)(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 64, in wrapped_app\n", + " raise exc\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 53, in wrapped_app\n", + " await app(scope, receive, sender)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 758, in __call__\n", + " await self.middleware_stack(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 778, in app\n", + " await route.handle(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 299, in handle\n", + " await self.app(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 79, in app\n", + " await wrap_app_handling_exceptions(app, request)(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 64, in wrapped_app\n", + " raise exc\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 53, in wrapped_app\n", + " await app(scope, receive, sender)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 74, in app\n", + " response = await func(request)\n", + " ^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/routing.py\", line 315, in app\n", + " content = await serialize_response(\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/routing.py\", line 180, in serialize_response\n", + " return jsonable_encoder(response_content)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/encoders.py\", line 330, in jsonable_encoder\n", + " raise ValueError(errors) from e\n", + "ValueError: [TypeError(\"'async_generator' object is not iterable\"), TypeError('vars() argument must have __dict__ attribute')]\n", + "ERROR: Exception in ASGI application\n", + "Traceback (most recent call last):\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/encoders.py\", line 322, in jsonable_encoder\n", + " data = dict(obj)\n", + " ^^^^^^^^^\n", + "TypeError: 'async_generator' object is not iterable\n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/encoders.py\", line 327, in jsonable_encoder\n", + " data = vars(obj)\n", + " ^^^^^^^^^\n", + "TypeError: vars() argument must have __dict__ attribute\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/uvicorn/protocols/http/h11_impl.py\", line 412, in run_asgi\n", + " result = await app( # type: ignore[func-returns-value]\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/uvicorn/middleware/proxy_headers.py\", line 84, in __call__\n", + " return await self.app(scope, receive, send)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/applications.py\", line 1054, in __call__\n", + " await super().__call__(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/applications.py\", line 123, in __call__\n", + " await self.middleware_stack(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/errors.py\", line 186, in __call__\n", + " raise exc\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/errors.py\", line 164, in __call__\n", + " await self.app(scope, receive, _send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/cors.py\", line 83, in __call__\n", + " await self.app(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/exceptions.py\", line 62, in __call__\n", + " await wrap_app_handling_exceptions(self.app, conn)(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 64, in wrapped_app\n", + " raise exc\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 53, in wrapped_app\n", + " await app(scope, receive, sender)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 758, in __call__\n", + " await self.middleware_stack(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 778, in app\n", + " await route.handle(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 299, in handle\n", + " await self.app(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 79, in app\n", + " await wrap_app_handling_exceptions(app, request)(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 64, in wrapped_app\n", + " raise exc\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 53, in wrapped_app\n", + " await app(scope, receive, sender)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 74, in app\n", + " response = await func(request)\n", + " ^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/routing.py\", line 315, in app\n", + " content = await serialize_response(\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/routing.py\", line 180, in serialize_response\n", + " return jsonable_encoder(response_content)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/encoders.py\", line 330, in jsonable_encoder\n", + " raise ValueError(errors) from e\n", + "ValueError: [TypeError(\"'async_generator' object is not iterable\"), TypeError('vars() argument must have __dict__ attribute')]\n", + "ERROR: Exception in ASGI application\n", + "Traceback (most recent call last):\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/encoders.py\", line 322, in jsonable_encoder\n", + " data = dict(obj)\n", + " ^^^^^^^^^\n", + "TypeError: 'async_generator' object is not iterable\n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/encoders.py\", line 327, in jsonable_encoder\n", + " data = vars(obj)\n", + " ^^^^^^^^^\n", + "TypeError: vars() argument must have __dict__ attribute\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/uvicorn/protocols/http/h11_impl.py\", line 412, in run_asgi\n", + " result = await app( # type: ignore[func-returns-value]\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/uvicorn/middleware/proxy_headers.py\", line 84, in __call__\n", + " return await self.app(scope, receive, send)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/applications.py\", line 1054, in __call__\n", + " await super().__call__(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/applications.py\", line 123, in __call__\n", + " await self.middleware_stack(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/errors.py\", line 186, in __call__\n", + " raise exc\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/errors.py\", line 164, in __call__\n", + " await self.app(scope, receive, _send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/cors.py\", line 83, in __call__\n", + " await self.app(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/exceptions.py\", line 62, in __call__\n", + " await wrap_app_handling_exceptions(self.app, conn)(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 64, in wrapped_app\n", + " raise exc\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 53, in wrapped_app\n", + " await app(scope, receive, sender)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 758, in __call__\n", + " await self.middleware_stack(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 778, in app\n", + " await route.handle(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 299, in handle\n", + " await self.app(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 79, in app\n", + " await wrap_app_handling_exceptions(app, request)(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 64, in wrapped_app\n", + " raise exc\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 53, in wrapped_app\n", + " await app(scope, receive, sender)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 74, in app\n", + " response = await func(request)\n", + " ^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/routing.py\", line 315, in app\n", + " content = await serialize_response(\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/routing.py\", line 180, in serialize_response\n", + " return jsonable_encoder(response_content)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/encoders.py\", line 330, in jsonable_encoder\n", + " raise ValueError(errors) from e\n", + "ValueError: [TypeError(\"'async_generator' object is not iterable\"), TypeError('vars() argument must have __dict__ attribute')]\n", + "ERROR: Exception in ASGI application\n", + "Traceback (most recent call last):\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/encoders.py\", line 322, in jsonable_encoder\n", + " data = dict(obj)\n", + " ^^^^^^^^^\n", + "TypeError: 'async_generator' object is not iterable\n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/encoders.py\", line 327, in jsonable_encoder\n", + " data = vars(obj)\n", + " ^^^^^^^^^\n", + "TypeError: vars() argument must have __dict__ attribute\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/uvicorn/protocols/http/h11_impl.py\", line 412, in run_asgi\n", + " result = await app( # type: ignore[func-returns-value]\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/uvicorn/middleware/proxy_headers.py\", line 84, in __call__\n", + " return await self.app(scope, receive, send)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/applications.py\", line 1054, in __call__\n", + " await super().__call__(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/applications.py\", line 123, in __call__\n", + " await self.middleware_stack(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/errors.py\", line 186, in __call__\n", + " raise exc\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/errors.py\", line 164, in __call__\n", + " await self.app(scope, receive, _send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/cors.py\", line 83, in __call__\n", + " await self.app(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/exceptions.py\", line 62, in __call__\n", + " await wrap_app_handling_exceptions(self.app, conn)(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 64, in wrapped_app\n", + " raise exc\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 53, in wrapped_app\n", + " await app(scope, receive, sender)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 758, in __call__\n", + " await self.middleware_stack(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 778, in app\n", + " await route.handle(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 299, in handle\n", + " await self.app(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 79, in app\n", + " await wrap_app_handling_exceptions(app, request)(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 64, in wrapped_app\n", + " raise exc\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 53, in wrapped_app\n", + " await app(scope, receive, sender)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 74, in app\n", + " response = await func(request)\n", + " ^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/routing.py\", line 315, in app\n", + " content = await serialize_response(\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/routing.py\", line 180, in serialize_response\n", + " return jsonable_encoder(response_content)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/encoders.py\", line 330, in jsonable_encoder\n", + " raise ValueError(errors) from e\n", + "ValueError: [TypeError(\"'async_generator' object is not iterable\"), TypeError('vars() argument must have __dict__ attribute')]\n", + "ERROR: Exception in ASGI application\n", + "Traceback (most recent call last):\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/encoders.py\", line 322, in jsonable_encoder\n", + " data = dict(obj)\n", + " ^^^^^^^^^\n", + "TypeError: 'async_generator' object is not iterable\n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/encoders.py\", line 327, in jsonable_encoder\n", + " data = vars(obj)\n", + " ^^^^^^^^^\n", + "TypeError: vars() argument must have __dict__ attribute\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/uvicorn/protocols/http/h11_impl.py\", line 412, in run_asgi\n", + " result = await app( # type: ignore[func-returns-value]\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/uvicorn/middleware/proxy_headers.py\", line 84, in __call__\n", + " return await self.app(scope, receive, send)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/applications.py\", line 1054, in __call__\n", + " await super().__call__(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/applications.py\", line 123, in __call__\n", + " await self.middleware_stack(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/errors.py\", line 186, in __call__\n", + " raise exc\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/errors.py\", line 164, in __call__\n", + " await self.app(scope, receive, _send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/cors.py\", line 83, in __call__\n", + " await self.app(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/exceptions.py\", line 62, in __call__\n", + " await wrap_app_handling_exceptions(self.app, conn)(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 64, in wrapped_app\n", + " raise exc\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 53, in wrapped_app\n", + " await app(scope, receive, sender)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 758, in __call__\n", + " await self.middleware_stack(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 778, in app\n", + " await route.handle(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 299, in handle\n", + " await self.app(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 79, in app\n", + " await wrap_app_handling_exceptions(app, request)(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 64, in wrapped_app\n", + " raise exc\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 53, in wrapped_app\n", + " await app(scope, receive, sender)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 74, in app\n", + " response = await func(request)\n", + " ^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/routing.py\", line 315, in app\n", + " content = await serialize_response(\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/routing.py\", line 180, in serialize_response\n", + " return jsonable_encoder(response_content)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/encoders.py\", line 330, in jsonable_encoder\n", + " raise ValueError(errors) from e\n", + "ValueError: [TypeError(\"'async_generator' object is not iterable\"), TypeError('vars() argument must have __dict__ attribute')]\n", + "ERROR: Exception in ASGI application\n", + "Traceback (most recent call last):\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/encoders.py\", line 322, in jsonable_encoder\n", + " data = dict(obj)\n", + " ^^^^^^^^^\n", + "TypeError: 'async_generator' object is not iterable\n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/encoders.py\", line 327, in jsonable_encoder\n", + " data = vars(obj)\n", + " ^^^^^^^^^\n", + "TypeError: vars() argument must have __dict__ attribute\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/uvicorn/protocols/http/h11_impl.py\", line 412, in run_asgi\n", + " result = await app( # type: ignore[func-returns-value]\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/uvicorn/middleware/proxy_headers.py\", line 84, in __call__\n", + " return await self.app(scope, receive, send)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/applications.py\", line 1054, in __call__\n", + " await super().__call__(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/applications.py\", line 123, in __call__\n", + " await self.middleware_stack(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/errors.py\", line 186, in __call__\n", + " raise exc\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/errors.py\", line 164, in __call__\n", + " await self.app(scope, receive, _send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/cors.py\", line 83, in __call__\n", + " await self.app(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/exceptions.py\", line 62, in __call__\n", + " await wrap_app_handling_exceptions(self.app, conn)(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 64, in wrapped_app\n", + " raise exc\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 53, in wrapped_app\n", + " await app(scope, receive, sender)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 758, in __call__\n", + " await self.middleware_stack(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 778, in app\n", + " await route.handle(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 299, in handle\n", + " await self.app(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 79, in app\n", + " await wrap_app_handling_exceptions(app, request)(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 64, in wrapped_app\n", + " raise exc\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 53, in wrapped_app\n", + " await app(scope, receive, sender)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 74, in app\n", + " response = await func(request)\n", + " ^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/routing.py\", line 315, in app\n", + " content = await serialize_response(\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/routing.py\", line 180, in serialize_response\n", + " return jsonable_encoder(response_content)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/encoders.py\", line 330, in jsonable_encoder\n", + " raise ValueError(errors) from e\n", + "ValueError: [TypeError(\"'async_generator' object is not iterable\"), TypeError('vars() argument must have __dict__ attribute')]\n", + "ERROR: Exception in ASGI application\n", + "Traceback (most recent call last):\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/encoders.py\", line 322, in jsonable_encoder\n", + " data = dict(obj)\n", + " ^^^^^^^^^\n", + "TypeError: 'async_generator' object is not iterable\n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/encoders.py\", line 327, in jsonable_encoder\n", + " data = vars(obj)\n", + " ^^^^^^^^^\n", + "TypeError: vars() argument must have __dict__ attribute\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/uvicorn/protocols/http/h11_impl.py\", line 412, in run_asgi\n", + " result = await app( # type: ignore[func-returns-value]\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/uvicorn/middleware/proxy_headers.py\", line 84, in __call__\n", + " return await self.app(scope, receive, send)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/applications.py\", line 1054, in __call__\n", + " await super().__call__(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/applications.py\", line 123, in __call__\n", + " await self.middleware_stack(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/errors.py\", line 186, in __call__\n", + " raise exc\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/errors.py\", line 164, in __call__\n", + " await self.app(scope, receive, _send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/cors.py\", line 83, in __call__\n", + " await self.app(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/exceptions.py\", line 62, in __call__\n", + " await wrap_app_handling_exceptions(self.app, conn)(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 64, in wrapped_app\n", + " raise exc\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 53, in wrapped_app\n", + " await app(scope, receive, sender)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 758, in __call__\n", + " await self.middleware_stack(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 778, in app\n", + " await route.handle(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 299, in handle\n", + " await self.app(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 79, in app\n", + " await wrap_app_handling_exceptions(app, request)(scope, receive, send)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 64, in wrapped_app\n", + " raise exc\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 53, in wrapped_app\n", + " await app(scope, receive, sender)\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 74, in app\n", + " response = await func(request)\n", + " ^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/routing.py\", line 315, in app\n", + " content = await serialize_response(\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/routing.py\", line 180, in serialize_response\n", + " return jsonable_encoder(response_content)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/encoders.py\", line 330, in jsonable_encoder\n", + " raise ValueError(errors) from e\n", + "ValueError: [TypeError(\"'async_generator' object is not iterable\"), TypeError('vars() argument must have __dict__ attribute')]\n" + ] + } + ], + "source": [ + "from llmstudio.server import start_server\n", + "start_server()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Connected to LLMStudio Proxy @ 0.0.0.0:8001\n" + ] + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n", + "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n", + "\u001b[1;31mClick here for more info. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + } + ], + "source": [ + "from llmstudio.engine.provider import LLMProxyProvider\n", + "\n", + "\n", + "llm = LLMProxyProvider(provider=\"openai\", host=\"0.0.0.0\", port=\"8001\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import clients\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "from llmstudio import LLM\n", + "from pprint import pprint" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Connected to LLMStudio Proxy @ 0.0.0.0:8001\n" + ] + } + ], + "source": [ + "# You can set OPENAI_API_KEY and ANTHROPIC_API_KEY on .env file\n", + "from llmstudio.llm import ProxyConfig\n", + "proxy = ProxyConfig(host=\"0.0.0.0\", port=\"8001\")\n", + "\n", + "openai = LLM(\"openai\", proxy_config=proxy)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "openai._provider\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Chat (non-stream)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "ChatCompletion(id='bbdd240d-ef4a-4550-b31f-d3758228e9c6', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content=\"I’m an AI and don’t have a personal name, but you can call me whatever you like! Let me know if there's anything I can help you with.\", refusal=None, role='assistant', function_call=None, tool_calls=None))], created=1729004854, model='gpt-4o', object='chat.completion', service_tier=None, system_fingerprint=None, usage=None, chat_input=\"What's your name\", chat_output=\"I’m an AI and don’t have a personal name, but you can call me whatever you like! Let me know if there's anything I can help you with.\", context=[{'role': 'user', 'content': \"What's your name\"}], provider='openai', deployment='gpt-4o-2024-08-06', timestamp=1729004855.494591, parameters={}, metrics={'input_tokens': 4, 'output_tokens': 34, 'total_tokens': 38, 'cost_usd': 0.0005300000000000001, 'latency_s': 0.8200538158416748, 'time_to_first_token_s': 0.4039459228515625, 'inter_token_latency_s': 0.01220102871165556, 'tokens_per_second': 42.68012577208389})" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "openai.chat(\"What's your name\", model=\"gpt-4o\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Async version" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "ChatCompletion(id='65a23cb5-77fa-44ce-a1c1-2c421aff02c6', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content=\"I'm an AI language model created by OpenAI, and I don't have a personal name. You can call me Assistant or any name you prefer! How can I help you today?\", refusal=None, role='assistant', function_call=None, tool_calls=None))], created=1729004863, model='gpt-4o', object='chat.completion', service_tier=None, system_fingerprint=None, usage=None, chat_input=\"What's your name\", chat_output=\"I'm an AI language model created by OpenAI, and I don't have a personal name. You can call me Assistant or any name you prefer! How can I help you today?\", context=[{'role': 'user', 'content': \"What's your name\"}], provider='openai', deployment='gpt-4o-2024-08-06', timestamp=1729004864.533138, parameters={}, metrics={'input_tokens': 4, 'output_tokens': 38, 'total_tokens': 42, 'cost_usd': 0.00059, 'latency_s': 1.1568489074707031, 'time_to_first_token_s': 0.5351388454437256, 'inter_token_latency_s': 0.016760536142297694, 'tokens_per_second': 32.84785053139045})" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "await openai.achat(\"What's your name\", model=\"gpt-4o\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Chat (stream)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "ename": "Exception", + "evalue": "Internal Server Error", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mException\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[16], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[43mopenai\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchat\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mWrite a paragfraph about space\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mgpt-4o\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mis_stream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, chunk \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(response):\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m i\u001b[38;5;241m%\u001b[39m\u001b[38;5;241m20\u001b[39m\u001b[38;5;241m==\u001b[39m\u001b[38;5;241m0\u001b[39m:\n", + "File \u001b[0;32m~/fun/LLMstudio/llmstudio/llm/__init__.py:62\u001b[0m, in \u001b[0;36mLLM.chat\u001b[0;34m(self, chat_input, model, is_stream, retries, parameters, **kwargs)\u001b[0m\n\u001b[1;32m 57\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mchat\u001b[39m(\u001b[38;5;28mself\u001b[39m, chat_input: Any, \n\u001b[1;32m 58\u001b[0m model: \u001b[38;5;28mstr\u001b[39m, is_stream: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m, \n\u001b[1;32m 59\u001b[0m retries: \u001b[38;5;28mint\u001b[39m \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m, \n\u001b[1;32m 60\u001b[0m parameters: Optional[\u001b[38;5;28mdict\u001b[39m] \u001b[38;5;241m=\u001b[39m {},\n\u001b[1;32m 61\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ChatCompletionChunk \u001b[38;5;241m|\u001b[39m ChatCompletion:\n\u001b[0;32m---> 62\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_provider\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchat\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchat_input\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mis_stream\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretries\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparameters\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 63\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_session_local:\n\u001b[1;32m 64\u001b[0m log \u001b[38;5;241m=\u001b[39m schemas\u001b[38;5;241m.\u001b[39mLogDefaultCreate(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mresult\u001b[38;5;241m.\u001b[39mmetrics)\n", + "File \u001b[0;32m~/fun/LLMstudio/llmstudio/engine/provider.py:52\u001b[0m, in \u001b[0;36mLLMProxyProvider.chat\u001b[0;34m(self, chat_input, model, is_stream, retries, parameters, **kwargs)\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m response\u001b[38;5;241m.\u001b[39mok:\n\u001b[1;32m 51\u001b[0m error_data \u001b[38;5;241m=\u001b[39m response\u001b[38;5;241m.\u001b[39mtext\n\u001b[0;32m---> 52\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m(error_data)\n\u001b[1;32m 54\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_stream:\n\u001b[1;32m 55\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgenerate_chat(response)\n", + "\u001b[0;31mException\u001b[0m: Internal Server Error" + ] + } + ], + "source": [ + "response = openai.chat(\"Write a paragfraph about space\", model=\"gpt-4o\", is_stream=True)\n", + "for i, chunk in enumerate(response):\n", + " if i%20==0:\n", + " print(\"\\n\")\n", + " if not chunk.metrics:\n", + " print(chunk.chat_output, end=\"\", flush=True)\n", + " else:\n", + " print(\"\\n\\n## Metrics:\")\n", + " pprint(chunk.metrics)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Async version" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "i=0\n", + "async for chunk in await openai.achat(\"Write a paragfraph about space\", model=\"gpt-4o-mini\", is_stream=True):\n", + " if i%20==0:\n", + " print(\"\\n\")\n", + " if not chunk.metrics:\n", + " print(chunk.chat_output, end=\"\", flush=True)\n", + " else:\n", + " print(\"\\n\\n## Metrics:\")\n", + " pprint(chunk.metrics)\n", + " i+=1\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Batch version" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llmstudio import LLM\n", + "import nest_asyncio\n", + "nest_asyncio.apply()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gpt3 = LLM('openai/gpt-3.5-turbo')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "complex_prompt= [{\"role\": \"system\", \"content\": \"You are a tchill dude from jamaica\"},\n", + " {\"role\": \"user\", \"content\": \"Hello, how are you?\"},\n", + " {\"role\": \"assistant\", \"content\": \"Ye man doin fain man!\"},\n", + " {\"role\": \"user\", \"content\": \"How is the weather today?\"}]\n", + "\n", + "inputs = [\n", + " \"What's your name?\",\n", + " \"Tell me a joke.\",\n", + " \"What's the weather like?\",\n", + " \"Can you sing a song?\",\n", + " \"Tell me about yourself.\",\n", + " complex_prompt\n", + "]\n", + "\n", + "responses = gpt3.run_batch_chat_coroutine(inputs)\n", + "responses" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/03_langchain_integration.ipynb b/examples/03_langchain_integration.ipynb index f1bb5f57..5bdd71e9 100644 --- a/examples/03_langchain_integration.ipynb +++ b/examples/03_langchain_integration.ipynb @@ -16,18 +16,9 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 1, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running LLMstudio Engine on http://localhost:55189 Running LLMstudio Tracking on http://localhost:55190 \n", - "\n" - ] - } - ], + "outputs": [], "source": [ "from llmstudio.llm.langchain import ChatLLMstudio\n", "from llmstudio import LLM" @@ -35,9 +26,22 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "TypeError", + "evalue": "Can't instantiate abstract class LLMProxyProvider without an implementation for abstract method '_provider_config_name'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[2], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# llm = ChatLLMstudio(model_id='openai/gpt-3.5-turbo', temperature=0)\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m llm \u001b[38;5;241m=\u001b[39m \u001b[43mChatLLMstudio\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mvertexai/gemini-1.5-flash\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtemperature\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/fun/LLMstudio/llmstudio/llm/langchain.py:33\u001b[0m, in \u001b[0;36mChatLLMstudio.__init__\u001b[0;34m(self, model_id, **kwargs)\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, model_id: \u001b[38;5;28mstr\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 32\u001b[0m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m(model_id\u001b[38;5;241m=\u001b[39mmodel_id, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m---> 33\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mllm \u001b[38;5;241m=\u001b[39m \u001b[43mLLM\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[0;31mTypeError\u001b[0m: Can't instantiate abstract class LLMProxyProvider without an implementation for abstract method '_provider_config_name'" + ] + } + ], "source": [ "# llm = ChatLLMstudio(model_id='openai/gpt-3.5-turbo', temperature=0)\n", "llm = ChatLLMstudio(model_id='vertexai/gemini-1.5-flash', temperature=0)" @@ -52,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -62,16 +66,16 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "AIMessage(content='Hello! \\n\\nHow can I help you today? \\n', response_metadata={'token_usage': None, 'model_name': 'gemini-1.5-flash', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-5945f8bd-7151-4d85-bf90-3bcc1eaabc6c-0')" + "AIMessage(content='Hello! 👋 How can I help you today? 😊 \\n', response_metadata={'token_usage': None, 'model_name': 'gemini-1.5-flash', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-2e60f8e1-bba0-4cab-9e21-8ecdbe8d49a2-0')" ] }, - "execution_count": 35, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -388,7 +392,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.12.2" } }, "nbformat": 4, diff --git a/examples/llm_proxy.py b/examples/llm_proxy.py new file mode 100644 index 00000000..92c00da2 --- /dev/null +++ b/examples/llm_proxy.py @@ -0,0 +1,28 @@ +from llmstudio.server import start_server +start_server() + +from llmstudio.engine.provider import LLMProxyProvider + + +llm = LLMProxyProvider(provider="openai", host="0.0.0.0", port="8001") + +result = llm.chat("What's your name", model="gpt-4o") +print(result) + +import asyncio + +# stream +print("\nasync stream") +async def async_stream(): + + response_async = await llm.achat("What's your name", model="gpt-4o", is_stream=True) + async for p in response_async: + if "}" in p.chat_output: + p.chat_output + print("that: ",p.chat_output) + # pprint(p.choices[0].delta.content==p.chat_output) + # print("metrics: ", p.metrics) + # print(p) + if p.metrics: + print(p) +asyncio.run(async_stream()) diff --git a/libs/core/llmstudio_core/providers/provider.py b/libs/core/llmstudio_core/providers/provider.py index f111e2bc..ba799b29 100644 --- a/libs/core/llmstudio_core/providers/provider.py +++ b/libs/core/llmstudio_core/providers/provider.py @@ -73,7 +73,8 @@ async def achat( model: str, is_stream: Optional[bool] = False, retries: Optional[int] = 0, - parameters: Optional[dict] = {} + parameters: Optional[dict] = {}, + **kwargs ) -> Coroutine[Any, Any, Union[ChatCompletionChunk, ChatCompletion]]: raise NotImplementedError("Providers needs to have achat method implemented.") @@ -84,7 +85,8 @@ def chat( model: str, is_stream: Optional[bool] = False, retries: Optional[int] = 0, - parameters: Optional[dict] = {} + parameters: Optional[dict] = {}, + **kwargs ) -> Union[ChatCompletionChunk, ChatCompletion]: raise NotImplementedError("Providers needs to have chat method implemented.") @@ -103,7 +105,8 @@ async def achat( model: str, is_stream: Optional[bool] = False, retries: Optional[int] = 0, - parameters: Optional[dict] = {} + parameters: Optional[dict] = {}, + **kwargs ): """Makes a chat connection with the provider's API""" @@ -145,7 +148,8 @@ def chat( model: str, is_stream: Optional[bool] = False, retries: Optional[int] = 0, - parameters: Optional[dict] = {} + parameters: Optional[dict] = {}, + **kwargs ): """Makes a chat connection with the provider's API""" diff --git a/llmstudio/cli.py b/llmstudio/cli.py index 2c6197ec..f0c4c7da 100644 --- a/llmstudio/cli.py +++ b/llmstudio/cli.py @@ -1,5 +1,6 @@ import os import signal +import threading import click @@ -25,11 +26,14 @@ def server(ui): print("Servers are running. Press CTRL+C to stop.") + stop_event = threading.Event() try: - signal.pause() + stop_event.wait() # Wait indefinitely until the event is set except KeyboardInterrupt: print("Shutting down servers...") if __name__ == "__main__": - main() + # main() + server() + print(4) diff --git a/llmstudio/engine/__init__.py b/llmstudio/engine/__init__.py index d541f775..ce1339bc 100644 --- a/llmstudio/engine/__init__.py +++ b/llmstudio/engine/__init__.py @@ -9,10 +9,11 @@ from fastapi import FastAPI, Request from fastapi.middleware.cors import CORSMiddleware from fastapi.responses import StreamingResponse -from pydantic import BaseModel, ValidationError +from pydantic import BaseModel from llmstudio.config import ENGINE_HOST, ENGINE_PORT -from llmstudio.engine.providers import * +from llmstudio_core.providers import _load_engine_config +from llmstudio_core.providers.provider import provider_registry ENGINE_BASE_ENDPOINT = "/api/engine" ENGINE_HEALTH_ENDPOINT = "/health" @@ -47,38 +48,6 @@ class EngineConfig(BaseModel): providers: Dict[str, ProviderConfig] -def _load_engine_config() -> EngineConfig: - default_config_path = Path(os.path.join(os.path.dirname(__file__), "config.yaml")) - local_config_path = Path(os.getcwd(), "config.yaml") - - def _merge_configs(config1, config2): - for key in config2: - if key in config1: - if isinstance(config1[key], dict) and isinstance(config2[key], dict): - _merge_configs(config1[key], config2[key]) - elif isinstance(config1[key], list) and isinstance(config2[key], list): - config1[key].extend(config2[key]) - else: - config1[key] = config2[key] - else: - config1[key] = config2[key] - return config1 - - try: - default_config_data = yaml.safe_load(default_config_path.read_text()) - local_config_data = ( - yaml.safe_load(local_config_path.read_text()) - if local_config_path.exists() - else {} - ) - config_data = _merge_configs(default_config_data, local_config_data) - return EngineConfig(**config_data) - except yaml.YAMLError as e: - raise RuntimeError(f"Error parsing YAML configuration: {e}") - except ValidationError as e: - raise RuntimeError(f"Error in configuration data: {e}") - - def create_engine_app( started_event: Event, config: EngineConfig = _load_engine_config() ) -> FastAPI: @@ -124,9 +93,11 @@ def get_models(provider: Optional[str] = None): def create_chat_handler(provider_config): async def chat_handler(request: Request): """Endpoint for chat functionality.""" - provider_class = provider_registry.get(f"{provider_config.name}Provider") + provider_class = provider_registry.get(f"{provider_config.name}".lower()) provider_instance = provider_class(provider_config) - return await provider_instance.chat(await request.json()) + request_dict = await request.json() + result = await provider_instance.achat(**request_dict) + return result return chat_handler diff --git a/llmstudio/engine/provider.py b/llmstudio/engine/provider.py new file mode 100644 index 00000000..2db14a22 --- /dev/null +++ b/llmstudio/engine/provider.py @@ -0,0 +1,236 @@ +import asyncio +from typing import Any, Coroutine, Dict, List, Union + +import requests +from libs.core.llmstudio_core.providers.provider import BaseProvider, ProviderABC +from llmstudio.server import is_server_running +from openai.types.chat import ChatCompletion, ChatCompletionChunk +from tqdm.asyncio import tqdm_asyncio + +from llmstudio.config import ENGINE_HOST, ENGINE_PORT +from llmstudio.llm.semaphore import DynamicSemaphore + +class LLMProxyProvider(ProviderABC): + def __init__(self, provider: str, + host: str, + port: str, + **kwargs): + self.provider = provider + self.engine_host = host + self.engine_port = port + if is_server_running(host=host, port=port): + print(f"Connected to LLMStudio Proxy @ {host}:{port}") + else: + raise Exception(f"LLMStudio Proxy is not running @ {host}:{port}") + + @staticmethod + def _provider_config_name(): + raise "proxy" + + def chat(self, chat_input: str, + model: str, + is_stream: bool = False, + retries: int = 0, + parameters: Dict = {}, + **kwargs) -> Union[ChatCompletion]: + response = requests.post( + f"http://{self.engine_host}:{self.engine_port}/api/engine/chat/{self.provider}", + json={ + "chat_input": chat_input, + "model": model, + "is_stream": is_stream, + "retries": retries, + "parameters": parameters, + **kwargs, + }, + stream=is_stream, + headers={"Content-Type": "application/json"}, + ) + + if not response.ok: + error_data = response.text + raise Exception(error_data) + + if is_stream: + return self.generate_chat(response) + else: + return ChatCompletion(**response.json()) + + def generate_chat(self, response): + for chunk in response.iter_content(chunk_size=None): + if chunk: + yield chunk.decode("utf-8") + + async def achat(self, chat_input: Any, + model: str, + is_stream: bool | None = False, + retries: int | None = 0, + parameters: Dict | None = {}, + **kwargs) -> Coroutine[Any, Any, Coroutine[Any, Any, ChatCompletionChunk | ChatCompletion]]: + if is_stream: + return self.async_stream(model=model, + chat_input=chat_input, + retries=retries, + parameters=parameters) + else: + return await self.async_non_stream(model=model, + chat_input=chat_input, + retries=retries, + parameters=parameters) + + async def chat_coroutine( + self, + input: Union[str, List[Dict[str, str]]], + semaphore, + retries, + error_threshold, + increment, + verbose, + ): + + async with semaphore: + try: + response = await self.async_non_stream( + input, max_tokens=semaphore.get_max_tokens(), retries=retries + ) + semaphore.update_computed_max_tokens(response.metrics["total_tokens"]) + return response + except Exception as e: + semaphore.error_requests += 1 + semaphore.error_requests_since_last_increase += 1 + return e + finally: + semaphore.finished_requests += 1 + semaphore.requests_since_last_increase += 1 + semaphore.try_increase_permits(error_threshold, increment) + if verbose > 0: + print( + f"Finished requests: {semaphore.finished_requests}/{semaphore.batch_size}" + ) + print( + f"Amount of parallel requests being allowed: {semaphore._permits}" + ) + print(f"Max tokens being used: {semaphore.get_max_tokens()}") + print( + f"Requests finished with an error: {semaphore.error_requests}" + ) + + async def batch_chat_coroutine( + self, + inputs: List[Union[str, List[Dict[str, str]]]], + coroutines, + retries, + error_threshold, + increment, + max_tokens, + verbose, + ) -> List[str]: + + semaphore = DynamicSemaphore( + coroutines, len(inputs), given_max_tokens=max_tokens + ) + + if verbose > 0: + responses = await asyncio.gather( + *[ + self.chat_coroutine( + input=input, + semaphore=semaphore, + retries=retries, + error_threshold=error_threshold, + increment=increment, + verbose=verbose, + ) + for input in inputs + ], + ) + return responses + else: + responses = await tqdm_asyncio.gather( + *[ + self.chat_coroutine( + input=input, + semaphore=semaphore, + retries=retries, + error_threshold=error_threshold, + increment=increment, + verbose=verbose, + ) + for input in inputs + ], + desc="Getting chat responses: ", + ) + return responses + + def batch_chat( + self, + inputs: List[Union[str, List[Dict[str, str]]]], + coroutines: int = 20, + retries: int = 5, + error_threshold: int = 5, + increment: int = 5, + max_tokens=None, + verbose=0, + ) -> List[str]: + + if coroutines > len(inputs): + raise Exception( + "num_coroutines can not be higher than the amount of inputs." + ) + + responses = asyncio.run( + self.batch_chat_coroutine( + inputs, + coroutines, + retries, + error_threshold, + increment, + max_tokens, + verbose, + ) + ) + return responses + + async def async_non_stream(self, model:str, chat_input: str, retries: int, parameters, **kwargs): + response = await asyncio.to_thread( + requests.post, + f"http://{self.engine_host}:{self.engine_port}/api/engine/chat/{self.provider}", + json={ + "chat_input": chat_input, + "model": model, + "is_stream": False, + "retries": retries, + "parameters": parameters, + **kwargs, + }, + headers={"Content-Type": "application/json"}, + ) + if not response.ok: + error_data = response.text + raise Exception(error_data) + + return ChatCompletion(**response.json()) + + async def async_stream(self, model:str, chat_input: str, retries: int, parameters, **kwargs): + response = await asyncio.to_thread( + requests.post, + f"http://{self.engine_host}:{self.engine_port}/api/engine/chat/{self.provider}", + json={ + "chat_input": chat_input, + "model": model, + "is_stream": True, + "retries": retries, + "parameters": parameters, + **kwargs, + }, + stream=True, + headers={"Content-Type": "application/json"}, + ) + + if not response.ok: + error_data = response.text + raise Exception(error_data) + + for chunk in response.iter_content(chunk_size=None): + if chunk: + yield chunk.decode("utf-8") diff --git a/llmstudio/llm/__init__.py b/llmstudio/llm/__init__.py index 8f42086b..b0af969f 100644 --- a/llmstudio/llm/__init__.py +++ b/llmstudio/llm/__init__.py @@ -1,290 +1,78 @@ -import asyncio -from typing import Dict, List, Union +from typing import Any, Coroutine, Optional +from llmstudio_core import LLM as LLM_factory +from llmstudio_core.providers.provider import ProviderABC +from openai.types.chat import ChatCompletion, ChatCompletionChunk +from pydantic import BaseModel -import requests -from openai.types.chat import ChatCompletion -from tqdm.asyncio import tqdm_asyncio +from llmstudio.engine.provider import LLMProxyProvider +from llmstudio.tracking.database import create_tracking_engine +from llmstudio.tracking.logs import crud, schemas -from llmstudio.config import ENGINE_HOST, ENGINE_PORT -from llmstudio.llm.semaphore import DynamicSemaphore -from llmstudio.server import start_server +from sqlalchemy.orm import declarative_base, sessionmaker -start_server() +class ProxyConfig(BaseModel): + host: str + port: int + username: Optional[str] = None + password: Optional[str] = None -class LLM: - def __init__(self, model_id: str, **kwargs): - self.provider, self.model = model_id.split("/") - self.session_id = kwargs.get("session_id") - self.api_key = kwargs.get("api_key") - self.api_endpoint = kwargs.get("api_endpoint") - self.api_version = kwargs.get("api_version") - self.base_url = kwargs.get("base_url") - self.temperature = kwargs.get("temperature") - self.top_p = kwargs.get("top_p") - self.top_k = kwargs.get("top_k") - self.max_tokens = kwargs.get("max_tokens") - self.frequency_penalty = kwargs.get("frequency_penalty") - self.presence_penalty = kwargs.get("presence_penalty") +class TrackingConfig(BaseModel): + database_uri: str - def chat(self, input: str, is_stream: bool = False, retries: int = 0, **kwargs): - response = requests.post( - f"http://{ENGINE_HOST}:{ENGINE_PORT}/api/engine/chat/{self.provider}", - json={ - "model": self.model, - "session_id": self.session_id, - "api_key": self.api_key, - "api_endpoint": self.api_endpoint, - "api_version": self.api_version, - "base_url": self.base_url, - "chat_input": input, - "is_stream": is_stream, - "retries": retries, - "parameters": { - key: value - for key, value in { - "temperature": kwargs.get("temperature") or self.temperature, - "top_p": kwargs.get("top_p") or self.top_p, - "top_k": kwargs.get("top_k") or self.top_k, - "max_tokens": kwargs.get("max_tokens") or self.max_tokens, - "max_output_tokens": kwargs.get("max_tokens") - or self.max_tokens, - "frequency_penalty": kwargs.get("frequency_penalty") - or self.frequency_penalty, - "presence_penalty": kwargs.get("presence_penalty") - or self.presence_penalty, - }.items() - if value is not None - }, - **kwargs, - }, - stream=is_stream, - headers={"Content-Type": "application/json"}, - ) - if not response.ok: - try: - error_data = response.json().get("detail", "LLMstudio Engine error") - except ValueError: - error_data = response.text - raise Exception(error_data) +class LLM(ProviderABC): - if is_stream: - return self.generate_chat(response) - else: - return ChatCompletion(**response.json()) - - def generate_chat(self, response): - for chunk in response.iter_content(chunk_size=None): - if chunk: - yield chunk.decode("utf-8") - async def async_chat(self, input: str, is_stream=False, retries: int = 0, **kwargs): - if is_stream: - return self.async_stream(input, retries=retries, **kwargs) + def __init__(self, + provider: str, + proxy_config: Optional[ProxyConfig] = None, + tracking_config: Optional[TrackingConfig] = None, + **kwargs): + + if proxy_config is not None: + self._provider = LLMProxyProvider(provider=provider, + host=proxy_config.host, + port=proxy_config.port, + **kwargs + ) else: - return await self.async_non_stream(input, retries=retries, **kwargs) - - async def chat_coroutine( - self, - input: Union[str, List[Dict[str, str]]], - semaphore, - retries, - error_threshold, - increment, - verbose, - ): - - async with semaphore: - try: - response = await self.async_non_stream( - input, max_tokens=semaphore.get_max_tokens(), retries=retries - ) - semaphore.update_computed_max_tokens(response.metrics["total_tokens"]) - return response - except Exception as e: - semaphore.error_requests += 1 - semaphore.error_requests_since_last_increase += 1 - return e - finally: - semaphore.finished_requests += 1 - semaphore.requests_since_last_increase += 1 - semaphore.try_increase_permits(error_threshold, increment) - if verbose > 0: - print( - f"Finished requests: {semaphore.finished_requests}/{semaphore.batch_size}" - ) - print( - f"Amount of parallel requests being allowed: {semaphore._permits}" - ) - print(f"Max tokens being used: {semaphore.get_max_tokens()}") - print( - f"Requests finished with an error: {semaphore.error_requests}" - ) - - async def batch_chat_coroutine( - self, - inputs: List[Union[str, List[Dict[str, str]]]], - coroutines, - retries, - error_threshold, - increment, - max_tokens, - verbose, - ) -> List[str]: - - semaphore = DynamicSemaphore( - coroutines, len(inputs), given_max_tokens=max_tokens - ) - - if verbose > 0: - responses = await asyncio.gather( - *[ - self.chat_coroutine( - input=input, - semaphore=semaphore, - retries=retries, - error_threshold=error_threshold, - increment=increment, - verbose=verbose, - ) - for input in inputs - ], - ) - return responses - else: - responses = await tqdm_asyncio.gather( - *[ - self.chat_coroutine( - input=input, - semaphore=semaphore, - retries=retries, - error_threshold=error_threshold, - increment=increment, - verbose=verbose, - ) - for input in inputs - ], - desc="Getting chat responses: ", - ) - return responses - - def batch_chat( - self, - inputs: List[Union[str, List[Dict[str, str]]]], - coroutines: int = 20, - retries: int = 5, - error_threshold: int = 5, - increment: int = 5, - max_tokens=None, - verbose=0, - ) -> List[str]: - - if coroutines > len(inputs): - raise Exception( - "num_coroutines can not be higher than the amount of inputs." - ) - - responses = asyncio.run( - self.batch_chat_coroutine( - inputs, - coroutines, - retries, - error_threshold, - increment, - max_tokens, - verbose, - ) - ) - return responses - - async def async_non_stream(self, input: str, retries: int, **kwargs): - response = await asyncio.to_thread( - requests.post, - f"http://{ENGINE_HOST}:{ENGINE_PORT}/api/engine/chat/{self.provider}", - json={ - "model": self.model, - "session_id": self.session_id, - "api_key": self.api_key, - "api_endpoint": self.api_endpoint, - "api_version": self.api_version, - "base_url": self.base_url, - "chat_input": input, - "is_stream": False, - "retries": retries, - "parameters": { - key: value - for key, value in { - "temperature": kwargs.get("temperature") or self.temperature, - "top_p": kwargs.get("top_p") or self.top_p, - "top_k": kwargs.get("top_k") or self.top_k, - "max_tokens": kwargs.get("max_tokens") or self.max_tokens, - "max_output_tokens": kwargs.get("max_tokens") - or self.max_tokens, - "frequency_penalty": kwargs.get("frequency_penalty") - or self.frequency_penalty, - "presence_penalty": kwargs.get("presence_penalty") - or self.presence_penalty, - }.items() - if value is not None - }, - **kwargs, - }, - headers={"Content-Type": "application/json"}, - ) - - if not response.ok: - try: - error_data = response.json().get("detail", "LLMstudio Engine error") - except ValueError: - error_data = response.text - raise Exception(error_data) - - return ChatCompletion(**response.json()) - - async def async_stream(self, input: str, retries: int, **kwargs): - response = await asyncio.to_thread( - requests.post, - f"http://{ENGINE_HOST}:{ENGINE_PORT}/api/engine/chat/{self.provider}", - json={ - "model": self.model, - "session_id": self.session_id, - "api_key": self.api_key, - "api_endpoint": self.api_endpoint, - "api_version": self.api_version, - "base_url": self.base_url, - "chat_input": input, - "is_stream": True, - "retries": retries, - "parameters": { - key: value - for key, value in { - "temperature": kwargs.get("temperature") or self.temperature, - "top_p": kwargs.get("top_p") or self.top_p, - "top_k": kwargs.get("top_k") or self.top_k, - "max_tokens": kwargs.get("max_tokens") or self.max_tokens, - "max_output_tokens": kwargs.get("max_tokens") - or self.max_tokens, - "frequency_penalty": kwargs.get("frequency_penalty") - or self.frequency_penalty, - "presence_penalty": kwargs.get("presence_penalty") - or self.presence_penalty, - }.items() - if value is not None - }, - **kwargs, - }, - headers={"Content-Type": "application/json"}, - stream=True, - ) - - if not response.ok: - try: - error_data = response.json().get("detail", "LLMstudio Engine error") - except ValueError: - error_data = response.text - raise Exception(error_data) - - for chunk in response.iter_content(chunk_size=None): - if chunk: - yield chunk.decode("utf-8") + self._provider = LLM_factory(provider, **kwargs) + + self._session_local = None + if tracking_config is not None: + engine = create_tracking_engine(tracking_config.database_uri) + self._session_local = sessionmaker(autocommit=False, autoflush=False, bind=engine) + + def _provider_config_name(self): + return self._provider._provider_config_name() + + def get_db(self): + db = self.session_local() + try: + yield db + finally: + db.close() + + def chat(self, chat_input: Any, + model: str, is_stream: bool | None = False, + retries: int | None = 0, + parameters: Optional[dict] = {}, + **kwargs) -> ChatCompletionChunk | ChatCompletion: + result = self._provider.chat(chat_input, model, is_stream, retries, parameters, **kwargs) + if self._session_local: + log = schemas.LogDefaultCreate(**result.metrics) + crud.add_log(db=self._session_local, log=log) + return result + + async def achat(self, chat_input: Any, + model: str, + is_stream: bool | None = False, + retries: int | None = 0, + parameters: Optional[dict] = {}, + **kwargs) -> Coroutine[Any, Any, Coroutine[Any, Any, ChatCompletionChunk | ChatCompletion]]: + result = await self._provider.achat(chat_input, model, is_stream, retries, parameters, **kwargs) + if self._session_local and result.metrics: + log = schemas.LogDefaultCreate(**result.metrics) + crud.add_log(db=self._session_local, log=log) + return result diff --git a/llmstudio/llm/langchain.py b/llmstudio/llm/langchain.py index e25610f3..42c12d3b 100644 --- a/llmstudio/llm/langchain.py +++ b/llmstudio/llm/langchain.py @@ -10,10 +10,23 @@ from llmstudio.llm import LLM +from pydantic import BaseModel + +class ProxyConfig(BaseModel): + host: str + port: int + username: Optional[str] = None + password: Optional[str] = None + +class TrackingConfig(BaseModel): + database_uri: str + class ChatLLMstudio(BaseChatModel): model_id: str llm: LLM = None + proxy_config: Optional[ProxyConfig] = None + tracking_config: Optional[TrackingConfig] = None def __init__(self, model_id: str, **kwargs): super().__init__(model_id=model_id, **kwargs) diff --git a/poetry.lock b/poetry.lock index 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langchain-experimental = "^0.0" +llmstudio-core = {path = "libs/core", develop = true} [tool.poetry.scripts] llmstudio = "llmstudio.cli:main" From b77aff2bdddecbf93e4ea615c9f8ee5adf555305 Mon Sep 17 00:00:00 2001 From: Diogo Goncalves Date: Wed, 16 Oct 2024 11:58:41 +0100 Subject: [PATCH 18/58] feat: adapt proxy --- examples/01_intro_to_llmstudio.ipynb | 115 +-- .../01_intro_to_llmstudio_with_proxy.ipynb | 727 ++++-------------- examples/llm_proxy.py | 31 +- libs/core/llmstudio_core/__init__.py | 9 +- .../core/llmstudio_core/providers/provider.py | 6 +- llmstudio/engine/__init__.py | 6 + llmstudio/engine/provider.py | 39 +- llmstudio/llm/__init__.py | 33 +- 8 files changed, 270 insertions(+), 696 deletions(-) diff --git a/examples/01_intro_to_llmstudio.ipynb b/examples/01_intro_to_llmstudio.ipynb index 2dfb3eaf..3c46805c 100644 --- a/examples/01_intro_to_llmstudio.ipynb +++ b/examples/01_intro_to_llmstudio.ipynb @@ -32,7 +32,7 @@ "source": [ "# You can set OPENAI_API_KEY and ANTHROPIC_API_KEY on .env file\n", "\n", - "anthropic = LLM(\"anthropic\") # or you can pass api_key as an argument here\n", + "# anthropic = LLM(\"anthropic\") # or you can pass api_key as an argument here\n", "openai = LLM(\"openai\")\n" ] }, @@ -51,7 +51,7 @@ { "data": { "text/plain": [ - "ChatCompletion(id='0388f750-9076-42bd-a963-d4eab24bb1ba', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='I’m called ChatGPT. How can I assist you today?', refusal=None, role='assistant', function_call=None, tool_calls=None))], created=1728478112, model='gpt-4o', object='chat.completion', service_tier=None, system_fingerprint='fp_2f406b9113', usage=CompletionUsage(completion_tokens=13, prompt_tokens=10, total_tokens=23, completion_tokens_details=CompletionTokensDetails(reasoning_tokens=0), prompt_tokens_details={'cached_tokens': 0}), chat_input=\"What's your name\", chat_output='I’m called ChatGPT. How can I assist you today?', context=[{'role': 'user', 'content': \"What's your name\"}], provider='openai', deployment='gpt-4o-2024-08-06', timestamp=1728478112.735258, parameters={}, metrics={'input_tokens': 10, 'output_tokens': 13, 'total_tokens': 23, 'cost_usd': 0.000245, 'latency_s': 1.051265001296997, 'time_to_first_token_s': None, 'inter_token_latency_s': None, 'tokens_per_second': 21.878403610529958})" + "ChatCompletion(id='3f7489ca-f338-4e30-a0a2-1491eef7d4c6', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='I’m called ChatGPT. How can I assist you today?', refusal=None, role='assistant', function_call=None, tool_calls=None))], created=1729071290, model='gpt-4o', object='chat.completion', service_tier=None, system_fingerprint=None, usage=None, chat_input=\"What's your name\", chat_output='I’m called ChatGPT. How can I assist you today?', context=[{'role': 'user', 'content': \"What's your name\"}], provider='openai', deployment='gpt-4o-2024-08-06', timestamp=1729071290.334091, parameters={}, metrics={'input_tokens': 4, 'output_tokens': 14, 'total_tokens': 18, 'cost_usd': 0.00023, 'latency_s': 0.8791909217834473, 'time_to_first_token_s': 0.7296760082244873, 'inter_token_latency_s': 0.01027270725795201, 'tokens_per_second': 17.06114067871897})" ] }, "execution_count": 3, @@ -78,7 +78,7 @@ { "data": { "text/plain": [ - "ChatCompletion(id='7f196166-935d-4539-a1a9-ba9ca32ef62a', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='I’m an AI developed by OpenAI and don’t have a personal name, but you can call me Assistant. How can I help you today?', refusal=None, role='assistant', function_call=None, tool_calls=None))], created=1728478113, model='gpt-4o', object='chat.completion', service_tier=None, system_fingerprint='fp_e5e4913e83', usage=CompletionUsage(completion_tokens=30, prompt_tokens=10, total_tokens=40, completion_tokens_details=CompletionTokensDetails(reasoning_tokens=0), prompt_tokens_details={'cached_tokens': 0}), chat_input=\"What's your name\", chat_output='I’m an AI developed by OpenAI and don’t have a personal name, but you can call me Assistant. How can I help you today?', context=[{'role': 'user', 'content': \"What's your name\"}], provider='openai', deployment='gpt-4o-2024-08-06', timestamp=1728478113.728288, parameters={}, metrics={'input_tokens': 10, 'output_tokens': 30, 'total_tokens': 40, 'cost_usd': 0.0005, 'latency_s': 0.9794328212738037, 'time_to_first_token_s': None, 'inter_token_latency_s': None, 'tokens_per_second': 40.83996281437445})" + "ChatCompletion(id='1920a936-eb77-45a5-acfe-06cdee9000ad', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='I don’t have a personal name, but you can call me Assistant. How can I help you today?', refusal=None, role='assistant', function_call=None, tool_calls=None))], created=1729071291, model='gpt-4o', object='chat.completion', service_tier=None, system_fingerprint=None, usage=None, chat_input=\"What's your name\", chat_output='I don’t have a personal name, but you can call me Assistant. How can I help you today?', chat_output_stream='', context=[{'role': 'user', 'content': \"What's your name\"}], provider='openai', deployment='gpt-4o-2024-08-06', timestamp=1729071291.715447, parameters={}, metrics={'input_tokens': 4, 'output_tokens': 22, 'total_tokens': 26, 'cost_usd': 0.00035, 'latency_s': 0.9404149055480957, 'time_to_first_token_s': 0.6825647354125977, 'inter_token_latency_s': 0.01118978210117506, 'tokens_per_second': 25.52065036231241})" ] }, "execution_count": 4, @@ -99,7 +99,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -107,34 +107,23 @@ "output_type": "stream", "text": [ "\n", - "\n", - "Space, an awe-inspiring expanse that stretches beyond the grasp of human imagination, is the\n", - "\n", - " vast, seemingly infinite void that envelops planets, stars, galaxies, and all celestial bodies. It\n", - "\n", - " serves as both the birthplace and the playground of cosmic phenomena, from the swirling, fiery dance of stars\n", - "\n", - " being born in nebulae to the mysterious, unseen forces of dark matter and dark energy that constitute\n", - "\n", - " the universe's unseen framework. This vast universe is not only a subject of scientific inquiry and discovery but\n", - "\n", - " also a source of inspiration and wonder. Human space exploration endeavors reflect our innate curiosity and longing to understand\n", - "\n", - " our place in the cosmos, driving the development of advanced technology and international cooperation. Space, with its\n", - "\n", - " breathtaking beauty and infinite mysteries, challenges our understanding and continually beckons us to explore further and dream bigger\n", - "\n", - ".\n", - "\n", - "## Metrics:\n", - "{'cost_usd': 0.002455,\n", - " 'input_tokens': 8,\n", - " 'inter_token_latency_s': 0.010445242342741592,\n", - " 'latency_s': 2.4184927940368652,\n", - " 'output_tokens': 161,\n", - " 'time_to_first_token_s': 0.7361259460449219,\n", - " 'tokens_per_second': 66.98386714214482,\n", - " 'total_tokens': 169}\n" + "\n" + ] + }, + { + "ename": "AttributeError", + "evalue": "'ChatCompletionChunk' object has no attribute 'chat_output_stream'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/fun/LLMstudio/venv/lib/python3.12/site-packages/pydantic/main.py:848\u001b[0m, in \u001b[0;36mBaseModel.__getattr__\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m 847\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 848\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpydantic_extra\u001b[49m\u001b[43m[\u001b[49m\u001b[43mitem\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m 849\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n", + "\u001b[0;31mKeyError\u001b[0m: 'chat_output_stream'", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[5], line 5\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m i\u001b[38;5;241m%\u001b[39m\u001b[38;5;241m20\u001b[39m\u001b[38;5;241m==\u001b[39m\u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mchunk\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchat_output_stream\u001b[49m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28mprint\u001b[39m(chunk\u001b[38;5;241m.\u001b[39mchat_output_stream, end\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m, flush\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", + "File \u001b[0;32m~/fun/LLMstudio/venv/lib/python3.12/site-packages/pydantic/main.py:850\u001b[0m, in \u001b[0;36mBaseModel.__getattr__\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m 848\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m pydantic_extra[item]\n\u001b[1;32m 849\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[0;32m--> 850\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m object has no attribute \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mitem\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mexc\u001b[39;00m\n\u001b[1;32m 851\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 852\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m, item):\n", + "\u001b[0;31mAttributeError\u001b[0m: 'ChatCompletionChunk' object has no attribute 'chat_output_stream'" ] } ], @@ -143,8 +132,8 @@ "for i, chunk in enumerate(response):\n", " if i%20==0:\n", " print(\"\\n\")\n", - " if chunk.chat_output is not None:\n", - " print(chunk.chat_output, end=\"\", flush=True)\n", + " if chunk.chat_output_stream is not None:\n", + " print(chunk.chat_output_stream, end=\"\", flush=True)\n", " else:\n", " print(\"\\n\\n## Metrics:\")\n", " pprint(chunk.metrics)" @@ -159,29 +148,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "Hi! How can I assist you today?\n", - "\n", - "## Metrics:\n", - "{'cost_usd': 5.85e-06,\n", - " 'input_tokens': 3,\n", - " 'inter_token_latency_s': 0.01939098834991455,\n", - " 'latency_s': 0.8029687404632568,\n", - " 'output_tokens': 9,\n", - " 'time_to_first_token_s': 0.6087050437927246,\n", - " 'tokens_per_second': 13.699163424037863,\n", - " 'total_tokens': 12}\n" - ] - } - ], + "outputs": [], "source": [ "i=0\n", "async for chunk in await openai.achat(\"Say hi back\", model=\"gpt-4o-mini\", is_stream=True):\n", @@ -204,21 +173,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'llmstudio'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[7], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mllmstudio\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m LLM\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnest_asyncio\u001b[39;00m\n\u001b[1;32m 3\u001b[0m nest_asyncio\u001b[38;5;241m.\u001b[39mapply()\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'llmstudio'" - ] - } - ], + "outputs": [], "source": [ "from llmstudio import LLM\n", "import nest_asyncio\n", @@ -238,23 +195,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[ChatCompletion(id='bce77107-790e-4194-b709-c1f973908e13', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='I am a virtual assistant and I do not have a personal name. You can simply refer to me as \"assistant\". How can I assist you today?', role='assistant', function_call=None, tool_calls=None))], created=1718357127, model='gpt-3.5-turbo', object='chat.completion', system_fingerprint=None, usage=None, session_id=None, chat_input=\"What's your name?\", chat_output='I am a virtual assistant and I do not have a personal name. You can simply refer to me as \"assistant\". How can I assist you today?', context=[{'role': 'user', 'content': \"What's your name?\"}], provider='openai', timestamp=1718357130.3642838, parameters={'temperature': 1, 'max_tokens': 2048, 'top_p': 1, 'frequency_penalty': 0, 'presence_penalty': 0}, metrics={'input_tokens': 5, 'output_tokens': 31, 'total_tokens': 36, 'cost_usd': 4.9e-05, 'latency_s': 2.8777408599853516, 'time_to_first_token_s': 2.8716890811920166, 'inter_token_latency_s': 8.686631917953491e-05, 'tokens_per_second': 11.46732857668358}),\n", - " ChatCompletion(id='f5058611-d4e5-48cc-810f-5cfbf82bd70f', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content=\"Why couldn't the bicycle find its way home? Because it lost its bearings!\", role='assistant', function_call=None, tool_calls=None))], created=1718357129, model='gpt-3.5-turbo', object='chat.completion', system_fingerprint=None, usage=None, session_id=None, chat_input='Tell me a joke.', chat_output=\"Why couldn't the bicycle find its way home? Because it lost its bearings!\", context=[{'role': 'user', 'content': 'Tell me a joke.'}], provider='openai', timestamp=1718357130.392538, parameters={'temperature': 1, 'max_tokens': 2048, 'top_p': 1, 'frequency_penalty': 0, 'presence_penalty': 0}, metrics={'input_tokens': 5, 'output_tokens': 16, 'total_tokens': 21, 'cost_usd': 2.65e-05, 'latency_s': 0.9708259105682373, 'time_to_first_token_s': 0.9693810939788818, 'inter_token_latency_s': 7.653236389160156e-05, 'tokens_per_second': 18.540914291692484}),\n", - " ChatCompletion(id='bb195dce-30b4-4836-a9bc-9d73ea81efcc', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content=\"I'm sorry, I am an AI and do not have the ability to determine the current weather. You can check the weather in your area by using a weather app on your phone or by visiting a weather website.\", role='assistant', function_call=None, tool_calls=None))], created=1718357129, model='gpt-3.5-turbo', object='chat.completion', system_fingerprint=None, usage=None, session_id=None, chat_input=\"What's the weather like?\", chat_output=\"I'm sorry, I am an AI and do not have the ability to determine the current weather. You can check the weather in your area by using a weather app on your phone or by visiting a weather website.\", context=[{'role': 'user', 'content': \"What's the weather like?\"}], provider='openai', timestamp=1718357130.386699, parameters={'temperature': 1, 'max_tokens': 2048, 'top_p': 1, 'frequency_penalty': 0, 'presence_penalty': 0}, metrics={'input_tokens': 6, 'output_tokens': 43, 'total_tokens': 49, 'cost_usd': 6.75e-05, 'latency_s': 1.5981099605560303, 'time_to_first_token_s': 1.594458818435669, 'inter_token_latency_s': 7.893822409889914e-05, 'tokens_per_second': 28.158262641916803}),\n", - " ChatCompletion(id='2e689f44-7977-4a21-9b45-4ac9fcf17e0e', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content=\"I'm an AI assistant, so I don't have a physical voice to sing with. However, I can provide lyrics or information about songs if you'd like! Just let me know how I can assist you.\", role='assistant', function_call=None, tool_calls=None))], created=1718357128, model='gpt-3.5-turbo', object='chat.completion', system_fingerprint=None, usage=None, session_id=None, chat_input='Can you sing a song?', chat_output=\"I'm an AI assistant, so I don't have a physical voice to sing with. However, I can provide lyrics or information about songs if you'd like! Just let me know how I can assist you.\", context=[{'role': 'user', 'content': 'Can you sing a song?'}], provider='openai', timestamp=1718357130.378707, parameters={'temperature': 1, 'max_tokens': 2048, 'top_p': 1, 'frequency_penalty': 0, 'presence_penalty': 0}, metrics={'input_tokens': 6, 'output_tokens': 43, 'total_tokens': 49, 'cost_usd': 6.75e-05, 'latency_s': 2.229912042617798, 'time_to_first_token_s': 2.22599196434021, 'inter_token_latency_s': 8.436224677345969e-05, 'tokens_per_second': 20.180168159086847}),\n", - " ChatCompletion(id='257fb034-90c5-4d98-8ff7-1830ff3a0954', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='I am an AI assistant designed to provide information and assistance in various topics. I am constantly learning and improving my abilities to help users with their queries and tasks. I am programmed to provide accurate and reliable information based on the data available to me. If you have any specific questions or need assistance, feel free to ask!', role='assistant', function_call=None, tool_calls=None))], created=1718357130, model='gpt-3.5-turbo', object='chat.completion', system_fingerprint=None, usage=None, session_id=None, chat_input='Tell me about yourself.', chat_output='I am an AI assistant designed to provide information and assistance in various topics. I am constantly learning and improving my abilities to help users with their queries and tasks. I am programmed to provide accurate and reliable information based on the data available to me. If you have any specific questions or need assistance, feel free to ask!', context=[{'role': 'user', 'content': 'Tell me about yourself.'}], provider='openai', timestamp=1718357132.529829, parameters={'temperature': 1, 'max_tokens': 2048, 'top_p': 1, 'frequency_penalty': 0, 'presence_penalty': 0}, metrics={'input_tokens': 5, 'output_tokens': 64, 'total_tokens': 69, 'cost_usd': 9.850000000000001e-05, 'latency_s': 2.184094190597534, 'time_to_first_token_s': 0.6790041923522949, 'inter_token_latency_s': 0.023148767764751728, 'tokens_per_second': 30.21847697051171}),\n", - " ChatCompletion(id='813d3a00-2765-479c-b158-bbf916232706', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content=\"De sun be shinin' bright and de breeze be cool, perfect weather for jammin' on de beach! How 'bout you, how's de weather by you?\", role='assistant', function_call=None, tool_calls=None))], created=1718357131, model='gpt-3.5-turbo', object='chat.completion', system_fingerprint=None, usage=None, session_id=None, chat_input='How is the weather today?', chat_output=\"De sun be shinin' bright and de breeze be cool, perfect weather for jammin' on de beach! How 'bout you, how's de weather by you?\", context=[{'role': 'system', 'content': 'You are a tchill dude from jamaica'}, {'role': 'user', 'content': 'Hello, how are you?'}, {'role': 'assistant', 'content': 'Ye man doin fain man!'}, {'role': 'user', 'content': 'How is the weather today?'}], provider='openai', timestamp=1718357132.537878, parameters={'temperature': 1, 'max_tokens': 2048, 'top_p': 1, 'frequency_penalty': 0, 'presence_penalty': 0}, metrics={'input_tokens': 31, 'output_tokens': 35, 'total_tokens': 66, 'cost_usd': 6.8e-05, 'latency_s': 1.5229389667510986, 'time_to_first_token_s': 1.5202741622924805, 'inter_token_latency_s': 6.913476520114475e-05, 'tokens_per_second': 24.29512988227787})]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "complex_prompt= [{\"role\": \"system\", \"content\": \"You are a tchill dude from jamaica\"},\n", " {\"role\": \"user\", \"content\": \"Hello, how are you?\"},\n", diff --git a/examples/01_intro_to_llmstudio_with_proxy.ipynb b/examples/01_intro_to_llmstudio_with_proxy.ipynb index ec254841..0b5a14ef 100644 --- a/examples/01_intro_to_llmstudio_with_proxy.ipynb +++ b/examples/01_intro_to_llmstudio_with_proxy.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -26,524 +26,6 @@ "Running LLMstudio Engine on http://0.0.0.0:8001 \n", "Running LLMstudio Tracking on http://0.0.0.0:8002 \n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "ERROR: Exception in ASGI application\n", - "Traceback (most recent call last):\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/encoders.py\", line 322, in jsonable_encoder\n", - " data = dict(obj)\n", - " ^^^^^^^^^\n", - "TypeError: 'async_generator' object is not iterable\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/encoders.py\", line 327, in jsonable_encoder\n", - " data = vars(obj)\n", - " ^^^^^^^^^\n", - "TypeError: vars() argument must have __dict__ attribute\n", - "\n", - "The above exception was the direct cause of the following exception:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/uvicorn/protocols/http/h11_impl.py\", line 412, in run_asgi\n", - " result = await app( # type: ignore[func-returns-value]\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/uvicorn/middleware/proxy_headers.py\", line 84, in __call__\n", - " return await self.app(scope, receive, send)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/applications.py\", line 1054, in __call__\n", - " await super().__call__(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/applications.py\", line 123, in __call__\n", - " await self.middleware_stack(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/errors.py\", line 186, in __call__\n", - " raise exc\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/errors.py\", line 164, in __call__\n", - " await self.app(scope, receive, _send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/cors.py\", line 83, in __call__\n", - " await self.app(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/exceptions.py\", line 62, in __call__\n", - " await wrap_app_handling_exceptions(self.app, conn)(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 64, in wrapped_app\n", - " raise exc\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 53, in wrapped_app\n", - " await app(scope, receive, sender)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 758, in __call__\n", - " await self.middleware_stack(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 778, in app\n", - " await route.handle(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 299, in handle\n", - " await self.app(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 79, in app\n", - " await wrap_app_handling_exceptions(app, request)(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 64, in wrapped_app\n", - " raise exc\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 53, in wrapped_app\n", - " await app(scope, receive, sender)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 74, in app\n", - " response = await func(request)\n", - " ^^^^^^^^^^^^^^^^^^^\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/routing.py\", line 315, in app\n", - " content = await serialize_response(\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/routing.py\", line 180, in serialize_response\n", - " return jsonable_encoder(response_content)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/encoders.py\", line 330, in jsonable_encoder\n", - " raise ValueError(errors) from e\n", - "ValueError: [TypeError(\"'async_generator' object is not iterable\"), TypeError('vars() argument must have __dict__ attribute')]\n", - "ERROR: Exception in ASGI application\n", - "Traceback (most recent call last):\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/encoders.py\", line 322, in jsonable_encoder\n", - " data = dict(obj)\n", - " ^^^^^^^^^\n", - "TypeError: 'async_generator' object is not iterable\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/encoders.py\", line 327, in jsonable_encoder\n", - " data = vars(obj)\n", - " ^^^^^^^^^\n", - "TypeError: vars() argument must have __dict__ attribute\n", - "\n", - "The above exception was the direct cause of the following exception:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/uvicorn/protocols/http/h11_impl.py\", line 412, in run_asgi\n", - " result = await app( # type: ignore[func-returns-value]\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/uvicorn/middleware/proxy_headers.py\", line 84, in __call__\n", - " return await self.app(scope, receive, send)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/applications.py\", line 1054, in __call__\n", - " await super().__call__(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/applications.py\", line 123, in __call__\n", - " await self.middleware_stack(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/errors.py\", line 186, in __call__\n", - " raise exc\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/errors.py\", line 164, in __call__\n", - " await self.app(scope, receive, _send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/cors.py\", line 83, in __call__\n", - " await self.app(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/exceptions.py\", line 62, in __call__\n", - " await wrap_app_handling_exceptions(self.app, conn)(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 64, in wrapped_app\n", - " raise exc\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 53, in wrapped_app\n", - " await app(scope, receive, sender)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 758, in __call__\n", - " await self.middleware_stack(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 778, in app\n", - " await route.handle(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 299, in handle\n", - " await self.app(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 79, in app\n", - " await wrap_app_handling_exceptions(app, request)(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 64, in wrapped_app\n", - " raise exc\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 53, in wrapped_app\n", - " await app(scope, receive, sender)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 74, in app\n", - " response = await func(request)\n", - " ^^^^^^^^^^^^^^^^^^^\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/routing.py\", line 315, in app\n", - " content = await serialize_response(\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/routing.py\", line 180, in serialize_response\n", - " return jsonable_encoder(response_content)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/encoders.py\", line 330, in jsonable_encoder\n", - " raise ValueError(errors) from e\n", - "ValueError: [TypeError(\"'async_generator' object is not iterable\"), TypeError('vars() argument must have __dict__ attribute')]\n", - "ERROR: Exception in ASGI application\n", - "Traceback (most recent call last):\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/encoders.py\", line 322, in jsonable_encoder\n", - " data = dict(obj)\n", - " ^^^^^^^^^\n", - "TypeError: 'async_generator' object is not iterable\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/encoders.py\", line 327, in jsonable_encoder\n", - " data = vars(obj)\n", - " ^^^^^^^^^\n", - "TypeError: vars() argument must have __dict__ attribute\n", - "\n", - "The above exception was the direct cause of the following exception:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/uvicorn/protocols/http/h11_impl.py\", line 412, in run_asgi\n", - " result = await app( # type: ignore[func-returns-value]\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/uvicorn/middleware/proxy_headers.py\", line 84, in __call__\n", - " return await self.app(scope, receive, send)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/applications.py\", line 1054, in __call__\n", - " await super().__call__(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/applications.py\", line 123, in __call__\n", - " await self.middleware_stack(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/errors.py\", line 186, in __call__\n", - " raise exc\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/errors.py\", line 164, in __call__\n", - " await self.app(scope, receive, _send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/cors.py\", line 83, in __call__\n", - " await self.app(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/exceptions.py\", line 62, in __call__\n", - " await wrap_app_handling_exceptions(self.app, conn)(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 64, in wrapped_app\n", - " raise exc\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 53, in wrapped_app\n", - " await app(scope, receive, sender)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 758, in __call__\n", - " await self.middleware_stack(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 778, in app\n", - " await route.handle(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 299, in handle\n", - " await self.app(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 79, in app\n", - " await wrap_app_handling_exceptions(app, request)(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 64, in wrapped_app\n", - " raise exc\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 53, in wrapped_app\n", - " await app(scope, receive, sender)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 74, in app\n", - " response = await func(request)\n", - " ^^^^^^^^^^^^^^^^^^^\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/routing.py\", line 315, in app\n", - " content = await serialize_response(\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/routing.py\", line 180, in serialize_response\n", - " return jsonable_encoder(response_content)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/encoders.py\", line 330, in jsonable_encoder\n", - " raise ValueError(errors) from e\n", - "ValueError: [TypeError(\"'async_generator' object is not iterable\"), TypeError('vars() argument must have __dict__ attribute')]\n", - "ERROR: Exception in ASGI application\n", - "Traceback (most recent call last):\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/encoders.py\", line 322, in jsonable_encoder\n", - " data = dict(obj)\n", - " ^^^^^^^^^\n", - "TypeError: 'async_generator' object is not iterable\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/encoders.py\", line 327, in jsonable_encoder\n", - " data = vars(obj)\n", - " ^^^^^^^^^\n", - "TypeError: vars() argument must have __dict__ attribute\n", - "\n", - "The above exception was the direct cause of the following exception:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/uvicorn/protocols/http/h11_impl.py\", line 412, in run_asgi\n", - " result = await app( # type: ignore[func-returns-value]\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/uvicorn/middleware/proxy_headers.py\", line 84, in __call__\n", - " return await self.app(scope, receive, send)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/applications.py\", line 1054, in __call__\n", - " await super().__call__(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/applications.py\", line 123, in __call__\n", - " await self.middleware_stack(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/errors.py\", line 186, in __call__\n", - " raise exc\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/errors.py\", line 164, in __call__\n", - " await self.app(scope, receive, _send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/cors.py\", line 83, in __call__\n", - " await self.app(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/exceptions.py\", line 62, in __call__\n", - " await wrap_app_handling_exceptions(self.app, conn)(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 64, in wrapped_app\n", - " raise exc\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 53, in wrapped_app\n", - " await app(scope, receive, sender)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 758, in __call__\n", - " await self.middleware_stack(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 778, in app\n", - " await route.handle(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 299, in handle\n", - " await self.app(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 79, in app\n", - " await wrap_app_handling_exceptions(app, request)(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 64, in wrapped_app\n", - " raise exc\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 53, in wrapped_app\n", - " await app(scope, receive, sender)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 74, in app\n", - " response = await func(request)\n", - " ^^^^^^^^^^^^^^^^^^^\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/routing.py\", line 315, in app\n", - " content = await serialize_response(\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/routing.py\", line 180, in serialize_response\n", - " return jsonable_encoder(response_content)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/encoders.py\", line 330, in jsonable_encoder\n", - " raise ValueError(errors) from e\n", - "ValueError: [TypeError(\"'async_generator' object is not iterable\"), TypeError('vars() argument must have __dict__ attribute')]\n", - "ERROR: Exception in ASGI application\n", - "Traceback (most recent call last):\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/encoders.py\", line 322, in jsonable_encoder\n", - " data = dict(obj)\n", - " ^^^^^^^^^\n", - "TypeError: 'async_generator' object is not iterable\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/encoders.py\", line 327, in jsonable_encoder\n", - " data = vars(obj)\n", - " ^^^^^^^^^\n", - "TypeError: vars() argument must have __dict__ attribute\n", - "\n", - "The above exception was the direct cause of the following exception:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/uvicorn/protocols/http/h11_impl.py\", line 412, in run_asgi\n", - " result = await app( # type: ignore[func-returns-value]\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/uvicorn/middleware/proxy_headers.py\", line 84, in __call__\n", - " return await self.app(scope, receive, send)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/applications.py\", line 1054, in __call__\n", - " await super().__call__(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/applications.py\", line 123, in __call__\n", - " await self.middleware_stack(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/errors.py\", line 186, in __call__\n", - " raise exc\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/errors.py\", line 164, in __call__\n", - " await self.app(scope, receive, _send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/cors.py\", line 83, in __call__\n", - " await self.app(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/exceptions.py\", line 62, in __call__\n", - " await wrap_app_handling_exceptions(self.app, conn)(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 64, in wrapped_app\n", - " raise exc\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 53, in wrapped_app\n", - " await app(scope, receive, sender)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 758, in __call__\n", - " await self.middleware_stack(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 778, in app\n", - " await route.handle(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 299, in handle\n", - " await self.app(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 79, in app\n", - " await wrap_app_handling_exceptions(app, request)(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 64, in wrapped_app\n", - " raise exc\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 53, in wrapped_app\n", - " await app(scope, receive, sender)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 74, in app\n", - " response = await func(request)\n", - " ^^^^^^^^^^^^^^^^^^^\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/routing.py\", line 315, in app\n", - " content = await serialize_response(\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/routing.py\", line 180, in serialize_response\n", - " return jsonable_encoder(response_content)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/encoders.py\", line 330, in jsonable_encoder\n", - " raise ValueError(errors) from e\n", - "ValueError: [TypeError(\"'async_generator' object is not iterable\"), TypeError('vars() argument must have __dict__ attribute')]\n", - "ERROR: Exception in ASGI application\n", - "Traceback (most recent call last):\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/encoders.py\", line 322, in jsonable_encoder\n", - " data = dict(obj)\n", - " ^^^^^^^^^\n", - "TypeError: 'async_generator' object is not iterable\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/encoders.py\", line 327, in jsonable_encoder\n", - " data = vars(obj)\n", - " ^^^^^^^^^\n", - "TypeError: vars() argument must have __dict__ attribute\n", - "\n", - "The above exception was the direct cause of the following exception:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/uvicorn/protocols/http/h11_impl.py\", line 412, in run_asgi\n", - " result = await app( # type: ignore[func-returns-value]\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/uvicorn/middleware/proxy_headers.py\", line 84, in __call__\n", - " return await self.app(scope, receive, send)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/applications.py\", line 1054, in __call__\n", - " await super().__call__(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/applications.py\", line 123, in __call__\n", - " await self.middleware_stack(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/errors.py\", line 186, in __call__\n", - " raise exc\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/errors.py\", line 164, in __call__\n", - " await self.app(scope, receive, _send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/cors.py\", line 83, in __call__\n", - " await self.app(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/exceptions.py\", line 62, in __call__\n", - " await wrap_app_handling_exceptions(self.app, conn)(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 64, in wrapped_app\n", - " raise exc\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 53, in wrapped_app\n", - " await app(scope, receive, sender)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 758, in __call__\n", - " await self.middleware_stack(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 778, in app\n", - " await route.handle(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 299, in handle\n", - " await self.app(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 79, in app\n", - " await wrap_app_handling_exceptions(app, request)(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 64, in wrapped_app\n", - " raise exc\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 53, in wrapped_app\n", - " await app(scope, receive, sender)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 74, in app\n", - " response = await func(request)\n", - " ^^^^^^^^^^^^^^^^^^^\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/routing.py\", line 315, in app\n", - " content = await serialize_response(\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/routing.py\", line 180, in serialize_response\n", - " return jsonable_encoder(response_content)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/encoders.py\", line 330, in jsonable_encoder\n", - " raise ValueError(errors) from e\n", - "ValueError: [TypeError(\"'async_generator' object is not iterable\"), TypeError('vars() argument must have __dict__ attribute')]\n", - "ERROR: Exception in ASGI application\n", - "Traceback (most recent call last):\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/encoders.py\", line 322, in jsonable_encoder\n", - " data = dict(obj)\n", - " ^^^^^^^^^\n", - "TypeError: 'async_generator' object is not iterable\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/encoders.py\", line 327, in jsonable_encoder\n", - " data = vars(obj)\n", - " ^^^^^^^^^\n", - "TypeError: vars() argument must have __dict__ attribute\n", - "\n", - "The above exception was the direct cause of the following exception:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/uvicorn/protocols/http/h11_impl.py\", line 412, in run_asgi\n", - " result = await app( # type: ignore[func-returns-value]\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/uvicorn/middleware/proxy_headers.py\", line 84, in __call__\n", - " return await self.app(scope, receive, send)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/applications.py\", line 1054, in __call__\n", - " await super().__call__(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/applications.py\", line 123, in __call__\n", - " await self.middleware_stack(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/errors.py\", line 186, in __call__\n", - " raise exc\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/errors.py\", line 164, in __call__\n", - " await self.app(scope, receive, _send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/cors.py\", line 83, in __call__\n", - " await self.app(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/exceptions.py\", line 62, in __call__\n", - " await wrap_app_handling_exceptions(self.app, conn)(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 64, in wrapped_app\n", - " raise exc\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 53, in wrapped_app\n", - " await app(scope, receive, sender)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 758, in __call__\n", - " await self.middleware_stack(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 778, in app\n", - " await route.handle(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 299, in handle\n", - " await self.app(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 79, in app\n", - " await wrap_app_handling_exceptions(app, request)(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 64, in wrapped_app\n", - " raise exc\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 53, in wrapped_app\n", - " await app(scope, receive, sender)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 74, in app\n", - " response = await func(request)\n", - " ^^^^^^^^^^^^^^^^^^^\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/routing.py\", line 315, in app\n", - " content = await serialize_response(\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/routing.py\", line 180, in serialize_response\n", - " return jsonable_encoder(response_content)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/encoders.py\", line 330, in jsonable_encoder\n", - " raise ValueError(errors) from e\n", - "ValueError: [TypeError(\"'async_generator' object is not iterable\"), TypeError('vars() argument must have __dict__ attribute')]\n", - "ERROR: Exception in ASGI application\n", - "Traceback (most recent call last):\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/encoders.py\", line 322, in jsonable_encoder\n", - " data = dict(obj)\n", - " ^^^^^^^^^\n", - "TypeError: 'async_generator' object is not iterable\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/encoders.py\", line 327, in jsonable_encoder\n", - " data = vars(obj)\n", - " ^^^^^^^^^\n", - "TypeError: vars() argument must have __dict__ attribute\n", - "\n", - "The above exception was the direct cause of the following exception:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/uvicorn/protocols/http/h11_impl.py\", line 412, in run_asgi\n", - " result = await app( # type: ignore[func-returns-value]\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/uvicorn/middleware/proxy_headers.py\", line 84, in __call__\n", - " return await self.app(scope, receive, send)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/applications.py\", line 1054, in __call__\n", - " await super().__call__(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/applications.py\", line 123, in __call__\n", - " await self.middleware_stack(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/errors.py\", line 186, in __call__\n", - " raise exc\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/errors.py\", line 164, in __call__\n", - " await self.app(scope, receive, _send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/cors.py\", line 83, in __call__\n", - " await self.app(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/middleware/exceptions.py\", line 62, in __call__\n", - " await wrap_app_handling_exceptions(self.app, conn)(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 64, in wrapped_app\n", - " raise exc\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 53, in wrapped_app\n", - " await app(scope, receive, sender)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 758, in __call__\n", - " await self.middleware_stack(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 778, in app\n", - " await route.handle(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 299, in handle\n", - " await self.app(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 79, in app\n", - " await wrap_app_handling_exceptions(app, request)(scope, receive, send)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 64, in wrapped_app\n", - " raise exc\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/_exception_handler.py\", line 53, in wrapped_app\n", - " await app(scope, receive, sender)\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/starlette/routing.py\", line 74, in app\n", - " response = await func(request)\n", - " ^^^^^^^^^^^^^^^^^^^\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/routing.py\", line 315, in app\n", - " content = await serialize_response(\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/routing.py\", line 180, in serialize_response\n", - " return jsonable_encoder(response_content)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/Users/diogo/fun/LLMstudio/venv/lib/python3.12/site-packages/fastapi/encoders.py\", line 330, in jsonable_encoder\n", - " raise ValueError(errors) from e\n", - "ValueError: [TypeError(\"'async_generator' object is not iterable\"), TypeError('vars() argument must have __dict__ attribute')]\n" - ] } ], "source": [ @@ -553,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -562,24 +44,54 @@ "text": [ "Connected to LLMStudio Proxy @ 0.0.0.0:8001\n" ] - }, - { - "ename": "", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n", - "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n", - "\u001b[1;31mClick here for more info. \n", - "\u001b[1;31mView Jupyter log for further details." - ] } ], "source": [ - "from llmstudio.engine.provider import LLMProxyProvider\n", + "from llmstudio.engine.provider import LLMProxyProvider as LLM\n", + "from llmstudio.engine.provider import ProxyConfig\n", "\n", + "# from llmstudio_core import LLMCore as LLM\n", + "# from llmstudio import LLM\n", "\n", - "llm = LLMProxyProvider(provider=\"openai\", host=\"0.0.0.0\", port=\"8001\")" + "llm = LLM(provider=\"openai\", \n", + " proxy_config=ProxyConfig(host=\"0.0.0.0\", port=\"8001\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "result = llm.chat(\"olá\", model=\"gpt-4o\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "('Olá! Como posso ajudar você hoje?',\n", + " {'input_tokens': 2,\n", + " 'output_tokens': 11,\n", + " 'total_tokens': 13,\n", + " 'cost_usd': 0.000175,\n", + " 'latency_s': 0.6496210098266602,\n", + " 'time_to_first_token_s': 0.5341501235961914,\n", + " 'inter_token_latency_s': 0.01241710450914171,\n", + " 'tokens_per_second': 15.393590799454474})" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result.chat_output, result.metrics" ] }, { @@ -591,7 +103,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -601,37 +113,39 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "host='0.0.0.0' port='8001' url=None username=None password=None\n", "Connected to LLMStudio Proxy @ 0.0.0.0:8001\n" ] } ], "source": [ "# You can set OPENAI_API_KEY and ANTHROPIC_API_KEY on .env file\n", - "from llmstudio.llm import ProxyConfig\n", + "from llmstudio.engine.provider import ProxyConfig\n", "proxy = ProxyConfig(host=\"0.0.0.0\", port=\"8001\")\n", + "print(proxy)\n", "\n", "openai = LLM(\"openai\", proxy_config=proxy)\n" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 13, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -656,16 +170,16 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "ChatCompletion(id='bbdd240d-ef4a-4550-b31f-d3758228e9c6', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content=\"I’m an AI and don’t have a personal name, but you can call me whatever you like! Let me know if there's anything I can help you with.\", refusal=None, role='assistant', function_call=None, tool_calls=None))], created=1729004854, model='gpt-4o', object='chat.completion', service_tier=None, system_fingerprint=None, usage=None, chat_input=\"What's your name\", chat_output=\"I’m an AI and don’t have a personal name, but you can call me whatever you like! Let me know if there's anything I can help you with.\", context=[{'role': 'user', 'content': \"What's your name\"}], provider='openai', deployment='gpt-4o-2024-08-06', timestamp=1729004855.494591, parameters={}, metrics={'input_tokens': 4, 'output_tokens': 34, 'total_tokens': 38, 'cost_usd': 0.0005300000000000001, 'latency_s': 0.8200538158416748, 'time_to_first_token_s': 0.4039459228515625, 'inter_token_latency_s': 0.01220102871165556, 'tokens_per_second': 42.68012577208389})" + "ChatCompletion(id='7a435f11-ab93-473e-b439-37202e554a1f', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='I don’t have a personal name, but you can call me Assistant. How can I help you today?', refusal=None, role='assistant', function_call=None, tool_calls=None))], created=1729076083, model='gpt-4o', object='chat.completion', service_tier=None, system_fingerprint=None, usage=None, chat_input=\"What's your name\", chat_output='I don’t have a personal name, but you can call me Assistant. How can I help you today?', chat_output_stream='', context=[{'role': 'user', 'content': \"What's your name\"}], provider='openai', deployment='gpt-4o-2024-08-06', timestamp=1729076085.7050822, parameters={}, metrics={'input_tokens': 4, 'output_tokens': 22, 'total_tokens': 26, 'cost_usd': 0.00035, 'latency_s': 3.0498709678649902, 'time_to_first_token_s': 0.5896170139312744, 'inter_token_latency_s': 0.10692943697390349, 'tokens_per_second': 7.869185369766901})" ] }, - "execution_count": 14, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -683,16 +197,16 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "ChatCompletion(id='65a23cb5-77fa-44ce-a1c1-2c421aff02c6', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content=\"I'm an AI language model created by OpenAI, and I don't have a personal name. You can call me Assistant or any name you prefer! How can I help you today?\", refusal=None, role='assistant', function_call=None, tool_calls=None))], created=1729004863, model='gpt-4o', object='chat.completion', service_tier=None, system_fingerprint=None, usage=None, chat_input=\"What's your name\", chat_output=\"I'm an AI language model created by OpenAI, and I don't have a personal name. You can call me Assistant or any name you prefer! How can I help you today?\", context=[{'role': 'user', 'content': \"What's your name\"}], provider='openai', deployment='gpt-4o-2024-08-06', timestamp=1729004864.533138, parameters={}, metrics={'input_tokens': 4, 'output_tokens': 38, 'total_tokens': 42, 'cost_usd': 0.00059, 'latency_s': 1.1568489074707031, 'time_to_first_token_s': 0.5351388454437256, 'inter_token_latency_s': 0.016760536142297694, 'tokens_per_second': 32.84785053139045})" + "ChatCompletion(id='a94bffc5-88ab-4290-8cce-d349c9829e57', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='I’m called ChatGPT. How can I assist you today?', refusal=None, role='assistant', function_call=None, tool_calls=None))], created=1729076085, model='gpt-4o', object='chat.completion', service_tier=None, system_fingerprint=None, usage=None, chat_input=\"What's your name\", chat_output='I’m called ChatGPT. How can I assist you today?', chat_output_stream='', context=[{'role': 'user', 'content': \"What's your name\"}], provider='openai', deployment='gpt-4o-2024-08-06', timestamp=1729076086.277256, parameters={}, metrics={'input_tokens': 4, 'output_tokens': 14, 'total_tokens': 18, 'cost_usd': 0.00023, 'latency_s': 0.5512211322784424, 'time_to_first_token_s': 0.3975660800933838, 'inter_token_latency_s': 0.010929771832057409, 'tokens_per_second': 27.212309401126042})" ] }, - "execution_count": 15, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -710,20 +224,46 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 10, "metadata": {}, "outputs": [ { - "ename": "Exception", - "evalue": "Internal Server Error", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mException\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[16], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[43mopenai\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchat\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mWrite a paragfraph about space\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mgpt-4o\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mis_stream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, chunk \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(response):\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m i\u001b[38;5;241m%\u001b[39m\u001b[38;5;241m20\u001b[39m\u001b[38;5;241m==\u001b[39m\u001b[38;5;241m0\u001b[39m:\n", - "File \u001b[0;32m~/fun/LLMstudio/llmstudio/llm/__init__.py:62\u001b[0m, in \u001b[0;36mLLM.chat\u001b[0;34m(self, chat_input, model, is_stream, retries, parameters, **kwargs)\u001b[0m\n\u001b[1;32m 57\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mchat\u001b[39m(\u001b[38;5;28mself\u001b[39m, chat_input: Any, \n\u001b[1;32m 58\u001b[0m model: \u001b[38;5;28mstr\u001b[39m, is_stream: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m, \n\u001b[1;32m 59\u001b[0m retries: \u001b[38;5;28mint\u001b[39m \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m, \n\u001b[1;32m 60\u001b[0m parameters: Optional[\u001b[38;5;28mdict\u001b[39m] \u001b[38;5;241m=\u001b[39m {},\n\u001b[1;32m 61\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ChatCompletionChunk \u001b[38;5;241m|\u001b[39m ChatCompletion:\n\u001b[0;32m---> 62\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_provider\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchat\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchat_input\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mis_stream\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretries\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparameters\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 63\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_session_local:\n\u001b[1;32m 64\u001b[0m log \u001b[38;5;241m=\u001b[39m schemas\u001b[38;5;241m.\u001b[39mLogDefaultCreate(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mresult\u001b[38;5;241m.\u001b[39mmetrics)\n", - "File \u001b[0;32m~/fun/LLMstudio/llmstudio/engine/provider.py:52\u001b[0m, in \u001b[0;36mLLMProxyProvider.chat\u001b[0;34m(self, chat_input, model, is_stream, retries, parameters, **kwargs)\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m response\u001b[38;5;241m.\u001b[39mok:\n\u001b[1;32m 51\u001b[0m error_data \u001b[38;5;241m=\u001b[39m response\u001b[38;5;241m.\u001b[39mtext\n\u001b[0;32m---> 52\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m(error_data)\n\u001b[1;32m 54\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_stream:\n\u001b[1;32m 55\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgenerate_chat(response)\n", - "\u001b[0;31mException\u001b[0m: Internal Server Error" + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Space, often referred to as the cosmos, is an expansive and mysterious expanse that extends beyond\n", + "\n", + " Earth's atmosphere. It is a vast, seemingly infinite realm filled with celestial bodies such as stars, planets\n", + "\n", + ", meteoroids, comets, and galaxies, each exhibiting unique characteristics and phenomena. The study\n", + "\n", + " of space, known as astronomy, has intrigued humans for millennia, driving them to explore and understand\n", + "\n", + " the universe's origins, structure, and future. With advancements in technology, humans have embarked on remarkable\n", + "\n", + " journeys into space, from landing on the Moon to deploying telescopes like the Hubble Space Telescope,\n", + "\n", + " which capture breathtaking images of distant galaxies and nebulas. Despite these achievements, the cosmos remains largely unexpl\n", + "\n", + "ored and holds countless mysteries, fueling our curiosity about the possibility of extraterrestrial life, the nature of\n", + "\n", + " dark matter and dark energy, and the ultimate fate of the universe itself. The exploration of space continues\n", + "\n", + " to inspire innovation, collaboration, and a profound sense of wonder, reminding us of our small yet significant\n", + "\n", + " place in the grand scheme of the universe.\n", + "\n", + "## Metrics:\n", + "{'cost_usd': 0.0032050000000000004,\n", + " 'input_tokens': 8,\n", + " 'inter_token_latency_s': 0.01872186113202401,\n", + " 'latency_s': 4.415300130844116,\n", + " 'output_tokens': 211,\n", + " 'time_to_first_token_s': 0.5010969638824463,\n", + " 'tokens_per_second': 47.56188566503003,\n", + " 'total_tokens': 219}\n" ] } ], @@ -733,7 +273,7 @@ " if i%20==0:\n", " print(\"\\n\")\n", " if not chunk.metrics:\n", - " print(chunk.chat_output, end=\"\", flush=True)\n", + " print(chunk.chat_output_stream, end=\"\", flush=True)\n", " else:\n", " print(\"\\n\\n## Metrics:\")\n", " pprint(chunk.metrics)\n" @@ -748,16 +288,54 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Space, the vast expanse beyond our planet's atmosphere, is a realm of awe and mystery\n", + "\n", + " that has captivated humanity for centuries. It stretches infinitely, hosting a myriad of celestial bodies, including stars\n", + "\n", + ", planets, moons, asteroids, and comets, all bound by the forces of gravity.\n", + "\n", + " The cosmos is not just a backdrop for astronomical phenomena; it's a dynamic environment where the laws of physics\n", + "\n", + " come to life. Black holes bend time and space, while supernovae illuminate the cosmos with explosive\n", + "\n", + " brilliance. Beyond our Milky Way galaxy, billions of other galaxies swirl in a cosmic dance, each\n", + "\n", + " potentially hosting its own unique systems and perhaps even life. As we continue to explore this enigmatic frontier through\n", + "\n", + " telescopes and space missions, we uncover the origins of the universe, the nature of dark matter,\n", + "\n", + " and the possibility of extraterrestrial life, igniting our imagination and expanding our understanding of existence itself.\n", + "\n", + "\n", + "\n", + "## Metrics:\n", + "{'cost_usd': 0.0001098,\n", + " 'input_tokens': 8,\n", + " 'inter_token_latency_s': 0.027061593532562257,\n", + " 'latency_s': 5.691463232040405,\n", + " 'output_tokens': 181,\n", + " 'time_to_first_token_s': 0.8199222087860107,\n", + " 'tokens_per_second': 31.802015162120444,\n", + " 'total_tokens': 189}\n" + ] + } + ], "source": [ "i=0\n", "async for chunk in await openai.achat(\"Write a paragfraph about space\", model=\"gpt-4o-mini\", is_stream=True):\n", " if i%20==0:\n", " print(\"\\n\")\n", " if not chunk.metrics:\n", - " print(chunk.chat_output, end=\"\", flush=True)\n", + " print(chunk.chat_output_stream, end=\"\", flush=True)\n", " else:\n", " print(\"\\n\\n## Metrics:\")\n", " pprint(chunk.metrics)\n", @@ -773,7 +351,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -784,18 +362,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ - "gpt3 = LLM('openai/gpt-3.5-turbo')" + "gpt3 = LLM('openai')" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'LLM' object has no attribute 'run_batch_chat_coroutine'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[15], line 15\u001b[0m\n\u001b[1;32m 1\u001b[0m complex_prompt\u001b[38;5;241m=\u001b[39m [{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrole\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msystem\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcontent\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYou are a tchill dude from jamaica\u001b[39m\u001b[38;5;124m\"\u001b[39m},\n\u001b[1;32m 2\u001b[0m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrole\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muser\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcontent\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHello, how are you?\u001b[39m\u001b[38;5;124m\"\u001b[39m},\n\u001b[1;32m 3\u001b[0m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrole\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124massistant\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcontent\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYe man doin fain man!\u001b[39m\u001b[38;5;124m\"\u001b[39m},\n\u001b[1;32m 4\u001b[0m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrole\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muser\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcontent\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHow is the weather today?\u001b[39m\u001b[38;5;124m\"\u001b[39m}]\n\u001b[1;32m 6\u001b[0m inputs \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 7\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWhat\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124ms your name?\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 8\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTell me a joke.\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 12\u001b[0m complex_prompt\n\u001b[1;32m 13\u001b[0m ]\n\u001b[0;32m---> 15\u001b[0m responses \u001b[38;5;241m=\u001b[39m \u001b[43mgpt3\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_batch_chat_coroutine\u001b[49m(inputs)\n\u001b[1;32m 16\u001b[0m responses\n", + "\u001b[0;31mAttributeError\u001b[0m: 'LLM' object has no attribute 'run_batch_chat_coroutine'" + ] + } + ], "source": [ "complex_prompt= [{\"role\": \"system\", \"content\": \"You are a tchill dude from jamaica\"},\n", " {\"role\": \"user\", \"content\": \"Hello, how are you?\"},\n", @@ -814,6 +404,13 @@ "responses = gpt3.run_batch_chat_coroutine(inputs)\n", "responses" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/examples/llm_proxy.py b/examples/llm_proxy.py index 92c00da2..93485425 100644 --- a/examples/llm_proxy.py +++ b/examples/llm_proxy.py @@ -6,23 +6,38 @@ llm = LLMProxyProvider(provider="openai", host="0.0.0.0", port="8001") -result = llm.chat("What's your name", model="gpt-4o") +result = llm.chat("Write a paragfraph about space", model="gpt-4o") print(result) + +response = llm.chat("Write a paragfraph about space", model="gpt-4o", is_stream=True) +for i, chunk in enumerate(response): + if i%20==0: + print("\n") + if not chunk.metrics: + print(chunk.chat_output, end="", flush=True) + else: + print("\n\n## Metrics:") + print(chunk.metrics) + + import asyncio # stream print("\nasync stream") async def async_stream(): - response_async = await llm.achat("What's your name", model="gpt-4o", is_stream=True) - async for p in response_async: - if "}" in p.chat_output: - p.chat_output - print("that: ",p.chat_output) + response_async = await llm.achat("Write a paragfraph about space", model="gpt-4o", is_stream=False) + print(response_async) + + response_async_stream = await llm.achat("Write a paragfraph about space", model="gpt-4o", is_stream=True) + async for p in response_async_stream: + # pprint(p.choices[0].delta.content==p.chat_output) # print("metrics: ", p.metrics) # print(p) - if p.metrics: - print(p) + if not p.metrics: + print(p.chat_output, end="", flush=True) + else: + print(p.metrics) asyncio.run(async_stream()) diff --git a/libs/core/llmstudio_core/__init__.py b/libs/core/llmstudio_core/__init__.py index 82b776b4..e7fc2642 100644 --- a/libs/core/llmstudio_core/__init__.py +++ b/libs/core/llmstudio_core/__init__.py @@ -7,7 +7,7 @@ _engine_config = _load_engine_config() -def LLM(provider: str, api_key: Optional[str] = None, **kwargs) -> BaseProvider: +def LLMCore(provider: str, api_key: Optional[str] = None, **kwargs) -> BaseProvider: """ Factory method to create an instance of a provider. @@ -35,7 +35,7 @@ def LLM(provider: str, api_key: Optional[str] = None, **kwargs) -> BaseProvider: load_dotenv() def test_stuff(provider, model, api_key, **kwargs): - llm = LLM(provider=provider, api_key=api_key, **kwargs) + llm = LLMCore(provider=provider, api_key=api_key, **kwargs) latencies = {} chat_request = { @@ -76,9 +76,8 @@ async def async_stream(): response_async = await llm.achat(**chat_request) async for p in response_async: - if "}" in p.chat_output: - p.chat_output - print("that: ",p.chat_output) + if not p.metrics: + print("that: ",p.chat_output_stream) # pprint(p.choices[0].delta.content==p.chat_output) # print("metrics: ", p.metrics) # print(p) diff --git a/libs/core/llmstudio_core/providers/provider.py b/libs/core/llmstudio_core/providers/provider.py index ba799b29..f804318b 100644 --- a/libs/core/llmstudio_core/providers/provider.py +++ b/libs/core/llmstudio_core/providers/provider.py @@ -229,7 +229,8 @@ async def ahandle_response( if isinstance(request.chat_input, str) else request.chat_input[-1]["content"] ), - "chat_output": chat_output if chat_output else "", + "chat_output": None, + "chat_output_stream": chat_output if chat_output else "", "context": ( [{"role": "user", "content": request.chat_input}] if isinstance(request.chat_input, str) @@ -276,7 +277,8 @@ async def ahandle_response( if isinstance(request.chat_input, str) else request.chat_input[-1]["content"] ), - "chat_output": "" if request.is_stream else output_string, + "chat_output": output_string, + "chat_output_stream": "", "context": ( [{"role": "user", "content": request.chat_input}] if isinstance(request.chat_input, str) diff --git a/llmstudio/engine/__init__.py b/llmstudio/engine/__init__.py index ce1339bc..23cb0aaf 100644 --- a/llmstudio/engine/__init__.py +++ b/llmstudio/engine/__init__.py @@ -96,7 +96,13 @@ async def chat_handler(request: Request): provider_class = provider_registry.get(f"{provider_config.name}".lower()) provider_instance = provider_class(provider_config) request_dict = await request.json() + result = await provider_instance.achat(**request_dict) + if request_dict.get("is_stream", False): + async def result_generator(): + async for chunk in result: + yield json.dumps(chunk.dict()) + return StreamingResponse(result_generator(), media_type="application/json") return result return chat_handler diff --git a/llmstudio/engine/provider.py b/llmstudio/engine/provider.py index 2db14a22..2dec16e4 100644 --- a/llmstudio/engine/provider.py +++ b/llmstudio/engine/provider.py @@ -1,27 +1,40 @@ import asyncio -from typing import Any, Coroutine, Dict, List, Union +import json +from typing import Any, Coroutine, Dict, List, Optional, Union +from pydantic import BaseModel import requests -from libs.core.llmstudio_core.providers.provider import BaseProvider, ProviderABC +from llmstudio_core.providers.provider import ProviderABC from llmstudio.server import is_server_running from openai.types.chat import ChatCompletion, ChatCompletionChunk from tqdm.asyncio import tqdm_asyncio -from llmstudio.config import ENGINE_HOST, ENGINE_PORT from llmstudio.llm.semaphore import DynamicSemaphore + +class ProxyConfig(BaseModel): + host: Optional[str] = None + port: Optional[str] = None + url: Optional[str] = None + username: Optional[str] = None + password: Optional[str] = None + def __init__(self, **data): + super().__init__(**data) + if (self.host is None and self.port is None) and self.url is None: + raise ValueError("Either both 'host' and 'port' must be provided, or 'url' must be specified.") + + class LLMProxyProvider(ProviderABC): def __init__(self, provider: str, - host: str, - port: str, - **kwargs): + proxy_config: ProxyConfig): self.provider = provider - self.engine_host = host - self.engine_port = port - if is_server_running(host=host, port=port): - print(f"Connected to LLMStudio Proxy @ {host}:{port}") + + self.engine_host = proxy_config.host + self.engine_port = proxy_config.port + if is_server_running(host=self.engine_host, port=self.engine_port): + print(f"Connected to LLMStudio Proxy @ {self.engine_host}:{self.engine_port}") else: - raise Exception(f"LLMStudio Proxy is not running @ {host}:{port}") + raise Exception(f"LLMStudio Proxy is not running @ {self.engine_host}:{self.engine_port}") @staticmethod def _provider_config_name(): @@ -59,7 +72,7 @@ def chat(self, chat_input: str, def generate_chat(self, response): for chunk in response.iter_content(chunk_size=None): if chunk: - yield chunk.decode("utf-8") + yield ChatCompletionChunk(**json.loads(chunk.decode("utf-8"))) async def achat(self, chat_input: Any, model: str, @@ -233,4 +246,4 @@ async def async_stream(self, model:str, chat_input: str, retries: int, parameter for chunk in response.iter_content(chunk_size=None): if chunk: - yield chunk.decode("utf-8") + yield ChatCompletionChunk(**json.loads(chunk.decode("utf-8"))) diff --git a/llmstudio/llm/__init__.py b/llmstudio/llm/__init__.py index b0af969f..cd912e55 100644 --- a/llmstudio/llm/__init__.py +++ b/llmstudio/llm/__init__.py @@ -1,24 +1,25 @@ from typing import Any, Coroutine, Optional -from llmstudio_core import LLM as LLM_factory +from llmstudio_core import LLMCore from llmstudio_core.providers.provider import ProviderABC from openai.types.chat import ChatCompletion, ChatCompletionChunk from pydantic import BaseModel -from llmstudio.engine.provider import LLMProxyProvider +from llmstudio.engine.provider import LLMProxyProvider, ProxyConfig from llmstudio.tracking.database import create_tracking_engine from llmstudio.tracking.logs import crud, schemas -from sqlalchemy.orm import declarative_base, sessionmaker - - -class ProxyConfig(BaseModel): - host: str - port: int - username: Optional[str] = None - password: Optional[str] = None +from sqlalchemy.orm import sessionmaker class TrackingConfig(BaseModel): - database_uri: str + database_uri: Optional[str] = None + host: Optional[str] = None + port: Optional[int] = None + url: Optional[str] = None + + def __init__(self, **data): + super().__init__(**data) + if (self.host and self.port) or self.url or self.database_uri: + raise ValueError("You must provide either both 'host' and 'port', or 'url', or 'database_uri'.") class LLM(ProviderABC): @@ -26,18 +27,18 @@ class LLM(ProviderABC): def __init__(self, provider: str, + api_key: Optional[str] = None, proxy_config: Optional[ProxyConfig] = None, tracking_config: Optional[TrackingConfig] = None, **kwargs): if proxy_config is not None: self._provider = LLMProxyProvider(provider=provider, - host=proxy_config.host, - port=proxy_config.port, - **kwargs - ) + proxy_config=proxy_config) else: - self._provider = LLM_factory(provider, **kwargs) + self._provider = LLMCore(provider=provider, + api_key=api_key, + **kwargs) self._session_local = None if tracking_config is not None: From 98bb2af826c81a96c40a4a4199e9cad31189e9cf Mon Sep 17 00:00:00 2001 From: Diogo Goncalves Date: Wed, 16 Oct 2024 12:10:23 +0100 Subject: [PATCH 19/58] feat: add chat_output_stream --- libs/core/llmstudio_core/providers/provider.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/libs/core/llmstudio_core/providers/provider.py b/libs/core/llmstudio_core/providers/provider.py index f804318b..6edba202 100644 --- a/libs/core/llmstudio_core/providers/provider.py +++ b/libs/core/llmstudio_core/providers/provider.py @@ -328,6 +328,7 @@ def handle_response( chunk = chunk[0] if isinstance(chunk, tuple) else chunk model = chunk.get("model") if chunk.get("choices")[0].get("finish_reason") != "stop": + chat_output = chunk.get("choices")[0].get("delta").get("content") chunk = { **chunk, "id": str(uuid.uuid4()), @@ -336,7 +337,8 @@ def handle_response( if isinstance(request.chat_input, str) else request.chat_input[-1]["content"] ), - "chat_output": chunk.get("choices")[0].get("delta").get("content"), + "chat_output": None, + "chat_output_stream": chat_output if chat_output else "", "context": ( [{"role": "user", "content": request.chat_input}] if isinstance(request.chat_input, str) @@ -383,7 +385,8 @@ def handle_response( if isinstance(request.chat_input, str) else request.chat_input[-1]["content"] ), - "chat_output": "" if request.is_stream else output_string, + "chat_output": output_string, + "chat_output_stream": "", "context": ( [{"role": "user", "content": request.chat_input}] if isinstance(request.chat_input, str) From c453d3d6908ca18a422df22f68e23ae353356a9b Mon Sep 17 00:00:00 2001 From: Diogo Goncalves Date: Wed, 16 Oct 2024 14:16:00 +0100 Subject: [PATCH 20/58] feat: adapt for modularization --- examples/01_intro_to_llmstudio copy.ipynb | 2 +- examples/01_intro_to_llmstudio_core.ipynb | 501 + .../01_intro_to_llmstudio_with_proxy.ipynb | 50 +- examples/llm_proxy.py | 2 +- libs/core/llmstudio_core/__init__.py | 4 +- .../core/llmstudio_core/providers/__init__.py | 2 +- libs/core/poetry.lock | 1837 ---- libs/core/pyproject.toml | 2 - libs/llmstudio/README.md | 70 + libs/llmstudio/llmstudio/__init__.py | 1 + .../llmstudio/llmstudio}/cli.py | 6 +- .../llmstudio/llmstudio}/client.py | 0 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"metadata": {}, + "source": [ + "### Import clients\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from llmstudio_core import LLMCore as LLM\n", + "from pprint import pprint" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mnotebook controller is DISPOSED. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mnotebook controller is DISPOSED. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + } + ], + "source": [ + "# You can set OPENAI_API_KEY and ANTHROPIC_API_KEY on .env file\n", + "\n", + "# anthropic = LLM(\"anthropic\") # or you can pass api_key as an argument here\n", + "openai = LLM(\"openai\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Chat (non-stream)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "ChatCompletion(id='a77d892b-4d27-43f5-a0e9-85310a233d0a', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content=\"I'm ChatGPT, an AI developed by OpenAI. How can I assist you today?\", refusal=None, role='assistant', function_call=None, tool_calls=None))], created=1729076559, model='gpt-4o', object='chat.completion', service_tier=None, system_fingerprint=None, usage=None, chat_input=\"What's your name\", chat_output=\"I'm ChatGPT, an AI developed by OpenAI. How can I assist you today?\", chat_output_stream='', context=[{'role': 'user', 'content': \"What's your name\"}], provider='openai', deployment='gpt-4o-2024-08-06', timestamp=1729076560.3108108, parameters={}, metrics={'input_tokens': 4, 'output_tokens': 20, 'total_tokens': 24, 'cost_usd': 0.00032, 'latency_s': 1.042776107788086, 'time_to_first_token_s': 0.8006670475006104, 'inter_token_latency_s': 0.012167516507600484, 'tokens_per_second': 19.179572537793913})" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mnotebook controller is DISPOSED. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mnotebook controller is DISPOSED. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mnotebook controller is DISPOSED. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + } + ], + "source": [ + "openai.chat(\"What's your name\", model=\"gpt-4o\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Async version" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "ChatCompletion(id='44fb46c2-d4dc-4287-a223-6ecff694105d', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='I am called Assistant, but you can call me whatever you like! How can I assist you today?', refusal=None, role='assistant', function_call=None, tool_calls=None))], created=1729076560, model='gpt-4o', object='chat.completion', service_tier=None, system_fingerprint=None, usage=None, chat_input=\"What's your name\", chat_output='I am called Assistant, but you can call me whatever you like! How can I assist you today?', chat_output_stream='', context=[{'role': 'user', 'content': \"What's your name\"}], provider='openai', deployment='gpt-4o-2024-08-06', timestamp=1729076561.568102, parameters={}, metrics={'input_tokens': 4, 'output_tokens': 21, 'total_tokens': 25, 'cost_usd': 0.000335, 'latency_s': 1.246669054031372, 'time_to_first_token_s': 0.949782133102417, 'inter_token_latency_s': 0.013458403674038973, 'tokens_per_second': 18.449162530845346})" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mnotebook controller is DISPOSED. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mnotebook controller is DISPOSED. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mnotebook controller is DISPOSED. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + } + ], + "source": [ + "await openai.achat(\"What's your name\", model=\"gpt-4o\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Chat (stream)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Space, the final frontier, is a vast and mysterious expanse that has fascinated humanity for centuries\n", + "\n", + ". Stretching beyond our planet's atmosphere, it consists of an endless array of celestial bodies, including\n", + "\n", + " stars, planets, moons, asteroids, and galaxies, all bound together in a cosmic dance governed\n", + "\n", + " by the laws of physics. Our exploration of space has revealed the breathtaking beauty and complexity of the universe\n", + "\n", + ", from the swirling arms of the Milky Way to the enigmatic black holes that challenge our understanding of\n", + "\n", + " nature. Space exploration has not only expanded our scientific knowledge but has also inspired us to dream of new\n", + "\n", + " possibilities, driving the development of advanced technologies and international collaborations. As we continue to venture further into space\n", + "\n", + ", we seek answers to profound questions about our place in the cosmos and the potential for life beyond Earth\n", + "\n", + ", opening up a new era of discovery and innovation.\n", + "\n", + "## Metrics:\n", + "{'cost_usd': 0.002575,\n", + " 'input_tokens': 8,\n", + " 'inter_token_latency_s': 0.012563287166126987,\n", + " 'latency_s': 2.640441656112671,\n", + " 'output_tokens': 169,\n", + " 'time_to_first_token_s': 0.49152684211730957,\n", + " 'tokens_per_second': 65.14061751821588,\n", + " 'total_tokens': 177}\n" + ] + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mnotebook controller is DISPOSED. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mnotebook controller is DISPOSED. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mnotebook controller is DISPOSED. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + } + ], + "source": [ + "response = openai.chat(\"Write a paragfraph about space\", model=\"gpt-4o\", is_stream=True)\n", + "for i, chunk in enumerate(response):\n", + " if i%20==0:\n", + " print(\"\\n\")\n", + " if chunk.chat_output_stream:\n", + " print(chunk.chat_output_stream, end=\"\", flush=True)\n", + " elif chunk.metrics:\n", + " print(\"\\n\\n## Metrics:\")\n", + " pprint(chunk.metrics)\n", + " i+=1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Async version" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Hi there! How can I assist you today?\n", + "\n", + "## Metrics:\n", + "{'cost_usd': 6.45e-06,\n", + " 'input_tokens': 3,\n", + " 'inter_token_latency_s': 0.005659731951626864,\n", + " 'latency_s': 0.8615350723266602,\n", + " 'output_tokens': 10,\n", + " 'time_to_first_token_s': 0.7987778186798096,\n", + " 'tokens_per_second': 13.92862622248543,\n", + " 'total_tokens': 13}\n" + ] + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mnotebook controller is DISPOSED. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mnotebook controller is DISPOSED. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mnotebook controller is DISPOSED. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + } + ], + "source": [ + "i=0\n", + "async for chunk in await openai.achat(\"Say hi back\", model=\"gpt-4o-mini\", is_stream=True):\n", + " if i%20==0:\n", + " print(\"\\n\")\n", + " if chunk.chat_output_stream:\n", + " print(chunk.chat_output_stream, end=\"\", flush=True)\n", + " elif chunk.metrics:\n", + " print(\"\\n\\n## Metrics:\")\n", + " pprint(chunk.metrics)\n", + " i+=1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Batch version" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mnotebook controller is DISPOSED. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mnotebook controller is DISPOSED. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mnotebook controller is DISPOSED. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + } + ], + "source": [ + "from llmstudio import LLM\n", + "import nest_asyncio\n", + "nest_asyncio.apply()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'NoneType' object has no attribute 'id'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[8], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m gpt3 \u001b[38;5;241m=\u001b[39m \u001b[43mLLM\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mopenai/gpt-3.5-turbo\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/fun/LLMstudio/llmstudio/llm/__init__.py:39\u001b[0m, in \u001b[0;36mLLM.__init__\u001b[0;34m(self, provider, api_key, proxy_config, tracking_config, **kwargs)\u001b[0m\n\u001b[1;32m 36\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_provider \u001b[38;5;241m=\u001b[39m LLMProxyProvider(provider\u001b[38;5;241m=\u001b[39mprovider,\n\u001b[1;32m 37\u001b[0m proxy_config\u001b[38;5;241m=\u001b[39mproxy_config)\n\u001b[1;32m 38\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 39\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_provider \u001b[38;5;241m=\u001b[39m \u001b[43mLLMCore\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprovider\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprovider\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 40\u001b[0m \u001b[43m \u001b[49m\u001b[43mapi_key\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mapi_key\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m 41\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 43\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_session_local \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 44\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tracking_config \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[0;32m~/fun/LLMstudio/libs/core/llmstudio_core/__init__.py:25\u001b[0m, in \u001b[0;36mLLMCore\u001b[0;34m(provider, api_key, **kwargs)\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 12\u001b[0m \u001b[38;5;124;03mFactory method to create an instance of a provider.\u001b[39;00m\n\u001b[1;32m 13\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[38;5;124;03m NotImplementedError: If the provider is not found in the provider map.\u001b[39;00m\n\u001b[1;32m 23\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 24\u001b[0m provider_config \u001b[38;5;241m=\u001b[39m _engine_config\u001b[38;5;241m.\u001b[39mproviders\u001b[38;5;241m.\u001b[39mget(provider)\n\u001b[0;32m---> 25\u001b[0m provider_class \u001b[38;5;241m=\u001b[39m provider_registry\u001b[38;5;241m.\u001b[39mget(\u001b[43mprovider_config\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mid\u001b[49m)\n\u001b[1;32m 26\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m provider_class:\n\u001b[1;32m 27\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m provider_class(config\u001b[38;5;241m=\u001b[39mprovider_config, api_key\u001b[38;5;241m=\u001b[39mapi_key, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'id'" + ] + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mnotebook controller is DISPOSED. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mnotebook controller is DISPOSED. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mnotebook controller is DISPOSED. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + } + ], + "source": [ + "gpt3 = LLM('openai/gpt-3.5-turbo')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mnotebook controller is DISPOSED. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mnotebook controller is DISPOSED. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mnotebook controller is DISPOSED. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + } + ], + "source": [ + "complex_prompt= [{\"role\": \"system\", \"content\": \"You are a tchill dude from jamaica\"},\n", + " {\"role\": \"user\", \"content\": \"Hello, how are you?\"},\n", + " {\"role\": \"assistant\", \"content\": \"Ye man doin fain man!\"},\n", + " {\"role\": \"user\", \"content\": \"How is the weather today?\"}]\n", + "\n", + "inputs = [\n", + " \"What's your name?\",\n", + " \"Tell me a joke.\",\n", + " \"What's the weather like?\",\n", + " \"Can you sing a song?\",\n", + " \"Tell me about yourself.\",\n", + " complex_prompt\n", + "]\n", + "\n", + "responses = gpt3.run_batch_chat_coroutine(inputs)\n", + "responses" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/01_intro_to_llmstudio_with_proxy.ipynb b/examples/01_intro_to_llmstudio_with_proxy.ipynb index 0b5a14ef..0181f356 100644 --- a/examples/01_intro_to_llmstudio_with_proxy.ipynb +++ b/examples/01_intro_to_llmstudio_with_proxy.ipynb @@ -23,14 +23,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Running LLMstudio Engine on http://0.0.0.0:8001 \n", + "Running LLMstudio Proxy on http://0.0.0.0:8001 \n", "Running LLMstudio Tracking on http://0.0.0.0:8002 \n" ] } ], "source": [ - "from llmstudio.server import start_server\n", - "start_server()" + "from llmstudio.server import start_servers\n", + "start_servers()" ] }, { @@ -47,8 +47,8 @@ } ], "source": [ - "from llmstudio.engine.provider import LLMProxyProvider as LLM\n", - "from llmstudio.engine.provider import ProxyConfig\n", + "from llmstudio_proxy.provider import LLMProxyProvider as LLM\n", + "from llmstudio_proxy.provider import ProxyConfig\n", "\n", "# from llmstudio_core import LLMCore as LLM\n", "# from llmstudio import LLM\n", @@ -61,7 +61,20 @@ "cell_type": "code", "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "Exception", + "evalue": "{\"detail\":\"Not Found\"}", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mException\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[3], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mllm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchat\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43molá\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mgpt-4o\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/fun/LLMstudio/libs/proxy/llmstudio_proxy/provider.py:63\u001b[0m, in \u001b[0;36mLLMProxyProvider.chat\u001b[0;34m(self, chat_input, model, is_stream, retries, parameters, **kwargs)\u001b[0m\n\u001b[1;32m 61\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m response\u001b[38;5;241m.\u001b[39mok:\n\u001b[1;32m 62\u001b[0m error_data \u001b[38;5;241m=\u001b[39m response\u001b[38;5;241m.\u001b[39mtext\n\u001b[0;32m---> 63\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m(error_data)\n\u001b[1;32m 65\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_stream:\n\u001b[1;32m 66\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgenerate_chat(response)\n", + "\u001b[0;31mException\u001b[0m: {\"detail\":\"Not Found\"}" + ] + } + ], "source": [ "result = llm.chat(\"olá\", model=\"gpt-4o\")\n" ] @@ -74,15 +87,15 @@ { "data": { "text/plain": [ - "('Olá! Como posso ajudar você hoje?',\n", + "('Olá! Como posso ajudá-lo hoje?',\n", " {'input_tokens': 2,\n", " 'output_tokens': 11,\n", " 'total_tokens': 13,\n", " 'cost_usd': 0.000175,\n", - " 'latency_s': 0.6496210098266602,\n", - " 'time_to_first_token_s': 0.5341501235961914,\n", - " 'inter_token_latency_s': 0.01241710450914171,\n", - " 'tokens_per_second': 15.393590799454474})" + " 'latency_s': 0.9914300441741943,\n", + " 'time_to_first_token_s': 0.8828918933868408,\n", + " 'inter_token_latency_s': 0.011675781673855253,\n", + " 'tokens_per_second': 10.086440348223903})" ] }, "execution_count": 4, @@ -117,17 +130,20 @@ "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "host='0.0.0.0' port='8001' url=None username=None password=None\n", - "Connected to LLMStudio Proxy @ 0.0.0.0:8001\n" + "ename": "ModuleNotFoundError", + "evalue": "No module named 'llmstudio_proxyprovider'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[6], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# You can set OPENAI_API_KEY and ANTHROPIC_API_KEY on .env file\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mllmstudio_proxyprovider\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ProxyConfig\n\u001b[1;32m 3\u001b[0m proxy \u001b[38;5;241m=\u001b[39m ProxyConfig(host\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m0.0.0.0\u001b[39m\u001b[38;5;124m\"\u001b[39m, port\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m8001\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28mprint\u001b[39m(proxy)\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'llmstudio_proxyprovider'" ] } ], "source": [ "# You can set OPENAI_API_KEY and ANTHROPIC_API_KEY on .env file\n", - "from llmstudio.engine.provider import ProxyConfig\n", + "from llmstudio_proxyprovider import ProxyConfig\n", "proxy = ProxyConfig(host=\"0.0.0.0\", port=\"8001\")\n", "print(proxy)\n", "\n", diff --git a/examples/llm_proxy.py b/examples/llm_proxy.py index 93485425..cd67b74f 100644 --- a/examples/llm_proxy.py +++ b/examples/llm_proxy.py @@ -1,7 +1,7 @@ from llmstudio.server import start_server start_server() -from llmstudio.engine.provider import LLMProxyProvider +from llmstudio_proxy.provider import LLMProxyProvider llm = LLMProxyProvider(provider="openai", host="0.0.0.0", port="8001") diff --git a/libs/core/llmstudio_core/__init__.py b/libs/core/llmstudio_core/__init__.py index e7fc2642..0c28cd45 100644 --- a/libs/core/llmstudio_core/__init__.py +++ b/libs/core/llmstudio_core/__init__.py @@ -2,9 +2,9 @@ from typing import Optional from llmstudio_core.providers.provider import BaseProvider, provider_registry -from llmstudio_core.providers import _load_engine_config +from llmstudio_core.providers import _load_providers_config -_engine_config = _load_engine_config() +_engine_config = _load_providers_config() def LLMCore(provider: str, api_key: Optional[str] = None, **kwargs) -> BaseProvider: diff --git a/libs/core/llmstudio_core/providers/__init__.py b/libs/core/llmstudio_core/providers/__init__.py index 4d42f9e7..4834a00b 100644 --- a/libs/core/llmstudio_core/providers/__init__.py +++ b/libs/core/llmstudio_core/providers/__init__.py @@ -38,7 +38,7 @@ class EngineConfig(BaseModel): providers: Dict[str, ProviderConfig] -def _load_engine_config() -> EngineConfig: +def _load_providers_config() -> EngineConfig: #TODO read from github default_config_path = Path(os.path.join(os.path.dirname(__file__), "config.yaml")) local_config_path = Path(os.getcwd(), "config.yaml") diff --git a/libs/core/poetry.lock b/libs/core/poetry.lock deleted file mode 100644 index e12e04a1..00000000 --- a/libs/core/poetry.lock +++ /dev/null @@ -1,1837 +0,0 @@ -# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. - 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We suggest that you create and activate a new environment using `conda` + +```bash +pip install llmstudio +``` + +Install `bun` if you want to use the UI + +```bash +curl -fsSL https://bun.sh/install | bash +``` + +Create a `.env` file at the same path you'll run **LLMstudio** + +```bash +OPENAI_API_KEY="sk-api_key" +ANTHROPIC_API_KEY="sk-api_key" +``` + +Now you should be able to run **LLMstudio** using the following command. + +```bash +llmstudio server --ui +``` + +When the `--ui` flag is set, you'll be able to access the UI at [http://localhost:3000](http://localhost:3000) + +## 📖 Documentation + +- [Visit our docs to learn how the SDK works](https://docs.LLMstudio.ai) (coming soon) +- Checkout our [notebook examples](https://github.com/TensorOpsAI/LLMstudio/tree/main/examples) to follow along with interactive tutorials + +## 👨‍💻 Contributing + +- Head on to our [Contribution Guide](https://github.com/TensorOpsAI/LLMstudio/tree/main/CONTRIBUTING.md) to see how you can help LLMstudio. +- Join our [Discord](https://discord.gg/GkAfPZR9wy) to talk with other LLMstudio enthusiasts. + +## Training + +[![Banner](https://imgur.com/XTRFZ4m.png)](https://www.tensorops.ai/llm-studio-workshop) + +--- + +Thank you for choosing LLMstudio. Your journey to perfecting AI interactions starts here. diff --git a/libs/llmstudio/llmstudio/__init__.py b/libs/llmstudio/llmstudio/__init__.py index e69de29b..8dc4ba24 100644 --- a/libs/llmstudio/llmstudio/__init__.py +++ b/libs/llmstudio/llmstudio/__init__.py @@ -0,0 +1 @@ +from llmstudio.llm import LLM diff --git a/llmstudio/cli.py b/libs/llmstudio/llmstudio/cli.py similarity index 85% rename from llmstudio/cli.py rename to libs/llmstudio/llmstudio/cli.py index f0c4c7da..3468f778 100644 --- a/llmstudio/cli.py +++ b/libs/llmstudio/llmstudio/cli.py @@ -18,11 +18,11 @@ def main(): @main.command() -@click.option("--ui", is_flag=True, help="Start the UI server.") -def server(ui): +@click.option(help="Start the server.") +def server(): signal.signal(signal.SIGINT, handle_shutdown) - start_server(ui=ui) + start_server() print("Servers are running. Press CTRL+C to stop.") diff --git a/llmstudio/client.py b/libs/llmstudio/llmstudio/client.py similarity index 100% rename from llmstudio/client.py rename to libs/llmstudio/llmstudio/client.py diff --git a/llmstudio/config.py b/libs/llmstudio/llmstudio/config.py similarity index 100% rename from llmstudio/config.py rename to libs/llmstudio/llmstudio/config.py diff --git a/llmstudio/llm/langchain.py b/libs/llmstudio/llmstudio/langchain.py similarity index 100% rename from llmstudio/llm/langchain.py rename to libs/llmstudio/llmstudio/langchain.py diff --git a/llmstudio/llm/__init__.py b/libs/llmstudio/llmstudio/llm/__init__.py similarity index 94% rename from llmstudio/llm/__init__.py rename to libs/llmstudio/llmstudio/llm/__init__.py index cd912e55..6d25a1d2 100644 --- a/llmstudio/llm/__init__.py +++ b/libs/llmstudio/llmstudio/llm/__init__.py @@ -4,9 +4,9 @@ from openai.types.chat import ChatCompletion, ChatCompletionChunk from pydantic import BaseModel -from llmstudio.engine.provider import LLMProxyProvider, ProxyConfig -from llmstudio.tracking.database import create_tracking_engine -from llmstudio.tracking.logs import crud, schemas +from llmstudio_proxy.provider import LLMProxyProvider, ProxyConfig +from llmstudio_tracker.database import create_tracking_engine +from llmstudio_tracker.logs import crud, schemas from sqlalchemy.orm import sessionmaker diff --git a/libs/llmstudio/llmstudio/server.py b/libs/llmstudio/llmstudio/server.py new file mode 100644 index 00000000..f4b6a4b9 --- /dev/null +++ b/libs/llmstudio/llmstudio/server.py @@ -0,0 +1,96 @@ +import threading +from threading import Event + +import requests + +from llmstudio_proxy.config import ( + ENGINE_HOST, + ENGINE_PORT, +) + +from llmstudio_tracker.config import ( + TRACKING_HOST, + TRACKING_PORT, +) + +from llmstudio_proxy import run_proxy_app +from llmstudio_tracker import run_tracker_app + +_proxy_started, _tracker_started = False, False + + +def is_server_running(host, port, path="/health"): + try: + response = requests.get(f"http://{host}:{port}{path}") + if response.status_code == 200 and response.json().get("status") == "healthy": + return True + except requests.ConnectionError: + pass + return False + + +def start_server_component(host, port, run_func, server_name): + if not is_server_running(host, port): + started_event = Event() + thread = threading.Thread(target=run_func, daemon=True, args=(started_event,)) + thread.start() + started_event.wait() # wait for startup, this assumes the event is set somewhere + return thread + else: + print(f"{server_name} server already running on {host}:{port}") + return None + + +def setup_servers(proxy, tracking): + global _servers_started + proxy_thread, tracking_thread = None, None + if _servers_started: + return proxy_thread, tracking_thread + + if proxy: + proxy_thread = start_server_component( + ENGINE_HOST, ENGINE_PORT, run_proxy_app, "Proxy" + ) + + if tracking: + tracking_thread = start_server_component( + TRACKING_HOST, TRACKING_PORT, run_tracking_app, "Tracker" + ) + + _servers_started = True + return proxy_thread, tracking_thread + +def setup_proxy(proxy_host, proxy_port): + global _proxy_started + proxy_thread = None + if _proxy_started: + return proxy_thread + + proxy_thread = start_server_component( + proxy_host, proxy_port, run_proxy_app, "proxy" + ) + + _proxy_started = True + return proxy_thread + +def setup_tracker(tracker_host, tracker_port): + global _tracker_started + tracker_thread = None + if _tracker_started: + return tracker_thread + + tracker_thread = start_server_component( + tracker_host, tracker_port, run_tracker_app, "Tracker" + ) + + _tracker_started = True + return tracker_thread + + +def start_servers(proxy=True, tracker=True): + global _proxy_started, _tracker_started + if proxy and not _proxy_started: + setup_proxy(ENGINE_HOST, ENGINE_PORT) + + if tracker and not _tracker_started: + setup_tracker(TRACKING_HOST, TRACKING_PORT) diff --git a/poetry.lock 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Lemos "] +license = "MIT" +homepage = "https://llmstudio.ai/" +repository = "https://github.com/tensoropsai/llmstudio" +documentation = "https://docs.llmstudio.ai" readme = "README.md" +keywords = ["ml", "ai", "llm", "llmops", "openai", "langchain", "chatgpt", "llmstudio", "tensorops"] + +[tool.poetry.urls] +"Bug Tracker" = "https://github.com/tensoropsai/llmstudio/issues" [tool.poetry.dependencies] -python = "^3.12" +python = "^3.9" +pydantic = "^2.5" +requests = "^2.31" +fastapi = "^0.109" +uvicorn = "^0.27" +openai = "^1.26" +tiktoken = "^0.7" +python-dotenv = "^0" +langchain-experimental = "^0.0" +llmstudio-core = { path = "../core/", develop = true } +llmstudio-tracker = { path = "../tracker/", develop = true } +llmstudio-proxy = { path = "../proxy/", develop = true } +[tool.poetry.scripts] +llmstudio = "llmstudio.cli:main" [build-system] -requires = ["poetry-core"] +requires = ["poetry-core>=1.0.0"] build-backend = "poetry.core.masonry.api" diff --git a/libs/proxy/README.md b/libs/proxy/README.md new file mode 100644 index 00000000..11fc5a66 --- /dev/null +++ b/libs/proxy/README.md @@ -0,0 +1,70 @@ +# LLMstudio by [TensorOps](http://tensorops.ai "TensorOps") + +Prompt Engineering at your fingertips + +![LLMstudio logo](https://imgur.com/Xqsj6V2.gif) + +## 🌟 Features + +![LLMstudio UI](https://imgur.com/wrwiIUs.png) + +- **LLM Proxy Access**: Seamless access to all the latest LLMs by OpenAI, Anthropic, Google. +- **Custom and Local LLM Support**: Use custom or local open-source LLMs through Ollama. +- **Prompt Playground UI**: A user-friendly interface for engineering and fine-tuning your prompts. +- **Python SDK**: Easily integrate LLMstudio into your existing workflows. +- **Monitoring and Logging**: Keep track of your usage and performance for all requests. +- **LangChain Integration**: LLMstudio integrates with your already existing LangChain projects. +- **Batch Calling**: Send multiple requests at once for improved efficiency. +- **Smart Routing and Fallback**: Ensure 24/7 availability by routing your requests to trusted LLMs. +- **Type Casting (soon)**: Convert data types as needed for your specific use case. + +## 🚀 Quickstart + +Don't forget to check out [https://docs.llmstudio.ai](docs) page. + +## Installation + +Install the latest version of **LLMstudio** using `pip`. We suggest that you create and activate a new environment using `conda` + +```bash +pip install llmstudio +``` + +Install `bun` if you want to use the UI + +```bash +curl -fsSL https://bun.sh/install | bash +``` + +Create a `.env` file at the same path you'll run **LLMstudio** + +```bash +OPENAI_API_KEY="sk-api_key" +ANTHROPIC_API_KEY="sk-api_key" +``` + +Now you should be able to run **LLMstudio** using the following command. + +```bash +llmstudio server --ui +``` + +When the `--ui` flag is set, you'll be able to access the UI at [http://localhost:3000](http://localhost:3000) + +## 📖 Documentation + +- [Visit our docs to learn how the SDK works](https://docs.LLMstudio.ai) (coming soon) +- Checkout our [notebook examples](https://github.com/TensorOpsAI/LLMstudio/tree/main/examples) to follow along with interactive tutorials + +## 👨‍💻 Contributing + +- Head on to our [Contribution Guide](https://github.com/TensorOpsAI/LLMstudio/tree/main/CONTRIBUTING.md) to see how you can help LLMstudio. +- Join our [Discord](https://discord.gg/GkAfPZR9wy) to talk with other LLMstudio enthusiasts. + +## Training + +[![Banner](https://imgur.com/XTRFZ4m.png)](https://www.tensorops.ai/llm-studio-workshop) + +--- + +Thank you for choosing LLMstudio. Your journey to perfecting AI interactions starts here. diff --git a/llmstudio/engine/__init__.py b/libs/proxy/llmstudio_proxy/__init__.py similarity index 87% rename from llmstudio/engine/__init__.py rename to libs/proxy/llmstudio_proxy/__init__.py index 23cb0aaf..487ba22e 100644 --- a/llmstudio/engine/__init__.py +++ b/libs/proxy/llmstudio_proxy/__init__.py @@ -1,23 +1,21 @@ import json -import os from pathlib import Path from threading import Event from typing import Any, Dict, List, Optional, Union import uvicorn -import yaml from fastapi import FastAPI, Request from fastapi.middleware.cors import CORSMiddleware from fastapi.responses import StreamingResponse from pydantic import BaseModel -from llmstudio.config import ENGINE_HOST, ENGINE_PORT -from llmstudio_core.providers import _load_engine_config +from llmstudio_proxy.config import ENGINE_HOST, ENGINE_PORT +from llmstudio_core.providers import _load_providers_config from llmstudio_core.providers.provider import provider_registry -ENGINE_BASE_ENDPOINT = "/api/engine" +ENGINE_BASE_ENDPOINT = "/api/proxy" ENGINE_HEALTH_ENDPOINT = "/health" -ENGINE_TITLE = "LLMstudio Engine API" +ENGINE_TITLE = "LLMstudio Proxy API" ENGINE_DESCRIPTION = "The core API for LLM interactions" ENGINE_VERSION = "0.1.0" @@ -44,12 +42,12 @@ class ProviderConfig(BaseModel): parameters: Optional[Dict[str, Any]] = None -class EngineConfig(BaseModel): +class ProxyConfig(BaseModel): providers: Dict[str, ProviderConfig] -def create_engine_app( - started_event: Event, config: EngineConfig = _load_engine_config() +def create_proxy_app( + started_event: Event, config: ProxyConfig = _load_providers_config() ) -> FastAPI: app = FastAPI( title=ENGINE_TITLE, @@ -68,7 +66,7 @@ def create_engine_app( @app.get(ENGINE_HEALTH_ENDPOINT) def health_check(): """Health check endpoint to ensure the API is running.""" - return {"status": "healthy", "message": "Engine is up and running"} + return {"status": "healthy", "message": "Proxy is up and running"} @app.get(f"{ENGINE_BASE_ENDPOINT}/providers") def get_providers(): @@ -143,19 +141,19 @@ async def export(request: Request): @app.on_event("startup") async def startup_event(): started_event.set() - print(f"Running LLMstudio Engine on http://{ENGINE_HOST}:{ENGINE_PORT} ") + print(f"Running LLMstudio Proxy on http://{ENGINE_HOST}:{ENGINE_PORT} ") return app -def run_engine_app(started_event: Event): +def run_proxy_app(started_event: Event): try: - engine = create_engine_app(started_event) + proxy = create_proxy_app(started_event) uvicorn.run( - engine, + proxy, host=ENGINE_HOST, port=ENGINE_PORT, log_level="warning", ) except Exception as e: - print(f"Error running LLMstudio Engine: {e}") + print(f"Error running LLMstudio Proxy: {e}") diff --git a/libs/proxy/llmstudio_proxy/config.py b/libs/proxy/llmstudio_proxy/config.py new file mode 100644 index 00000000..9cb813f2 --- /dev/null +++ b/libs/proxy/llmstudio_proxy/config.py @@ -0,0 +1,32 @@ +import os +import socket + +from dotenv import load_dotenv + +load_dotenv(os.path.join(os.getcwd(), ".env")) + + +def assign_port(default_port=None): + with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: + try: + if default_port is not None: + s.bind(("", default_port)) + return default_port + else: + s.bind(("", 0)) + return s.getsockname()[1] + except OSError: + s.bind(("", 0)) + return s.getsockname()[1] + + +defaults = { + "LLMSTUDIO_ENGINE_HOST": "localhost", + "LLMSTUDIO_ENGINE_PORT": str(assign_port(50001)), +} + +for key, default in defaults.items(): + os.environ[key] = os.getenv(key, default) + +ENGINE_HOST = os.environ["LLMSTUDIO_ENGINE_HOST"] +ENGINE_PORT = os.environ["LLMSTUDIO_ENGINE_PORT"] diff --git a/llmstudio/engine/provider.py b/libs/proxy/llmstudio_proxy/provider.py similarity index 57% rename from llmstudio/engine/provider.py rename to libs/proxy/llmstudio_proxy/provider.py index 2dec16e4..dabbe53a 100644 --- a/llmstudio/engine/provider.py +++ b/libs/proxy/llmstudio_proxy/provider.py @@ -9,8 +9,6 @@ from openai.types.chat import ChatCompletion, ChatCompletionChunk from tqdm.asyncio import tqdm_asyncio -from llmstudio.llm.semaphore import DynamicSemaphore - class ProxyConfig(BaseModel): host: Optional[str] = None @@ -91,119 +89,6 @@ async def achat(self, chat_input: Any, retries=retries, parameters=parameters) - async def chat_coroutine( - self, - input: Union[str, List[Dict[str, str]]], - semaphore, - retries, - error_threshold, - increment, - verbose, - ): - - async with semaphore: - try: - response = await self.async_non_stream( - input, max_tokens=semaphore.get_max_tokens(), retries=retries - ) - semaphore.update_computed_max_tokens(response.metrics["total_tokens"]) - return response - except Exception as e: - semaphore.error_requests += 1 - semaphore.error_requests_since_last_increase += 1 - return e - finally: - semaphore.finished_requests += 1 - semaphore.requests_since_last_increase += 1 - semaphore.try_increase_permits(error_threshold, increment) - if verbose > 0: - print( - f"Finished requests: {semaphore.finished_requests}/{semaphore.batch_size}" - ) - print( - f"Amount of parallel requests being allowed: {semaphore._permits}" - ) - print(f"Max tokens being used: {semaphore.get_max_tokens()}") - print( - f"Requests finished with an error: {semaphore.error_requests}" - ) - - async def batch_chat_coroutine( - self, - inputs: List[Union[str, List[Dict[str, str]]]], - coroutines, - retries, - error_threshold, - increment, - max_tokens, - verbose, - ) -> List[str]: - - semaphore = DynamicSemaphore( - coroutines, len(inputs), given_max_tokens=max_tokens - ) - - if verbose > 0: - responses = await asyncio.gather( - *[ - self.chat_coroutine( - input=input, - semaphore=semaphore, - retries=retries, - error_threshold=error_threshold, - increment=increment, - verbose=verbose, - ) - for input in inputs - ], - ) - return responses - else: - responses = await tqdm_asyncio.gather( - *[ - self.chat_coroutine( - input=input, - semaphore=semaphore, - retries=retries, - error_threshold=error_threshold, - increment=increment, - verbose=verbose, - ) - for input in inputs - ], - desc="Getting chat responses: ", - ) - return responses - - def batch_chat( - self, - inputs: List[Union[str, List[Dict[str, str]]]], - coroutines: int = 20, - retries: int = 5, - error_threshold: int = 5, - increment: int = 5, - max_tokens=None, - verbose=0, - ) -> List[str]: - - if coroutines > len(inputs): - raise Exception( - "num_coroutines can not be higher than the amount of inputs." - ) - - responses = asyncio.run( - self.batch_chat_coroutine( - inputs, - coroutines, - retries, - error_threshold, - increment, - max_tokens, - verbose, - ) - ) - return responses - async def async_non_stream(self, model:str, chat_input: str, retries: int, parameters, **kwargs): response = await asyncio.to_thread( requests.post, diff --git a/libs/proxy/pyproject.toml b/libs/proxy/pyproject.toml index f8c01068..d1f44ff3 100644 --- a/libs/proxy/pyproject.toml +++ b/libs/proxy/pyproject.toml @@ -6,8 +6,15 @@ authors = ["Diogo Goncalves "] readme = "README.md" [tool.poetry.dependencies] -python = "^3.12" - +python = "^3.9" +pydantic = "^2.5" +requests = "^2.31" +fastapi = "^0.109" +uvicorn = "^0.27" +tiktoken = "^0.7" +python-dotenv = "^0" +llmstudio-core = { path = "../core/", develop = true } +llmstudio-tracker = { path = "../tracker/", develop = true } [build-system] requires = ["poetry-core"] diff --git a/libs/tracker/README.md b/libs/tracker/README.md new file mode 100644 index 00000000..11fc5a66 --- /dev/null +++ b/libs/tracker/README.md @@ -0,0 +1,70 @@ +# LLMstudio by [TensorOps](http://tensorops.ai "TensorOps") + +Prompt Engineering at your fingertips + +![LLMstudio logo](https://imgur.com/Xqsj6V2.gif) + +## 🌟 Features + +![LLMstudio UI](https://imgur.com/wrwiIUs.png) + +- **LLM Proxy Access**: Seamless access to all the latest LLMs by OpenAI, Anthropic, Google. +- **Custom and Local LLM Support**: Use custom or local open-source LLMs through Ollama. +- **Prompt Playground UI**: A user-friendly interface for engineering and fine-tuning your prompts. +- **Python SDK**: Easily integrate LLMstudio into your existing workflows. +- **Monitoring and Logging**: Keep track of your usage and performance for all requests. +- **LangChain Integration**: LLMstudio integrates with your already existing LangChain projects. +- **Batch Calling**: Send multiple requests at once for improved efficiency. +- **Smart Routing and Fallback**: Ensure 24/7 availability by routing your requests to trusted LLMs. +- **Type Casting (soon)**: Convert data types as needed for your specific use case. + +## 🚀 Quickstart + +Don't forget to check out [https://docs.llmstudio.ai](docs) page. + +## Installation + +Install the latest version of **LLMstudio** using `pip`. We suggest that you create and activate a new environment using `conda` + +```bash +pip install llmstudio +``` + +Install `bun` if you want to use the UI + +```bash +curl -fsSL https://bun.sh/install | bash +``` + +Create a `.env` file at the same path you'll run **LLMstudio** + +```bash +OPENAI_API_KEY="sk-api_key" +ANTHROPIC_API_KEY="sk-api_key" +``` + +Now you should be able to run **LLMstudio** using the following command. + +```bash +llmstudio server --ui +``` + +When the `--ui` flag is set, you'll be able to access the UI at [http://localhost:3000](http://localhost:3000) + +## 📖 Documentation + +- [Visit our docs to learn how the SDK works](https://docs.LLMstudio.ai) (coming soon) +- Checkout our [notebook examples](https://github.com/TensorOpsAI/LLMstudio/tree/main/examples) to follow along with interactive tutorials + +## 👨‍💻 Contributing + +- Head on to our [Contribution Guide](https://github.com/TensorOpsAI/LLMstudio/tree/main/CONTRIBUTING.md) to see how you can help LLMstudio. +- Join our [Discord](https://discord.gg/GkAfPZR9wy) to talk with other LLMstudio enthusiasts. + +## Training + +[![Banner](https://imgur.com/XTRFZ4m.png)](https://www.tensorops.ai/llm-studio-workshop) + +--- + +Thank you for choosing LLMstudio. Your journey to perfecting AI interactions starts here. diff --git a/llmstudio/tracking/__init__.py b/libs/tracker/llmstudio_tracker/__init__.py similarity index 89% rename from llmstudio/tracking/__init__.py rename to libs/tracker/llmstudio_tracker/__init__.py index bbd05232..5324a55f 100644 --- a/llmstudio/tracking/__init__.py +++ b/libs/tracker/llmstudio_tracker/__init__.py @@ -5,8 +5,8 @@ from fastapi.middleware.cors import CORSMiddleware from llmstudio.config import TRACKING_HOST, TRACKING_PORT -from llmstudio.tracking.logs.endpoints import LogsRoutes -from llmstudio.tracking.session.endpoints import SessionsRoutes +from llmstudio_tracker.logs.endpoints import LogsRoutes +from llmstudio_tracker.session.endpoints import SessionsRoutes TRACKING_HEALTH_ENDPOINT = "/health" TRACKING_TITLE = "LLMstudio Tracking API" @@ -50,7 +50,7 @@ async def startup_event(): return app -def run_tracking_app(started_event: Event): +def run_tracker_app(started_event: Event): try: tracking = create_tracking_app(started_event) uvicorn.run( @@ -64,4 +64,4 @@ def run_tracking_app(started_event: Event): if __name__ == "__main__": - run_tracking_app() + run_tracker_app() diff --git a/libs/tracker/llmstudio_tracker/config.py b/libs/tracker/llmstudio_tracker/config.py new file mode 100644 index 00000000..c03df0cd --- /dev/null +++ b/libs/tracker/llmstudio_tracker/config.py @@ -0,0 +1,32 @@ +import os +import socket + +from dotenv import load_dotenv + +load_dotenv(os.path.join(os.getcwd(), ".env")) + + +def assign_port(default_port=None): + with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: + try: + if default_port is not None: + s.bind(("", default_port)) + return default_port + else: + s.bind(("", 0)) + return s.getsockname()[1] + except OSError: + s.bind(("", 0)) + return s.getsockname()[1] + + +defaults = { + "LLMSTUDIO_TRACKING_HOST": "localhost", + "LLMSTUDIO_TRACKING_PORT": str(assign_port(50002)), +} + +for key, default in defaults.items(): + os.environ[key] = os.getenv(key, default) + +TRACKING_HOST = os.environ["LLMSTUDIO_TRACKING_HOST"] +TRACKING_PORT = os.environ["LLMSTUDIO_TRACKING_PORT"] diff --git a/llmstudio/tracking/database.py b/libs/tracker/llmstudio_tracker/database.py similarity index 100% rename from llmstudio/tracking/database.py rename to libs/tracker/llmstudio_tracker/database.py diff --git a/llmstudio/tracking/logs/__init__.py b/libs/tracker/llmstudio_tracker/logs/__init__.py similarity index 100% rename from llmstudio/tracking/logs/__init__.py rename to libs/tracker/llmstudio_tracker/logs/__init__.py diff --git a/llmstudio/tracking/logs/crud.py b/libs/tracker/llmstudio_tracker/logs/crud.py similarity index 93% rename from llmstudio/tracking/logs/crud.py rename to libs/tracker/llmstudio_tracker/logs/crud.py index ae899095..b16a2f5d 100644 --- a/llmstudio/tracking/logs/crud.py +++ b/libs/tracker/llmstudio_tracker/logs/crud.py @@ -1,6 +1,6 @@ from sqlalchemy.orm import Session -from llmstudio.tracking.logs import models, schemas +from llmstudio_tracker.logs import models, schemas def get_project_by_name(db: Session, name: str): diff --git a/llmstudio/tracking/logs/endpoints.py b/libs/tracker/llmstudio_tracker/logs/endpoints.py similarity index 92% rename from llmstudio/tracking/logs/endpoints.py rename to libs/tracker/llmstudio_tracker/logs/endpoints.py index ccb40714..dd8b65a3 100644 --- a/llmstudio/tracking/logs/endpoints.py +++ b/libs/tracker/llmstudio_tracker/logs/endpoints.py @@ -3,8 +3,8 @@ from fastapi import APIRouter, Depends from sqlalchemy.orm import Session -from llmstudio.tracking.database import engine, get_db -from llmstudio.tracking.logs import crud, models, schemas +from llmstudio_tracker.database import engine, get_db +from llmstudio_tracker.logs import crud, models, schemas models.Base.metadata.create_all(bind=engine) diff --git a/llmstudio/tracking/logs/models.py b/libs/tracker/llmstudio_tracker/logs/models.py similarity index 92% rename from llmstudio/tracking/logs/models.py rename to libs/tracker/llmstudio_tracker/logs/models.py index a24ba7b8..5ed5873c 100644 --- a/llmstudio/tracking/logs/models.py +++ b/libs/tracker/llmstudio_tracker/logs/models.py @@ -1,7 +1,7 @@ from sqlalchemy import JSON, Column, DateTime, Integer, String from sqlalchemy.sql import func -from llmstudio.tracking.database import Base +from llmstudio_tracker.database import Base class LogDefault(Base): diff --git a/llmstudio/tracking/logs/schemas.py b/libs/tracker/llmstudio_tracker/logs/schemas.py similarity index 100% rename from llmstudio/tracking/logs/schemas.py rename to libs/tracker/llmstudio_tracker/logs/schemas.py diff --git a/llmstudio/tracking/session/__init__.py b/libs/tracker/llmstudio_tracker/session/__init__.py similarity index 100% rename from llmstudio/tracking/session/__init__.py rename to libs/tracker/llmstudio_tracker/session/__init__.py diff --git a/llmstudio/tracking/session/crud.py b/libs/tracker/llmstudio_tracker/session/crud.py similarity index 95% rename from llmstudio/tracking/session/crud.py rename to libs/tracker/llmstudio_tracker/session/crud.py index f085a8ed..a5f6592e 100644 --- a/llmstudio/tracking/session/crud.py +++ b/libs/tracker/llmstudio_tracker/session/crud.py @@ -1,6 +1,6 @@ from sqlalchemy.orm import Session -from llmstudio.tracking.session import models, schemas +from llmstudio_tracker.session import models, schemas def get_project_by_name(db: Session, name: str): diff --git a/llmstudio/tracking/session/endpoints.py b/libs/tracker/llmstudio_tracker/session/endpoints.py similarity index 91% rename from llmstudio/tracking/session/endpoints.py rename to libs/tracker/llmstudio_tracker/session/endpoints.py index 993dbd8e..bbb25317 100644 --- a/llmstudio/tracking/session/endpoints.py +++ b/libs/tracker/llmstudio_tracker/session/endpoints.py @@ -3,8 +3,8 @@ from fastapi import APIRouter, Depends from sqlalchemy.orm import Session -from llmstudio.tracking.database import engine, get_db -from llmstudio.tracking.session import crud, models, schemas +from llmstudio_tracker.database import engine, get_db +from llmstudio_tracker.session import crud, models, schemas models.Base.metadata.create_all(bind=engine) diff --git a/llmstudio/tracking/session/models.py b/libs/tracker/llmstudio_tracker/session/models.py similarity index 91% rename from llmstudio/tracking/session/models.py rename to libs/tracker/llmstudio_tracker/session/models.py index 39285c5c..0fe8ddc5 100644 --- a/llmstudio/tracking/session/models.py +++ b/libs/tracker/llmstudio_tracker/session/models.py @@ -1,7 +1,7 @@ from sqlalchemy import JSON, Column, DateTime, Integer, String from sqlalchemy.sql import func -from llmstudio.tracking.database import Base +from llmstudio_tracker.database import Base class SessionDefault(Base): diff --git a/llmstudio/tracking/session/schemas.py b/libs/tracker/llmstudio_tracker/session/schemas.py similarity index 100% rename from llmstudio/tracking/session/schemas.py rename to libs/tracker/llmstudio_tracker/session/schemas.py diff --git a/llmstudio/tracking/tracker.py b/libs/tracker/llmstudio_tracker/tracker.py similarity index 100% rename from llmstudio/tracking/tracker.py rename to libs/tracker/llmstudio_tracker/tracker.py diff --git a/libs/tracker/pyproject.toml b/libs/tracker/pyproject.toml index 1e1d869b..9e050652 100644 --- a/libs/tracker/pyproject.toml +++ b/libs/tracker/pyproject.toml @@ -6,7 +6,7 @@ authors = ["Diogo Goncalves "] readme = "README.md" [tool.poetry.dependencies] -python = "^3.12" +python = "^3.9" pydantic = "^2.5" sqlalchemy = "^2" diff --git a/llmstudio/__init__.py b/llmstudio/__init__.py deleted file mode 100644 index 77af7b28..00000000 --- a/llmstudio/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from llmstudio.llm import LLM -from llmstudio.llm.parser import Parser -from llmstudio.llm.semaphore import DynamicSemaphore diff --git a/llmstudio/engine/config.yaml b/llmstudio/engine/config.yaml deleted file mode 100644 index ae82394b..00000000 --- a/llmstudio/engine/config.yaml +++ /dev/null @@ -1,374 +0,0 @@ -providers: - anthropic: - id: anthropic - name: Anthropic - chat: true - embed: true - keys: - - ANTHROPIC_API_KEY - models: - claude-3-opus-20240229: - mode: chat - max_tokens: 200000 - input_token_cost: 0.000015 - output_token_cost: 0.000075 - claude-3-sonnet-2024022: - mode: chat - max_tokens: 200000 - input_token_cost: 0.000003 - output_token_cost: 0.000015 - claude-3-haiku-20240307: - mode: chat - max_tokens: 200000 - input_token_cost: 0.00000025 - output_token_cost: 0.00000125 - claude-2.1: - mode: chat - max_tokens: 200000 - input_token_cost: 0.000008 - output_token_cost: 0.000024 - claude-2: - mode: chat - max_tokens: 100000 - input_token_cost: 0.000008 - output_token_cost: 0.000024 - claude-instant-1.2: - mode: chat - max_tokens: 100000 - input_token_cost: 0.00000163 - output_token_cost: 0.00000551 - parameters: - temperature: - name: "Temperature" - type: float - default: 1 - min: 0 - max: 1 - step: 0.01 - max_tokens: - name: "Maximum tokens" - type: float - default: 256 - min: 1 - max: 4096 - step: 0.01 - top_p: - name: "Top P" - type: float - default: 1 - min: 0 - max: 1 - step: 0.01 - top_k: - name: "Top K" - type: float - default: 5 - min: 0 - max: 500 - step: 1 - ollama: - id: ollama - name: Ollama - chat: true - embed: true - keys: - models: - llama2: - mode: chat - max_tokens: 0 - input_token_cost: 0 - output_token_cost: 0 - parameters: - temperature: - name: "Temperature" - type: float - default: 1 - min: 0 - max: 1 - step: 0.01 - max_tokens: - name: "Maximum tokens" - type: float - default: 256 - min: 1 - max: 4096 - step: 0.01 - top_p: - name: "Top P" - type: float - default: 1 - min: 0 - max: 1 - step: 0.01 - top_k: - name: "Top K" - type: float - default: 5 - min: 0 - max: 500 - step: 1 - openai: - id: openai - name: OpenAI - chat: true - embed: true - keys: - - OPENAI_API_KEY - models: - gpt-4o-mini: - mode: chat - max_tokens: 128000 - input_token_cost: 0.00000015 - output_token_cost: 0.00000060 - gpt-4o: - mode: chat - max_tokens: 128000 - input_token_cost: 0.000005 - output_token_cost: 0.000015 - gpt-4-turbo: - mode: chat - max_tokens: 128000 - input_token_cost: 0.00001 - output_token_cost: 0.00003 - gpt-4: - mode: chat - max_tokens: 8192 - input_token_cost: 0.00003 - output_token_cost: 0.00006 - gpt-3.5-turbo: - mode: chat - max_tokens: 16385 - input_token_cost: 0.0000005 - output_token_cost: 0.0000015 - gpt-3.5-turbo-instruct: - mode: chat - max_tokens: 4096 - input_token_cost: 0.0000015 - output_token_cost: 0.000002 - parameters: - temperature: - name: "Temperature" - type: float - default: 1 - min: 0 - max: 2 - step: 0.01 - max_tokens: - name: "Maximum length" - type: int - default: 256 - min: 1 - max: 4096 - step: 1 - top_p: - name: "Top P" - type: float - default: 1 - min: 0 - max: 1 - step: 0.01 - frequency_penalty: - name: "Frequency Penalty" - type: float - default: 0 - min: 0 - max: 2 - step: 0.01 - presence_penalty: - name: "Presence Penalty" - type: float - default: 0 - min: 0 - max: 2 - step: 0.01 - azure: - id: azure - name: Azure - chat: true - embed: true - keys: - - AZURE_API_KEY - - AZURE_API_ENDPOINT - - AZURE_API_VERSION - models: - gpt-4o-mini: - mode: chat - max_tokens: 128000 - input_token_cost: 0.00000015 - output_token_cost: 0.0000006 - gpt-4o: - mode: chat - max_tokens: 128000 - input_token_cost: 0.000005 - output_token_cost: 0.000015 - gpt-4-turbo: - mode: chat - max_tokens: 128000 - input_token_cost: 0.00001 - output_token_cost: 0.00003 - gpt-4: - mode: chat - max_tokens: 8192 - input_token_cost: 0.00003 - output_token_cost: 0.00006 - gpt-35-turbo: - mode: chat - max_tokens: 4097 - input_token_cost: 0.0000005 - output_token_cost: 0.0000015 - Meta-Llama-3.1-405B-Instruct: - mode: chat - max_tokens: 128000 - input_token_cost: 0.00533 - output_token_cost: 0.016 - Meta-Llama-3.1-70B-Instruct: - mode: chat - max_tokens: 128000 - input_token_cost: 0.00268 - output_token_cost: 0.00354 - Meta-Llama-3.1-8B-Instruct: - mode: chat - max_tokens: 128000 - input_token_cost: 0.0003 - output_token_cost: 0.00061 - Meta-Llama-3-70B-Instruct: - mode: chat - max_tokens: 8192 - input_token_cost: 0.00378 - output_token_cost: 0.01134 - Meta-Llama-3-8B-Instruct: - mode: chat - max_tokens: 8192 - input_token_cost: 0.00037 - output_token_cost: 0.0011 - Meta-Llama-2-70B: - mode: chat - max_tokens: 4096 - input_token_cost: 0.00154 - output_token_cost: 0.00177 - Meta-Llama-2-70B-Chat: - mode: chat - max_tokens: 4096 - input_token_cost: 0.00154 - output_token_cost: 0.00177 - Meta-Llama-2-13B-Chat: - mode: chat - max_tokens: 4096 - input_token_cost: 0.00081 - output_token_cost: 0.00094 - Meta-Llama-2-7B: - mode: chat - max_tokens: 4096 - input_token_cost: 0.00052 - output_token_cost: 0.00067 - parameters: - temperature: - name: "Temperature" - type: float - default: 1 - min: 0 - max: 2 - step: 0.01 - max_tokens: - name: "Maximum length" - type: int - default: 256 - min: 1 - max: 4096 - step: 1 - top_p: - name: "Top P" - type: float - default: 1 - min: 0 - max: 1 - step: 0.01 - frequency_penalty: - name: "Frequency Penalty" - type: float - default: 0 - min: 0 - max: 2 - step: 0.01 - presence_penalty: - name: "Presence Penalty" - type: float - default: 0 - min: 0 - max: 2 - step: 0.01 - vertexai: - id: vertexai - name: VertexAI - chat: true - embed: true - keys: - - GOOGLE_API_KEY - models: - gemini-1.5-flash: - mode: chat - max_tokens: 1000000 - input_token_cost: - - range: [0, 128000] - cost: 0.00000035 - - range: [128001, null] - cost: 0.0000007 - output_token_cost: - - range: [0, 128000] - cost: 0.00000105 - - range: [128001, null] - cost: 0.0000021 - gemini-1.5-pro-latest: - mode: chat - max_tokens: 1000000 - input_token_cost: - - range: [0, 128000] - cost: 0.00000035 - - range: [128001, null] - cost: 0.0000007 - output_token_cost: - - range: [0, 128000] - cost: 0.0000105 - - range: [128001, null] - cost: 0.000021 - gemini-1.0-pro: - mode: chat - max_tokens: 1000000 - input_token_cost: 0.0000005 - output_token_cost: 0.0000015 - parameters: - temperature: - name: "Temperature" - type: float - default: 1 - min: 0 - max: 2 - step: 0.01 - max_tokens: - name: "Maximum length" - type: int - default: 256 - min: 1 - max: 4096 - step: 1 - top_p: - name: "Top P" - type: float - default: 1 - min: 0 - max: 1 - step: 0.01 - frequency_penalty: - name: "Frequency Penalty" - type: float - default: 0 - min: 0 - max: 2 - step: 0.01 - presence_penalty: - name: "Presence Penalty" - type: float - default: 0 - min: 0 - max: 2 - step: 0.01 diff --git a/llmstudio/llm/parser.py b/llmstudio/llm/parser.py deleted file mode 100644 index 002430ac..00000000 --- a/llmstudio/llm/parser.py +++ /dev/null @@ -1,21 +0,0 @@ -import json - -from pydantic import BaseModel, ValidationError - - -class Parser: - def parse_response(self, json_str: str, response_model: BaseModel): - try: - start_index = json_str.find("{") - end_index = json_str.rfind("}") - trimmed_str = json_str[start_index : end_index + 1] - if start_index == -1 or end_index == -1: - raise ValueError("Failed to find JSON in the response.") - parsed_response = response_model.model_validate(json.loads(trimmed_str)) - return parsed_response - except json.JSONDecodeError as e: - raise ValueError( - f"Failed to decode JSON string: {str(e)}. JSON string: {trimmed_str}\n" - ) - except Exception as e: - return e diff --git a/llmstudio/llm/semaphore.py b/llmstudio/llm/semaphore.py deleted file mode 100644 index 3ff04418..00000000 --- a/llmstudio/llm/semaphore.py +++ /dev/null @@ -1,54 +0,0 @@ -import asyncio - - -class DynamicSemaphore: - def __init__(self, initial_permits, batch_size, given_max_tokens): - self.batch_size = batch_size - self.given_max_tokens = given_max_tokens - self.initial_permits = initial_permits - self._permits = initial_permits - self._semaphore = asyncio.Semaphore(initial_permits) - self.computed_max_tokens = 0 - self.requests_since_last_increase = 0 - self.error_requests_since_last_increase = 0 - self.finished_requests = 0 - self.error_requests = 0 - - def increase_permits(self, additional_permits): - for _ in range(additional_permits): - self._semaphore.release() - self._permits += additional_permits - - async def __aenter__(self): - await self._semaphore.acquire() - return self - - async def __aexit__(self, exc_type, exc, tb): - self._semaphore.release() - - def try_increase_permits(self, error_threshold, increment): - if ( - self.requests_since_last_increase >= self._permits - and self.error_requests_since_last_increase <= error_threshold - ): - self.increase_permits(increment) - self.requests_since_last_increase = 0 - self.error_requests_since_last_increase = 0 - - def update_computed_max_tokens(self, tokens): - if self.finished_requests < self.initial_permits: - self.computed_max_tokens = max(self.computed_max_tokens, tokens) - - def get_max_tokens(self): - - # If user provided max tokes, use that value - if self.given_max_tokens != None: - return self.given_max_tokens - - # If we are still computing max tokens, use models default value - elif self.finished_requests < self.initial_permits: - return 10 - - # If we finished computing max tokens, return that value - elif self.finished_requests >= self.initial_permits: - return int(self.computed_max_tokens + (self.computed_max_tokens * 0.10)) diff --git a/llmstudio/server.py b/llmstudio/server.py deleted file mode 100644 index c69a5b96..00000000 --- a/llmstudio/server.py +++ /dev/null @@ -1,69 +0,0 @@ -import threading -from threading import Event - -import requests - -from llmstudio.config import ( - ENGINE_HOST, - ENGINE_PORT, - TRACKING_HOST, - TRACKING_PORT, - UI_HOST, - UI_PORT, -) -from llmstudio.engine import run_engine_app -from llmstudio.tracking import run_tracking_app -from llmstudio.ui import run_ui_app - -_servers_started = False - - -def is_server_running(host, port, path="/health"): - try: - response = requests.get(f"http://{host}:{port}{path}") - if response.status_code == 200 and response.json().get("status") == "healthy": - return True - except requests.ConnectionError: - pass - return False - - -def start_server_component(host, port, run_func, server_name): - if not is_server_running(host, port): - started_event = Event() - thread = threading.Thread(target=run_func, daemon=True, args=(started_event,)) - thread.start() - started_event.wait() # wait for startup, this assumes the event is set somewhere - return thread - else: - print(f"{server_name} server already running on {host}:{port}") - return None - - -def setup_servers(engine, tracking, ui): - global _servers_started - engine_thread, tracking_thread, ui_thread = None, None, None - if _servers_started: - return engine_thread, tracking_thread, ui_thread - - if engine: - engine_thread = start_server_component( - ENGINE_HOST, ENGINE_PORT, run_engine_app, "Engine" - ) - - if tracking: - tracking_thread = start_server_component( - TRACKING_HOST, TRACKING_PORT, run_tracking_app, "Tracking" - ) - - if ui: - ui_thread = start_server_component(UI_HOST, UI_PORT, run_ui_app, "UI") - - _servers_started = True - return engine_thread, tracking_thread, ui_thread - - -def start_server(engine=True, tracking=True, ui=False): - global _servers_started - if not _servers_started: - setup_servers(engine, tracking, ui) diff --git a/llmstudio/ui/.eslintrc.json b/llmstudio/ui/.eslintrc.json deleted file mode 100644 index bffb357a..00000000 --- a/llmstudio/ui/.eslintrc.json +++ /dev/null @@ -1,3 +0,0 @@ -{ - "extends": "next/core-web-vitals" -} diff --git a/llmstudio/ui/.gitignore b/llmstudio/ui/.gitignore deleted file mode 100644 index fd3dbb57..00000000 --- a/llmstudio/ui/.gitignore +++ /dev/null @@ -1,36 +0,0 @@ -# See https://help.github.com/articles/ignoring-files/ for more about ignoring files. - -# dependencies -/node_modules -/.pnp -.pnp.js -.yarn/install-state.gz - -# testing -/coverage - -# next.js -/.next/ -/out/ - -# production -/build - -# misc -.DS_Store -*.pem - -# debug -npm-debug.log* -yarn-debug.log* -yarn-error.log* - -# local env files -.env*.local - -# vercel -.vercel - -# typescript -*.tsbuildinfo -next-env.d.ts diff --git a/llmstudio/ui/.prettierrc.json b/llmstudio/ui/.prettierrc.json deleted file mode 100644 index f5a575dc..00000000 --- a/llmstudio/ui/.prettierrc.json +++ /dev/null @@ -1,8 +0,0 @@ -{ - "trailingComma": "es5", - "semi": true, - "tabWidth": 2, - "singleQuote": true, - "jsxSingleQuote": true, - "plugins": ["prettier-plugin-tailwindcss"] -} diff --git a/llmstudio/ui/__init__.py b/llmstudio/ui/__init__.py deleted file mode 100644 index 1569aa6f..00000000 --- a/llmstudio/ui/__init__.py +++ /dev/null @@ -1,26 +0,0 @@ -import os -import subprocess -from pathlib import Path -import threading -from threading import Event - -from llmstudio.config import UI_PORT - - -def run_bun_in_thread(): - ui_dir = Path(os.path.join(os.path.dirname(__file__))) - try: - subprocess.run(["bun", "install", "--silent"], cwd=ui_dir, check=True) - subprocess.run( - ["bun", "dev", "--port", UI_PORT], - cwd=ui_dir, - check=True, - ) - except Exception as e: - print(f"Error running LLMstudio UI: {e}") - - -def run_ui_app(started_event: Event): - thread = threading.Thread(target=run_bun_in_thread) - thread.start() - started_event.set() #just here for compatibility diff --git a/llmstudio/ui/components.json b/llmstudio/ui/components.json deleted file mode 100644 index e4e8e1a7..00000000 --- a/llmstudio/ui/components.json +++ /dev/null @@ -1,16 +0,0 @@ -{ - "$schema": "https://ui.shadcn.com/schema.json", - "style": "default", - "rsc": true, - "tsx": true, - "tailwind": { - "config": "tailwind.config.js", - "css": "src/app/globals.css", - "baseColor": "slate", - "cssVariables": true - }, - "aliases": { - "components": "@/components", - "utils": "@/lib/utils" - } -} \ No newline at end of file diff --git a/llmstudio/ui/global.d.ts b/llmstudio/ui/global.d.ts deleted file mode 100644 index 858595c7..00000000 --- a/llmstudio/ui/global.d.ts +++ /dev/null @@ -1,5 +0,0 @@ -declare namespace JSX { - interface IntrinsicElements { - "dotlottie-player": any; - } -} \ No newline at end of file diff --git a/llmstudio/ui/next.config.js b/llmstudio/ui/next.config.js deleted file mode 100644 index 658404ac..00000000 --- a/llmstudio/ui/next.config.js +++ /dev/null @@ -1,4 +0,0 @@ -/** @type {import('next').NextConfig} */ -const nextConfig = {}; - -module.exports = nextConfig; diff --git a/llmstudio/ui/package-lock.json b/llmstudio/ui/package-lock.json deleted file mode 100644 index 8976df5c..00000000 --- a/llmstudio/ui/package-lock.json +++ /dev/null @@ -1,8283 +0,0 @@ -{ - "name": "llmstudio", - "version": "0.1.0", - "lockfileVersion": 3, - "requires": true, - "packages": { - "": { - "name": "llmstudio", - "version": "0.1.0", - "dependencies": { - "@hookform/resolvers": "^3.3.2", - "@lottiefiles/react-lottie-player": "^3.5.3", - "@radix-ui/react-accordion": "^1.1.2", - "@radix-ui/react-alert-dialog": "^1.0.5", - "@radix-ui/react-aspect-ratio": "^1.0.3", - "@radix-ui/react-avatar": "^1.0.4", - "@radix-ui/react-checkbox": "^1.0.4", - "@radix-ui/react-collapsible": "^1.0.3", - "@radix-ui/react-context-menu": "^2.1.5", - "@radix-ui/react-dialog": "^1.0.5", - "@radix-ui/react-dropdown-menu": "^2.0.6", - "@radix-ui/react-hover-card": "^1.0.7", - 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@@ - - - diff --git a/llmstudio/ui/public/svg/compare.svg b/llmstudio/ui/public/svg/compare.svg deleted file mode 100644 index 72c59f29..00000000 --- a/llmstudio/ui/public/svg/compare.svg +++ /dev/null @@ -1,7 +0,0 @@ - - - - - - - diff --git a/llmstudio/ui/public/svg/home.svg b/llmstudio/ui/public/svg/home.svg deleted file mode 100644 index 79fcd43d..00000000 --- a/llmstudio/ui/public/svg/home.svg +++ /dev/null @@ -1,12 +0,0 @@ - - - - - - diff --git a/llmstudio/ui/public/svg/load.svg b/llmstudio/ui/public/svg/load.svg deleted file mode 100644 index 5765efef..00000000 --- a/llmstudio/ui/public/svg/load.svg +++ /dev/null @@ -1,4 +0,0 @@ - - - - diff --git a/llmstudio/ui/public/svg/magic.svg b/llmstudio/ui/public/svg/magic.svg deleted file mode 100644 index bcdc96e3..00000000 --- a/llmstudio/ui/public/svg/magic.svg +++ /dev/null @@ -1,10 +0,0 @@ - - - - - - - - - - diff --git a/llmstudio/ui/public/svg/play.svg b/llmstudio/ui/public/svg/play.svg deleted file mode 100644 index 29850243..00000000 --- a/llmstudio/ui/public/svg/play.svg +++ /dev/null @@ -1,11 +0,0 @@ - - - - - - - diff --git a/llmstudio/ui/public/svg/playground.svg b/llmstudio/ui/public/svg/playground.svg deleted file mode 100644 index 84eb6be2..00000000 --- a/llmstudio/ui/public/svg/playground.svg +++ /dev/null @@ -1,8 +0,0 @@ - - - - - - - - diff --git a/llmstudio/ui/public/svg/plus.svg b/llmstudio/ui/public/svg/plus.svg deleted file mode 100644 index 0a5f35fe..00000000 --- a/llmstudio/ui/public/svg/plus.svg +++ /dev/null @@ -1,9 +0,0 @@ - - - - - - diff --git a/llmstudio/ui/public/svg/settings.svg b/llmstudio/ui/public/svg/settings.svg deleted file mode 100644 index eeed24ef..00000000 --- a/llmstudio/ui/public/svg/settings.svg +++ /dev/null @@ -1,15 +0,0 @@ - - - - - - - diff --git a/llmstudio/ui/public/svg/sparkles.svg b/llmstudio/ui/public/svg/sparkles.svg deleted file mode 100644 index d6a9a1b7..00000000 --- a/llmstudio/ui/public/svg/sparkles.svg +++ /dev/null @@ -1,3 +0,0 @@ - - - diff --git a/llmstudio/ui/src/app/(llm)/compare/page.tsx b/llmstudio/ui/src/app/(llm)/compare/page.tsx deleted file mode 100644 index 24874ce7..00000000 --- a/llmstudio/ui/src/app/(llm)/compare/page.tsx +++ /dev/null @@ -1,8 +0,0 @@ -import Image from 'next/image' - -export default function Home() { - return ( -
-
- ) -} diff --git a/llmstudio/ui/src/app/(llm)/dashboard/hooks/useDashboardFetch.tsx b/llmstudio/ui/src/app/(llm)/dashboard/hooks/useDashboardFetch.tsx deleted file mode 100644 index 73fa0f95..00000000 --- a/llmstudio/ui/src/app/(llm)/dashboard/hooks/useDashboardFetch.tsx +++ /dev/null @@ -1,39 +0,0 @@ -import { useState, useEffect } from 'react'; - -interface DashboardMetrics { - request_by_provider: Array>; - request_by_model: Array>; - total_cost_by_provider: Array>; - total_cost_by_model: Array>; - average_latency: Array>; - average_ttft: Array>; - average_itl: Array>; - average_tps: Array>; -} - -export function useDashboardFetch() { - const [metrics, setMetrics] = useState({ - request_by_provider: [], - request_by_model: [], - total_cost_by_provider: [], - total_cost_by_model: [], - average_latency: [], - average_ttft: [], - average_itl: [], - average_tps: [] - }); - - useEffect(() => { - async function fetchDashboardMetrics() { - const queryParams = new URLSearchParams({ year: '2024', month: '1' }); - const response = await fetch( - `http://${process.env.NEXT_PUBLIC_LLMSTUDIO_TRACKING_HOST}:${process.env.NEXT_PUBLIC_LLMSTUDIO_TRACKING_PORT}/api/tracking/dashboard/metrics?${queryParams}` - ); - const data = await response.json(); - setMetrics(data); - } - fetchDashboardMetrics(); - }, []); - - return { metrics }; -} diff --git a/llmstudio/ui/src/app/(llm)/dashboard/page.tsx b/llmstudio/ui/src/app/(llm)/dashboard/page.tsx deleted file mode 100644 index 02cb1496..00000000 --- a/llmstudio/ui/src/app/(llm)/dashboard/page.tsx +++ /dev/null @@ -1,118 +0,0 @@ -'use client'; -import { AreaChart, DonutChart, BarChart, Card, Title } from '@tremor/react'; -import { useRef } from 'react'; -import { useDashboardFetch } from '@/app/(llm)/dashboard/hooks/useDashboardFetch'; -import { Tabs, TabsContent, TabsList, TabsTrigger } from '@/components/ui/tabs'; - -export default function Dashboard() { - const { metrics } = useDashboardFetch(); - const toggleRef = useRef(null); - - if (metrics) { - const providers = - metrics.request_by_model.length > 0 - ? Object.keys(metrics.request_by_provider[0]).filter( - (k) => k !== 'date' - ) - : []; - const models = - metrics.request_by_model.length > 0 - ? Object.keys(metrics.request_by_model[0]).filter((k) => k !== 'date') - : []; - - return ( - <> -
-

Dashboard

-
- - Requests by Provider - - - - Requests by Model - - -
-
- -
- Total Cost - - - Model - Provider - - -
- - - - Average Latency - - - - Average Time To First Token - - - - Average Inter Token Latency - - - - Average Tokens Per Second - - -
-
- - ); - } -} diff --git a/llmstudio/ui/src/app/(llm)/playground/components/DataTable/ColumnHeader.tsx b/llmstudio/ui/src/app/(llm)/playground/components/DataTable/ColumnHeader.tsx deleted file mode 100644 index cae04679..00000000 --- a/llmstudio/ui/src/app/(llm)/playground/components/DataTable/ColumnHeader.tsx +++ /dev/null @@ -1,71 +0,0 @@ -import { - ArrowDownIcon, - ArrowUpIcon, - CaretSortIcon, - EyeNoneIcon, -} from '@radix-ui/react-icons'; -import { Column } from '@tanstack/react-table'; - -import { cn } from '@/lib/utils'; -import { Button } from '@/components/ui/button'; -import { - DropdownMenu, - DropdownMenuContent, - DropdownMenuItem, - DropdownMenuSeparator, - DropdownMenuTrigger, -} from '@/components/ui/dropdown-menu'; - -interface DataTableColumnHeaderProps - extends React.HTMLAttributes { - column: Column; - title: string; -} - -export function DataTableColumnHeader({ - column, - title, - className, -}: DataTableColumnHeaderProps) { - if (!column.getCanSort()) { - return
{title}
; - } - - return ( -
- - - - - - column.toggleSorting(false)}> - - Asc - - column.toggleSorting(true)}> - - Desc - - - column.toggleVisibility(false)}> - - Hide - - - -
- ); -} diff --git a/llmstudio/ui/src/app/(llm)/playground/components/DataTable/DataTable.tsx b/llmstudio/ui/src/app/(llm)/playground/components/DataTable/DataTable.tsx deleted file mode 100644 index 7afdb787..00000000 --- a/llmstudio/ui/src/app/(llm)/playground/components/DataTable/DataTable.tsx +++ /dev/null @@ -1,129 +0,0 @@ -'use client'; - -import * as React from 'react'; -import { - ColumnDef, - ColumnFiltersState, - SortingState, - VisibilityState, - flexRender, - getCoreRowModel, - getFacetedRowModel, - getFacetedUniqueValues, - getFilteredRowModel, - getPaginationRowModel, - getSortedRowModel, - useReactTable, -} from '@tanstack/react-table'; - -import { - Table, - TableBody, - TableCell, - TableHead, - TableHeader, - TableRow, -} from '@/components/ui/table'; - -import { DataTablePagination } from '@/app/(llm)/playground/components/DataTable/Pagination'; -import { DataTableToolbar } from '@/app/(llm)/playground/components/DataTable/Toolbar'; -import { columns } from '@/app/(llm)/playground/components/DataTable/columns'; -import { useLogsFetch } from '@/app/(llm)/playground/hooks/useLogsFetch'; -import { useStore } from '@/app/(llm)/playground/store'; -import { Cedarville_Cursive } from 'next/font/google'; - -export default function DataTable() { - const [rowSelection, setRowSelection] = React.useState({}); - const [columnVisibility, setColumnVisibility] = - React.useState({}); - const [columnFilters, setColumnFilters] = React.useState( - [] - ); - const [sorting, setSorting] = React.useState([ - { - id: 'log_id', - desc: true, - }, - ]); - useLogsFetch(); - const { logs } = useStore(); - - const table = useReactTable({ - data: logs, - columns, - state: { - sorting, - columnVisibility, - rowSelection, - columnFilters, - }, - enableRowSelection: true, - onRowSelectionChange: setRowSelection, - onSortingChange: setSorting, - onColumnFiltersChange: setColumnFilters, - onColumnVisibilityChange: setColumnVisibility, - getCoreRowModel: getCoreRowModel(), - getFilteredRowModel: getFilteredRowModel(), - getPaginationRowModel: getPaginationRowModel(), - getSortedRowModel: getSortedRowModel(), - getFacetedRowModel: getFacetedRowModel(), - getFacetedUniqueValues: getFacetedUniqueValues(), - }); - - return ( -
- -
- - - {table.getHeaderGroups().map((headerGroup) => ( - - {headerGroup.headers.map((header) => { - return ( - - {header.isPlaceholder - ? null - : flexRender( - header.column.columnDef.header, - header.getContext() - )} - - ); - })} - - ))} - - - {table.getRowModel().rows?.length ? ( - table.getRowModel().rows.map((row) => ( - - {row.getVisibleCells().map((cell) => ( - - {flexRender( - cell.column.columnDef.cell, - cell.getContext() - )} - - ))} - - )) - ) : ( - - - No results. - - - )} - -
-
- -
- ); -} diff --git a/llmstudio/ui/src/app/(llm)/playground/components/DataTable/FacetedFilter.tsx b/llmstudio/ui/src/app/(llm)/playground/components/DataTable/FacetedFilter.tsx deleted file mode 100644 index 6a39884e..00000000 --- a/llmstudio/ui/src/app/(llm)/playground/components/DataTable/FacetedFilter.tsx +++ /dev/null @@ -1,147 +0,0 @@ -import * as React from 'react'; -import { CheckIcon, PlusCircledIcon } from '@radix-ui/react-icons'; -import { Column } from '@tanstack/react-table'; - -import { cn } from '@/lib/utils'; -import { Badge } from '@/components/ui/badge'; -import { Button } from '@/components/ui/button'; -import { - Command, - CommandEmpty, - CommandGroup, - CommandInput, - CommandItem, - CommandList, - CommandSeparator, -} from '@/components/ui/command'; -import { - Popover, - PopoverContent, - PopoverTrigger, -} from '@/components/ui/popover'; -import { Separator } from '@/components/ui/separator'; - -interface DataTableFacetedFilterProps { - column?: Column; - title?: string; - options: { - label: string; - value: string; - icon?: React.ComponentType<{ className?: string }>; - }[]; -} - -export function DataTableFacetedFilter({ - column, - title, - options, -}: DataTableFacetedFilterProps) { - const facets = column?.getFacetedUniqueValues(); - const selectedValues = new Set(column?.getFilterValue() as string[]); - - return ( - - - - - - - - - No results found. - - {options.map((option) => { - const isSelected = selectedValues.has(option.value); - return ( - { - if (isSelected) { - selectedValues.delete(option.value); - } else { - selectedValues.add(option.value); - } - const filterValues = Array.from(selectedValues); - column?.setFilterValue( - filterValues.length ? filterValues : undefined - ); - }} - > -
- -
- {option.icon && ( - - )} - {option.label} - {facets?.get(option.value) && ( - - {facets.get(option.value)} - - )} - - ); - })} - - {selectedValues.size > 0 && ( - <> - - - column?.setFilterValue(undefined)} - className='justify-center text-center' - > - Clear filters - - - - )} - - - - - ); -} diff --git a/llmstudio/ui/src/app/(llm)/playground/components/DataTable/Pagination.tsx b/llmstudio/ui/src/app/(llm)/playground/components/DataTable/Pagination.tsx deleted file mode 100644 index 17a6a52e..00000000 --- a/llmstudio/ui/src/app/(llm)/playground/components/DataTable/Pagination.tsx +++ /dev/null @@ -1,97 +0,0 @@ -import { - ChevronLeftIcon, - ChevronRightIcon, - DoubleArrowLeftIcon, - DoubleArrowRightIcon, -} from '@radix-ui/react-icons'; -import { Table } from '@tanstack/react-table'; - -import { Button } from '@/components/ui/button'; -import { - Select, - SelectContent, - SelectItem, - SelectTrigger, - SelectValue, -} from '@/components/ui/select'; - -interface DataTablePaginationProps { - table: Table; -} - -export function DataTablePagination({ - table, -}: DataTablePaginationProps) { - return ( -
-
- {table.getFilteredSelectedRowModel().rows.length} of{' '} - {table.getFilteredRowModel().rows.length} row(s) selected. -
-
-
-

Rows per page

- -
-
- Page {table.getState().pagination.pageIndex + 1} of{' '} - {table.getPageCount()} -
-
- - - - -
-
-
- ); -} diff --git a/llmstudio/ui/src/app/(llm)/playground/components/DataTable/RowActions.tsx b/llmstudio/ui/src/app/(llm)/playground/components/DataTable/RowActions.tsx deleted file mode 100644 index 2242855e..00000000 --- a/llmstudio/ui/src/app/(llm)/playground/components/DataTable/RowActions.tsx +++ /dev/null @@ -1,57 +0,0 @@ -'use client'; - -import { DotsHorizontalIcon } from '@radix-ui/react-icons'; -import { Row } from '@tanstack/react-table'; - -import { Button } from '@/components/ui/button'; -import { - DropdownMenu, - DropdownMenuContent, - DropdownMenuItem, - DropdownMenuShortcut, - DropdownMenuTrigger, -} from '@/components/ui/dropdown-menu'; - -import { Log, useStore } from '@/app/(llm)/playground/store'; - -interface DataTableRowActionsProps { - row: Row; -} - -export function DataTableRowActions({ - row, -}: DataTableRowActionsProps) { - const { setInput, setOutput, setModel, setProvider, setParameter } = - useStore(); - - const restore = () => { - setInput(row.original.chat_input); - setOutput(row.original.chat_output); - setModel(row.original.model); - setProvider(row.original.provider); - Object.entries(row.original.parameters).forEach(([key, value]) => - setParameter(key, value) - ); - }; - - return ( - - - - - - Restore - -
Delete
- ⌘⌫ - - - - ); -} diff --git a/llmstudio/ui/src/app/(llm)/playground/components/DataTable/Toolbar.tsx b/llmstudio/ui/src/app/(llm)/playground/components/DataTable/Toolbar.tsx deleted file mode 100644 index 3cd32714..00000000 --- a/llmstudio/ui/src/app/(llm)/playground/components/DataTable/Toolbar.tsx +++ /dev/null @@ -1,50 +0,0 @@ -'use client'; - -import { Table } from '@tanstack/react-table'; - -import { Button } from '@/components/ui/button'; -import { - DropdownMenu, - DropdownMenuContent, - DropdownMenuItem, - DropdownMenuTrigger, -} from '@/components/ui/dropdown-menu'; - -import { useExport } from '@/app/(llm)/playground/hooks/useExport'; - -interface DataTableToolbarProps { - table: Table; -} - -export function DataTableToolbar({ - table, -}: DataTableToolbarProps) { - const exportLogs = useExport(); - - return ( -
- - - - - - exportLogs()}> - Export all - - - exportLogs( - table.getSelectedRowModel().rows.map((row) => row.original) - ) - } - > - Export selected - - - -
- ); -} diff --git a/llmstudio/ui/src/app/(llm)/playground/components/DataTable/UserNav.tsx b/llmstudio/ui/src/app/(llm)/playground/components/DataTable/UserNav.tsx deleted file mode 100644 index 6cd0675d..00000000 --- a/llmstudio/ui/src/app/(llm)/playground/components/DataTable/UserNav.tsx +++ /dev/null @@ -1,58 +0,0 @@ -import { Avatar, AvatarFallback, AvatarImage } from '@/components/ui/avatar'; -import { Button } from '@/components/ui/button'; -import { - DropdownMenu, - DropdownMenuContent, - DropdownMenuGroup, - DropdownMenuItem, - DropdownMenuLabel, - DropdownMenuSeparator, - DropdownMenuShortcut, - DropdownMenuTrigger, -} from '@/components/ui/dropdown-menu'; - -export function UserNav() { - return ( - - - - - - -
-

shadcn

-

- m@example.com -

-
- - - - - Profile - ⇧⌘P - - - Billing - ⌘B - - - Settings - ⌘S - - New Team - - - - Log out - ⇧⌘Q - - - - ); -} diff --git a/llmstudio/ui/src/app/(llm)/playground/components/DataTable/ViewOptions.tsx b/llmstudio/ui/src/app/(llm)/playground/components/DataTable/ViewOptions.tsx deleted file mode 100644 index 654c42d9..00000000 --- a/llmstudio/ui/src/app/(llm)/playground/components/DataTable/ViewOptions.tsx +++ /dev/null @@ -1,59 +0,0 @@ -'use client'; - -import { DropdownMenuTrigger } from '@radix-ui/react-dropdown-menu'; -import { MixerHorizontalIcon } from '@radix-ui/react-icons'; -import { Table } from '@tanstack/react-table'; - -import { Button } from '@/components/ui/button'; -import { - DropdownMenu, - DropdownMenuCheckboxItem, - DropdownMenuContent, - DropdownMenuLabel, - DropdownMenuSeparator, -} from '@/components/ui/dropdown-menu'; - -interface DataTableViewOptionsProps { - table: Table; -} - -export function DataTableViewOptions({ - table, -}: DataTableViewOptionsProps) { - return ( - - - - - - Toggle columns - - {table - .getAllColumns() - .filter( - (column) => - typeof column.accessorFn !== 'undefined' && column.getCanHide() - ) - .map((column) => { - return ( - column.toggleVisibility(!!value)} - > - {column.id} - - ); - })} - - - ); -} diff --git a/llmstudio/ui/src/app/(llm)/playground/components/DataTable/columns.tsx b/llmstudio/ui/src/app/(llm)/playground/components/DataTable/columns.tsx deleted file mode 100644 index 8f7a50a1..00000000 --- a/llmstudio/ui/src/app/(llm)/playground/components/DataTable/columns.tsx +++ /dev/null @@ -1,265 +0,0 @@ -'use client'; - -import { ColumnDef } from '@tanstack/react-table'; - -import { Badge } from '@/components/ui/badge'; -import { Checkbox } from '@/components/ui/checkbox'; - -import { DataTableColumnHeader } from '@/app/(llm)/playground/components/DataTable/ColumnHeader'; -import { DataTableRowActions } from '@/app/(llm)/playground/components/DataTable/RowActions'; - -import { - Sheet, - SheetContent, - SheetDescription, - SheetHeader, - SheetTitle, - SheetTrigger, -} from '@/components/ui/sheet'; - -import CodeBlock from '@/components/CodeBlock'; -import { Maximize2 } from 'lucide-react'; - -import { Button } from '@/components/ui/button'; - -export const columns: ColumnDef[] = [ - { - accessorKey: 'context', - header: () => <>, - cell: ({ row }) => ( - - - - - - - Context - - {row.original.context.map((item: any, index: any) => ( -
-
- Role: {item.role} -
- {item.content && ( -
- Content: {item.content} -
- )} - {item.function_call && ( - - )} - {item.name && ( -
- Function Name: {item.name} -
- )} -
- ))} - - - - - ), - enablePinning: true, - }, - { - id: 'select', - header: ({ table }) => ( - table.toggleAllPageRowsSelected(!!value)} - aria-label='Select all' - className='translate-y-[2px]' - /> - ), - cell: ({ row }) => ( - row.toggleSelected(!!value)} - aria-label='Select row' - className='translate-y-[2px]' - /> - ), - enablePinning: true, - }, - { - accessorKey: 'log_id', - id: 'log_id', - header: ({ column }) => ( - - ), - cell: ({ row }) =>
{row.original.log_id}
, - enableSorting: true, - enableHiding: false, - }, - { - accessorKey: 'chat_input', - header: ({ column }) => ( - - ), - cell: ({ row }) => { - return ( -
- {/* {row.original.label && ( - {row.original.label} - )} */} - - {row.original.chat_input} - -
- ); - }, - enableSorting: false, - }, - { - accessorKey: 'chat_output', - header: ({ column }) => ( - - ), - cell: ({ row }) => { - return ( -
- - {row.original.chat_output} - -
- ); - }, - enableSorting: false, - }, - { - accessorKey: 'model', - header: ({ column }) => ( - - ), - cell: ({ row }) => { - return ( -
- {row.original.provider} - - {row.original.model} - -
- ); - }, - enableSorting: false, - }, - { - accessorKey: 'cost_usd', - header: ({ column }) => ( - - ), - cell: ({ row }) => { - return ( -
- - ${row.original.metrics.cost_usd.toFixed(6)} - -
- ); - }, - }, - { - accessorKey: 'input_tokens', - header: ({ column }) => ( - - ), - cell: ({ row }) => { - return ( -
- - {row.original.metrics.input_tokens} - -
- ); - }, - }, - { - accessorKey: 'output_tokens', - header: ({ column }) => ( - - ), - cell: ({ row }) => { - return ( -
- - {row.original.metrics.output_tokens} - -
- ); - }, - }, - { - accessorKey: 'latency_s', - header: ({ column }) => ( - - ), - cell: ({ row }) => { - return ( -
- - {row.original.metrics.latency_s.toFixed(3)}s - -
- ); - }, - }, - { - accessorKey: 'ttft', - header: ({ column }) => ( - - ), - cell: ({ row }) => { - return ( -
- - {row.original.metrics.time_to_first_token_s.toFixed(3)}s - -
- ); - }, - }, - { - accessorKey: 'itl', - header: ({ column }) => ( - - ), - cell: ({ row }) => { - return ( -
- - {row.original.metrics.inter_token_latency_s.toFixed(3)}s - -
- ); - }, - }, - { - accessorKey: 'tps', - header: ({ column }) => ( - - ), - cell: ({ row }) => { - return ( -
- - {row.original.metrics.tokens_per_second.toFixed(0)} - -
- ); - }, - }, - { - id: 'actions', - cell: ({ row }) => , - }, -]; diff --git a/llmstudio/ui/src/app/(llm)/playground/components/Input.tsx b/llmstudio/ui/src/app/(llm)/playground/components/Input.tsx deleted file mode 100644 index 428005d6..00000000 --- a/llmstudio/ui/src/app/(llm)/playground/components/Input.tsx +++ /dev/null @@ -1,61 +0,0 @@ -'use client'; -import { useRef } from 'react'; -import { useStore } from '@/app/(llm)/playground/store'; -import { useChat } from '@/app/(llm)/playground/hooks/useChat'; -import { Button } from '@/components/ui/button'; -import { Loader2 } from 'lucide-react'; - -export default function Input() { - const { input, status, setInput, setStatus } = useStore(); - const textareaRef = useRef(null); - const submitChat = useChat(); - - const onSubmit = (e: React.FormEvent) => { - e.preventDefault(); - setStatus('loading'); - textareaRef.current?.blur(); - submitChat(); - }; - - const onKeyDown = (e: React.KeyboardEvent) => { - if ((e.metaKey || e.ctrlKey) && e.key === 'Enter') - onSubmit(e as unknown as React.FormEvent); - }; - - const onInputChange = (e: React.ChangeEvent) => { - setInput(e.target.value); - setStatus('idle'); - }; - - return ( -
-
-
-

Input

- -
-
- -
-
-
- ); -} diff --git a/llmstudio/ui/src/app/(llm)/playground/components/LogSheet.tsx b/llmstudio/ui/src/app/(llm)/playground/components/LogSheet.tsx deleted file mode 100644 index edad565b..00000000 --- a/llmstudio/ui/src/app/(llm)/playground/components/LogSheet.tsx +++ /dev/null @@ -1,51 +0,0 @@ -'use client'; - -import { Button } from '@/components/ui/button'; -import { Input } from '@/components/ui/input'; -import { Label } from '@/components/ui/label'; -import { - Sheet, - SheetClose, - SheetContent, - SheetDescription, - SheetFooter, - SheetHeader, - SheetTitle, - SheetTrigger, -} from '@/components/ui/sheet'; - -export default function LogSheet() { - return ( -
- - - - Edit profile - - Make changes to your profile here. Click save when youre done. - - -
-
- - -
-
- - -
-
- - - - - - - -
- ); -} diff --git a/llmstudio/ui/src/app/(llm)/playground/components/ModelItem.tsx b/llmstudio/ui/src/app/(llm)/playground/components/ModelItem.tsx deleted file mode 100644 index 15e7d191..00000000 --- a/llmstudio/ui/src/app/(llm)/playground/components/ModelItem.tsx +++ /dev/null @@ -1,28 +0,0 @@ -'use client'; -import { CheckIcon } from '@radix-ui/react-icons'; - -import { cn } from '@/lib/utils'; -import { CommandItem } from '@/components/ui/command'; - -interface ModelItemProps { - id: string; - isSelected: boolean; - onSelect: () => void; -} - -export function ModelItem({ id, isSelected, onSelect }: ModelItemProps) { - return ( - - {id} - - - ); -} diff --git a/llmstudio/ui/src/app/(llm)/playground/components/ModelSelector.tsx b/llmstudio/ui/src/app/(llm)/playground/components/ModelSelector.tsx deleted file mode 100644 index 860a1f59..00000000 --- a/llmstudio/ui/src/app/(llm)/playground/components/ModelSelector.tsx +++ /dev/null @@ -1,75 +0,0 @@ -'use client'; -import React from 'react'; -import { PopoverProps } from '@radix-ui/react-popover'; -import { CaretSortIcon, CheckIcon } from '@radix-ui/react-icons'; - -import { Button } from '@/components/ui/button'; -import { Label } from '@/components/ui/label'; -import { - Command, - CommandEmpty, - CommandGroup, - CommandInput, - CommandList, -} from '@/components/ui/command'; -import { - Popover, - PopoverContent, - PopoverTrigger, -} from '@/components/ui/popover'; - -import { ModelItem } from '@/app/(llm)/playground/components/ModelItem'; -import { useModelFetch } from '@/app/(llm)/playground/hooks/useModelFetch'; -import { useStore } from '@/app/(llm)/playground/store'; - -interface ModelSelectorProps extends PopoverProps {} - -export default function ModelSelector(props: ModelSelectorProps) { - const [open, setOpen] = React.useState(false); - const { providers } = useModelFetch(); - const { model, setModel, setProvider } = useStore(); - - return ( -
- - - - - - - - - - No Models found. - {providers.map((provider) => ( - - {provider.models.map((singleModel) => ( - { - setProvider(provider.name); - setModel(singleModel); - setOpen(false); - }} - /> - ))} - - ))} - - - - -
- ); -} diff --git a/llmstudio/ui/src/app/(llm)/playground/components/Output.tsx b/llmstudio/ui/src/app/(llm)/playground/components/Output.tsx deleted file mode 100644 index 3ac421b8..00000000 --- a/llmstudio/ui/src/app/(llm)/playground/components/Output.tsx +++ /dev/null @@ -1,33 +0,0 @@ -'use client'; -import { useEffect, useRef } from 'react'; -import { useStore } from '@/app/(llm)/playground/store'; -import Markdown from '@/components/Markdown'; - -export default function Output() { - const { output, status } = useStore(); - const textareaRef = useRef(null); - - useEffect(() => { - if (textareaRef.current) - textareaRef.current.scrollTop = textareaRef.current.scrollHeight; - }); - - const getStatusColor = (status: string) => { - if (status === 'idle') return 'bg-slate-400'; - if (status === 'waiting') return 'bg-yellow-400'; - if (status === 'done') return 'bg-green-500'; - if (status === 'error') return 'bg-red-600'; - }; - - return ( -
-
-

Output

-
-
-
- -
-
- ); -} diff --git a/llmstudio/ui/src/app/(llm)/playground/components/Parameters.tsx b/llmstudio/ui/src/app/(llm)/playground/components/Parameters.tsx deleted file mode 100644 index ebdd8dbe..00000000 --- a/llmstudio/ui/src/app/(llm)/playground/components/Parameters.tsx +++ /dev/null @@ -1,81 +0,0 @@ -'use client'; -import React from 'react'; -import { useEffect } from 'react'; -import { Settings } from 'lucide-react'; -import { Button } from '@/components/ui/button'; -import { Input } from '@/components/ui/input'; -import { Label } from '@/components/ui/label'; -import { Slider } from '@/components/ui/slider'; -import { PopoverProps } from '@radix-ui/react-popover'; -import { - Popover, - PopoverContent, - PopoverTrigger, -} from '@/components/ui/popover'; - -import { - useParameterFetch, - ParameterType, -} from '@/app/(llm)/playground/hooks/useParameterFetch'; -import { useStore } from '@/app/(llm)/playground/store'; - -interface ModelSelectorProps extends PopoverProps {} - -export default function ModelSelector(props: ModelSelectorProps) { - const [open, setOpen] = React.useState(false); - const { parameters } = useParameterFetch(); - const { setParameter, getParameter } = useStore(); - - useEffect(() => { - for (const param of parameters) setParameter(param.id, param.default); - }, [parameters, setParameter]); - - return ( -
- - - - - -
-
-

Parameters

-
- - {parameters.map((param: ParameterType) => { - if (param.type === 'float' || param.type === 'int') - return ( -
-
- - { - setParameter(param.id, parseFloat(e.target.value)); - }} - /> -
- - setParameter(param.id, value[0]) - } - /> -
- ); - })} -
- - -
- ); -} diff --git a/llmstudio/ui/src/app/(llm)/playground/components/index.tsx b/llmstudio/ui/src/app/(llm)/playground/components/index.tsx deleted file mode 100644 index 3894db09..00000000 --- a/llmstudio/ui/src/app/(llm)/playground/components/index.tsx +++ /dev/null @@ -1,5 +0,0 @@ -export { default as Input } from './Input'; -export { default as Output } from './Output'; -export { default as ModelSelector } from './ModelSelector'; -export { default as Parameters } from './Parameters'; -export { default as DataTable } from './DataTable/DataTable'; diff --git a/llmstudio/ui/src/app/(llm)/playground/hooks/useChat.tsx b/llmstudio/ui/src/app/(llm)/playground/hooks/useChat.tsx deleted file mode 100644 index 3d60f2ae..00000000 --- a/llmstudio/ui/src/app/(llm)/playground/hooks/useChat.tsx +++ /dev/null @@ -1,43 +0,0 @@ -import { useCallback } from 'react'; -import { useStore } from '@/app/(llm)/playground/store'; -import { generateStream } from '@/lib/utils'; -import { toast } from 'sonner'; - -export const useChat = (): (() => Promise) => { - const { input, provider, model, parameters, setOutput, setStatus } = - useStore(); - - const submitChat = useCallback(async () => { - setStatus('waiting'); - setOutput(''); - - generateStream( - `http://${process.env.NEXT_PUBLIC_LLMSTUDIO_ENGINE_HOST}:${ - process.env.NEXT_PUBLIC_LLMSTUDIO_ENGINE_PORT - }/api/engine/chat/${provider.replace(/\s+/g, '').toLowerCase()}`, - { - chat_input: input, - model: model, - is_stream: true, - has_end_token: true, - parameters: parameters, - } - ) - .then(async (stream) => { - for await (const chunk of stream) - setOutput( - chunk.includes('') - ? chunk.split('')[0] - : chunk, - true - ); - setStatus('done'); - }) - .catch((e: Error) => { - toast.error(e.message); - setStatus('error'); - }); - }, [input, model, parameters, provider, setStatus, setOutput]); - - return submitChat; -}; diff --git a/llmstudio/ui/src/app/(llm)/playground/hooks/useExport.tsx b/llmstudio/ui/src/app/(llm)/playground/hooks/useExport.tsx deleted file mode 100644 index 1cdc37ba..00000000 --- a/llmstudio/ui/src/app/(llm)/playground/hooks/useExport.tsx +++ /dev/null @@ -1,34 +0,0 @@ -import { useCallback } from 'react'; -import { useStore } from '@/app/(llm)/playground/store'; - -export const useExport = (): ((selected?: any[]) => void) => { - const { logs } = useStore(); - - const exportLogs = useCallback( - (selected?: any[]): void => { - fetch( - `http://${process.env.NEXT_PUBLIC_LLMSTUDIO_ENGINE_HOST}:${process.env.NEXT_PUBLIC_LLMSTUDIO_ENGINE_PORT}/api/export`, - { - method: 'POST', - headers: { - Accept: 'application/json, text/plain', - 'Content-Type': 'application/json;charset=UTF-8', - }, - body: JSON.stringify(selected || logs), - } - ) - .then((response) => response.blob()) - .then((blob) => { - const url = URL.createObjectURL(blob); - const link = document.createElement('a'); - link.href = url; - link.download = 'parameters.csv'; - link.click(); - URL.revokeObjectURL(url); - }); - }, - [logs] - ); - - return exportLogs; -}; diff --git a/llmstudio/ui/src/app/(llm)/playground/hooks/useLogsFetch.tsx b/llmstudio/ui/src/app/(llm)/playground/hooks/useLogsFetch.tsx deleted file mode 100644 index 6839d473..00000000 --- a/llmstudio/ui/src/app/(llm)/playground/hooks/useLogsFetch.tsx +++ /dev/null @@ -1,23 +0,0 @@ -import { useEffect } from 'react'; -import { toast } from 'sonner'; -import { useStore } from '@/app/(llm)/playground/store'; - -export function useLogsFetch() { - const { status, setLogs } = useStore(); - - useEffect(() => { - async function fetchLogs() { - try { - const response = await fetch( - `http://${process.env.NEXT_PUBLIC_LLMSTUDIO_TRACKING_HOST}:${process.env.NEXT_PUBLIC_LLMSTUDIO_TRACKING_PORT}/api/tracking/logs` - ); - const data = await response.json(); - setLogs(data); - } catch (e) { - toast.error('Tracking API is not available'); - } - } - - fetchLogs(); - }, [status, setLogs]); -} diff --git a/llmstudio/ui/src/app/(llm)/playground/hooks/useModelFetch.tsx b/llmstudio/ui/src/app/(llm)/playground/hooks/useModelFetch.tsx deleted file mode 100644 index 98922a51..00000000 --- a/llmstudio/ui/src/app/(llm)/playground/hooks/useModelFetch.tsx +++ /dev/null @@ -1,35 +0,0 @@ -import { useState, useEffect } from 'react'; -import { toast } from 'sonner'; - -interface ModelType { - name: string; - models: string[]; -} - -export function useModelFetch() { - const [providers, setProviders] = useState([]); - - useEffect(() => { - async function fetchModels() { - try { - const response = await fetch( - `http://${process.env.NEXT_PUBLIC_LLMSTUDIO_ENGINE_HOST}:${process.env.NEXT_PUBLIC_LLMSTUDIO_ENGINE_PORT}/api/engine/models` - ); - const data = await response.json(); - const fetchedProviders: ModelType[] = Object.keys(data).map((key) => ({ - name: data[key].name, - models: data[key].models.map( - (modelName: string, index: number) => modelName - ), - })); - setProviders(fetchedProviders); - } catch (e) { - toast.error('LLMstudio Engine is not running'); - } - } - - fetchModels(); - }, []); - - return { providers }; -} diff --git a/llmstudio/ui/src/app/(llm)/playground/hooks/useParameterFetch.tsx b/llmstudio/ui/src/app/(llm)/playground/hooks/useParameterFetch.tsx deleted file mode 100644 index b0dc86e1..00000000 --- a/llmstudio/ui/src/app/(llm)/playground/hooks/useParameterFetch.tsx +++ /dev/null @@ -1,39 +0,0 @@ -import { useState, useEffect } from 'react'; -import { toast } from 'sonner'; -import { useStore } from '@/app/(llm)/playground/store'; - -export interface ParameterType { - id: string; - name: string; - type: string; - default: number; - min?: number; - max?: number; - step?: number; -} - -export function useParameterFetch() { - const [parameters, setParameters] = useState([]); - const { provider } = useStore(); - - useEffect(() => { - async function fetchParameters() { - try { - const queryParams = new URLSearchParams({ - provider: provider.replace(/\s+/g, '').toLowerCase(), - }); - const response = await fetch( - `http://${process.env.NEXT_PUBLIC_LLMSTUDIO_ENGINE_HOST}:${process.env.NEXT_PUBLIC_LLMSTUDIO_ENGINE_PORT}/api/engine/parameters?${queryParams}` - ); - const data: ParameterType[] = await response.json(); - setParameters(data); - } catch (e) { - toast.error('LLMstudio Engine is not running'); - } - } - - if (provider) fetchParameters(); - }, [provider]); - - return { parameters }; -} diff --git a/llmstudio/ui/src/app/(llm)/playground/page.tsx b/llmstudio/ui/src/app/(llm)/playground/page.tsx deleted file mode 100644 index 1729861f..00000000 --- a/llmstudio/ui/src/app/(llm)/playground/page.tsx +++ /dev/null @@ -1,32 +0,0 @@ -import Image from 'next/image'; -import { - Input, - Output, - ModelSelector, - Parameters, - DataTable, -} from '@/app/(llm)/playground/components'; - -export default function Playground() { - return ( - <> -
-
- - -
-
-
- -
-
- -
-
-
-
- -
- - ); -} diff --git a/llmstudio/ui/src/app/(llm)/playground/store.tsx b/llmstudio/ui/src/app/(llm)/playground/store.tsx deleted file mode 100644 index 22ee1102..00000000 --- a/llmstudio/ui/src/app/(llm)/playground/store.tsx +++ /dev/null @@ -1,74 +0,0 @@ -import { create } from 'zustand'; - -export interface Log { - log_id: string; - chat_input: string; - chat_output: string; - provider: string; - model: string; - created_at: Date; - parameters: Record; - metrics: { - input_tokens: number; - output_tokens: number; - total_tokens: number; - cost_usd: number; - latency_s: number; - time_to_first_token_s: number; - inter_token_latency_s: number; - tokens_per_second: number; - }; -} - -interface Store { - status: string; - logs: Log[]; - input: string; - output: string; - provider: string; - model: string; - parameters: Record; - setStatus: (status: string) => void; - setLogs: (logs: Log[]) => void; - setInput: (input: string) => void; - setOutput: (output: string, isChunk?: boolean) => void; - setModel: (model: string) => void; - setProvider: (provider: string) => void; - setParameter: (parameter: string, value: any) => void; - getParameter: ( - parameter: string - ) => string | number | readonly string[] | undefined; -} - -export const useStore = create()((set, get) => ({ - status: 'idle', - logs: [], - input: '', - output: '', - provider: 'Open AI', - model: 'gpt-4o', - parameters: {}, - setStatus: (status) => set({ status: status }), - setLogs: (logs: Log[]) => set({ logs: logs }), - setInput: (input) => set({ input: input }), - setOutput: (output, isChunk = false) => - set((state) => ({ - output: isChunk ? state.output + output : output, - })), - setModel: (model) => set({ model: model }), - setProvider: (provider) => set({ provider: provider }), - setParameter: (parameter, value) => { - set((state) => ({ - parameters: { - ...state.parameters, - [parameter]: Number(value), - }, - })); - }, - getParameter: (parameter: string) => - get().parameters[parameter] as - | string - | number - | readonly string[] - | undefined, -})); diff --git a/llmstudio/ui/src/app/favicon.ico b/llmstudio/ui/src/app/favicon.ico deleted file mode 100644 index 2f2fe6e72be9c0949ebe07bba33b255ab3d13e8e..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 15406 zcmeHN|8G-O6u)MK4s`n+Iy!-Aw z=bg_x@AKZf@7~LphUu8i#`xaJp42n8kTK?Tjvp6JW2~3gxm-8f3m6N|V62eaa2M_| zUe9+?x3;lALExX~VBu>!pknhD3H}59fC$yqe;$MdFTGB=u0C{3st@JfXthB>QyY}5 zTTk$G?t`4VWiZ=YL&r-7KL(9f3$0ggfO%FnxVP_u#cy{>_YVCZ{OYW9jORuF$~SgW z-6P*#Apg37X!?Fs;qTtw361BkhSk^f4Z?~q zf>hUh@n=}HWe51W4~N-=@+bDmX*lXjA%RMK&p0`J9Qb}x+r<4z?*53piM$C72o2n7 z4cJRu^ex8ku^Otn4gw-td+F29!2_S1qBir)Ea{tEd-%Pm_L2N8Idg%3D`Eos8Hi|h zMUS<^@)KWDo7Q09@&6(Yc`F*IZu(uNFmsVxf?9mf?jGuoV4oxsYcBsL#kbv26xQDI z({)9B?4#(-W-v^#K+6xmNI8j&jmM$u_xJS2v58< zenfY5-@fY5-@ zfY5-@Ksspv=e;YpzL^m2?d*o83u6Q<$MVOq<29Flg@;akC)v5T?Ft)D!!hsb-gl+; zXy;SQ>FsyBL1)ZJ2-duOXgD)Wz;Y~q9G`C-83k`wuViPmW^(@H^8(0g2MmQ%Y_;amQ~%h2-Ub@0aEKigj`@po_A33bOlQXn4w z#ZPqr&dp2+oCo0J(RdF+`8Qgv;N{Pf!uRH!fg1mY;5pD5jKqKH+&uDE;~K4AuAh>1 z8^WHeX!i2DaQ>O#i!o=U#^1GJqm=vk&umI4K605%{*5Eoz+PNR{4+9bQ2*)awBjEa z_z*It+lha+w;EOtT}~VRxZbR+<9S*;rqSsD-wCbPZln!=oEMz6xQcR*^VmxUPbAEJ zGWB2o*=atfJcan%i_4(-V(fWk`FlfNo|NY|!vrPm9mE~KeHAahl2q;|EC0CO5;cR| z^|zUM|KWUE@t5~Ea_?J~G - - -
- - {children} - - - - ); -} diff --git a/llmstudio/ui/src/app/page.tsx b/llmstudio/ui/src/app/page.tsx deleted file mode 100644 index 01db15ae..00000000 --- a/llmstudio/ui/src/app/page.tsx +++ /dev/null @@ -1,12 +0,0 @@ -'use client'; - -import { useEffect } from 'react'; -import { redirect } from 'next/navigation'; - -export default function Home() { - useEffect(() => { - redirect('/playground'); - }); - - return
; -} diff --git a/llmstudio/ui/src/components/CodeBlock/index.tsx b/llmstudio/ui/src/components/CodeBlock/index.tsx deleted file mode 100644 index 43bda642..00000000 --- a/llmstudio/ui/src/components/CodeBlock/index.tsx +++ /dev/null @@ -1,71 +0,0 @@ -import { useState } from 'react'; -import hljs from 'highlight.js'; - -import { cn } from '@/lib/utils'; - -import CopyButton from '@/components/CopyButton'; - -function CodeBlock({ - language, - value, - className, - codeClass, - copyable = true, - codeWrap = false, - copyOnHover = false, - noCodeFont = false, - noMask = true, -}: { - language: string; - value: string; - className?: string; - codeClass?: string; - copyable?: boolean; - codeWrap?: boolean; - copyOnHover?: boolean; - noCodeFont?: boolean; - noMask?: boolean; -}) { - value = value || ''; - hljs.getLanguage(language) ? (language = language) : (language = 'plaintext'); - const highlightedCode = hljs.highlight(value, { language }).value; - const [isBlockHovered, setIsBlockHovered] = useState(false); - - return ( - 
 {
-        setIsBlockHovered(true);
-      }}
-      onMouseLeave={() => {
-        setIsBlockHovered(false);
-      }}
-    >
-      
-
-      {!noMask && (
-        

-      )}
-      
-
- ); -} - -export default CodeBlock; diff --git a/llmstudio/ui/src/components/CopyButton/index.tsx b/llmstudio/ui/src/components/CopyButton/index.tsx deleted file mode 100644 index e6ddddf1..00000000 --- a/llmstudio/ui/src/components/CopyButton/index.tsx +++ /dev/null @@ -1,96 +0,0 @@ -import { useCallback, useState } from 'react'; -import copy from 'copy-to-clipboard'; -import { AnimatePresence, MotionConfig, motion } from 'framer-motion'; - -// import '../styles/editor.css'; -import { cn } from '@/lib/utils'; - -export default function CopyButton({ - value, - copyable = true, - isBlockHovered, - className, -}: { - value: string; - copyable?: boolean; - isBlockHovered?: boolean; - className?: string; -}) { - const [copying, setCopying] = useState(0); - - const onCopy = useCallback(() => { - copy(value); - setCopying((c) => c + 1); - setTimeout(() => { - setCopying((c) => c - 1); - }, 2000); - }, [value]); - - if (!value || !copyable) return <>; - - const variants = { - visible: { opacity: 1, scale: 1 }, - hidden: { opacity: 0, scale: 0.5 }, - }; - return ( - - ); -} diff --git a/llmstudio/ui/src/components/Header/index.tsx b/llmstudio/ui/src/components/Header/index.tsx deleted file mode 100644 index 7c7e6abe..00000000 --- a/llmstudio/ui/src/components/Header/index.tsx +++ /dev/null @@ -1,60 +0,0 @@ -'use client'; -import React from 'react'; -import { Player } from '@lottiefiles/react-lottie-player'; -import { Toggle } from '@/components/Theme'; - -import Link from 'next/link'; -import { Shapes, GitCompare, BarChart3, BookAIcon } from 'lucide-react'; -import { Button } from '@/components/ui/button'; - -const links = [ - { - name: 'Playground', - route: '/playground', - icon: Shapes, - }, - // { - // name: 'Compare', - // route: '/compare', - // icon: GitCompare, - // }, - { - name: 'Dashboard', - route: '/dashboard', - icon: BarChart3, - }, - { - name: 'Docs', - route: 'https://docs.llmstudio.ai', - icon: BookAIcon, - }, -]; - -export default function Header() { - return ( -
-
- - - -
-
- {links.map((link) => ( - - ))} - -
-
- ); -} diff --git a/llmstudio/ui/src/components/Markdown/index.tsx b/llmstudio/ui/src/components/Markdown/index.tsx deleted file mode 100644 index e6286d58..00000000 --- a/llmstudio/ui/src/components/Markdown/index.tsx +++ /dev/null @@ -1,235 +0,0 @@ -import React from 'react'; -import ReactMarkdown from 'react-markdown'; -import rehypeAutolinkHeadings from 'rehype-autolink-headings'; -import rehypeKatex from 'rehype-katex'; -import rehypeRaw from 'rehype-raw'; -import rehypeSlug from 'rehype-slug'; -import remarkEmoji from 'remark-emoji'; -import remarkGfm from 'remark-gfm'; -import remarkMath from 'remark-math'; - -import { cn } from '@/lib/utils'; - -import CodeBlock from '@/components/CodeBlock'; - -// import 'katex/dist/katex.min.css'; - -interface ComponentTypes { - className?: string; - href?: string; - [key: string]: any; -} - -function Markdown({ - code, - codeCopyable = false, - codeClass = '', - className, -}: { - code: string; - codeCopyable?: boolean; - codeClass?: string; - className?: string; -}) { - const components = { - h1: ({ className, ...props }: ComponentTypes) => ( -

- ), - h2: ({ className, ...props }: ComponentTypes) => ( -

- ), - h3: ({ className, ...props }: ComponentTypes) => ( -

- ), - h4: ({ className, ...props }: ComponentTypes) => ( -

- ), - h5: ({ className, ...props }: ComponentTypes) => ( -

- ), - h6: ({ className, ...props }: ComponentTypes) => ( -
- ), - a: ({ className, href, ...props }: ComponentTypes) => { - const isHashURL = href?.includes('#'); // Ex. https://example.com#section - - return ( - - ); - }, - p: ({ className, ...props }: ComponentTypes) => ( -

- ), - ul: ({ className, ...props }: ComponentTypes) => ( -

    - ), - ol: ({ className, ...props }: ComponentTypes) => ( -
      - ), - li: ({ className, ...props }: ComponentTypes) => ( -
    1. - ), - blockquote: ({ className, ...props }: ComponentTypes) => ( -
      *]:text-muted-foreground', - className - )} - {...props} - /> - ), - img: ({ - className, - alt, - ...props - }: React.ImgHTMLAttributes) => ( - // eslint-disable-next-line @next/next/no-img-element - {alt} - ), - iframe: ({ className, ...props }: ComponentTypes) => ( -