diff --git a/os_computer_use/config.py b/os_computer_use/config.py index e6c66d6..9b7d531 100644 --- a/os_computer_use/config.py +++ b/os_computer_use/config.py @@ -5,15 +5,19 @@ grounding_model = providers.OSAtlasProvider() # grounding_model = providers.ShowUIProvider() -# vision_model = providers.FireworksProvider("llama3.2") -# vision_model = providers.OpenAIProvider("gpt-4o") -# vision_model = providers.AnthropicProvider("claude-3.5-sonnet") -vision_model = providers.GroqProvider("llama3.2") -# vision_model = providers.MistralProvider("pixtral") # pixtral-large-latest has vision capabilities +# Vision models using LiteLLM: +vision_model = providers.LiteLLMProvider("pixtral") # Mistral +# vision_model = providers.LiteLLMProvider("llama3.2", provider="fireworks") # Fireworks +# vision_model = providers.LiteLLMProvider("gpt-4-vision") # OpenAI +# vision_model = providers.LiteLLMProvider("llama3.2", provider="groq") # Groq +# vision_model = providers.LiteLLMProvider("claude-3-5-sonnet") # Anthropic +# vision_model = providers.LiteLLMProvider("gemini-2.0-flash", provider="gemini") # Gemini -# action_model = providers.FireworksProvider("llama3.3") -# action_model = providers.OpenAIProvider("gpt-4o") -# action_model = providers.AnthropicProvider("claude-3.5-sonnet") -action_model = providers.GroqProvider("llama3.3") -# action_model = providers.MistralProvider("large") # mistral-large-latest for non-vision tasks +# Action models using LiteLLM: +action_model = providers.LiteLLMProvider("large") # Mistral +# action_model = providers.LiteLLMProvider("llama3.3", provider="fireworks") # Fireworks +# action_model = providers.LiteLLMProvider("llama3.3", provider="groq") # Groq +# action_model = providers.LiteLLMProvider("gpt-4") # OpenAI +# action_model = providers.LiteLLMProvider("claude-3-5-sonnet") # Anthropic +# action_model = providers.LiteLLMProvider("gemini-2.0-flash", provider="gemini") # Gemini diff --git a/os_computer_use/llm_provider.py b/os_computer_use/llm_provider.py index 4482249..5d5ef05 100644 --- a/os_computer_use/llm_provider.py +++ b/os_computer_use/llm_provider.py @@ -4,6 +4,7 @@ import json import re import base64 +import imghdr def Message(content, role="assistant"): @@ -22,6 +23,29 @@ def parse_json(s): return None +def extract_json_objects(s): + """Extract all balanced JSON objects from a string.""" + objects = [] + brace_level = 0 + start_index = None + for i, char in enumerate(s): + if char == "{": + if brace_level == 0: + start_index = i + brace_level += 1 + elif char == "}": + brace_level -= 1 + if brace_level == 0 and start_index is not None: + candidate = s[start_index : i + 1] + try: + obj = json.loads(candidate) + objects.append(obj) + except json.JSONDecodeError: + pass + start_index = None + return objects + + class LLMProvider: """ The LLM provider is used to make calls to an LLM given a provider and model name, with optional tool use support @@ -52,6 +76,13 @@ def create_function_schema(self, definitions): properties[param_name] = {"type": "string", "description": param_desc} required.append(param_name) + # Add a dummy property if no parameters are provided, because providers like Gemini require a non-empty "properties" object. + if not properties: + properties["noop"] = { + "type": "string", + "description": "Dummy parameter for function with no parameters.", + } + function_def = self.create_function_def(name, details, properties, required) functions.append(function_def) @@ -68,8 +99,7 @@ def create_tool_call(self, name, parameters): # Wrap a content block in a text or an image object def wrap_block(self, block): if isinstance(block, bytes): - encoded_image = base64.b64encode(block).decode("utf-8") - return self.create_image_block(encoded_image) + return self.create_image_block(block) else: return Text(block) @@ -117,10 +147,17 @@ def create_function_def(self, name, details, properties, required): }, } - def create_image_block(self, base64_image): + def create_image_block(self, image_data): + # Detect the image type using imghdr. + image_type = imghdr.what(None, image_data) + if image_type is None: + image_type = "png" # fallback if type cannot be detected + + # Base64-encode the raw image bytes. + encoded = base64.b64encode(image_data).decode("utf-8") return { "type": "image_url", - "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}, + "image_url": {"url": f"data:image/{image_type};base64,{encoded}"}, } def call(self, messages, functions=None): @@ -140,18 +177,17 @@ def call(self, messages, functions=None): if parse_json(tool_call.function.arguments) is not None ] - # Sometimes, function calls are returned unparsed by the inference provider. This code parses them manually. + # Sometimes, function calls are returned unparsed by the inference provider. if message.content and not tool_calls: - tool_call_matches = re.search(r"\{.*\}", message.content) - if tool_call_matches: - tool_call = parse_json(tool_call_matches.group(0)) - # Some models use "arguments" as the key instead of "parameters" - parameters = tool_call.get("parameters", tool_call.get("arguments")) - if tool_call.get("name") and parameters: + json_objs = extract_json_objects(message.content) + for obj in json_objs: + parameters = obj.get("parameters", obj.get("arguments")) + if obj.get("name") and parameters is not None: combined_tool_calls.append( - self.create_tool_call(tool_call.get("name"), parameters) + self.create_tool_call(obj.get("name"), parameters) ) - return None, combined_tool_calls + if combined_tool_calls: + return None, combined_tool_calls return message.content, combined_tool_calls @@ -160,70 +196,46 @@ def call(self, messages, functions=None): return message.content -class AnthropicBaseProvider(LLMProvider): +class LiteLLMBaseProvider(OpenAIBaseProvider): + """Base provider using LiteLLM""" def create_client(self): - return Anthropic(api_key=self.api_key).messages + from litellm import completion - def create_function_def(self, name, details, properties, required): - return { - "name": name, - "description": details["description"], - "input_schema": { - "type": "object", - "properties": properties, - "required": required, - }, - } + import litellm - def create_image_block(self, base64_image): - return { - "type": "image", - "source": { - "type": "base64", - "media_type": "image/png", - "data": base64_image, - }, - } - - def call(self, messages, functions=None): - tools = self.create_function_schema(functions) if functions else None - - # Move all messages with the system role to a system parameter - system = "\n".join( - msg.get("content") for msg in messages if msg.get("role") == "system" - ) - messages = [msg for msg in messages if msg.get("role") != "system"] - - # Call the Anthropic API - completion = self.completion( - messages, system=system, tools=tools, max_tokens=4096 - ) - text = "".join(getattr(block, "text", "") for block in completion.content) + # Enable dropping unsupported params globally + litellm.drop_params = True + litellm.modify_params = True + # Enable debug mode for better error messages + # litellm._turn_on_debug() + return completion - # Return response text and tool calls separately - if functions: - tool_calls = [ - self.create_tool_call(block.name, block.input) - for block in completion.content - if block.type == "tool_use" - ] - return text, tool_calls + def completion(self, messages, **kwargs): + # Skip the tools parameter if it's None + filtered_kwargs = {k: v for k, v in kwargs.items() if v is not None} - # Only return response text - else: - return text + # No need to remove tools; pass tools so that function calling works with Claude. + # Wrap content blocks in image or text objects if necessary + new_messages = [self.transform_message(message) for message in messages] -class MistralBaseProvider(OpenAIBaseProvider): - def create_function_def(self, name, details, properties, required): - # If description is wrapped in a dict, extract the inner string - if isinstance(details.get("description"), dict): - details["description"] = details["description"].get("description", "") - return super().create_function_def(name, details, properties, required) + # Call LiteLLM completion + completion_response = self.client( + model=self.model, + messages=new_messages, + api_key=self.api_key, + **filtered_kwargs, + ) + return completion_response + # Added method to adjust the final message role for Mistral-based models only def call(self, messages, functions=None): - if messages and messages[-1].get("role") == "assistant": + if ( + "mistral" in self.model.lower() + and messages + and messages[-1].get("role") == "assistant" + ): prefix = messages.pop()["content"] if messages and messages[-1].get("role") == "user": messages[-1]["content"] = ( @@ -231,4 +243,5 @@ def call(self, messages, functions=None): ) else: messages.append({"role": "user", "content": prefix}) + return super().call(messages, functions) diff --git a/os_computer_use/providers.py b/os_computer_use/providers.py index 9f0ce17..2a9e369 100644 --- a/os_computer_use/providers.py +++ b/os_computer_use/providers.py @@ -1,10 +1,6 @@ import os from dotenv import load_dotenv -from os_computer_use.llm_provider import ( - OpenAIBaseProvider, - AnthropicBaseProvider, - MistralBaseProvider, -) +from os_computer_use.llm_provider import LiteLLMBaseProvider from os_computer_use.osatlas_provider import OSAtlasProvider from os_computer_use.showui_provider import ShowUIProvider @@ -14,66 +10,102 @@ # LLM providers use the OpenAI specification and require a base URL: -class LlamaProvider(OpenAIBaseProvider): - base_url = "https://api.llama-api.com" - api_key = os.getenv("LLAMA_API_KEY") - aliases = {"llama3.2": "llama3.2-90b-vision", "llama3.3": "llama3.3-70b"} +class LiteLLMProvider(LiteLLMBaseProvider): + """Universal provider for all LLM models using LiteLLM""" - -class OpenRouterProvider(OpenAIBaseProvider): - base_url = "https://openrouter.ai/api/v1" - api_key = os.getenv("OPENROUTER_API_KEY") - aliases = {"llama3.2": "meta-llama/llama-3.2-90b-vision-instruct"} - - -class FireworksProvider(OpenAIBaseProvider): - base_url = "https://api.fireworks.ai/inference/v1" - api_key = os.getenv("FIREWORKS_API_KEY") - aliases = { - "llama3.2": "accounts/fireworks/models/llama-v3p2-90b-vision-instruct", - "llama3.3": "accounts/fireworks/models/llama-v3p3-70b-instruct", + VISION_MODELS = { + "llama3.2": { + "groq": "groq/llama-3.2-90b-vision-preview", + "fireworks": "fireworks/llama-v3p2-90b-vision-instruct", + "openrouter": "openrouter/meta-llama/llama-3.2-90b-vision-instruct", + }, + "gpt-4-vision": "openai/gpt-4-vision-preview", + "gemini-pro-vision": "gemini/gemini-pro-vision", + "pixtral": "mistral/pixtral-large-latest", + "gemini-2.0-flash-vision": "gemini/gemini-2.0-flash-vision", } - -class DeepSeekProvider(OpenAIBaseProvider): - base_url = "https://api.deepseek.com" - api_key = os.getenv("DEEPSEEK_API_KEY") - - -class OpenAIProvider(OpenAIBaseProvider): - base_url = "https://api.openai.com/v1" - api_key = os.getenv("OPENAI_API_KEY") - - -class GeminiProvider(OpenAIBaseProvider): - base_url = "https://generativelanguage.googleapis.com/v1beta/openai" - api_key = os.getenv("GEMINI_API_KEY") - - -class AnthropicProvider(AnthropicBaseProvider): - api_key = os.getenv("ANTHROPIC_API_KEY") - aliases = { - "claude-3.5-sonnet": "claude-3-5-sonnet-20241022", - "claude-3.5-haiku": "claude-3-5-haiku-20241022", - "claude-3-opus": "claude-3-opus-20240229", + TEXT_MODELS = { + "llama3.3": { + "groq": "groq/llama-3.3-70b-versatile", + "fireworks": "fireworks/llama-v3p3-70b-instruct", + }, + "small": "mistral/mistral-small-latest", + "medium": "mistral/mistral-medium-latest", + "large": "mistral/mistral-large-latest", + "gpt-4": "openai/gpt-4-turbo-preview", + "claude-3-opus": "anthropic/claude-3-opus-20240229", + "claude-3-5-sonnet": "anthropic/claude-3-5-sonnet-20241022", + "claude-3-haiku": "anthropic/claude-3-haiku-20240229", + "gemini-pro": "gemini/gemini-pro", + "gemini-2.0-flash": "gemini/gemini-2.0-flash", + "gemini-2.0-flash-exp": "gemini/gemini-2.0-flash-exp", + "gemini-2.0-flash-lite-preview-02-05": "gemini/gemini-2.0-flash-lite-preview-02-05", + "deepseek-coder": "deepseek/deepseek-coder-33b-instruct", } - -class GroqProvider(OpenAIBaseProvider): - base_url = "https://api.groq.com/openai/v1" - api_key = os.getenv("GROQ_API_KEY") - aliases = { - "llama3.2": "llama-3.2-90b-vision-preview", - "llama3.3": "llama-3.3-70b-versatile", + aliases = {**VISION_MODELS, **TEXT_MODELS} + + # Model mappings for different providers + PROVIDER_MODEL_MAPPINGS = { + "fireworks": { + "provider": "fireworks_ai", + "model_template": "fireworks_ai/accounts/fireworks/models/{model}", + }, + "openai": {"provider": "openai", "model_template": "openai/{model}"}, + "anthropic": {"provider": "anthropic", "model_template": "anthropic/{model}"}, + "groq": {"provider": "groq", "model_template": "groq/{model}"}, + "mistral": {"provider": "mistral", "model_template": "mistral/{model}"}, + "gemini": {"provider": "gemini", "model_template": "gemini/{model}"}, } - -class MistralProvider(MistralBaseProvider): - base_url = "https://api.mistral.ai/v1" - api_key = os.getenv("MISTRAL_API_KEY") - aliases = { - "small": "mistral-small-latest", - "medium": "mistral-medium-latest", - "large": "mistral-large-latest", - "pixtral": "pixtral-large-latest", - } + def __init__(self, model, provider=None): + # If model has multiple providers, use specified provider or default + model_info = self.aliases.get(model) + if isinstance(model_info, dict): + if not provider: + # Default to first available provider + provider = next(iter(model_info)) + model_path = model_info[provider] + else: + model_path = model_info + + # Get provider mapping from PROVIDER_MODEL_MAPPINGS. + parts = model_path.split("/") + provider_name = parts[0] + provider_config = self.PROVIDER_MODEL_MAPPINGS.get( + provider_name, + {"provider": provider_name, "model_template": f"{provider_name}/{{model}}"}, + ) + + # Transform model path according to provider requirements. + # This always formats the model as per the mapping. + if "/" in model_path: + _, model_name = model_path.split("/", 1) + self.model = provider_config["model_template"].format(model=model_name) + self.provider = provider_config["provider"] + else: + self.model = model_path + self.provider = provider_name + + # Set API key based on provider + self.api_key = os.getenv(f"{provider_name.upper()}_API_KEY") + super().__init__(self.model) + + @classmethod + def get_vision_models(cls): + """Get list of available vision models""" + return list(cls.VISION_MODELS.keys()) + + @classmethod + def get_text_models(cls): + """Get list of available text-only models""" + return list(cls.TEXT_MODELS.keys()) + + @classmethod + def get_providers(cls, model): + """Get available providers for a model""" + model_info = cls.aliases.get(model) + if isinstance(model_info, dict): + return list(model_info.keys()) + return [model_info.split("/")[0]] if model_info else [] diff --git a/poetry.lock b/poetry.lock index d10be88..d55650b 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,135 @@ -# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.0.1 and should not be changed by hand. + +[[package]] +name = "aiohappyeyeballs" +version = "2.4.4" +description = "Happy Eyeballs for asyncio" +optional = false +python-versions = ">=3.8" +groups = ["main"] +files = [ + {file = "aiohappyeyeballs-2.4.4-py3-none-any.whl", hash = "sha256:a980909d50efcd44795c4afeca523296716d50cd756ddca6af8c65b996e27de8"}, + {file = 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open("./tests/test_screenshot.png", "rb") as f: @@ -45,42 +40,40 @@ def take_screenshot(): ) ] -# Anthropic -opus = AnthropicProvider("claude-3-opus") -print(opus.call(toolcall_messages, tools)[1]) -print(opus.call(messages)) -# OpenAI -gpt4o = OpenAIProvider("gpt-4o") -print(gpt4o.call(toolcall_messages, tools)[1]) -print(gpt4o.call(messages)) +# Test LiteLLM with Pixtral +litellm = LiteLLMProvider("pixtral") +print("\nTesting LiteLLM with Pixtral:") +print(litellm.call(toolcall_messages, tools)[1]) +print(litellm.call(messages)) -# Groq -groq = GroqProvider("llama3.2") -print(groq.call(toolcall_messages, tools)[1]) -print(groq.call(messages)) - -# Fireworks -fireworks = FireworksProvider("llama3.2") -print(fireworks.call(toolcall_messages, tools)[1]) -print(fireworks.call(messages)) +# Test LiteLLM with Mistral Large (non-vision) +litellm_large = LiteLLMProvider("large") # Using mistral-large-latest +text_messages = [Message("What is the capital of France?", role="user")] +print("\nTesting LiteLLM Mistral Large with text-only:") +print(litellm_large.call(text_messages)) +# Test tool calls for Mistral Large using text-only messages +text_tool_messages = [Message("Click on the submit button", role="user")] +print("\nTesting LiteLLM Mistral Large Tool Calls with text:") +print(litellm_large.call(text_tool_messages, tools)[1]) +# Test LiteLLM with Claude-3-Opus +litellm_claude = LiteLLMProvider("claude-3-opus") +print("\nTesting LiteLLM with Claude-3-Opus:") +print(litellm_claude.call(toolcall_messages, tools)[1]) +print(litellm_claude.call(messages)) -# Pixtral -mistral = MistralProvider("pixtral") -print("\nTesting Mistral :") -print(mistral.call(toolcall_messages, tools)[1]) -print(mistral.call(messages)) +# Test LiteLLM with Claude-3-Sonnet +litellm_claude_sonnet = LiteLLMProvider("claude-3-sonnet") +print("\nTesting LiteLLM with Claude-3-Sonnet:") +print(litellm_claude_sonnet.call(toolcall_messages, tools)[1]) +print(litellm_claude_sonnet.call(messages)) -# Mistral Large (non-vision) using text-only messages -mistral_large = MistralProvider("large") # Using mistral-large-latest for non-vision tasks -text_messages = [Message("What is the capital of France?", role="user")] -print("\nTesting Mistral Large with text-only:") -print(mistral_large.call(text_messages)) -# Test tool calls for Mistral Large using text-only messages (no image data) -text_tool_messages = [Message("Click on the submit button", role="user")] -print("\nTesting Mistral Large Tool Calls with text:") -print(mistral_large.call(text_tool_messages, tools)[1]) \ No newline at end of file +# Test LiteLLM with Gemini 2.0 +gemini_llm = LiteLLMProvider("gemini-2.0-flash") +print("\nTesting LiteLLM with Gemini-2.0-Flash:") +print(gemini_llm.call(toolcall_messages, tools)[1]) +print(gemini_llm.call(messages))