diff --git a/docs/how-to/deploy-on-gcp/deploy-on-google-cloud-platform.mdx b/docs/how-to/deploy-on-gcp/deploy-on-google-cloud-platform.mdx new file mode 100644 index 00000000..f39a2d2d --- /dev/null +++ b/docs/how-to/deploy-on-gcp/deploy-on-google-cloud-platform.mdx @@ -0,0 +1,160 @@ +Learn how to deploy LLMstudio as a containerized application on Google Kubernetes Engine and make calls from a local repository. + + +## Prerequisites +To follow this guide you need to have the following set-up: + +- A **project** on google cloud platform. +- **Kubernetes Engine** API enabled on your project. +- **Kubernetes Engine Admin** role for the user performing the guide. + +## Deploy LLMstudio + +This example demonstrates a public deployment. For a private service accessible only within your enterprise infrastructure, deploy it within your own Virtual Private Cloud (VPC). + + + Begin by navigating to the Kubernetes Engine page. + + + Go to **Workloads** and **Create a new Deployment**. + + + + + + Rename your project. We will call the one in this guide **llmstudio-on-gcp**. + + + + + + Choose between **creating a new cluster** or **using an existing cluster**. + For this guide, we will create a new cluster and use the default region. + + + + + + Once done done with the **Deployment configuration**, proceed to **Container details**. + + + In the new container section, select **Existing container image**. + + + Copy the path to LLMstudio's image available on Docker Hub. + ```bash Image Path + tensoropsai/llmstudio:latest + ``` + Set it as the **Image path** to your container. + + + + + + Configure the following mandatory environment variables: +| Environment Variable | Value | +|----------------------------|-----------| +| `LLMSTUDIO_ENGINE_HOST` | 0.0.0.0 | +| `LLMSTUDIO_ENGINE_PORT` | YOUR_DESIRED_PORT | +| `LLMSTUDIO_TRACKING_HOST` | 0.0.0.0 | +| `LLMSTUDIO_TRACKING_PORT` | YOUR_DESIRED_PORT | + +Additionally, set the `GOOGLE_API_KEY` environment variable to enable calls to Google's Gemini models. +Refer to **SDK/LLM/Providers** for instructions on setting up other providers. + + + + + + + + After configuring your container, proceed to **Expose (Optional)**. + + + Select **Expose deployment as a new service** and leave the first item as is. + + + + + + Add two other items, and expose the ports defined in the **Set Environment Variables** step. + + + + + + + After setting up and exposing the ports, press **Deploy**. + You have successfully deployed **LLMstudio on Google Cloud Platform**! + + + + +## Make a Call +Now let's make a call to our LLMstudio instance on GCP! + + + + + + Setup a simple project with this two files: + 1. `simple-call.ipynb` + 2. `.env` + + + + + + + Go to your newly deployed **Workload**, scroll to the **Exposing services** section, and take note of the Host of your endpoint. + + + + + Create your `.env` file with the following: + + ```env .env + LLMSTUDIO_ENGINE_HOST = "YOUR_HOST" + LLMSTUDIO_ENGINE_PORT = "YOUR_ENGINE_PORT" + LLMSTUDIO_TRACKING_HOST = "YOUR_TRACKING_PORT" + LLMSTUDIO_TRACKING_PORT = "YOUR_HOST" + ``` + + You are done seting up you **.env** file! + + + + + + + Start by importing llmstudio: + ```python 1st cell + from llmstudio import LLM + ``` + + Set up your LLM. We will be using `gemini-1.5-flash` for this guide. + ```python 2nd cell + llm = LLM('vertexai/gemini-1.5-flash') + ``` + + Chat with your model. + ```python 3rd cell + llm.chat('Hello!') + print(response.chat_output) + ``` + + + + + + + You are done calling llmstudio on GCP! + + + + + + + + + diff --git a/docs/how-to/deploy-on-gcp/step-2.png b/docs/how-to/deploy-on-gcp/step-2.png new file mode 100644 index 00000000..4d2bb3ee Binary files /dev/null and b/docs/how-to/deploy-on-gcp/step-2.png differ diff --git a/docs/how-to/deploy-on-gcp/step-3.png b/docs/how-to/deploy-on-gcp/step-3.png new file mode 100644 index 00000000..adb4eac3 Binary files /dev/null and b/docs/how-to/deploy-on-gcp/step-3.png differ diff --git a/docs/how-to/deploy-on-gcp/step-4.png b/docs/how-to/deploy-on-gcp/step-4.png new file mode 100644 index 00000000..cc18c845 Binary files /dev/null and b/docs/how-to/deploy-on-gcp/step-4.png differ diff --git a/docs/how-to/deploy-on-gcp/step-6.png b/docs/how-to/deploy-on-gcp/step-6.png new file mode 100644 index 00000000..6ac8b8b1 Binary files /dev/null and b/docs/how-to/deploy-on-gcp/step-6.png differ diff --git a/docs/how-to/deploy-on-gcp/step-7-1.png b/docs/how-to/deploy-on-gcp/step-7-1.png new file mode 100644 index 00000000..e3e523a3 Binary files /dev/null and b/docs/how-to/deploy-on-gcp/step-7-1.png differ diff --git a/docs/how-to/deploy-on-gcp/step-7.png b/docs/how-to/deploy-on-gcp/step-7.png new file mode 100644 index 00000000..b8325854 Binary files /dev/null and b/docs/how-to/deploy-on-gcp/step-7.png differ diff --git a/docs/how-to/deploy-on-gcp/step-9-1.png b/docs/how-to/deploy-on-gcp/step-9-1.png new file mode 100644 index 00000000..328ca889 Binary files /dev/null and b/docs/how-to/deploy-on-gcp/step-9-1.png differ diff --git a/docs/how-to/deploy-on-gcp/step-9-2.png b/docs/how-to/deploy-on-gcp/step-9-2.png new file mode 100644 index 00000000..64d9ddb6 Binary files /dev/null and b/docs/how-to/deploy-on-gcp/step-9-2.png differ diff --git a/docs/how-to/deploy-on-gcp/step-9.png b/docs/how-to/deploy-on-gcp/step-9.png new file mode 100644 index 00000000..1953805a Binary files /dev/null and b/docs/how-to/deploy-on-gcp/step-9.png differ diff --git a/docs/how-to/deploy-on-gcp/step-env.png b/docs/how-to/deploy-on-gcp/step-env.png new file mode 100644 index 00000000..392a49ad Binary files /dev/null and b/docs/how-to/deploy-on-gcp/step-env.png differ diff --git a/docs/how-to/deploy-on-gcp/step-llmstudio-call.png b/docs/how-to/deploy-on-gcp/step-llmstudio-call.png new file mode 100644 index 00000000..e05609fb Binary files /dev/null and b/docs/how-to/deploy-on-gcp/step-llmstudio-call.png differ diff --git a/docs/mint.json b/docs/mint.json index 63b3defc..b878a0c3 100644 --- a/docs/mint.json +++ b/docs/mint.json @@ -87,3 +87,4 @@ "baseUrl": "http://localhost:8000" } } + \ No newline at end of file diff --git a/docs/sdk/llm/chat.mdx b/docs/sdk/llm/chat.mdx new file mode 100644 index 00000000..cc8852f4 --- /dev/null +++ b/docs/sdk/llm/chat.mdx @@ -0,0 +1,86 @@ +Make chat calls using your LLM. + +## Parameters +The llm.chat method can have the following parameters. +| Parameter | Type | Description | +|-------------------|--------|-----------------------------------------------------------------------------| +| `input ` | str | The input message to send to the chat model. | +| `is_stream` | bool | The temperature parameter for the model. | +| `**kwargs` | dict | Additional parameters to pass to the chat model. | + +Refer to your provider-specific documentation for additional kwargs you can use. + +## Returns +| Output | Type | Description | +|-------------------|--------|-----------------------------------------------------------------------------| +| `ChatCompletion` | object | A chat completion object in the OpenAI format + metrics computed by LLMstudio.| + + +## Usage +Here's how to use `.chat()` to make calls to your LLM. + + + + Start by importing LLM. + ```python + from llmstudio import LLM + ``` + + + Set up an LLM from your desired provider. + ```python + llm = LLM('openai/gpt-4o') + ``` + + + Create your message. Your message can be a simple `string` or a message in the `OpenAI format`. + + + + ```python + message = "Hello, how are you today?" + ``` + + + ```python + message = [ + {"role": "system", "content": "You are a helpfull assistant."}, + {"role": "user", "content": "Hello, how are you today?"} + ] + ``` + + + + + + + + + Get your response. + ```python + response = llm.chat(message) + ``` + + Vizualize your response. + ```python + print(response) + ``` + + + Get your response. + ```python + response = llm.chat(message, is_stream = True) + ``` + + Vizualize your response. + ```python + for chunk in response: + print(chunk) + ``` + + + + You are done chating with your **LLMstudio LLM**! + + + \ No newline at end of file diff --git a/docs/sdk/llm/providers/anthropic.mdx b/docs/sdk/llm/providers/anthropic.mdx new file mode 100644 index 00000000..eeff4915 --- /dev/null +++ b/docs/sdk/llm/providers/anthropic.mdx @@ -0,0 +1,101 @@ +Interact with your Anthropic models using LLMstudios LLM. + +## Supported models +1. `claude-3-opus-20240229` +2. `claude-3-sonnet-2024022` +3. `claude-3-haiku-20240307` +4. `claude-2.1` +5. `claude-2` +6. `claude-instant-1.2` + +## Parameters +An Anthropic LLM interface can have the following parameters: +| Parameter | Type | Description | +|-------------------|--------|-----------------------------------------------------------------------------| +| `api_key` | str | The API key for authentication. | +| `temperature` | float | The temperature parameter for the model. | +| `top_p` | float | The top-p parameter for the model. | +| `max_tokens` | int | The maximum number of tokens for the model's output. | +| `top_k` | int | The top-k parameter for the model. | + + +## Usage +Here is how you setup an interface to interact with your Anthropic models. + + + + + + Create a `.env` file with you `ANTHROPIC_API_KEY` + + Make sure you call your environment variable ANTHROPIC_API_KEY + ```bash + ANTHROPIC_API_KEY="YOUR-KEY" + ``` + + + In your python code, import LLM from llmstudio. + ```python + from llmstudio import LLM + ``` + + + Create your **llm** instance. + ```python + llm = LLM('anthropic/{model}') + ``` + + + **Optional:** You can add your parameters as follows: + ```python + llm = LLM('anthropic/model', + temperature= ..., + max_tokens= ..., + top_p= ..., + frequency_penalty= ..., + presence_penalty= ...) + ``` + You are done setting up your **Anthropic LLM**! + + + + + + + In your python code, import LLM from llmstudio. + ```python + from llmstudio import LLM + ``` + + + Create your **llm** instance. + ```python + llm = LLM('anthropic/{model}',api_key="YOUR_API_KEY") + ``` + + + **Optional:** You can add your parameters as follows: + ```python + llm = LLM('anthropic/model', + temperature= ..., + max_tokens= ..., + top_p= ..., + frequency_penalty= ..., + presence_penalty= ...) + ``` + You are done setting up your **Anthropic LLM**! + + + + + + +## What's next? + + + Learn how to send messeges and recieve responses next! + + + Learn how to build a tool calling agent using llmstudio. + + diff --git a/docs/sdk/llm/providers/azure.mdx b/docs/sdk/llm/providers/azure.mdx new file mode 100644 index 00000000..161a2491 --- /dev/null +++ b/docs/sdk/llm/providers/azure.mdx @@ -0,0 +1,126 @@ +Interact with your Azure models using LLM. + +## Parameters +An Azure LLM interface can have the following parameters: +| Parameter | Type | Description | +|---------------------|--------|-----------------------------------------------------------------------------| +| `temperature` | float | The temperature parameter for the model. | +| `max_tokens` | int | The maximum number of tokens to generate. | +| `top_p` | float | The top-p parameter for the model. | +| `frequency_penalty` | float | The frequency penalty parameter for the model. | +| `presence_penalty` | float | The presence penalty parameter for the model. | + + +## Usage +Here is how you setup an interface to interact with your Azure models. + + + + + Create a `config.yaml` file in the same directory as your code. + 1. 📁 src + 1. 🐍 PythonCode.py + 2. 🐍 PyNotebook.ipynb + 3. 📄 **config.yaml** + + + Define your Azure OpenAI provider and models inside the `config.yaml` file. + ```yaml + providers: + azure: + id: azure + name: Azure + chat: true + embed: true + models: + YOUR_MODEL: <- Replace with your model name + mode: chat + max_tokens: ... + input_token_cost: ... + output_token_cost: ... + ``` + If you are not sure, you can leave `max_tokens`, `input_tokens` and the other parameters as **0** + + + Create your **llm** instance. + ```python + llm = LLM('azure/YOUR_MODEL', + api_key = YOUR_API_KEY, + api_endpoint = YOUR_ENDPOINT, + api_version = YOUR_API_VERSION) + ``` + + + **Optional:** You can add your parameters as follows: + ```python + llm = LLM('azure/model', + temperature= ..., + max_tokens= ..., + top_p= ..., + frequency_penalty= ..., + presence_penalty= ...) + ``` + You are done setting up your **Azure LLM**! + + + + + + + Create a `config.yaml` file in the same directory as your code. + 1. 📁 src + 1. 🐍 PythonCode.py + 2. 🐍 PyNotebook.ipynb + 3. 📄 **config.yaml** + + + Define your Azure provider and models inside the `config.yaml` file. + ```yaml + providers: + azure: + id: azure + name: Azure + chat: true + embed: true + models: + YOUR_MODEL: <- Replace with your model name + mode: chat + max_tokens: ... + input_token_cost: ... + output_token_cost: ... + ``` + If you are not sure, you can leave `max_tokens`, `input_tokens` and the other parameters as **0** + + + Create your **llm** instance. + ```python + llm = LLM('azure/YOUR_MODEL', + api_key = YOUR_API_KEY, + base_url = YOUR_ENDPOINT) + ``` + + + **Optional:** You can add your parameters as follows: + ```python + llm = LLM('azure/model', + temperature= ..., + max_tokens= ..., + top_p= ..., + frequency_penalty= ..., + presence_penalty= ...) + ``` + You are done setting up your **Azure LLM**! + + + + + +## What's next? + + + Learn how to send messeges and recieve responses next! + + + Learn how to build a tool calling agent using llmstudio. + + diff --git a/docs/sdk/llm/providers/ollama.mdx b/docs/sdk/llm/providers/ollama.mdx new file mode 100644 index 00000000..eae25f25 --- /dev/null +++ b/docs/sdk/llm/providers/ollama.mdx @@ -0,0 +1,70 @@ +Interact with your Ollama models using LLM. + +## Parameters +An Ollama LLM interface can have the following parameters: +| Parameter | Type | Description | +|-------------------|--------|-----------------------------------------------------------------------------| +| `temperature` | float | The temperature parameter for the model. | +| `top_p` | float | The top-p parameter for the model. | +| `num_predict` | int | The number of tokens to predict. | +| `top_k` | int | The top-k parameter for the model. | + + +## Usage +Here is how you setup an interface to interact with your Ollama models. + + + + Create a `config.yaml` in the same directory your code is in. + 1. src + 1. yourPythonCode.py + 2. yourPyNotebook.py + 3. **config.yaml** + + + Define your Ollama provider and models inside the `config.yaml` file. + ```yaml + providers: + ollama: + id: ollama + name: Ollama + chat: true + embed: true + keys: + models: + YOUR_MODEL: <- Replace with your model name + mode: chat + max_tokens: ... + input_token_cost: ... + output_token_cost: ... + ``` + If you are not sure about any of these parameters, you can just leave them as **0** + + + Create your **llm** instance. + ```python + llm = LLM('ollama/{YOUR_MODEL}') + ``` + + + **Optional:** You can add your parameters as follows: + ```python + llm = LLM('ollama/model', + temperature= ..., + num_predict= ..., + top_p= ..., + top_k= ...,) + ``` + You are done setting up your **Ollama LLM**! + + + +## What's next? + + + Learn how to send messeges and recieve responses next! + + + Learn how to build a tool calling agent using llmstudio. + + diff --git a/docs/sdk/llm/providers/openai.mdx b/docs/sdk/llm/providers/openai.mdx new file mode 100644 index 00000000..4c6d3528 --- /dev/null +++ b/docs/sdk/llm/providers/openai.mdx @@ -0,0 +1,100 @@ +Interact with your OpenAI models using LLM. + +## Supported models +1. `gpt-4o` +2. `gpt-4-turbo` +3. `gpt-4` +4. `gpt-3.5-turbo` +5. `gpt-3.5-turbo-instruct` + +## Parameters +An OpenAI LLM interface can have the following parameters: +| Parameter | Type | Description | +|-------------------|--------|-----------------------------------------------------------------------------| +| `api_key` | str | The API key for authentication. | +| `temperature` | float | The temperature parameter for the model. | +| `top_p` | float | The top-p parameter for the model. | +| `max_tokens` | int | The maximum number of tokens for the model's output. | +| `frequency_penalty` | float | The frequency penalty parameter for the model. | +| `presence_penalty` | float | The presence penalty parameter for the model. | + + +## Usage +Here is how you setup an interface to interact with your OpenAI models. + + + + + + Create a `.env` file with you `OPENAI_API_KEY` + + Make sure you call your environment variable OPENAI_API_KEY + ```bash + OPENAI_API_KEY="YOUR-KEY" + ``` + + + In your python code, import LLM from llmstudio. + ```python + from llmstudio import LLM + ``` + + + Create your **llm** instance. + ```python + llm = LLM('openai/{model}') + ``` + + + **Optional:** You can add your parameters as follows: + ```python + llm = LLM('openai/model', + temperature= ..., + max_tokens= ..., + top_p= ..., + frequency_penalty= ..., + presence_penalty= ...) + ``` + You are done setting up your **OpenAI LLM**! + + + + + + + In your python code, import LLM from llmstudio. + ```python + from llmstudio import LLM + ``` + + + Create your **llm** instance. + ```python + llm = LLM('openai/{model}',api_key="YOUR_API_KEY") + ``` + + + **Optional:** You can add your parameters as follows: + ```python + llm = LLM('openai/model', + temperature= ..., + max_tokens= ..., + top_p= ..., + frequency_penalty= ..., + presence_penalty= ...) + ``` + You are done setting up your **OpenAI LLM**! + + + + + +## What's next? + + + Learn how to send messeges and recieve responses next! + + + Learn how to build a tool calling agent using llmstudio. + + diff --git a/docs/sdk/llm/providers/vertexai.mdx b/docs/sdk/llm/providers/vertexai.mdx new file mode 100644 index 00000000..1f5a1c81 --- /dev/null +++ b/docs/sdk/llm/providers/vertexai.mdx @@ -0,0 +1,98 @@ +Interact with your VertexAI models using LLM. + +## Supported models +1. `gemini-1.5-flash` +2. `gemini-1.5-pro` +3. `gemini-1.0-pro` + +## Parameters +A VertexAI LLM interface can have the following parameters: +| Parameter | Type | Description | +|-------------------|--------|-----------------------------------------------------------------------------| +| `api_key` | str | The API key for authentication. | +| `temperature` | float | The temperature parameter for the model. | +| `top_p` | float | The top-p parameter for the model. | +| `max_tokens` | int | The maximum number of tokens for the model's output. | +| `frequency_penalty` | float | The frequency penalty parameter for the model. | +| `presence_penalty` | float | The presence penalty parameter for the model. | + + +## Usage +Here is how you setup an interface to interact with your VertexAI models. + + + + + + Create a `.env` file with you `GOOGLE_API_KEY` + + Make sure you call your environment variable GOOGLE_API_KEY + ```bash + GOOGLE_API_KEY="YOUR-KEY" + ``` + + + In your python code, import LLM from llmstudio. + ```python + from llmstudio import LLM + ``` + + + Create your **llm** instance. + ```python + llm = LLM('vertexai/{model}') + ``` + + + **Optional:** You can add your parameters as follows: + ```python + llm = LLM('vertexai/model', + temperature= ..., + max_tokens= ..., + top_p= ..., + frequency_penalty= ..., + presence_penalty= ...) + ``` + You are done setting up your **VertexAI LLM**! + + + + + + + In your python code, import LLM from llmstudio. + ```python + from llmstudio import LLM + ``` + + + Create your **llm** instance. + ```python + llm = LLM('vertexai/{model}',api_key="YOUR_API_KEY") + ``` + + + **Optional:** You can add your parameters as follows: + ```python + llm = LLM('vertexai/model', + temperature= ..., + max_tokens= ..., + top_p= ..., + frequency_penalty= ..., + presence_penalty= ...) + ``` + You are done setting up your **VertexAI LLM**! + + + + + +## What's next? + + + Learn how to send messeges and recieve responses next! + + + Learn how to build a tool calling agent using llmstudio. + + \ No newline at end of file diff --git a/examples/06_gcloud_guide.ipynb b/examples/06_gcloud_guide.ipynb new file mode 100644 index 00000000..fa29d234 --- /dev/null +++ b/examples/06_gcloud_guide.ipynb @@ -0,0 +1,108 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Engine server already running on 0.0.0.0:8001\n", + "Tracking server already running on 0.0.0.0:8002\n" + ] + } + ], + "source": [ + "from llmstudio import LLM" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "llm = LLM('openai/gpt-3.5-turbo')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "ename": "ConnectionError", + "evalue": "('Connection aborted.', RemoteDisconnected('Remote end closed connection without response'))", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mRemoteDisconnected\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m/opt/anaconda3/envs/llmstudiodev/lib/python3.11/site-packages/urllib3/connectionpool.py:793\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[0m\n\u001b[1;32m 792\u001b[0m \u001b[38;5;66;03m# Make the request on the HTTPConnection object\u001b[39;00m\n\u001b[0;32m--> 793\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 794\u001b[0m \u001b[43m \u001b[49m\u001b[43mconn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 795\u001b[0m \u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 796\u001b[0m \u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 797\u001b[0m \u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 798\u001b[0m \u001b[43m \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 799\u001b[0m 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\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mresponse_kw\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 806\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 808\u001b[0m \u001b[38;5;66;03m# Everything went great!\u001b[39;00m\n", + "File \u001b[0;32m/opt/anaconda3/envs/llmstudiodev/lib/python3.11/site-packages/urllib3/connectionpool.py:537\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[0;34m(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)\u001b[0m\n\u001b[1;32m 536\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 537\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[43mconn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgetresponse\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 538\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (BaseSSLError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m e:\n", + "File \u001b[0;32m/opt/anaconda3/envs/llmstudiodev/lib/python3.11/site-packages/urllib3/connection.py:466\u001b[0m, in \u001b[0;36mHTTPConnection.getresponse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 465\u001b[0m \u001b[38;5;66;03m# Get the response from http.client.HTTPConnection\u001b[39;00m\n\u001b[0;32m--> 466\u001b[0m httplib_response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgetresponse\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 468\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n", + "File \u001b[0;32m/opt/anaconda3/envs/llmstudiodev/lib/python3.11/http/client.py:1395\u001b[0m, in \u001b[0;36mHTTPConnection.getresponse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1394\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1395\u001b[0m \u001b[43mresponse\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbegin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1396\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mConnectionError\u001b[39;00m:\n", + "File \u001b[0;32m/opt/anaconda3/envs/llmstudiodev/lib/python3.11/http/client.py:325\u001b[0m, in \u001b[0;36mHTTPResponse.begin\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 324\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m--> 325\u001b[0m version, status, reason \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_read_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 326\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m status \u001b[38;5;241m!=\u001b[39m CONTINUE:\n", + "File \u001b[0;32m/opt/anaconda3/envs/llmstudiodev/lib/python3.11/http/client.py:294\u001b[0m, in \u001b[0;36mHTTPResponse._read_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 291\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m line:\n\u001b[1;32m 292\u001b[0m \u001b[38;5;66;03m# Presumably, the server closed the connection before\u001b[39;00m\n\u001b[1;32m 293\u001b[0m \u001b[38;5;66;03m# sending a valid response.\u001b[39;00m\n\u001b[0;32m--> 294\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m RemoteDisconnected(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRemote end closed connection without\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 295\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m response\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 296\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n", + "\u001b[0;31mRemoteDisconnected\u001b[0m: Remote end closed connection without response", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mProtocolError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m/opt/anaconda3/envs/llmstudiodev/lib/python3.11/site-packages/requests/adapters.py:589\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m 588\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 589\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[43mconn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43murlopen\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 590\u001b[0m \u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 591\u001b[0m \u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 592\u001b[0m \u001b[43m \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 593\u001b[0m \u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 594\u001b[0m \u001b[43m \u001b[49m\u001b[43mredirect\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 595\u001b[0m \u001b[43m \u001b[49m\u001b[43massert_same_host\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 596\u001b[0m \u001b[43m \u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 597\u001b[0m \u001b[43m \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 598\u001b[0m \u001b[43m \u001b[49m\u001b[43mretries\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[43mmax_retries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 599\u001b[0m \u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 600\u001b[0m \u001b[43m \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 601\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 603\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (ProtocolError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m err:\n", + "File \u001b[0;32m/opt/anaconda3/envs/llmstudiodev/lib/python3.11/site-packages/urllib3/connectionpool.py:847\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[0m\n\u001b[1;32m 845\u001b[0m new_e \u001b[38;5;241m=\u001b[39m ProtocolError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConnection aborted.\u001b[39m\u001b[38;5;124m\"\u001b[39m, new_e)\n\u001b[0;32m--> 847\u001b[0m retries \u001b[38;5;241m=\u001b[39m \u001b[43mretries\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mincrement\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 848\u001b[0m \u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merror\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnew_e\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_pool\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_stacktrace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msys\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexc_info\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[1;32m 849\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 850\u001b[0m retries\u001b[38;5;241m.\u001b[39msleep()\n", + "File \u001b[0;32m/opt/anaconda3/envs/llmstudiodev/lib/python3.11/site-packages/urllib3/util/retry.py:470\u001b[0m, in \u001b[0;36mRetry.increment\u001b[0;34m(self, method, url, response, error, _pool, _stacktrace)\u001b[0m\n\u001b[1;32m 469\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m read \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m method \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_method_retryable(method):\n\u001b[0;32m--> 470\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[43mreraise\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mtype\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43merror\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merror\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_stacktrace\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 471\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m read \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/opt/anaconda3/envs/llmstudiodev/lib/python3.11/site-packages/urllib3/util/util.py:38\u001b[0m, in \u001b[0;36mreraise\u001b[0;34m(tp, value, tb)\u001b[0m\n\u001b[1;32m 37\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m value\u001b[38;5;241m.\u001b[39m__traceback__ \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m tb:\n\u001b[0;32m---> 38\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m value\u001b[38;5;241m.\u001b[39mwith_traceback(tb)\n\u001b[1;32m 39\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m value\n", + "File \u001b[0;32m/opt/anaconda3/envs/llmstudiodev/lib/python3.11/site-packages/urllib3/connectionpool.py:793\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[0m\n\u001b[1;32m 792\u001b[0m \u001b[38;5;66;03m# Make the request on the HTTPConnection object\u001b[39;00m\n\u001b[0;32m--> 793\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 794\u001b[0m \u001b[43m \u001b[49m\u001b[43mconn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 795\u001b[0m \u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 796\u001b[0m \u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 797\u001b[0m \u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 798\u001b[0m \u001b[43m \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 799\u001b[0m \u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 800\u001b[0m \u001b[43m \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 801\u001b[0m \u001b[43m \u001b[49m\u001b[43mretries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mretries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 802\u001b[0m \u001b[43m \u001b[49m\u001b[43mresponse_conn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresponse_conn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 803\u001b[0m \u001b[43m \u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpreload_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 804\u001b[0m \u001b[43m \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecode_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 805\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mresponse_kw\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 806\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 808\u001b[0m \u001b[38;5;66;03m# Everything went great!\u001b[39;00m\n", + "File \u001b[0;32m/opt/anaconda3/envs/llmstudiodev/lib/python3.11/site-packages/urllib3/connectionpool.py:537\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[0;34m(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)\u001b[0m\n\u001b[1;32m 536\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 537\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[43mconn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgetresponse\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 538\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (BaseSSLError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m e:\n", + "File \u001b[0;32m/opt/anaconda3/envs/llmstudiodev/lib/python3.11/site-packages/urllib3/connection.py:466\u001b[0m, in \u001b[0;36mHTTPConnection.getresponse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 465\u001b[0m \u001b[38;5;66;03m# Get the response from http.client.HTTPConnection\u001b[39;00m\n\u001b[0;32m--> 466\u001b[0m httplib_response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgetresponse\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 468\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n", + "File \u001b[0;32m/opt/anaconda3/envs/llmstudiodev/lib/python3.11/http/client.py:1395\u001b[0m, in \u001b[0;36mHTTPConnection.getresponse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1394\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1395\u001b[0m \u001b[43mresponse\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbegin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1396\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mConnectionError\u001b[39;00m:\n", + "File \u001b[0;32m/opt/anaconda3/envs/llmstudiodev/lib/python3.11/http/client.py:325\u001b[0m, in \u001b[0;36mHTTPResponse.begin\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 324\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m--> 325\u001b[0m version, status, reason \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_read_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 326\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m status \u001b[38;5;241m!=\u001b[39m CONTINUE:\n", + "File \u001b[0;32m/opt/anaconda3/envs/llmstudiodev/lib/python3.11/http/client.py:294\u001b[0m, in \u001b[0;36mHTTPResponse._read_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 291\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m line:\n\u001b[1;32m 292\u001b[0m \u001b[38;5;66;03m# Presumably, the server closed the connection before\u001b[39;00m\n\u001b[1;32m 293\u001b[0m \u001b[38;5;66;03m# sending a valid response.\u001b[39;00m\n\u001b[0;32m--> 294\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m RemoteDisconnected(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRemote end closed connection without\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 295\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m response\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 296\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n", + "\u001b[0;31mProtocolError\u001b[0m: ('Connection aborted.', RemoteDisconnected('Remote end closed connection without response'))", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mConnectionError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[3], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \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;43mHi\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/GitHub/LLMstudio/llmstudio/llm/__init__.py:31\u001b[0m, in \u001b[0;36mLLM.chat\u001b[0;34m(self, input, is_stream, retries, **kwargs)\u001b[0m\n\u001b[1;32m 30\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mchat\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: \u001b[38;5;28mstr\u001b[39m, is_stream: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m, retries: \u001b[38;5;28mint\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m---> 31\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[43mrequests\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpost\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 32\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mhttp://\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mENGINE_HOST\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m:\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mENGINE_PORT\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m/api/engine/chat/\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprovider\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 33\u001b[0m \u001b[43m \u001b[49m\u001b[43mjson\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\n\u001b[1;32m 34\u001b[0m \u001b[43m 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\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mchat_input\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 41\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mis_stream\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[43m,\u001b[49m\n\u001b[1;32m 42\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mretries\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mretries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 43\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mparameters\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\n\u001b[1;32m 44\u001b[0m \u001b[43m 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\u001b[0;32m/opt/anaconda3/envs/llmstudiodev/lib/python3.11/site-packages/requests/api.py:115\u001b[0m, in \u001b[0;36mpost\u001b[0;34m(url, data, json, **kwargs)\u001b[0m\n\u001b[1;32m 103\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpost\u001b[39m(url, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, json\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 104\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Sends a POST request.\u001b[39;00m\n\u001b[1;32m 105\u001b[0m \n\u001b[1;32m 106\u001b[0m \u001b[38;5;124;03m :param url: URL for the new :class:`Request` object.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 112\u001b[0m \u001b[38;5;124;03m :rtype: requests.Response\u001b[39;00m\n\u001b[1;32m 113\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 115\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m 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open which can trigger a ResourceWarning in some\u001b[39;00m\n\u001b[1;32m 57\u001b[0m \u001b[38;5;66;03m# cases, and look like a memory leak in others.\u001b[39;00m\n\u001b[1;32m 58\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m sessions\u001b[38;5;241m.\u001b[39mSession() \u001b[38;5;28;01mas\u001b[39;00m session:\n\u001b[0;32m---> 59\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43msession\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43murl\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", + "File \u001b[0;32m/opt/anaconda3/envs/llmstudiodev/lib/python3.11/site-packages/requests/sessions.py:589\u001b[0m, in \u001b[0;36mSession.request\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m 584\u001b[0m send_kwargs \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 585\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtimeout\u001b[39m\u001b[38;5;124m\"\u001b[39m: timeout,\n\u001b[1;32m 586\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mallow_redirects\u001b[39m\u001b[38;5;124m\"\u001b[39m: allow_redirects,\n\u001b[1;32m 587\u001b[0m }\n\u001b[1;32m 588\u001b[0m send_kwargs\u001b[38;5;241m.\u001b[39mupdate(settings)\n\u001b[0;32m--> 589\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprep\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[43msend_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 591\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m resp\n", + "File \u001b[0;32m/opt/anaconda3/envs/llmstudiodev/lib/python3.11/site-packages/requests/sessions.py:703\u001b[0m, in \u001b[0;36mSession.send\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m 700\u001b[0m start \u001b[38;5;241m=\u001b[39m preferred_clock()\n\u001b[1;32m 702\u001b[0m \u001b[38;5;66;03m# Send the request\u001b[39;00m\n\u001b[0;32m--> 703\u001b[0m r \u001b[38;5;241m=\u001b[39m \u001b[43madapter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\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 705\u001b[0m \u001b[38;5;66;03m# Total elapsed time of the request (approximately)\u001b[39;00m\n\u001b[1;32m 706\u001b[0m elapsed \u001b[38;5;241m=\u001b[39m preferred_clock() \u001b[38;5;241m-\u001b[39m start\n", + "File \u001b[0;32m/opt/anaconda3/envs/llmstudiodev/lib/python3.11/site-packages/requests/adapters.py:604\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m 589\u001b[0m resp \u001b[38;5;241m=\u001b[39m conn\u001b[38;5;241m.\u001b[39murlopen(\n\u001b[1;32m 590\u001b[0m method\u001b[38;5;241m=\u001b[39mrequest\u001b[38;5;241m.\u001b[39mmethod,\n\u001b[1;32m 591\u001b[0m url\u001b[38;5;241m=\u001b[39murl,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 600\u001b[0m chunked\u001b[38;5;241m=\u001b[39mchunked,\n\u001b[1;32m 601\u001b[0m )\n\u001b[1;32m 603\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (ProtocolError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m--> 604\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mConnectionError\u001b[39;00m(err, request\u001b[38;5;241m=\u001b[39mrequest)\n\u001b[1;32m 606\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m MaxRetryError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 607\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(e\u001b[38;5;241m.\u001b[39mreason, ConnectTimeoutError):\n\u001b[1;32m 608\u001b[0m \u001b[38;5;66;03m# TODO: Remove this in 3.0.0: see #2811\u001b[39;00m\n", + "\u001b[0;31mConnectionError\u001b[0m: ('Connection aborted.', RemoteDisconnected('Remote end closed connection without response'))" + ] + } + ], + "source": [ + "llm.chat('Hi')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "llmstudiodev", + "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": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/langchain_integration.py b/examples/langchain_integration.py index b22f2b28..4d408535 100644 --- a/examples/langchain_integration.py +++ b/examples/langchain_integration.py @@ -233,7 +233,7 @@ def dim_lights(brightness: float) -> bool: from langchain.agents import AgentExecutor, create_tool_calling_agent, create_openai_tools_agent from langchain_core.prompts import ChatPromptTemplate -def assistant_new(question: str)->str: +def assistant_new(question: str,chat_llm)->str: tools = [power_disco_ball, start_music, dim_lights] print(tools) @@ -264,7 +264,7 @@ def assistant_new(question: str)->str: api_endpoint=os.environ["AZURE_API_ENDPOINT"]) chat_llm = ChatLLMstudio(llm=llm, model = "gpt-4o-mini", parameters={"temperature":0}) -print("\n\nresult:\n", assistant_new("Turn this into a party!"),"\n") +print("\n\nresult:\n", assistant_new("Turn this into a party!",chat_llm),"\n") print("###### vertex") @@ -323,6 +323,66 @@ def assistant_vertex(question: str) -> str: print("\n\nresult:\n", assistant_vertex("Turn this into a party!"),"\n") + + +print("###### bedrock anthropic") +import os +from llmstudio.langchain import ChatLLMstudio +from llmstudio.providers import LLM + +llm = LLM(provider="bedrock", + region=os.environ["BEDROCK_REGION"], + secret_key=os.environ["BEDROCK_SECRET_KEY"], + access_key=os.environ["BEDROCK_ACCESS_KEY"]) +chat_llm = ChatLLMstudio(llm=llm, model = "anthropic.claude-3-5-sonnet-20240620-v1:0", parameters={"temperature":0}) + +from langchain.tools import tool + +@tool +def power_disco_ball(power: bool) -> bool: + """Powers the spinning disco ball.""" + print(f"Disco ball is {'spinning!' if power else 'stopped.'}") + return True + +@tool +def start_music(energetic: bool, loud: bool, bpm: int) -> str: + """Play some music matching the specified parameters.""" + print(f"Starting music! {energetic=} {loud=}, {bpm=}") + return "Never gonna give you up." + +@tool +def dim_lights(brightness: float) -> bool: + """Dim the lights.""" + print(f"Lights are now set to {brightness:.0%}") + return True + +from langchain.agents import AgentExecutor, create_tool_calling_agent +from langchain_core.prompts import ChatPromptTemplate + + +def assistant_bedrock_anthropic(question: str) -> str: + tools = [power_disco_ball, start_music, dim_lights] + print(tools) + + # Rebuild agent with new tools + prompt = ChatPromptTemplate.from_messages( + [ + ("system", "You are a helpful assistant"), + ("human", "{input}"), + ("placeholder", "{agent_scratchpad}"), + ] + ) + agent = create_tool_calling_agent(chat_llm, tools, prompt) + agent_executor = AgentExecutor(agent=agent, tools=tools) + + response = agent_executor.invoke({"input": question}) + + return response + + +print("\n\nresult:\n", assistant_bedrock_anthropic("Turn this into a party!"),"\n") + + print("###### proxy") from llmstudio.server import start_servers start_servers() diff --git a/libs/core/llmstudio_core/config.yaml b/libs/core/llmstudio_core/config.yaml index ae82394b..8a45f642 100644 --- a/libs/core/llmstudio_core/config.yaml +++ b/libs/core/llmstudio_core/config.yaml @@ -17,6 +17,11 @@ providers: max_tokens: 200000 input_token_cost: 0.000003 output_token_cost: 0.000015 + claude-3-sonnet: + mode: chat + max_tokens: 200000 + input_token_cost: 0.000003 + output_token_cost: 0.000015 claude-3-haiku-20240307: mode: chat max_tokens: 200000 @@ -66,6 +71,90 @@ providers: min: 0 max: 500 step: 1 + bedrock: + id: bedrock + name: Bedrock + chat: true + embed: true + keys: + - BEDROCK_SECRET_KEY + - BEDROCK_ACCESS_KEY + - BEDROCK_REGION + models: + anthropic.claude-3-sonnet-20240229-v1:0: + mode: chat + max_tokens: 200000 + input_token_cost: 0.000003 + output_token_cost: 0.000015 + anthropic.claude-3-5-sonnet-20240620-v1:0: + mode: chat + max_tokens: 200000 + input_token_cost: 0.000003 + output_token_cost: 0.000015 + anthropic.claude-3-5-sonnet-20241022-v2:0: + mode: chat + max_tokens: 200000 + input_token_cost: 0.000003 + output_token_cost: 0.000015 + anthropic.claude-3-haiku-20240307-v1:0: + mode: chat + max_tokens: 200000 + input_token_cost: 0.00000025 + output_token_cost: 0.00000125 + anthropic.claude-3-5-haiku-20241022-v1:0: + mode: chat + max_tokens: 200000 + input_token_cost: 0.000001 + output_token_cost: 0.000005 + anthropic.claude-3-opus-20240229-v1:0: + mode: chat + max_tokens: 200000 + input_token_cost: 0.000015 + output_token_cost: 0.000075 + anthropic.claude-instant-v1: + mode: chat + max_tokens: 100000 + input_token_cost: 0.0000008 + output_token_cost: 0.000024 + anthropic.claude-v2: + mode: chat + max_tokens: 100000 + input_token_cost: 0.000008 + output_token_cost: 0.000024 + anthropic.claude-v2:1: + mode: chat + max_tokens: 100000 + input_token_cost: 0.000008 + output_token_cost: 0.000024 + 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 diff --git a/libs/core/llmstudio_core/providers/__init__.py b/libs/core/llmstudio_core/providers/__init__.py index 972fca55..6aa6c4e7 100644 --- a/libs/core/llmstudio_core/providers/__init__.py +++ b/libs/core/llmstudio_core/providers/__init__.py @@ -2,6 +2,7 @@ from typing import Optional from llmstudio_core.providers.azure import AzureProvider +from llmstudio_core.providers.bedrock.provider import BedrockProvider # from llmstudio_core.providers.ollama import OllamaProvider #TODO: adapt it from llmstudio_core.providers.openai import OpenAIProvider diff --git a/libs/core/llmstudio_core/providers/azure.py b/libs/core/llmstudio_core/providers/azure.py index 6924f411..2dbd7307 100644 --- a/libs/core/llmstudio_core/providers/azure.py +++ b/libs/core/llmstudio_core/providers/azure.py @@ -1,5 +1,4 @@ import ast -import asyncio import json import os import time @@ -23,9 +22,15 @@ @provider class AzureProvider(ProviderCore): def __init__( - self, config, api_key=None, api_endpoint=None, api_version=None, base_url=None + self, + config, + api_key=None, + api_endpoint=None, + api_version=None, + base_url=None, + **kwargs, ): - super().__init__(config) + super().__init__(config, **kwargs) self.API_KEY = api_key or os.getenv("AZURE_API_KEY") self.API_ENDPOINT = api_endpoint self.API_VERSION = api_version or os.getenv("AZURE_API_VERSION") @@ -54,57 +59,7 @@ def validate_request(self, request: ChatRequest): 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.parameters.get("tools") is not None - self.has_functions = request.parameters.get("functions") is not None - - try: - 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": True, - } - - # Combine all arguments - combined_args = { - **base_args, - **tool_args, - **function_args, - **request.parameters, - } - # Perform the asynchronous call - return await asyncio.to_thread( - self._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()) + return self.generate_client(request=request) def generate_client(self, request: ChatRequest) -> Any: """Generate an AzureOpenAI client""" @@ -179,17 +134,11 @@ 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 or self.has_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 + result = self.parse_response(response=response, **kwargs) + for chunk in result: + yield chunk - def parse_response(self, response: Generator, **kwargs) -> Any: + def parse_response(self, response: AsyncGenerator, **kwargs) -> Any: if self.is_llama and (self.has_tools or self.has_functions): for chunk in self.handle_tool_response(response, **kwargs): if chunk: diff --git a/libs/core/llmstudio_core/providers/bedrock/__init__.py b/libs/core/llmstudio_core/providers/bedrock/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/libs/core/llmstudio_core/providers/bedrock/anthropic.py b/libs/core/llmstudio_core/providers/bedrock/anthropic.py new file mode 100644 index 00000000..f2b3cfc1 --- /dev/null +++ b/libs/core/llmstudio_core/providers/bedrock/anthropic.py @@ -0,0 +1,330 @@ +import json +import os +import time +import uuid +from typing import ( + Any, + AsyncGenerator, + Coroutine, + Dict, + Generator, + List, + Optional, + Union, +) + +import boto3 +from llmstudio_core.exceptions import ProviderError +from llmstudio_core.providers.provider import ChatRequest, ProviderCore, provider +from llmstudio_core.utils import OpenAIToolFunction +from openai.types.chat import ChatCompletionChunk +from openai.types.chat.chat_completion_chunk import ( + Choice, + ChoiceDelta, + ChoiceDeltaToolCall, + ChoiceDeltaToolCallFunction, +) +from pydantic import ValidationError + +SERVICE = "bedrock-runtime" + + +@provider +class BedrockAnthropicProvider(ProviderCore): + def __init__(self, config, **kwargs): + super().__init__(config, **kwargs) + self._client = boto3.client( + SERVICE, + region_name=self.region if self.region else os.getenv("BEDROCK_REGION"), + aws_access_key_id=self.access_key + if self.access_key + else os.getenv("BEDROCK_ACCESS_KEY"), + aws_secret_access_key=self.secret_key + if self.secret_key + else os.getenv("BEDROCK_SECRET_KEY"), + ) + + @staticmethod + def _provider_config_name(): + return "bedrock-antropic" + + def validate_request(self, request: ChatRequest): + return ChatRequest(**request) + + async def agenerate_client(self, request: ChatRequest) -> Coroutine[Any, Any, Any]: + """Generate an AWS Bedrock client""" + return self.generate_client(request=request) + + def generate_client(self, request: ChatRequest) -> Coroutine[Any, Any, Generator]: + """Generate an AWS Bedrock client""" + try: + messages, system_prompt = self._process_messages(request.chat_input) + tools = self._process_tools(request.parameters) + + system_prompt = ( + request.parameters.get("system") + if request.parameters.get("system") + else system_prompt + ) + + client_params = { + "modelId": request.model, + "messages": messages, + "inferenceConfig": self._process_parameters(request.parameters), + "system": system_prompt, + } + if tools: + client_params["toolConfig"] = tools + + return self._client.converse_stream(**client_params) + except Exception as e: + raise ProviderError(str(e)) + + async def aparse_response( + self, response: Any, **kwargs + ) -> AsyncGenerator[Any, None]: + return self.parse_response(response=response, **kwargs) + + def parse_response(self, response: AsyncGenerator[Any, None], **kwargs) -> Any: + tool_name = None + tool_arguments = "" + tool_id = None + + for chunk in response["stream"]: + if chunk.get("messageStart"): + first_chunk = ChatCompletionChunk( + id=str(uuid.uuid4()), + 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() + + elif chunk.get("contentBlockStart"): + if chunk["contentBlockStart"]["start"].get("toolUse"): + tool_name = chunk["contentBlockStart"]["start"]["toolUse"]["name"] + tool_arguments = "" + tool_id = chunk["contentBlockStart"]["start"]["toolUse"][ + "toolUseId" + ] + + elif chunk.get("contentBlockDelta"): + delta = chunk["contentBlockDelta"]["delta"] + if delta.get("text"): + # Regular content, yield it + text = delta["text"] + chunk = ChatCompletionChunk( + id=str(uuid.uuid4()), + choices=[ + Choice( + delta=ChoiceDelta(content=text), + finish_reason=None, + index=0, + ) + ], + created=int(time.time()), + model=kwargs.get("request").model, + object="chat.completion.chunk", + ) + yield chunk.model_dump() + + elif delta.get("toolUse"): + partial_json = delta["toolUse"]["input"] + tool_arguments += partial_json + + elif chunk.get("contentBlockStop") and tool_id: + name_chunk = ChatCompletionChunk( + id=str(uuid.uuid4()), + choices=[ + Choice( + delta=ChoiceDelta( + role="assistant", + tool_calls=[ + ChoiceDeltaToolCall( + index=chunk["contentBlockStop"][ + "contentBlockIndex" + ], + id=tool_id, + function=ChoiceDeltaToolCallFunction( + name=tool_name, + arguments="", + type="function", + ), + ) + ], + ), + finish_reason=None, + index=chunk["contentBlockStop"]["contentBlockIndex"], + ) + ], + created=int(time.time()), + model=kwargs.get("request").model, + object="chat.completion.chunk", + ) + yield name_chunk.model_dump() + + args_chunk = ChatCompletionChunk( + id=tool_id, + choices=[ + Choice( + delta=ChoiceDelta( + tool_calls=[ + ChoiceDeltaToolCall( + index=chunk["contentBlockStop"][ + "contentBlockIndex" + ], + function=ChoiceDeltaToolCallFunction( + arguments=tool_arguments, + ), + ) + ], + ), + finish_reason=None, + index=chunk["contentBlockStop"]["contentBlockIndex"], + ) + ], + created=int(time.time()), + model=kwargs.get("request").model, + object="chat.completion.chunk", + ) + yield args_chunk.model_dump() + + elif chunk.get("messageStop"): + stop_reason = chunk["messageStop"].get("stopReason") + final_chunk = ChatCompletionChunk( + id=str(uuid.uuid4()), + choices=[ + Choice( + delta=ChoiceDelta(), + finish_reason="tool_calls" + if stop_reason == "tool_use" + else "length" + if stop_reason == "max_tokens" + else "stop", + index=0, + ) + ], + created=int(time.time()), + model=kwargs.get("request").model, + object="chat.completion.chunk", + ) + yield final_chunk.model_dump() + + @staticmethod + def _process_messages( + chat_input: Union[str, List[Dict[str, str]]] + ) -> List[Dict[str, Union[List[Dict[str, str]], str]]]: + + if isinstance(chat_input, str): + return [ + { + "role": "user", + "content": [{"text": chat_input}], + } + ], [] + + elif isinstance(chat_input, list): + messages = [] + next_tool_result_message = False + system_prompt = [] + for message in chat_input: + if message.get("role") in ["assistant", "user"]: + next_tool_result_message = False + if message.get("tool_calls"): + tool_use = {"role": "assistant", "content": []} + for tool in message.get("tool_calls"): + tool_use["content"].append( + { + "toolUse": { + "toolUseId": tool["id"], + "name": tool["function"]["name"], + "input": json.loads( + tool["function"]["arguments"] + ), + } + } + ) + messages.append(tool_use) + else: + messages.append( + { + "role": message.get("role"), + "content": [{"text": message.get("content")}], + } + ) + if message.get("role") in ["tool"]: + if not next_tool_result_message: + tool_result = {"role": "user", "content": []} + next_tool_result_message = True + messages.append(tool_result) + + tool_result = { + "toolResult": { + "toolUseId": message["tool_call_id"], + "content": [{"json": {"text": message["content"]}}], + } + } + + messages[-1]["content"].append(tool_result) + + if message.get("role") in ["system"]: + system_prompt = [{"text": message.get("content")}] + + return messages, system_prompt + + @staticmethod + def _process_tools(parameters: dict) -> Optional[Dict]: + if parameters.get("tools") is None and parameters.get("functions") is None: + return None + + try: + if parameters.get("tools"): + parsed_tools = [ + OpenAIToolFunction(**tool["function"]) + for tool in parameters["tools"] + ] + + if parameters.get("functions"): + parsed_tools = [ + OpenAIToolFunction(**tool) for tool in parameters["functions"] + ] + + tool_configurations = [] + for tool in parsed_tools: + tool_config = { + "toolSpec": { + "name": tool.name, + "description": tool.description, + "inputSchema": { + "json": { + "type": tool.parameters.type, + "properties": tool.parameters.properties, + "required": tool.parameters.required, + } + }, + } + } + tool_configurations.append(tool_config) + return {"tools": tool_configurations} + + except ValidationError: + return parameters.get("tools", parameters.get("functions")) + + @staticmethod + def _process_parameters(parameters: dict) -> dict: + remove_keys = ["system", "stop", "tools", "functions"] + for key in remove_keys: + parameters.pop(key, None) + return parameters diff --git a/libs/core/llmstudio_core/providers/bedrock/provider.py b/libs/core/llmstudio_core/providers/bedrock/provider.py new file mode 100644 index 00000000..7087dac2 --- /dev/null +++ b/libs/core/llmstudio_core/providers/bedrock/provider.py @@ -0,0 +1,43 @@ +from typing import Any, AsyncGenerator, Coroutine, Generator + +from llmstudio_core.providers.bedrock.anthropic import BedrockAnthropicProvider +from llmstudio_core.providers.provider import ChatRequest, ProviderCore, provider + + +@provider +class BedrockProvider(ProviderCore): + def __init__(self, config, **kwargs): + super().__init__(config, **kwargs) + self.kwargs = kwargs + self.selected_model = None + + def _get_provider(self, model): + if "anthropic." in model: + return BedrockAnthropicProvider(config=self.config, **self.kwargs) + + raise ValueError(f" provider is not yet supported.") + + @staticmethod + def _provider_config_name(): + return "bedrock" + + def validate_request(self, request: ChatRequest): + return ChatRequest(**request) + + async def agenerate_client(self, request: ChatRequest) -> Coroutine[Any, Any, Any]: + self.selected_model = self._get_provider(request.model) + return await self.selected_model.agenerate_client(request) + + def generate_client(self, request: ChatRequest) -> Coroutine[Any, Any, Generator]: + self.selected_model = self._get_provider(request.model) + return self.selected_model.generate_client(request=request) + + async def aparse_response( + self, response: Any, **kwargs + ) -> AsyncGenerator[Any, None]: + result = await self.selected_model.aparse_response(response=response, **kwargs) + for chunk in result: + yield chunk + + def parse_response(self, response: AsyncGenerator[Any, None], **kwargs) -> Any: + return self.selected_model.parse_response(response=response, **kwargs) diff --git a/libs/core/llmstudio_core/providers/openai.py b/libs/core/llmstudio_core/providers/openai.py index bd8bb6dc..e75fcad4 100644 --- a/libs/core/llmstudio_core/providers/openai.py +++ b/libs/core/llmstudio_core/providers/openai.py @@ -1,4 +1,3 @@ -import asyncio import os from typing import Any, AsyncGenerator, Coroutine, Generator @@ -26,21 +25,7 @@ async def agenerate_client( self, request: ChatRequest ) -> Coroutine[Any, Any, Generator]: """Generate an OpenAI client""" - - try: - return await asyncio.to_thread( - self._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=True, - **request.parameters, - ) - except openai._exceptions.APIError as e: - raise ProviderError(str(e)) + return self.generate_client(request=request) def generate_client(self, request: ChatRequest) -> Generator: """Generate an OpenAI client""" @@ -62,8 +47,9 @@ def generate_client(self, request: ChatRequest) -> Generator: async def aparse_response( self, response: AsyncGenerator, **kwargs ) -> AsyncGenerator[str, None]: - for chunk in response: - yield chunk.model_dump() + result = self.parse_response(response=response, **kwargs) + for chunk in result: + yield chunk def parse_response(self, response: Generator, **kwargs) -> Generator: for chunk in response: diff --git a/libs/core/llmstudio_core/providers/provider.py b/libs/core/llmstudio_core/providers/provider.py index c06ce998..07e03835 100644 --- a/libs/core/llmstudio_core/providers/provider.py +++ b/libs/core/llmstudio_core/providers/provider.py @@ -63,13 +63,18 @@ def __init__( api_version: Optional[str] = None, base_url: Optional[str] = None, tokenizer: Optional[Any] = None, + access_key: Optional[str] = None, + secret_key: Optional[str] = None, + region: Optional[str] = None, ): self.config = config self.API_KEY = api_key self.api_endpoint = api_endpoint self.api_version = api_version self.base_url = base_url - + self.access_key = access_key + self.secret_key = secret_key + self.region = region self.tokenizer = tokenizer if tokenizer else self._get_tokenizer() self.count = 0 diff --git a/libs/core/llmstudio_core/providers/vertexai.py b/libs/core/llmstudio_core/providers/vertexai.py index 90e3a399..69eee514 100644 --- a/libs/core/llmstudio_core/providers/vertexai.py +++ b/libs/core/llmstudio_core/providers/vertexai.py @@ -1,4 +1,3 @@ -import asyncio import json import os import time @@ -17,6 +16,7 @@ import requests from llmstudio_core.exceptions import ProviderError from llmstudio_core.providers.provider import ChatRequest, ProviderCore, provider +from llmstudio_core.utils import OpenAIToolFunction from openai.types.chat import ChatCompletionChunk from openai.types.chat.chat_completion_chunk import ( Choice, @@ -24,50 +24,7 @@ ChoiceDeltaToolCall, ChoiceDeltaToolCallFunction, ) -from pydantic import BaseModel, ValidationError - - -class OpenAIToolParameter(BaseModel): - type: str - description: Optional[str] = None - - -class OpenAIToolParameters(BaseModel): - type: str - properties: Dict[str, OpenAIToolParameter] - required: List[str] - - -class OpenAIToolFunction(BaseModel): - name: str - description: str - parameters: OpenAIToolParameters - - -class OpenAITool(BaseModel): - type: str - function: OpenAIToolFunction - - -class VertexAIToolParameter(BaseModel): - type: str - description: str - - -class VertexAIToolParameters(BaseModel): - type: str - properties: Dict[str, VertexAIToolParameter] - required: List[str] - - -class VertexAIFunctionDeclaration(BaseModel): - name: str - description: str - parameters: VertexAIToolParameters - - -class VertexAI(BaseModel): - function_declarations: List[VertexAIFunctionDeclaration] +from pydantic import ValidationError @provider @@ -87,26 +44,7 @@ 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" - headers = { - "Content-Type": "application/json", - "x-goog-api-key": self.API_KEY, - } - - # 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 await asyncio.to_thread( - requests.post, url, headers=headers, json=message, stream=True - ) - - except Exception as e: - raise ProviderError(str(e)) + return self.generate_client(request=request) def generate_client(self, request: ChatRequest) -> Coroutine[Any, Any, Generator]: """Initialize Vertex AI""" @@ -119,12 +57,10 @@ def generate_client(self, request: ChatRequest) -> Coroutine[Any, Any, Generator "x-goog-api-key": self.API_KEY, } - # 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) + tool_payload = self._process_tools(request.parameters) + payload = self._create_request_payload(request.chat_input, tool_payload) - # Generate content - return requests.post(url, headers=headers, json=message, stream=True) + return requests.post(url, headers=headers, json=payload, stream=True) except Exception as e: raise ProviderError(str(e)) @@ -139,7 +75,6 @@ def parse_response(self, response: AsyncGenerator[Any, None], **kwargs) -> Any: if not chunk: continue - # Check if it is a function call if ( "functionCall" in chunk["parts"][0] and chunk["parts"][0]["functionCall"] is not None @@ -177,9 +112,9 @@ def parse_response(self, response: AsyncGenerator[Any, None], **kwargs) -> Any: index=index, id="call_" + str(uuid.uuid4())[:29], function=ChoiceDeltaToolCallFunction( - name=functioncall["functionCall"][ + name=functioncall["functionCall"].get( "name" - ], + ), arguments="", type="function", ), @@ -236,9 +171,9 @@ def parse_response(self, response: AsyncGenerator[Any, None], **kwargs) -> Any: 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=[ @@ -270,303 +205,102 @@ def parse_response(self, response: AsyncGenerator[Any, None], **kwargs) -> Any: 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: ")) - 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=chunk.get("parts")[0].get("text"), - role="assistant", - ), - finish_reason=None, - index=0, - ) - ], - created=int(time.time()), - model=kwargs.get("request").model, - object="chat.completion.chunk", - ).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() + result = self.parse_response(response=response, **kwargs) + for chunk in result: + yield chunk - def convert_input_to_vertexai( + def _create_request_payload( self, input_data: Union[Dict, str, List[Dict]], tool_payload: Optional[Any] ) -> Dict: - """ - Converts OpenAI formatted input to VertexAI format. - - Args: - input_data (Union[Dict, str, List[Dict]]): The input data in OpenAI format. - tool_payload (Optional[Any]): The tool payload for the request. - - Returns: - Dict: The converted input in VertexAI format. - """ - if isinstance(input_data, dict) and "input" in input_data: - return self._handle_simple_string_input(input_data["input"], tool_payload) if isinstance(input_data, str): - return self._handle_simple_string_input(input_data, tool_payload) - - if isinstance(input_data, list): - return self._convert_list_input_to_vertexai(input_data, tool_payload) - - raise ValueError("Invalid input type. Expected dict, str, or list.") - - def _handle_simple_string_input( - self, input_data: str, tool_payload: Optional[Any] - ) -> Dict: - """ - Handles simple string input and converts it to VertexAI format. - - Args: - input_data (str): The input data as a simple string. - tool_payload (Optional[Any]): The tool payload for the request. - - Returns: - Dict: The converted input in VertexAI format. - """ - return self._initialize_vertexai_message( - user_message=input_data, tool_payload=tool_payload - ) - - def _convert_list_input_to_vertexai( - self, input_data: List[Dict], tool_payload: Optional[Any] - ) -> Dict: - """ - Converts a list of messages in OpenAI format to VertexAI format. - - Args: - input_data (List[Dict]): The input data as a list of messages. - tool_payload (Optional[Any]): The tool payload for the request. + return self._create_vertexai_payload( + user_payload=input_data, tool_payload=tool_payload + ) - Returns: - Dict: The converted input in VertexAI format. - """ - vertexai_format = self._initialize_vertexai_message(tool_payload=tool_payload) - for message in input_data: - role = message.get("role") - if role == "system": - vertexai_format["system_instruction"]["parts"]["text"] = ( - message["content"] or "You are a helpful assistant" - ) - elif role == "user": - vertexai_format["contents"].append( - {"role": "user", "parts": [{"text": message["content"]}]} - ) - elif role == "assistant": - if message["content"] is None and "tool_calls" in message: - tool_call = message["tool_calls"][0] - vertexai_format["contents"].append( - { - "role": "model", - "parts": [ - { - "functionCall": { - "name": tool_call["function"]["name"], - "args": json.loads( - tool_call["function"]["arguments"] - ), + elif isinstance(input_data, list): + payload = self._create_vertexai_payload(tool_payload=tool_payload) + for message in input_data: + if message.get("role") == "system": + payload["system_instruction"]["parts"]["text"] = message["content"] + + if message.get("role") in ["user", "assistant"]: + if message.get("tool_calls"): + tool_call = message["tool_calls"][0] + payload["contents"].append( + { + "role": "model", + "parts": [ + { + "functionCall": { + "name": tool_call["function"]["name"], + "args": json.loads( + tool_call["function"]["arguments"] + ), + } } - } - ], - } - ) - else: - vertexai_format["contents"].append( - {"role": "model", "parts": [{"text": message["content"]}]} - ) - elif role == "tool": - function_name = message["name"] - response = message["content"] - vertexai_format["system_instruction"]["parts"][ - "text" - ] += f"\nYou have called {function_name} and got the following response: {response}." - else: - raise ValueError( - f"Invalid role: {role}. Expected 'system', 'user', 'assistant', or 'tool'." - ) - return vertexai_format + ], + } + ) + else: + payload["contents"].append( + { + "role": message.get("role"), + "parts": [{"text": message["content"]}], + } + ) + elif message.get("role") == "tool": + function_name = message["name"] + response = message["content"] + payload["system_instruction"]["parts"][ + "text" + ] += f"\nYou have called {function_name} and got the following response: {response}." - def _initialize_vertexai_message( - self, user_message: Optional[str] = None, tool_payload: Optional[Any] = None + return payload + + @staticmethod + def _create_vertexai_payload( + user_payload: Optional[str] = None, tool_payload: Optional[Any] = None ) -> Dict: """ - Initializes the basic structure of a VertexAI message. + Initializes the basic structure of a VertexAI payload. Args: - user_message (Optional[str]): The user's message to include in the request. - tool_payload (Optional[Any]): The tool payload for the request. + user_payload (Optional[str]): The user's payload to include in the + tool_payload (Optional[Any]): The tool payload for the Returns: - Dict: The initialized VertexAI message structure. + Dict: The initialized VertexAI payload structure. """ - message_format = { + return { "system_instruction": {"parts": {"text": "You are a helpful assistant"}}, - "contents": [], + "contents": [{"role": "user", "parts": [{"text": user_payload}]}] + if user_payload + else [], "tools": tool_payload, "tool_config": {"function_calling_config": {"mode": "AUTO"}}, } - if user_message: - message_format["contents"].append( - {"role": "user", "parts": [{"text": user_message}]} - ) - return message_format - def process_tools( - self, tools: Optional[Union[List[Dict], Dict]] - ) -> Optional[VertexAI]: - if tools is None: - return None + @staticmethod + def _process_tools(parameters: dict) -> dict: + if parameters.get("tools") is None and parameters.get("functions") is None: + return None try: - # Try to parse as OpenAI format - parsed_tools = ( - [OpenAITool(**tool) for tool in tools] - if isinstance(tools, list) - else [OpenAITool(**tools)] - ) - # Convert to VertexAI format + if parameters.get("tools"): + parsed_tools = [ + OpenAIToolFunction(**tool["function"]) + for tool in parameters["tools"] + ] + + if parameters.get("functions"): + parsed_tools = [ + OpenAIToolFunction(**tool) for tool in parameters["functions"] + ] + function_declarations = [] for tool in parsed_tools: - function = tool.function - properties = { - name: VertexAIToolParameter( - type=param.type, description=param.description or "" - ) - for name, param in function.parameters.properties.items() - } - function_decl = VertexAIFunctionDeclaration( - name=function.name, - description=function.description, - parameters=VertexAIToolParameters( - type=function.parameters.type, - properties=properties, - required=function.parameters.required, - ), - ) - function_declarations.append(function_decl) - return VertexAI(function_declarations=function_declarations).model_dump() + function_declarations.append(tool.model_dump()) + return {"function_declarations": function_declarations} except ValidationError: - # If the format is not OpenAI, attempt to validate as VertexAI format - try: - return VertexAI(**tools).model_dump() - except ValidationError: - # If it fails to validate as VertexAI, throw an error - raise ValueError( - "Invalid tool format. Tool data must be in OpenAI or VertexAI format." - ) + return parameters.get("tools", parameters.get("functions")) diff --git a/libs/core/llmstudio_core/utils.py b/libs/core/llmstudio_core/utils.py index a9ddbdef..04c6da49 100644 --- a/libs/core/llmstudio_core/utils.py +++ b/libs/core/llmstudio_core/utils.py @@ -6,6 +6,23 @@ from pydantic import BaseModel, ValidationError +class OpenAIToolParameters(BaseModel): + type: str + properties: Dict + required: List[str] + + +class OpenAIToolFunction(BaseModel): + name: str + description: str + parameters: OpenAIToolParameters + + +class OpenAITool(BaseModel): + type: str + function: OpenAIToolFunction + + class CostRange(BaseModel): range: List[Optional[int]] cost: float diff --git a/libs/core/poetry.lock b/libs/core/poetry.lock index cabef66a..16140a91 100644 --- a/libs/core/poetry.lock +++ b/libs/core/poetry.lock @@ -108,6 +108,47 @@ d = ["aiohttp (>=3.7.4)", "aiohttp (>=3.7.4,!=3.9.0)"] jupyter = ["ipython (>=7.8.0)", "tokenize-rt 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No proxy server required. For that use llmstudio[proxy]" authors = ["Cláudio Lemos "] license = "MIT" @@ -13,6 +13,7 @@ requests = "^2.31" openai = "^1.26" tiktoken = "^0.7" pyyaml = "^6" +boto3 = "^1.35.54" [tool.poetry.group.dev.dependencies] pytest = "^7.4" diff --git a/libs/llmstudio/llmstudio/langchain.py b/libs/llmstudio/llmstudio/langchain.py index 4a52b0d0..87180304 100644 --- a/libs/llmstudio/llmstudio/langchain.py +++ b/libs/llmstudio/llmstudio/langchain.py @@ -57,7 +57,7 @@ def _create_chat_result(self, response: Any) -> ChatResult: generation_info=generation_info, ) generations.append(gen) - token_usage = response.get("usage", {}) + token_usage = response.get("metrics", {}) llm_output = { "token_usage": token_usage, "model_name": response["model"], diff --git a/libs/llmstudio/llmstudio/providers/provider.py b/libs/llmstudio/llmstudio/providers/provider.py index 2569fa14..02620fbb 100644 --- a/libs/llmstudio/llmstudio/providers/provider.py +++ b/libs/llmstudio/llmstudio/providers/provider.py @@ -18,6 +18,9 @@ def __init__( api_endpoint: Optional[str] = None, api_version: Optional[str] = None, base_url: Optional[str] = None, + access_key: Optional[str] = None, + secret_key: Optional[str] = None, + region: Optional[str] = None, **kwargs, ): """ @@ -60,6 +63,9 @@ def __init__( api_endpoint=api_endpoint, api_version=api_version, base_url=base_url, + region=region, + secret_key=secret_key, + access_key=access_key, **kwargs, ) diff --git a/libs/llmstudio/poetry.lock b/libs/llmstudio/poetry.lock index fbebd4d7..81929482 100644 --- a/libs/llmstudio/poetry.lock +++ b/libs/llmstudio/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. 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