feat: add support for Anthropic provider, including configuration and conversion utilities
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295
src/providers/anthropic_provider.py
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295
src/providers/anthropic_provider.py
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# src/providers/anthropic_provider.py
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import json
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import logging
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from collections.abc import Generator
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from typing import Any
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from anthropic import Anthropic, Stream
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from anthropic.types import Message, MessageStreamEvent, TextDelta
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# Use relative imports for modules within the same package
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from providers.base import BaseProvider
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# Use absolute imports as per Ruff warning and user instructions
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from src.llm_models import MODELS
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from src.tools.conversion import convert_to_anthropic_tools
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logger = logging.getLogger(__name__)
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class AnthropicProvider(BaseProvider):
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"""Provider implementation for Anthropic Claude models."""
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def __init__(self, api_key: str, base_url: str | None = None):
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# Anthropic client doesn't use base_url in the same way, but store it if needed
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# Use default Anthropic endpoint if base_url is not provided or relevant
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effective_base_url = base_url or MODELS.get("anthropic", {}).get("endpoint")
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super().__init__(api_key, effective_base_url) # Pass base_url to parent, though Anthropic client might ignore it
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logger.info("Initializing AnthropicProvider")
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try:
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self.client = Anthropic(api_key=self.api_key)
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# Note: Anthropic client doesn't take base_url during init
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except Exception as e:
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logger.error(f"Failed to initialize Anthropic client: {e}", exc_info=True)
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raise
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def _convert_messages(self, messages: list[dict[str, Any]]) -> tuple[str | None, list[dict[str, Any]]]:
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"""Converts standard message format to Anthropic's format, extracting system prompt."""
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anthropic_messages = []
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system_prompt = None
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for i, message in enumerate(messages):
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role = message.get("role")
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content = message.get("content")
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if role == "system":
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if i == 0:
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system_prompt = content
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logger.debug("Extracted system prompt for Anthropic.")
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else:
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# Handle system message not at the start (append to previous user message or add as user)
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logger.warning("System message found not at the beginning. Treating as user message.")
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anthropic_messages.append({"role": "user", "content": f"[System Note]\n{content}"})
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continue
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# Handle tool results specifically
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if role == "tool":
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# Find the preceding assistant message with the corresponding tool_use block
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# This requires careful handling in the follow-up logic
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tool_use_id = message.get("tool_call_id")
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tool_content = content
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# Format as a tool_result content block
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anthropic_messages.append({"role": "user", "content": [{"type": "tool_result", "tool_use_id": tool_use_id, "content": tool_content}]})
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continue
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# Handle assistant message potentially containing tool_use blocks
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if role == "assistant":
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# Check if content is structured (e.g., from a previous tool call response)
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if isinstance(content, list): # Assuming tool calls might be represented as a list
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anthropic_messages.append({"role": "assistant", "content": content})
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else:
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anthropic_messages.append({"role": "assistant", "content": content}) # Regular text content
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continue
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# Regular user messages
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if role == "user":
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anthropic_messages.append({"role": "user", "content": content})
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continue
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logger.warning(f"Unsupported role '{role}' in message conversion for Anthropic.")
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# Ensure conversation starts with a user message if no system prompt was used
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if not system_prompt and anthropic_messages and anthropic_messages[0]["role"] != "user":
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logger.warning("Anthropic conversation must start with a user message. Prepending empty user message.")
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anthropic_messages.insert(0, {"role": "user", "content": "[Start of conversation]"}) # Or handle differently
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return system_prompt, anthropic_messages
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def create_chat_completion(
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self,
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messages: list[dict[str, str]],
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model: str,
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temperature: float = 0.4,
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max_tokens: int | None = None, # Anthropic requires max_tokens
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stream: bool = True,
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tools: list[dict[str, Any]] | None = None,
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) -> Stream[MessageStreamEvent] | Message:
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"""Creates a chat completion using the Anthropic API."""
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logger.debug(f"Anthropic create_chat_completion called. Stream: {stream}, Tools: {bool(tools)}")
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# Anthropic requires max_tokens
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if max_tokens is None:
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max_tokens = 4096 # Default value if not provided
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logger.warning(f"max_tokens not provided for Anthropic, defaulting to {max_tokens}")
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system_prompt, anthropic_messages = self._convert_messages(messages)
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try:
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completion_params = {
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"model": model,
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"messages": anthropic_messages,
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"temperature": temperature,
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"max_tokens": max_tokens,
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"stream": stream,
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}
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if system_prompt:
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completion_params["system"] = system_prompt
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if tools:
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completion_params["tools"] = tools
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# Anthropic doesn't have an explicit 'tool_choice' like OpenAI's 'auto' in the main API call
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# Remove None values (though Anthropic requires max_tokens)
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completion_params = {k: v for k, v in completion_params.items() if v is not None}
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log_params = completion_params.copy()
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if "messages" in log_params:
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log_params["messages"] = [{k: (v[:100] + "..." if isinstance(v, str) and len(v) > 100 else v) for k, v in msg.items()} for msg in log_params["messages"][-2:]]
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tools_log = log_params.get("tools", "Not Present")
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logger.debug(f"Calling Anthropic API. Model: {log_params.get('model')}, Stream: {log_params.get('stream')}, System: {bool(log_params.get('system'))}, Tools: {tools_log}")
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logger.debug(f"Full API Params (messages summarized): {log_params}")
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response = self.client.messages.create(**completion_params)
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logger.debug("Anthropic API call successful.")
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return response
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except Exception as e:
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logger.error(f"Anthropic API error: {e}", exc_info=True)
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raise
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def get_streaming_content(self, response: Stream[MessageStreamEvent]) -> Generator[str, None, None]:
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"""Yields content chunks from an Anthropic streaming response."""
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logger.debug("Processing Anthropic stream...")
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full_delta = ""
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try:
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# Iterate through events in the stream
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for event in response:
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if event.type == "content_block_delta":
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# Check if the delta is for text content before accessing .text
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if isinstance(event.delta, TextDelta):
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delta_text = event.delta.text
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if delta_text:
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full_delta += delta_text
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yield delta_text
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# Ignore other delta types like InputJSONDelta for text streaming
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# Other event types like 'message_start', 'content_block_start', etc., can be logged or handled if needed
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elif event.type == "message_start":
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logger.debug(f"Anthropic stream started. Model: {event.message.model}")
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elif event.type == "message_stop":
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# The stop_reason might be available on the 'message' object associated with the stream,
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# not directly on the stop event itself. We log that the stop event occurred.
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# Accessing the actual reason might require inspecting the final message state if needed.
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logger.debug("Anthropic stream message_stop event received.")
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elif event.type == "content_block_start":
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if event.content_block.type == "tool_use":
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logger.debug(f"Anthropic stream detected tool use start: ID {event.content_block.id}, Name: {event.content_block.name}")
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elif event.type == "content_block_stop":
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logger.debug(f"Anthropic stream detected content block stop. Index: {event.index}")
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logger.debug(f"Anthropic stream finished. Total delta length: {len(full_delta)}")
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except Exception as e:
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logger.error(f"Error processing Anthropic stream: {e}", exc_info=True)
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yield json.dumps({"error": f"Stream processing error: {str(e)}"})
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def get_content(self, response: Message) -> str:
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"""Extracts content from a non-streaming Anthropic response."""
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try:
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# Combine text content from all text blocks
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text_content = "".join([block.text for block in response.content if block.type == "text"])
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logger.debug(f"Extracted content (length {len(text_content)}) from non-streaming Anthropic response.")
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return text_content
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except Exception as e:
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logger.error(f"Error extracting content from Anthropic response: {e}", exc_info=True)
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return f"[Error extracting content: {str(e)}]"
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def has_tool_calls(self, response: Stream[MessageStreamEvent] | Message) -> bool:
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"""Checks if the Anthropic response contains tool calls."""
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try:
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if isinstance(response, Message): # Non-streaming
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# Check stop reason and content blocks
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has_tool_use_block = any(block.type == "tool_use" for block in response.content)
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has_calls = response.stop_reason == "tool_use" or has_tool_use_block
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logger.debug(f"Non-streaming Anthropic response check: stop_reason='{response.stop_reason}', has_tool_use_block={has_tool_use_block}. Result: {has_calls}")
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return has_calls
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elif isinstance(response, Stream):
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# Cannot reliably check an unconsumed stream without consuming it.
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# The LLMClient should handle this by checking after consumption or based on stop_reason if available post-stream.
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logger.warning("has_tool_calls check on an Anthropic stream is unreliable before consumption.")
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return False
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else:
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logger.warning(f"has_tool_calls received unexpected type for Anthropic: {type(response)}")
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return False
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except Exception as e:
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logger.error(f"Error checking for Anthropic tool calls: {e}", exc_info=True)
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return False
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def parse_tool_calls(self, response: Message) -> list[dict[str, Any]]:
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"""Parses tool calls from a non-streaming Anthropic response."""
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parsed_calls = []
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try:
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if not isinstance(response, Message):
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logger.error(f"parse_tool_calls expects Anthropic Message, got {type(response)}")
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return []
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if response.stop_reason != "tool_use":
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logger.debug("No tool use indicated by stop_reason.")
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# return [] # Might still have tool_use blocks even if stop_reason isn't tool_use? Check API docs. Let's check content anyway.
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tool_use_blocks = [block for block in response.content if block.type == "tool_use"]
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if not tool_use_blocks:
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logger.debug("No 'tool_use' content blocks found in Anthropic response.")
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return []
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logger.debug(f"Parsing {len(tool_use_blocks)} 'tool_use' blocks from Anthropic response.")
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for block in tool_use_blocks:
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# Adapt server/tool name splitting if needed (similar to OpenAI provider)
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# Assuming Anthropic tool names might also be prefixed like "server__tool"
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parts = block.name.split("__", 1)
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if len(parts) == 2:
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server_name, func_name = parts
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else:
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logger.warning(f"Could not determine server_name from Anthropic tool name '{block.name}'.")
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server_name = None
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func_name = block.name
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parsed_calls.append({
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"id": block.id,
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"server_name": server_name,
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"function_name": func_name,
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"arguments": json.dumps(block.input), # Anthropic input is already a dict, dump to string like OpenAI provider expects? Or keep as dict? Let's keep as dict for now.
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# "arguments": block.input, # Keep as dict? Let's try this first.
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})
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return parsed_calls
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except Exception as e:
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logger.error(f"Error parsing Anthropic tool calls: {e}", exc_info=True)
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return []
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def format_tool_results(self, tool_call_id: str, result: Any) -> dict[str, Any]:
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"""Formats a tool result for an Anthropic follow-up request."""
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# Anthropic expects a 'tool_result' content block
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# The content of the result block should typically be a string.
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try:
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if isinstance(result, dict):
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content_str = json.dumps(result)
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else:
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content_str = str(result)
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except Exception as e:
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logger.error(f"Error JSON-encoding tool result for Anthropic {tool_call_id}: {e}")
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content_str = json.dumps({"error": "Failed to encode tool result", "original_type": str(type(result))})
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logger.debug(f"Formatting Anthropic tool result for call ID {tool_call_id}")
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# This needs to be placed inside a "user" role message's content list
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return {
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"type": "tool_result",
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"tool_use_id": tool_call_id,
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"content": content_str,
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# Optionally add is_error=True if result indicates an error
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}
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def convert_tools(self, tools: list[dict[str, Any]]) -> list[dict[str, Any]]:
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"""Converts internal tool format to Anthropic's format."""
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# Use the conversion function, assuming it's correctly placed and imported
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logger.debug(f"Converting {len(tools)} tools to Anthropic format.")
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try:
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# The conversion function needs to handle the server__tool prefixing
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anthropic_tools = convert_to_anthropic_tools(tools)
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logger.debug(f"Tool conversion result: {anthropic_tools}")
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return anthropic_tools
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except Exception as e:
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logger.error(f"Error during Anthropic tool conversion: {e}", exc_info=True)
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return []
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# Helper needed by LLMClient's current tool handling logic (if adapting OpenAI's pattern)
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def get_original_message_with_calls(self, response: Message) -> dict[str, Any]:
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"""Extracts the assistant's message containing tool calls for Anthropic."""
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try:
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if isinstance(response, Message) and any(block.type == "tool_use" for block in response.content):
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# Anthropic's response structure is different. The 'message' itself is the assistant's turn.
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# We need to return a representation of this turn, including the tool_use blocks.
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# Convert Pydantic models within content to dicts
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content_list = [block.model_dump(exclude_unset=True) for block in response.content]
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return {"role": "assistant", "content": content_list}
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else:
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logger.warning("Could not extract original message with tool calls from Anthropic response.")
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return {"role": "assistant", "content": "[Could not extract tool calls message]"}
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except Exception as e:
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logger.error(f"Error extracting original Anthropic message with calls: {e}", exc_info=True)
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return {"role": "assistant", "content": f"[Error extracting tool calls message: {str(e)}]"}
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