diff --git a/pyproject.toml b/pyproject.toml index f64c0958..1b8a6716 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -9,7 +9,7 @@ dependencies = [ "uvicorn>=0.34.0", "httpx>=0.25.0", "pydantic>=2.0.0", - "litellm>=1.77.7", + "litellm>=1.81.0", "python-dotenv>=1.0.0", "google-auth>=2.41.1", "google-cloud-aiplatform>=1.120.0", diff --git a/server.py b/server.py index bf2ac6c1..15dacf35 100644 --- a/server.py +++ b/server.py @@ -21,59 +21,67 @@ # Configure logging logging.basicConfig( level=logging.WARN, # Change to INFO level to show more details - format='%(asctime)s - %(levelname)s - %(message)s', + format="%(asctime)s - %(levelname)s - %(message)s", ) logger = logging.getLogger(__name__) # Configure uvicorn to be quieter import uvicorn + # Tell uvicorn's loggers to be quiet logging.getLogger("uvicorn").setLevel(logging.WARNING) logging.getLogger("uvicorn.access").setLevel(logging.WARNING) logging.getLogger("uvicorn.error").setLevel(logging.WARNING) + # Create a filter to block any log messages containing specific strings class MessageFilter(logging.Filter): def filter(self, record): # Block messages containing these strings blocked_phrases = [ "LiteLLM completion()", - "HTTP Request:", + "HTTP Request:", "selected model name for cost calculation", "utils.py", - "cost_calculator" + "cost_calculator", ] - - if hasattr(record, 'msg') and isinstance(record.msg, str): + + if hasattr(record, "msg") and isinstance(record.msg, str): for phrase in blocked_phrases: if phrase in record.msg: return False return True + # Apply the filter to the root logger to catch all messages root_logger = logging.getLogger() root_logger.addFilter(MessageFilter()) + # Custom formatter for model mapping logs class ColorizedFormatter(logging.Formatter): """Custom formatter to highlight model mappings""" + BLUE = "\033[94m" GREEN = "\033[92m" YELLOW = "\033[93m" RED = "\033[91m" RESET = "\033[0m" BOLD = "\033[1m" - + def format(self, record): if record.levelno == logging.debug and "MODEL MAPPING" in record.msg: # Apply colors and formatting to model mapping logs return f"{self.BOLD}{self.GREEN}{record.msg}{self.RESET}" return super().format(record) + # Apply custom formatter to console handler for handler in logger.handlers: if isinstance(handler, logging.StreamHandler): - handler.setFormatter(ColorizedFormatter('%(asctime)s - %(levelname)s - %(message)s')) + handler.setFormatter( + ColorizedFormatter("%(asctime)s - %(levelname)s - %(message)s") + ) app = FastAPI() @@ -113,14 +121,12 @@ def format(self, record): "gpt-4o-mini", "gpt-4o-mini-audio-preview", "gpt-4.1", # Added default big model - "gpt-4.1-mini" # Added default small model + "gpt-4.1-mini", # Added default small model ] # List of Gemini models -GEMINI_MODELS = [ - "gemini-2.5-flash", - "gemini-2.5-pro" -] +GEMINI_MODELS = ["gemini-2.5-flash", "gemini-2.5-pro"] + # Helper function to clean schema for Gemini def clean_gemini_schema(schema: Any) -> Any: @@ -134,53 +140,76 @@ def clean_gemini_schema(schema: Any) -> Any: if schema.get("type") == "string" and "format" in schema: allowed_formats = {"enum", "date-time"} if schema["format"] not in allowed_formats: - logger.debug(f"Removing unsupported format '{schema['format']}' for string type in Gemini schema.") + logger.debug( + f"Removing unsupported format '{schema['format']}' for string type in Gemini schema." + ) schema.pop("format") # Recursively clean nested schemas (properties, items, etc.) - for key, value in list(schema.items()): # Use list() to allow modification during iteration + for key, value in list( + schema.items() + ): # Use list() to allow modification during iteration schema[key] = clean_gemini_schema(value) elif isinstance(schema, list): # Recursively clean items in a list return [clean_gemini_schema(item) for item in schema] return schema + # Models for Anthropic API requests class ContentBlockText(BaseModel): type: Literal["text"] text: str + class ContentBlockImage(BaseModel): type: Literal["image"] source: Dict[str, Any] + class ContentBlockToolUse(BaseModel): type: Literal["tool_use"] id: str name: str input: Dict[str, Any] + class ContentBlockToolResult(BaseModel): type: Literal["tool_result"] tool_use_id: str content: Union[str, List[Dict[str, Any]], Dict[str, Any], List[Any], Any] + class SystemContent(BaseModel): type: Literal["text"] text: str + class Message(BaseModel): - role: Literal["user", "assistant"] - content: Union[str, List[Union[ContentBlockText, ContentBlockImage, ContentBlockToolUse, ContentBlockToolResult]]] + role: Literal["user", "assistant"] + content: Union[ + str, + List[ + Union[ + ContentBlockText, + ContentBlockImage, + ContentBlockToolUse, + ContentBlockToolResult, + ] + ], + ] + class Tool(BaseModel): name: str description: Optional[str] = None input_schema: Dict[str, Any] + class ThinkingConfig(BaseModel): enabled: bool = True + class MessagesRequest(BaseModel): model: str max_tokens: int @@ -196,21 +225,23 @@ class MessagesRequest(BaseModel): tool_choice: Optional[Dict[str, Any]] = None thinking: Optional[ThinkingConfig] = None original_model: Optional[str] = None # Will store the original model name - - @field_validator('model') - def validate_model_field(cls, v, info): # Renamed to avoid conflict + + @field_validator("model") + def validate_model_field(cls, v, info): # Renamed to avoid conflict original_model = v - new_model = v # Default to original value + new_model = v # Default to original value - logger.debug(f"📋 MODEL VALIDATION: Original='{original_model}', Preferred='{PREFERRED_PROVIDER}', BIG='{BIG_MODEL}', SMALL='{SMALL_MODEL}'") + logger.debug( + f"📋 MODEL VALIDATION: Original='{original_model}', Preferred='{PREFERRED_PROVIDER}', BIG='{BIG_MODEL}', SMALL='{SMALL_MODEL}'" + ) # Remove provider prefixes for easier matching clean_v = v - if clean_v.startswith('anthropic/'): + if clean_v.startswith("anthropic/"): clean_v = clean_v[10:] - elif clean_v.startswith('openai/'): + elif clean_v.startswith("openai/"): clean_v = clean_v[7:] - elif clean_v.startswith('gemini/'): + elif clean_v.startswith("gemini/"): clean_v = clean_v[7:] # --- Mapping Logic --- START --- @@ -221,7 +252,7 @@ def validate_model_field(cls, v, info): # Renamed to avoid conflict mapped = True # Map Haiku to SMALL_MODEL based on provider preference - elif 'haiku' in clean_v.lower(): + elif "haiku" in clean_v.lower(): if PREFERRED_PROVIDER == "google" and SMALL_MODEL in GEMINI_MODELS: new_model = f"gemini/{SMALL_MODEL}" mapped = True @@ -230,7 +261,7 @@ def validate_model_field(cls, v, info): # Renamed to avoid conflict mapped = True # Map Sonnet to BIG_MODEL based on provider preference - elif 'sonnet' in clean_v.lower(): + elif "sonnet" in clean_v.lower(): if PREFERRED_PROVIDER == "google" and BIG_MODEL in GEMINI_MODELS: new_model = f"gemini/{BIG_MODEL}" mapped = True @@ -240,29 +271,32 @@ def validate_model_field(cls, v, info): # Renamed to avoid conflict # Add prefixes to non-mapped models if they match known lists elif not mapped: - if clean_v in GEMINI_MODELS and not v.startswith('gemini/'): + if clean_v in GEMINI_MODELS and not v.startswith("gemini/"): new_model = f"gemini/{clean_v}" - mapped = True # Technically mapped to add prefix - elif clean_v in OPENAI_MODELS and not v.startswith('openai/'): + mapped = True # Technically mapped to add prefix + elif clean_v in OPENAI_MODELS and not v.startswith("openai/"): new_model = f"openai/{clean_v}" - mapped = True # Technically mapped to add prefix + mapped = True # Technically mapped to add prefix # --- Mapping Logic --- END --- if mapped: logger.debug(f"📌 MODEL MAPPING: '{original_model}' ➡️ '{new_model}'") else: - # If no mapping occurred and no prefix exists, log warning or decide default - if not v.startswith(('openai/', 'gemini/', 'anthropic/')): - logger.warning(f"⚠️ No prefix or mapping rule for model: '{original_model}'. Using as is.") - new_model = v # Ensure we return the original if no rule applied + # If no mapping occurred and no prefix exists, log warning or decide default + if not v.startswith(("openai/", "gemini/", "anthropic/")): + logger.warning( + f"⚠️ No prefix or mapping rule for model: '{original_model}'. Using as is." + ) + new_model = v # Ensure we return the original if no rule applied # Store the original model in the values dictionary values = info.data if isinstance(values, dict): - values['original_model'] = original_model + values["original_model"] = original_model return new_model + class TokenCountRequest(BaseModel): model: str messages: List[Message] @@ -271,30 +305,32 @@ class TokenCountRequest(BaseModel): thinking: Optional[ThinkingConfig] = None tool_choice: Optional[Dict[str, Any]] = None original_model: Optional[str] = None # Will store the original model name - - @field_validator('model') - def validate_model_token_count(cls, v, info): # Renamed to avoid conflict + + @field_validator("model") + def validate_model_token_count(cls, v, info): # Renamed to avoid conflict # Use the same logic as MessagesRequest validator # NOTE: Pydantic validators might not share state easily if not class methods # Re-implementing the logic here for clarity, could be refactored original_model = v - new_model = v # Default to original value + new_model = v # Default to original value - logger.debug(f"📋 TOKEN COUNT VALIDATION: Original='{original_model}', Preferred='{PREFERRED_PROVIDER}', BIG='{BIG_MODEL}', SMALL='{SMALL_MODEL}'") + logger.debug( + f"📋 TOKEN COUNT VALIDATION: Original='{original_model}', Preferred='{PREFERRED_PROVIDER}', BIG='{BIG_MODEL}', SMALL='{SMALL_MODEL}'" + ) # Remove provider prefixes for easier matching clean_v = v - if clean_v.startswith('anthropic/'): + if clean_v.startswith("anthropic/"): clean_v = clean_v[10:] - elif clean_v.startswith('openai/'): + elif clean_v.startswith("openai/"): clean_v = clean_v[7:] - elif clean_v.startswith('gemini/'): + elif clean_v.startswith("gemini/"): clean_v = clean_v[7:] # --- Mapping Logic --- START --- mapped = False # Map Haiku to SMALL_MODEL based on provider preference - if 'haiku' in clean_v.lower(): + if "haiku" in clean_v.lower(): if PREFERRED_PROVIDER == "google" and SMALL_MODEL in GEMINI_MODELS: new_model = f"gemini/{SMALL_MODEL}" mapped = True @@ -303,7 +339,7 @@ def validate_model_token_count(cls, v, info): # Renamed to avoid conflict mapped = True # Map Sonnet to BIG_MODEL based on provider preference - elif 'sonnet' in clean_v.lower(): + elif "sonnet" in clean_v.lower(): if PREFERRED_PROVIDER == "google" and BIG_MODEL in GEMINI_MODELS: new_model = f"gemini/{BIG_MODEL}" mapped = True @@ -313,71 +349,81 @@ def validate_model_token_count(cls, v, info): # Renamed to avoid conflict # Add prefixes to non-mapped models if they match known lists elif not mapped: - if clean_v in GEMINI_MODELS and not v.startswith('gemini/'): + if clean_v in GEMINI_MODELS and not v.startswith("gemini/"): new_model = f"gemini/{clean_v}" - mapped = True # Technically mapped to add prefix - elif clean_v in OPENAI_MODELS and not v.startswith('openai/'): + mapped = True # Technically mapped to add prefix + elif clean_v in OPENAI_MODELS and not v.startswith("openai/"): new_model = f"openai/{clean_v}" - mapped = True # Technically mapped to add prefix + mapped = True # Technically mapped to add prefix # --- Mapping Logic --- END --- if mapped: logger.debug(f"📌 TOKEN COUNT MAPPING: '{original_model}' ➡️ '{new_model}'") else: - if not v.startswith(('openai/', 'gemini/', 'anthropic/')): - logger.warning(f"⚠️ No prefix or mapping rule for token count model: '{original_model}'. Using as is.") - new_model = v # Ensure we return the original if no rule applied + if not v.startswith(("openai/", "gemini/", "anthropic/")): + logger.warning( + f"⚠️ No prefix or mapping rule for token count model: '{original_model}'. Using as is." + ) + new_model = v # Ensure we return the original if no rule applied # Store the original model in the values dictionary values = info.data if isinstance(values, dict): - values['original_model'] = original_model + values["original_model"] = original_model return new_model + class TokenCountResponse(BaseModel): input_tokens: int + class Usage(BaseModel): input_tokens: int output_tokens: int cache_creation_input_tokens: int = 0 cache_read_input_tokens: int = 0 + class MessagesResponse(BaseModel): id: str model: str role: Literal["assistant"] = "assistant" content: List[Union[ContentBlockText, ContentBlockToolUse]] type: Literal["message"] = "message" - stop_reason: Optional[Literal["end_turn", "max_tokens", "stop_sequence", "tool_use"]] = None + stop_reason: Optional[ + Literal["end_turn", "max_tokens", "stop_sequence", "tool_use"] + ] = None stop_sequence: Optional[str] = None usage: Usage + @app.middleware("http") async def log_requests(request: Request, call_next): # Get request details method = request.method path = request.url.path - + # Log only basic request details at debug level logger.debug(f"Request: {method} {path}") - + # Process the request and get the response response = await call_next(request) - + return response + # Not using validation function as we're using the environment API key + def parse_tool_result_content(content): """Helper function to properly parse and normalize tool result content.""" if content is None: return "No content provided" - + if isinstance(content, str): return content - + if isinstance(content, list): result = "" for item in content: @@ -399,7 +445,7 @@ def parse_tool_result_content(content): except: result += "Unparseable content\n" return result.strip() - + if isinstance(content, dict): if content.get("type") == "text": return content.get("text", "") @@ -407,20 +453,21 @@ def parse_tool_result_content(content): return json.dumps(content) except: return str(content) - + # Fallback for any other type try: return str(content) except: return "Unparseable content" + def convert_anthropic_to_litellm(anthropic_request: MessagesRequest) -> Dict[str, Any]: """Convert Anthropic API request format to LiteLLM format (which follows OpenAI).""" # LiteLLM already handles Anthropic models when using the format model="anthropic/claude-3-opus-20240229" # So we just need to convert our Pydantic model to a dict in the expected format - + messages = [] - + # Add system message if present if anthropic_request.system: # Handle different formats of system messages @@ -431,14 +478,14 @@ def convert_anthropic_to_litellm(anthropic_request: MessagesRequest) -> Dict[str # List of content blocks system_text = "" for block in anthropic_request.system: - if hasattr(block, 'type') and block.type == "text": + if hasattr(block, "type") and block.type == "text": system_text += block.text + "\n\n" elif isinstance(block, dict) and block.get("type") == "text": system_text += block.get("text", "") + "\n\n" - + if system_text: messages.append({"role": "system", "content": system_text.strip()}) - + # Add conversation messages for idx, msg in enumerate(anthropic_request.messages): content = msg.content @@ -446,12 +493,16 @@ def convert_anthropic_to_litellm(anthropic_request: MessagesRequest) -> Dict[str messages.append({"role": msg.role, "content": content}) else: # Special handling for tool_result in user messages - # OpenAI/LiteLLM format expects the assistant to call the tool, + # OpenAI/LiteLLM format expects the assistant to call the tool, # and the user's next message to include the result as plain text - if msg.role == "user" and any(block.type == "tool_result" for block in content if hasattr(block, "type")): + if msg.role == "user" and any( + block.type == "tool_result" + for block in content + if hasattr(block, "type") + ): # For user messages with tool_result, split into separate messages text_content = "" - + # Extract all text parts and concatenate them for block in content: if hasattr(block, "type"): @@ -459,8 +510,12 @@ def convert_anthropic_to_litellm(anthropic_request: MessagesRequest) -> Dict[str text_content += block.text + "\n" elif block.type == "tool_result": # Add tool result as a message by itself - simulate the normal flow - tool_id = block.tool_use_id if hasattr(block, "tool_use_id") else "" - + tool_id = ( + block.tool_use_id + if hasattr(block, "tool_use_id") + else "" + ) + # Handle different formats of tool result content result_content = "" if hasattr(block, "content"): @@ -469,19 +524,33 @@ def convert_anthropic_to_litellm(anthropic_request: MessagesRequest) -> Dict[str elif isinstance(block.content, list): # If content is a list of blocks, extract text from each for content_block in block.content: - if hasattr(content_block, "type") and content_block.type == "text": + if ( + hasattr(content_block, "type") + and content_block.type == "text" + ): result_content += content_block.text + "\n" - elif isinstance(content_block, dict) and content_block.get("type") == "text": - result_content += content_block.get("text", "") + "\n" + elif ( + isinstance(content_block, dict) + and content_block.get("type") == "text" + ): + result_content += ( + content_block.get("text", "") + "\n" + ) elif isinstance(content_block, dict): # Handle any dict by trying to extract text or convert to JSON if "text" in content_block: - result_content += content_block.get("text", "") + "\n" + result_content += ( + content_block.get("text", "") + "\n" + ) else: try: - result_content += json.dumps(content_block) + "\n" + result_content += ( + json.dumps(content_block) + "\n" + ) except: - result_content += str(content_block) + "\n" + result_content += ( + str(content_block) + "\n" + ) elif isinstance(block.content, dict): # Handle dictionary content if block.content.get("type") == "text": @@ -497,10 +566,12 @@ def convert_anthropic_to_litellm(anthropic_request: MessagesRequest) -> Dict[str result_content = str(block.content) except: result_content = "Unparseable content" - + # In OpenAI format, tool results come from the user (rather than being content blocks) - text_content += f"Tool result for {tool_id}:\n{result_content}\n" - + text_content += ( + f"Tool result for {tool_id}:\n{result_content}\n" + ) + # Add as a single user message with all the content messages.append({"role": "user", "content": text_content.strip()}) else: @@ -509,49 +580,67 @@ def convert_anthropic_to_litellm(anthropic_request: MessagesRequest) -> Dict[str for block in content: if hasattr(block, "type"): if block.type == "text": - processed_content.append({"type": "text", "text": block.text}) + processed_content.append( + {"type": "text", "text": block.text} + ) elif block.type == "image": - processed_content.append({"type": "image", "source": block.source}) + processed_content.append( + {"type": "image", "source": block.source} + ) elif block.type == "tool_use": # Handle tool use blocks if needed - processed_content.append({ - "type": "tool_use", - "id": block.id, - "name": block.name, - "input": block.input - }) + processed_content.append( + { + "type": "tool_use", + "id": block.id, + "name": block.name, + "input": block.input, + } + ) elif block.type == "tool_result": # Handle different formats of tool result content processed_content_block = { "type": "tool_result", - "tool_use_id": block.tool_use_id if hasattr(block, "tool_use_id") else "" + "tool_use_id": block.tool_use_id + if hasattr(block, "tool_use_id") + else "", } - + # Process the content field properly if hasattr(block, "content"): if isinstance(block.content, str): # If it's a simple string, create a text block for it - processed_content_block["content"] = [{"type": "text", "text": block.content}] + processed_content_block["content"] = [ + {"type": "text", "text": block.content} + ] elif isinstance(block.content, list): # If it's already a list of blocks, keep it processed_content_block["content"] = block.content else: # Default fallback - processed_content_block["content"] = [{"type": "text", "text": str(block.content)}] + processed_content_block["content"] = [ + {"type": "text", "text": str(block.content)} + ] else: # Default empty content - processed_content_block["content"] = [{"type": "text", "text": ""}] - + processed_content_block["content"] = [ + {"type": "text", "text": ""} + ] + processed_content.append(processed_content_block) - + messages.append({"role": msg.role, "content": processed_content}) - + # Cap max_tokens for OpenAI models to their limit of 16384 max_tokens = anthropic_request.max_tokens - if anthropic_request.model.startswith("openai/") or anthropic_request.model.startswith("gemini/"): + if anthropic_request.model.startswith( + "openai/" + ) or anthropic_request.model.startswith("gemini/"): max_tokens = min(max_tokens, 16384) - logger.debug(f"Capping max_tokens to 16384 for OpenAI/Gemini model (original value: {anthropic_request.max_tokens})") - + logger.debug( + f"Capping max_tokens to 16384 for OpenAI/Gemini model (original value: {anthropic_request.max_tokens})" + ) + # Create LiteLLM request dict litellm_request = { "model": anthropic_request.model, # it understands "anthropic/claude-x" format @@ -568,13 +657,13 @@ def convert_anthropic_to_litellm(anthropic_request: MessagesRequest) -> Dict[str # Add optional parameters if present if anthropic_request.stop_sequences: litellm_request["stop"] = anthropic_request.stop_sequences - + if anthropic_request.top_p: litellm_request["top_p"] = anthropic_request.top_p - + if anthropic_request.top_k: litellm_request["top_k"] = anthropic_request.top_k - + # Convert tools to OpenAI format if anthropic_request.tools: openai_tools = [] @@ -582,21 +671,23 @@ def convert_anthropic_to_litellm(anthropic_request: MessagesRequest) -> Dict[str for tool in anthropic_request.tools: # Convert to dict if it's a pydantic model - if hasattr(tool, 'dict'): + if hasattr(tool, "dict"): tool_dict = tool.dict() else: # Ensure tool_dict is a dictionary, handle potential errors if 'tool' isn't dict-like try: tool_dict = dict(tool) if not isinstance(tool, dict) else tool except (TypeError, ValueError): - logger.error(f"Could not convert tool to dict: {tool}") - continue # Skip this tool if conversion fails + logger.error(f"Could not convert tool to dict: {tool}") + continue # Skip this tool if conversion fails # Clean the schema if targeting a Gemini model input_schema = tool_dict.get("input_schema", {}) if is_gemini_model: - logger.debug(f"Cleaning schema for Gemini tool: {tool_dict.get('name')}") - input_schema = clean_gemini_schema(input_schema) + logger.debug( + f"Cleaning schema for Gemini tool: {tool_dict.get('name')}" + ) + input_schema = clean_gemini_schema(input_schema) # Create OpenAI-compatible function tool openai_tool = { @@ -604,20 +695,20 @@ def convert_anthropic_to_litellm(anthropic_request: MessagesRequest) -> Dict[str "function": { "name": tool_dict["name"], "description": tool_dict.get("description", ""), - "parameters": input_schema # Use potentially cleaned schema - } + "parameters": input_schema, # Use potentially cleaned schema + }, } openai_tools.append(openai_tool) litellm_request["tools"] = openai_tools - + # Convert tool_choice to OpenAI format if present if anthropic_request.tool_choice: - if hasattr(anthropic_request.tool_choice, 'dict'): + if hasattr(anthropic_request.tool_choice, "dict"): tool_choice_dict = anthropic_request.tool_choice.dict() else: tool_choice_dict = anthropic_request.tool_choice - + # Handle Anthropic's tool_choice format choice_type = tool_choice_dict.get("type") if choice_type == "auto": @@ -627,84 +718,109 @@ def convert_anthropic_to_litellm(anthropic_request: MessagesRequest) -> Dict[str elif choice_type == "tool" and "name" in tool_choice_dict: litellm_request["tool_choice"] = { "type": "function", - "function": {"name": tool_choice_dict["name"]} + "function": {"name": tool_choice_dict["name"]}, } else: # Default to auto if we can't determine litellm_request["tool_choice"] = "auto" - + return litellm_request -def convert_litellm_to_anthropic(litellm_response: Union[Dict[str, Any], Any], - original_request: MessagesRequest) -> MessagesResponse: + +def convert_litellm_to_anthropic( + litellm_response: Union[Dict[str, Any], Any], original_request: MessagesRequest +) -> MessagesResponse: """Convert LiteLLM (OpenAI format) response to Anthropic API response format.""" - + # Enhanced response extraction with better error handling try: # Get the clean model name to check capabilities clean_model = original_request.model if clean_model.startswith("anthropic/"): - clean_model = clean_model[len("anthropic/"):] + clean_model = clean_model[len("anthropic/") :] elif clean_model.startswith("openai/"): - clean_model = clean_model[len("openai/"):] - + clean_model = clean_model[len("openai/") :] + # Check if this is a Claude model (which supports content blocks) is_claude_model = clean_model.startswith("claude-") - + # Handle ModelResponse object from LiteLLM - if hasattr(litellm_response, 'choices') and hasattr(litellm_response, 'usage'): + if hasattr(litellm_response, "choices") and hasattr(litellm_response, "usage"): # Extract data from ModelResponse object directly choices = litellm_response.choices message = choices[0].message if choices and len(choices) > 0 else None - content_text = message.content if message and hasattr(message, 'content') else "" - tool_calls = message.tool_calls if message and hasattr(message, 'tool_calls') else None - finish_reason = choices[0].finish_reason if choices and len(choices) > 0 else "stop" + content_text = ( + message.content if message and hasattr(message, "content") else "" + ) + tool_calls = ( + message.tool_calls + if message and hasattr(message, "tool_calls") + else None + ) + finish_reason = ( + choices[0].finish_reason if choices and len(choices) > 0 else "stop" + ) usage_info = litellm_response.usage - response_id = getattr(litellm_response, 'id', f"msg_{uuid.uuid4()}") + response_id = getattr(litellm_response, "id", f"msg_{uuid.uuid4()}") else: # For backward compatibility - handle dict responses # If response is a dict, use it, otherwise try to convert to dict try: - response_dict = litellm_response if isinstance(litellm_response, dict) else litellm_response.dict() + response_dict = ( + litellm_response + if isinstance(litellm_response, dict) + else litellm_response.dict() + ) except AttributeError: - # If .dict() fails, try to use model_dump or __dict__ + # If .dict() fails, try to use model_dump or __dict__ try: - response_dict = litellm_response.model_dump() if hasattr(litellm_response, 'model_dump') else litellm_response.__dict__ + response_dict = ( + litellm_response.model_dump() + if hasattr(litellm_response, "model_dump") + else litellm_response.__dict__ + ) except AttributeError: # Fallback - manually extract attributes response_dict = { - "id": getattr(litellm_response, 'id', f"msg_{uuid.uuid4()}"), - "choices": getattr(litellm_response, 'choices', [{}]), - "usage": getattr(litellm_response, 'usage', {}) + "id": getattr(litellm_response, "id", f"msg_{uuid.uuid4()}"), + "choices": getattr(litellm_response, "choices", [{}]), + "usage": getattr(litellm_response, "usage", {}), } - + # Extract the content from the response dict choices = response_dict.get("choices", [{}]) - message = choices[0].get("message", {}) if choices and len(choices) > 0 else {} + message = ( + choices[0].get("message", {}) if choices and len(choices) > 0 else {} + ) content_text = message.get("content", "") tool_calls = message.get("tool_calls", None) - finish_reason = choices[0].get("finish_reason", "stop") if choices and len(choices) > 0 else "stop" + finish_reason = ( + choices[0].get("finish_reason", "stop") + if choices and len(choices) > 0 + else "stop" + ) usage_info = response_dict.get("usage", {}) response_id = response_dict.get("id", f"msg_{uuid.uuid4()}") - + # Create content list for Anthropic format content = [] - + # Add text content block if present (text might be None or empty for pure tool call responses) if content_text is not None and content_text != "": content.append({"type": "text", "text": content_text}) - - # Add tool calls if present (tool_use in Anthropic format) - only for Claude models - if tool_calls and is_claude_model: + + # Add tool calls if present (tool_use in Anthropic format) + # For ALL models, not just Claude models - convert tool_calls to tool_use blocks + if tool_calls: logger.debug(f"Processing tool calls: {tool_calls}") - + # Convert to list if it's not already if not isinstance(tool_calls, list): tool_calls = [tool_calls] - + for idx, tool_call in enumerate(tool_calls): logger.debug(f"Processing tool call {idx}: {tool_call}") - + # Extract function data based on whether it's a dict or object if isinstance(tool_call, dict): function = tool_call.get("function", {}) @@ -715,66 +831,33 @@ def convert_litellm_to_anthropic(litellm_response: Union[Dict[str, Any], Any], function = getattr(tool_call, "function", None) tool_id = getattr(tool_call, "id", f"tool_{uuid.uuid4()}") name = getattr(function, "name", "") if function else "" - arguments = getattr(function, "arguments", "{}") if function else "{}" - + arguments = ( + getattr(function, "arguments", "{}") if function else "{}" + ) + # Convert string arguments to dict if needed if isinstance(arguments, str): try: arguments = json.loads(arguments) except json.JSONDecodeError: - logger.warning(f"Failed to parse tool arguments as JSON: {arguments}") + logger.warning( + f"Failed to parse tool arguments as JSON: {arguments}" + ) arguments = {"raw": arguments} - - logger.debug(f"Adding tool_use block: id={tool_id}, name={name}, input={arguments}") - - content.append({ - "type": "tool_use", - "id": tool_id, - "name": name, - "input": arguments - }) - elif tool_calls and not is_claude_model: - # For non-Claude models, convert tool calls to text format - logger.debug(f"Converting tool calls to text for non-Claude model: {clean_model}") - - # We'll append tool info to the text content - tool_text = "\n\nTool usage:\n" - - # Convert to list if it's not already - if not isinstance(tool_calls, list): - tool_calls = [tool_calls] - - for idx, tool_call in enumerate(tool_calls): - # Extract function data based on whether it's a dict or object - if isinstance(tool_call, dict): - function = tool_call.get("function", {}) - tool_id = tool_call.get("id", f"tool_{uuid.uuid4()}") - name = function.get("name", "") - arguments = function.get("arguments", "{}") - else: - function = getattr(tool_call, "function", None) - tool_id = getattr(tool_call, "id", f"tool_{uuid.uuid4()}") - name = getattr(function, "name", "") if function else "" - arguments = getattr(function, "arguments", "{}") if function else "{}" - - # Convert string arguments to dict if needed - if isinstance(arguments, str): - try: - args_dict = json.loads(arguments) - arguments_str = json.dumps(args_dict, indent=2) - except json.JSONDecodeError: - arguments_str = arguments - else: - arguments_str = json.dumps(arguments, indent=2) - - tool_text += f"Tool: {name}\nArguments: {arguments_str}\n\n" - - # Add or append tool text to content - if content and content[0]["type"] == "text": - content[0]["text"] += tool_text - else: - content.append({"type": "text", "text": tool_text}) - + + logger.debug( + f"Adding tool_use block: id={tool_id}, name={name}, input={arguments}" + ) + + content.append( + { + "type": "tool_use", + "id": tool_id, + "name": name, + "input": arguments, + } + ) + # Get usage information - extract values safely from object or dict if isinstance(usage_info, dict): prompt_tokens = usage_info.get("prompt_tokens", 0) @@ -782,7 +865,7 @@ def convert_litellm_to_anthropic(litellm_response: Union[Dict[str, Any], Any], else: prompt_tokens = getattr(usage_info, "prompt_tokens", 0) completion_tokens = getattr(usage_info, "completion_tokens", 0) - + # Map OpenAI finish_reason to Anthropic stop_reason stop_reason = None if finish_reason == "stop": @@ -793,11 +876,11 @@ def convert_litellm_to_anthropic(litellm_response: Union[Dict[str, Any], Any], stop_reason = "tool_use" else: stop_reason = "end_turn" # Default - + # Make sure content is never empty if not content: content.append({"type": "text", "text": ""}) - + # Create Anthropic-style response anthropic_response = MessagesResponse( id=response_id, @@ -806,62 +889,68 @@ def convert_litellm_to_anthropic(litellm_response: Union[Dict[str, Any], Any], content=content, stop_reason=stop_reason, stop_sequence=None, - usage=Usage( - input_tokens=prompt_tokens, - output_tokens=completion_tokens - ) + usage=Usage(input_tokens=prompt_tokens, output_tokens=completion_tokens), ) - + return anthropic_response - + except Exception as e: import traceback + error_traceback = traceback.format_exc() - error_message = f"Error converting response: {str(e)}\n\nFull traceback:\n{error_traceback}" + error_message = ( + f"Error converting response: {str(e)}\n\nFull traceback:\n{error_traceback}" + ) logger.error(error_message) - + # In case of any error, create a fallback response return MessagesResponse( id=f"msg_{uuid.uuid4()}", model=original_request.model, role="assistant", - content=[{"type": "text", "text": f"Error converting response: {str(e)}. Please check server logs."}], + content=[ + { + "type": "text", + "text": f"Error converting response: {str(e)}. Please check server logs.", + } + ], stop_reason="end_turn", - usage=Usage(input_tokens=0, output_tokens=0) + usage=Usage(input_tokens=0, output_tokens=0), ) + async def handle_streaming(response_generator, original_request: MessagesRequest): """Handle streaming responses from LiteLLM and convert to Anthropic format.""" try: # Send message_start event message_id = f"msg_{uuid.uuid4().hex[:24]}" # Format similar to Anthropic's IDs - + message_data = { - 'type': 'message_start', - 'message': { - 'id': message_id, - 'type': 'message', - 'role': 'assistant', - 'model': original_request.model, - 'content': [], - 'stop_reason': None, - 'stop_sequence': None, - 'usage': { - 'input_tokens': 0, - 'cache_creation_input_tokens': 0, - 'cache_read_input_tokens': 0, - 'output_tokens': 0 - } - } + "type": "message_start", + "message": { + "id": message_id, + "type": "message", + "role": "assistant", + "model": original_request.model, + "content": [], + "stop_reason": None, + "stop_sequence": None, + "usage": { + "input_tokens": 0, + "cache_creation_input_tokens": 0, + "cache_read_input_tokens": 0, + "output_tokens": 0, + }, + }, } yield f"event: message_start\ndata: {json.dumps(message_data)}\n\n" - + # Content block index for the first text block yield f"event: content_block_start\ndata: {json.dumps({'type': 'content_block_start', 'index': 0, 'content_block': {'type': 'text', 'text': ''}})}\n\n" - + # Send a ping to keep the connection alive (Anthropic does this) yield f"event: ping\ndata: {json.dumps({'type': 'ping'})}\n\n" - + tool_index = None current_tool_call = None tool_content = "" @@ -872,60 +961,58 @@ async def handle_streaming(response_generator, original_request: MessagesRequest output_tokens = 0 has_sent_stop_reason = False last_tool_index = 0 - + # Process each chunk async for chunk in response_generator: try: - - # Check if this is the end of the response with usage data - if hasattr(chunk, 'usage') and chunk.usage is not None: - if hasattr(chunk.usage, 'prompt_tokens'): + if hasattr(chunk, "usage") and chunk.usage is not None: + if hasattr(chunk.usage, "prompt_tokens"): input_tokens = chunk.usage.prompt_tokens - if hasattr(chunk.usage, 'completion_tokens'): + if hasattr(chunk.usage, "completion_tokens"): output_tokens = chunk.usage.completion_tokens - + # Handle text content - if hasattr(chunk, 'choices') and len(chunk.choices) > 0: + if hasattr(chunk, "choices") and len(chunk.choices) > 0: choice = chunk.choices[0] - + # Get the delta from the choice - if hasattr(choice, 'delta'): + if hasattr(choice, "delta"): delta = choice.delta else: # If no delta, try to get message - delta = getattr(choice, 'message', {}) - + delta = getattr(choice, "message", {}) + # Check for finish_reason to know when we're done - finish_reason = getattr(choice, 'finish_reason', None) - + finish_reason = getattr(choice, "finish_reason", None) + # Process text content delta_content = None - + # Handle different formats of delta content - if hasattr(delta, 'content'): + if hasattr(delta, "content"): delta_content = delta.content - elif isinstance(delta, dict) and 'content' in delta: - delta_content = delta['content'] - + elif isinstance(delta, dict) and "content" in delta: + delta_content = delta["content"] + # Accumulate text content if delta_content is not None and delta_content != "": accumulated_text += delta_content - + # Always emit text deltas if no tool calls started if tool_index is None and not text_block_closed: text_sent = True yield f"event: content_block_delta\ndata: {json.dumps({'type': 'content_block_delta', 'index': 0, 'delta': {'type': 'text_delta', 'text': delta_content}})}\n\n" - + # Process tool calls delta_tool_calls = None - + # Handle different formats of tool calls - if hasattr(delta, 'tool_calls'): + if hasattr(delta, "tool_calls"): delta_tool_calls = delta.tool_calls - elif isinstance(delta, dict) and 'tool_calls' in delta: - delta_tool_calls = delta['tool_calls'] - + elif isinstance(delta, dict) and "tool_calls" in delta: + delta_tool_calls = delta["tool_calls"] + # Process tool calls if any if delta_tool_calls: # First tool call we've seen - need to handle text properly @@ -936,7 +1023,11 @@ async def handle_streaming(response_generator, original_request: MessagesRequest yield f"event: content_block_stop\ndata: {json.dumps({'type': 'content_block_stop', 'index': 0})}\n\n" # If we've accumulated text but not sent it, we need to emit it now # This handles the case where the first delta has both text and a tool call - elif accumulated_text and not text_sent and not text_block_closed: + elif ( + accumulated_text + and not text_sent + and not text_block_closed + ): # Send the accumulated text text_sent = True yield f"event: content_block_delta\ndata: {json.dumps({'type': 'content_block_delta', 'index': 0, 'delta': {'type': 'text_delta', 'text': accumulated_text}})}\n\n" @@ -947,52 +1038,74 @@ async def handle_streaming(response_generator, original_request: MessagesRequest elif not text_block_closed: text_block_closed = True yield f"event: content_block_stop\ndata: {json.dumps({'type': 'content_block_stop', 'index': 0})}\n\n" - + # Convert to list if it's not already if not isinstance(delta_tool_calls, list): delta_tool_calls = [delta_tool_calls] - + for tool_call in delta_tool_calls: # Get the index of this tool call (for multiple tools) current_index = None - if isinstance(tool_call, dict) and 'index' in tool_call: - current_index = tool_call['index'] - elif hasattr(tool_call, 'index'): + if isinstance(tool_call, dict) and "index" in tool_call: + current_index = tool_call["index"] + elif hasattr(tool_call, "index"): current_index = tool_call.index else: current_index = 0 - + # Check if this is a new tool or a continuation if tool_index is None or current_index != tool_index: # New tool call - create a new tool_use block tool_index = current_index last_tool_index += 1 anthropic_tool_index = last_tool_index - + # Extract function info if isinstance(tool_call, dict): - function = tool_call.get('function', {}) - name = function.get('name', '') if isinstance(function, dict) else "" - tool_id = tool_call.get('id', f"toolu_{uuid.uuid4().hex[:24]}") + function = tool_call.get("function", {}) + name = ( + function.get("name", "") + if isinstance(function, dict) + else "" + ) + tool_id = tool_call.get( + "id", f"toolu_{uuid.uuid4().hex[:24]}" + ) else: - function = getattr(tool_call, 'function', None) - name = getattr(function, 'name', '') if function else '' - tool_id = getattr(tool_call, 'id', f"toolu_{uuid.uuid4().hex[:24]}") - + function = getattr(tool_call, "function", None) + name = ( + getattr(function, "name", "") + if function + else "" + ) + tool_id = getattr( + tool_call, + "id", + f"toolu_{uuid.uuid4().hex[:24]}", + ) + # Start a new tool_use block yield f"event: content_block_start\ndata: {json.dumps({'type': 'content_block_start', 'index': anthropic_tool_index, 'content_block': {'type': 'tool_use', 'id': tool_id, 'name': name, 'input': {}}})}\n\n" current_tool_call = tool_call tool_content = "" - + # Extract function arguments arguments = None - if isinstance(tool_call, dict) and 'function' in tool_call: - function = tool_call.get('function', {}) - arguments = function.get('arguments', '') if isinstance(function, dict) else '' - elif hasattr(tool_call, 'function'): - function = getattr(tool_call, 'function', None) - arguments = getattr(function, 'arguments', '') if function else '' - + if isinstance(tool_call, dict) and "function" in tool_call: + function = tool_call.get("function", {}) + arguments = ( + function.get("arguments", "") + if isinstance(function, dict) + else "" + ) + elif hasattr(tool_call, "function"): + function = getattr(tool_call, "function", None) + arguments = ( + getattr(function, "arguments", "") + if function + else "" + ) + # If we have arguments, send them as a delta if arguments: # Try to detect if arguments are valid JSON or just a fragment @@ -1007,22 +1120,24 @@ async def handle_streaming(response_generator, original_request: MessagesRequest except (json.JSONDecodeError, TypeError): # If it's a fragment, treat it as a string args_json = arguments - + # Add to accumulated tool content - tool_content += args_json if isinstance(args_json, str) else "" - + tool_content += ( + args_json if isinstance(args_json, str) else "" + ) + # Send the update yield f"event: content_block_delta\ndata: {json.dumps({'type': 'content_block_delta', 'index': anthropic_tool_index, 'delta': {'type': 'input_json_delta', 'partial_json': args_json}})}\n\n" - + # Process finish_reason - end the streaming response if finish_reason and not has_sent_stop_reason: has_sent_stop_reason = True - + # Close any open tool call blocks if tool_index is not None: for i in range(1, last_tool_index + 1): yield f"event: content_block_stop\ndata: {json.dumps({'type': 'content_block_stop', 'index': i})}\n\n" - + # If we accumulated text but never sent or closed text block, do it now if not text_block_closed: if accumulated_text and not text_sent: @@ -1030,7 +1145,7 @@ async def handle_streaming(response_generator, original_request: MessagesRequest yield f"event: content_block_delta\ndata: {json.dumps({'type': 'content_block_delta', 'index': 0, 'delta': {'type': 'text_delta', 'text': accumulated_text}})}\n\n" # Close the text block yield f"event: content_block_stop\ndata: {json.dumps({'type': 'content_block_stop', 'index': 0})}\n\n" - + # Map OpenAI finish_reason to Anthropic stop_reason stop_reason = "end_turn" if finish_reason == "length": @@ -1039,15 +1154,15 @@ async def handle_streaming(response_generator, original_request: MessagesRequest stop_reason = "tool_use" elif finish_reason == "stop": stop_reason = "end_turn" - + # Send message_delta with stop reason and usage usage = {"output_tokens": output_tokens} - + yield f"event: message_delta\ndata: {json.dumps({'type': 'message_delta', 'delta': {'stop_reason': stop_reason, 'stop_sequence': None}, 'usage': usage})}\n\n" - + # Send message_stop event yield f"event: message_stop\ndata: {json.dumps({'type': 'message_stop'})}\n\n" - + # Send final [DONE] marker to match Anthropic's behavior yield "data: [DONE]\n\n" return @@ -1055,80 +1170,85 @@ async def handle_streaming(response_generator, original_request: MessagesRequest # Log error but continue processing other chunks logger.error(f"Error processing chunk: {str(e)}") continue - + # If we didn't get a finish reason, close any open blocks if not has_sent_stop_reason: # Close any open tool call blocks if tool_index is not None: for i in range(1, last_tool_index + 1): yield f"event: content_block_stop\ndata: {json.dumps({'type': 'content_block_stop', 'index': i})}\n\n" - + # Close the text content block yield f"event: content_block_stop\ndata: {json.dumps({'type': 'content_block_stop', 'index': 0})}\n\n" - + # Send final message_delta with usage usage = {"output_tokens": output_tokens} - + yield f"event: message_delta\ndata: {json.dumps({'type': 'message_delta', 'delta': {'stop_reason': 'end_turn', 'stop_sequence': None}, 'usage': usage})}\n\n" - + # Send message_stop event yield f"event: message_stop\ndata: {json.dumps({'type': 'message_stop'})}\n\n" - + # Send final [DONE] marker to match Anthropic's behavior yield "data: [DONE]\n\n" - + except Exception as e: import traceback + error_traceback = traceback.format_exc() - error_message = f"Error in streaming: {str(e)}\n\nFull traceback:\n{error_traceback}" + error_message = ( + f"Error in streaming: {str(e)}\n\nFull traceback:\n{error_traceback}" + ) logger.error(error_message) - + # Send error message_delta yield f"event: message_delta\ndata: {json.dumps({'type': 'message_delta', 'delta': {'stop_reason': 'error', 'stop_sequence': None}, 'usage': {'output_tokens': 0}})}\n\n" - + # Send message_stop event yield f"event: message_stop\ndata: {json.dumps({'type': 'message_stop'})}\n\n" - + # Send final [DONE] marker yield "data: [DONE]\n\n" + @app.post("/v1/messages") -async def create_message( - request: MessagesRequest, - raw_request: Request -): +async def create_message(request: MessagesRequest, raw_request: Request): try: # print the body here body = await raw_request.body() - + # Parse the raw body as JSON since it's bytes - body_json = json.loads(body.decode('utf-8')) + body_json = json.loads(body.decode("utf-8")) original_model = body_json.get("model", "unknown") - + # Get the display name for logging, just the model name without provider prefix display_model = original_model if "/" in display_model: display_model = display_model.split("/")[-1] - + # Clean model name for capability check clean_model = request.model if clean_model.startswith("anthropic/"): - clean_model = clean_model[len("anthropic/"):] + clean_model = clean_model[len("anthropic/") :] elif clean_model.startswith("openai/"): - clean_model = clean_model[len("openai/"):] - - logger.debug(f"📊 PROCESSING REQUEST: Model={request.model}, Stream={request.stream}") - + clean_model = clean_model[len("openai/") :] + + logger.debug( + f"📊 PROCESSING REQUEST: Model={request.model}, Stream={request.stream}" + ) + # Convert Anthropic request to LiteLLM format litellm_request = convert_anthropic_to_litellm(request) - + # Determine which API key to use based on the model if request.model.startswith("openai/"): litellm_request["api_key"] = OPENAI_API_KEY # Use custom OpenAI base URL if configured if OPENAI_BASE_URL: litellm_request["api_base"] = OPENAI_BASE_URL - logger.debug(f"Using OpenAI API key and custom base URL {OPENAI_BASE_URL} for model: {request.model}") + logger.debug( + f"Using OpenAI API key and custom base URL {OPENAI_BASE_URL} for model: {request.model}" + ) else: logger.debug(f"Using OpenAI API key for model: {request.model}") elif request.model.startswith("gemini/"): @@ -1136,18 +1256,20 @@ async def create_message( litellm_request["vertex_project"] = VERTEX_PROJECT litellm_request["vertex_location"] = VERTEX_LOCATION litellm_request["custom_llm_provider"] = "vertex_ai" - logger.debug(f"Using Gemini ADC with project={VERTEX_PROJECT}, location={VERTEX_LOCATION} and model: {request.model}") + logger.debug( + f"Using Gemini ADC with project={VERTEX_PROJECT}, location={VERTEX_LOCATION} and model: {request.model}" + ) else: litellm_request["api_key"] = GEMINI_API_KEY logger.debug(f"Using Gemini API key for model: {request.model}") else: litellm_request["api_key"] = ANTHROPIC_API_KEY logger.debug(f"Using Anthropic API key for model: {request.model}") - + # For OpenAI models - modify request format to work with limitations if "openai" in litellm_request["model"] and "messages" in litellm_request: logger.debug(f"Processing OpenAI model request: {litellm_request['model']}") - + # For OpenAI models, we need to convert content blocks to simple strings # and handle other requirements for i, msg in enumerate(litellm_request["messages"]): @@ -1156,27 +1278,37 @@ async def create_message( if "content" in msg and isinstance(msg["content"], list): is_only_tool_result = True for block in msg["content"]: - if not isinstance(block, dict) or block.get("type") != "tool_result": + if ( + not isinstance(block, dict) + or block.get("type") != "tool_result" + ): is_only_tool_result = False break - + if is_only_tool_result and len(msg["content"]) > 0: - logger.warning(f"Found message with only tool_result content - special handling required") + logger.warning( + f"Found message with only tool_result content - special handling required" + ) # Extract the content from all tool_result blocks all_text = "" for block in msg["content"]: all_text += "Tool Result:\n" result_content = block.get("content", []) - + # Handle different formats of content if isinstance(result_content, list): for item in result_content: - if isinstance(item, dict) and item.get("type") == "text": + if ( + isinstance(item, dict) + and item.get("type") == "text" + ): all_text += item.get("text", "") + "\n" elif isinstance(item, dict): # Fall back to string representation of any dict try: - item_text = item.get("text", json.dumps(item)) + item_text = item.get( + "text", json.dumps(item) + ) all_text += item_text + "\n" except: all_text += str(item) + "\n" @@ -1187,12 +1319,16 @@ async def create_message( all_text += json.dumps(result_content) + "\n" except: all_text += str(result_content) + "\n" - + # Replace the list with extracted text - litellm_request["messages"][i]["content"] = all_text.strip() or "..." - logger.warning(f"Converted tool_result to plain text: {all_text.strip()[:200]}...") + litellm_request["messages"][i]["content"] = ( + all_text.strip() or "..." + ) + logger.warning( + f"Converted tool_result to plain text: {all_text.strip()[:200]}..." + ) continue # Skip normal processing for this message - + # 1. Handle content field - normal case if "content" in msg: # Check if content is a list (content blocks) @@ -1204,152 +1340,194 @@ async def create_message( # Handle different content block types if block.get("type") == "text": text_content += block.get("text", "") + "\n" - + # Handle tool_result content blocks - extract nested text elif block.get("type") == "tool_result": tool_id = block.get("tool_use_id", "unknown") text_content += f"[Tool Result ID: {tool_id}]\n" - + # Extract text from the tool_result content result_content = block.get("content", []) if isinstance(result_content, list): for item in result_content: - if isinstance(item, dict) and item.get("type") == "text": - text_content += item.get("text", "") + "\n" + if ( + isinstance(item, dict) + and item.get("type") == "text" + ): + text_content += ( + item.get("text", "") + "\n" + ) elif isinstance(item, dict): # Handle any dict by trying to extract text or convert to JSON if "text" in item: - text_content += item.get("text", "") + "\n" + text_content += ( + item.get("text", "") + "\n" + ) else: try: - text_content += json.dumps(item) + "\n" + text_content += ( + json.dumps(item) + "\n" + ) except: text_content += str(item) + "\n" elif isinstance(result_content, dict): # Handle dictionary content if result_content.get("type") == "text": - text_content += result_content.get("text", "") + "\n" + text_content += ( + result_content.get("text", "") + "\n" + ) else: try: - text_content += json.dumps(result_content) + "\n" + text_content += ( + json.dumps(result_content) + "\n" + ) except: - text_content += str(result_content) + "\n" + text_content += ( + str(result_content) + "\n" + ) elif isinstance(result_content, str): text_content += result_content + "\n" else: try: - text_content += json.dumps(result_content) + "\n" + text_content += ( + json.dumps(result_content) + "\n" + ) except: text_content += str(result_content) + "\n" - + # Handle tool_use content blocks elif block.get("type") == "tool_use": tool_name = block.get("name", "unknown") tool_id = block.get("id", "unknown") tool_input = json.dumps(block.get("input", {})) text_content += f"[Tool: {tool_name} (ID: {tool_id})]\nInput: {tool_input}\n\n" - + # Handle image content blocks elif block.get("type") == "image": text_content += "[Image content - not displayed in text format]\n" - + # Make sure content is never empty for OpenAI models if not text_content.strip(): text_content = "..." - + litellm_request["messages"][i]["content"] = text_content.strip() # Also check for None or empty string content elif msg["content"] is None: - litellm_request["messages"][i]["content"] = "..." # Empty content not allowed - + litellm_request["messages"][i]["content"] = ( + "..." # Empty content not allowed + ) + # 2. Remove any fields OpenAI doesn't support in messages for key in list(msg.keys()): - if key not in ["role", "content", "name", "tool_call_id", "tool_calls"]: - logger.warning(f"Removing unsupported field from message: {key}") + if key not in [ + "role", + "content", + "name", + "tool_call_id", + "tool_calls", + ]: + logger.warning( + f"Removing unsupported field from message: {key}" + ) del msg[key] - + # 3. Final validation - check for any remaining invalid values and dump full message details for i, msg in enumerate(litellm_request["messages"]): # Log the message format for debugging - logger.debug(f"Message {i} format check - role: {msg.get('role')}, content type: {type(msg.get('content'))}") - + logger.debug( + f"Message {i} format check - role: {msg.get('role')}, content type: {type(msg.get('content'))}" + ) + # If content is still a list or None, replace with placeholder if isinstance(msg.get("content"), list): - logger.warning(f"CRITICAL: Message {i} still has list content after processing: {json.dumps(msg.get('content'))}") + logger.warning( + f"CRITICAL: Message {i} still has list content after processing: {json.dumps(msg.get('content'))}" + ) # Last resort - stringify the entire content as JSON - litellm_request["messages"][i]["content"] = f"Content as JSON: {json.dumps(msg.get('content'))}" + litellm_request["messages"][i]["content"] = ( + f"Content as JSON: {json.dumps(msg.get('content'))}" + ) elif msg.get("content") is None: - logger.warning(f"Message {i} has None content - replacing with placeholder") - litellm_request["messages"][i]["content"] = "..." # Fallback placeholder - + logger.warning( + f"Message {i} has None content - replacing with placeholder" + ) + litellm_request["messages"][i]["content"] = ( + "..." # Fallback placeholder + ) + # Only log basic info about the request, not the full details - logger.debug(f"Request for model: {litellm_request.get('model')}, stream: {litellm_request.get('stream', False)}") - + logger.debug( + f"Request for model: {litellm_request.get('model')}, stream: {litellm_request.get('stream', False)}" + ) + # Handle streaming mode if request.stream: # Use LiteLLM for streaming num_tools = len(request.tools) if request.tools else 0 - + log_request_beautifully( - "POST", - raw_request.url.path, - display_model, - litellm_request.get('model'), - len(litellm_request['messages']), + "POST", + raw_request.url.path, + display_model, + litellm_request.get("model"), + len(litellm_request["messages"]), num_tools, - 200 # Assuming success at this point + 200, # Assuming success at this point ) # Ensure we use the async version for streaming response_generator = await litellm.acompletion(**litellm_request) - + return StreamingResponse( handle_streaming(response_generator, request), - media_type="text/event-stream" + media_type="text/event-stream", ) else: # Use LiteLLM for regular completion num_tools = len(request.tools) if request.tools else 0 - + log_request_beautifully( - "POST", - raw_request.url.path, - display_model, - litellm_request.get('model'), - len(litellm_request['messages']), + "POST", + raw_request.url.path, + display_model, + litellm_request.get("model"), + len(litellm_request["messages"]), num_tools, - 200 # Assuming success at this point + 200, # Assuming success at this point ) start_time = time.time() litellm_response = litellm.completion(**litellm_request) - logger.debug(f"✅ RESPONSE RECEIVED: Model={litellm_request.get('model')}, Time={time.time() - start_time:.2f}s") - + logger.debug( + f"✅ RESPONSE RECEIVED: Model={litellm_request.get('model')}, Time={time.time() - start_time:.2f}s" + ) + # Convert LiteLLM response to Anthropic format anthropic_response = convert_litellm_to_anthropic(litellm_response, request) - + return anthropic_response - + except Exception as e: import traceback + error_traceback = traceback.format_exc() - + # Capture as much info as possible about the error error_details = { "error": str(e), "type": type(e).__name__, - "traceback": error_traceback + "traceback": error_traceback, } - + # Check for LiteLLM-specific attributes - for attr in ['message', 'status_code', 'response', 'llm_provider', 'model']: + for attr in ["message", "status_code", "response", "llm_provider", "model"]: if hasattr(e, attr): error_details[attr] = getattr(e, attr) - + # Check for additional exception details in dictionaries - if hasattr(e, '__dict__'): + if hasattr(e, "__dict__"): for key, value in e.__dict__.items(): - if key not in error_details and key not in ['args', '__traceback__']: + if key not in error_details and key not in ["args", "__traceback__"]: error_details[key] = str(value) - + # Helper function to safely serialize objects for JSON def sanitize_for_json(obj): """递归地清理对象使其可以JSON序列化""" @@ -1357,9 +1535,9 @@ def sanitize_for_json(obj): return {k: sanitize_for_json(v) for k, v in obj.items()} elif isinstance(obj, list): return [sanitize_for_json(item) for item in obj] - elif hasattr(obj, '__dict__'): + elif hasattr(obj, "__dict__"): return sanitize_for_json(obj.__dict__) - elif hasattr(obj, 'text'): + elif hasattr(obj, "text"): return str(obj.text) else: try: @@ -1367,43 +1545,43 @@ def sanitize_for_json(obj): return obj except (TypeError, ValueError): return str(obj) - + # Log all error details with safe serialization sanitized_details = sanitize_for_json(error_details) - logger.error(f"Error processing request: {json.dumps(sanitized_details, indent=2)}") - + logger.error( + f"Error processing request: {json.dumps(sanitized_details, indent=2)}" + ) + # Format error for response error_message = f"Error: {str(e)}" - if 'message' in error_details and error_details['message']: + if "message" in error_details and error_details["message"]: error_message += f"\nMessage: {error_details['message']}" - if 'response' in error_details and error_details['response']: + if "response" in error_details and error_details["response"]: error_message += f"\nResponse: {error_details['response']}" - + # Return detailed error - status_code = error_details.get('status_code', 500) + status_code = error_details.get("status_code", 500) raise HTTPException(status_code=status_code, detail=error_message) + @app.post("/v1/messages/count_tokens") -async def count_tokens( - request: TokenCountRequest, - raw_request: Request -): +async def count_tokens(request: TokenCountRequest, raw_request: Request): try: # Log the incoming token count request original_model = request.original_model or request.model - + # Get the display name for logging, just the model name without provider prefix display_model = original_model if "/" in display_model: display_model = display_model.split("/")[-1] - + # Clean model name for capability check clean_model = request.model if clean_model.startswith("anthropic/"): - clean_model = clean_model[len("anthropic/"):] + clean_model = clean_model[len("anthropic/") :] elif clean_model.startswith("openai/"): - clean_model = clean_model[len("openai/"):] - + clean_model = clean_model[len("openai/") :] + # Convert the messages to a format LiteLLM can understand converted_request = convert_anthropic_to_litellm( MessagesRequest( @@ -1413,59 +1591,62 @@ async def count_tokens( system=request.system, tools=request.tools, tool_choice=request.tool_choice, - thinking=request.thinking + thinking=request.thinking, ) ) - + # Use LiteLLM's token_counter function try: # Import token_counter function from litellm import token_counter - + # Log the request beautifully num_tools = len(request.tools) if request.tools else 0 - + log_request_beautifully( "POST", raw_request.url.path, display_model, - converted_request.get('model'), - len(converted_request['messages']), + converted_request.get("model"), + len(converted_request["messages"]), num_tools, - 200 # Assuming success at this point + 200, # Assuming success at this point ) - + # Prepare token counter arguments token_counter_args = { "model": converted_request["model"], "messages": converted_request["messages"], } - + # Add custom base URL for OpenAI models if configured if request.model.startswith("openai/") and OPENAI_BASE_URL: token_counter_args["api_base"] = OPENAI_BASE_URL - + # Count tokens token_count = token_counter(**token_counter_args) - + # Return Anthropic-style response return TokenCountResponse(input_tokens=token_count) - + except ImportError: logger.error("Could not import token_counter from litellm") # Fallback to a simple approximation return TokenCountResponse(input_tokens=1000) # Default fallback - + except Exception as e: import traceback + error_traceback = traceback.format_exc() logger.error(f"Error counting tokens: {str(e)}\n{error_traceback}") raise HTTPException(status_code=500, detail=f"Error counting tokens: {str(e)}") + @app.get("/") async def root(): return {"message": "Anthropic Proxy for LiteLLM"} + # Define ANSI color codes for terminal output class Colors: CYAN = "\033[96m" @@ -1478,44 +1659,53 @@ class Colors: BOLD = "\033[1m" UNDERLINE = "\033[4m" DIM = "\033[2m" -def log_request_beautifully(method, path, claude_model, openai_model, num_messages, num_tools, status_code): + + +def log_request_beautifully( + method, path, claude_model, openai_model, num_messages, num_tools, status_code +): """Log requests in a beautiful, twitter-friendly format showing Claude to OpenAI mapping.""" # Format the Claude model name nicely claude_display = f"{Colors.CYAN}{claude_model}{Colors.RESET}" - + # Extract endpoint name endpoint = path if "?" in endpoint: endpoint = endpoint.split("?")[0] - + # Extract just the OpenAI model name without provider prefix openai_display = openai_model if "/" in openai_display: openai_display = openai_display.split("/")[-1] openai_display = f"{Colors.GREEN}{openai_display}{Colors.RESET}" - + # Format tools and messages tools_str = f"{Colors.MAGENTA}{num_tools} tools{Colors.RESET}" messages_str = f"{Colors.BLUE}{num_messages} messages{Colors.RESET}" - + # Format status code - status_str = f"{Colors.GREEN}✓ {status_code} OK{Colors.RESET}" if status_code == 200 else f"{Colors.RED}✗ {status_code}{Colors.RESET}" - + status_str = ( + f"{Colors.GREEN}✓ {status_code} OK{Colors.RESET}" + if status_code == 200 + else f"{Colors.RED}✗ {status_code}{Colors.RESET}" + ) # Put it all together in a clear, beautiful format log_line = f"{Colors.BOLD}{method} {endpoint}{Colors.RESET} {status_str}" model_line = f"{claude_display} → {openai_display} {tools_str} {messages_str}" - + # Print to console print(log_line) print(model_line) sys.stdout.flush() + if __name__ == "__main__": import sys + if len(sys.argv) > 1 and sys.argv[1] == "--help": print("Run with: uvicorn server:app --reload --host 0.0.0.0 --port 8082") sys.exit(0) - + # Configure uvicorn to run with minimal logs uvicorn.run(app, host="0.0.0.0", port=8082, log_level="error") diff --git a/uv.lock b/uv.lock index ac45fff0..416d956f 100644 --- a/uv.lock +++ b/uv.lock @@ -1,18 +1,25 @@ version = 1 +revision = 3 requires-python = ">=3.10" +resolution-markers = [ + "python_full_version >= '3.14'", + "python_full_version == '3.13.*'", + "python_full_version >= '3.11' and python_full_version < '3.13'", + "python_full_version < '3.11'", +] [[package]] name = "aiohappyeyeballs" version = "2.6.1" source = { registry = "https://pypi.org/simple" } -sdist = { url = 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