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250 lines
8.3 KiB
Python
250 lines
8.3 KiB
Python
"""Anthropic Claude Provider
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Direct integration with Anthropic's Claude API.
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Supports Claude 3.5 Sonnet, Claude 3 Opus, and other models.
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"""
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import json
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import os
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# Import base classes from parent module
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import sys
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from dataclasses import dataclass
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import requests
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from clients.llm_client import BaseLLMProvider, LLMResponse, ToolCall
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class AnthropicProvider(BaseLLMProvider):
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"""Anthropic Claude API provider.
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Provides direct integration with Anthropic's Claude models
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without going through OpenRouter.
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Supports:
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- Claude 3.5 Sonnet (claude-3-5-sonnet-20241022)
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- Claude 3 Opus (claude-3-opus-20240229)
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- Claude 3 Sonnet (claude-3-sonnet-20240229)
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- Claude 3 Haiku (claude-3-haiku-20240307)
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"""
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API_URL = "https://api.anthropic.com/v1/messages"
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API_VERSION = "2023-06-01"
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def __init__(
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self,
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api_key: str | None = None,
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model: str = "claude-3-5-sonnet-20241022",
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temperature: float = 0,
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max_tokens: int = 4096,
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):
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"""Initialize the Anthropic provider.
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Args:
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api_key: Anthropic API key. Defaults to ANTHROPIC_API_KEY env var.
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model: Model to use. Defaults to Claude 3.5 Sonnet.
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temperature: Sampling temperature (0-1).
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max_tokens: Maximum tokens in response.
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"""
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self.api_key = api_key or os.environ.get("ANTHROPIC_API_KEY", "")
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self.model = model
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self.temperature = temperature
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self.max_tokens = max_tokens
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def call(self, prompt: str, **kwargs) -> LLMResponse:
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"""Make a call to the Anthropic API.
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Args:
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prompt: The prompt to send.
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**kwargs: Additional options (model, temperature, max_tokens).
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Returns:
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LLMResponse with the generated content.
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Raises:
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ValueError: If API key is not set.
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requests.HTTPError: If the API request fails.
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"""
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if not self.api_key:
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raise ValueError("Anthropic API key is required")
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response = requests.post(
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self.API_URL,
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headers={
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"x-api-key": self.api_key,
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"anthropic-version": self.API_VERSION,
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"Content-Type": "application/json",
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},
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json={
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"model": kwargs.get("model", self.model),
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"max_tokens": kwargs.get("max_tokens", self.max_tokens),
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"temperature": kwargs.get("temperature", self.temperature),
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"messages": [{"role": "user", "content": prompt}],
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},
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timeout=120,
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)
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response.raise_for_status()
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data = response.json()
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# Extract content from response
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content = ""
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for block in data.get("content", []):
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if block.get("type") == "text":
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content += block.get("text", "")
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return LLMResponse(
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content=content,
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model=data.get("model", self.model),
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provider="anthropic",
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tokens_used=data.get("usage", {}).get("input_tokens", 0)
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+ data.get("usage", {}).get("output_tokens", 0),
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finish_reason=data.get("stop_reason"),
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)
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def call_with_tools(
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self,
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messages: list[dict],
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tools: list[dict] | None = None,
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**kwargs,
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) -> LLMResponse:
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"""Make a call to the Anthropic API with tool support.
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Args:
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messages: List of message dicts with 'role' and 'content'.
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tools: List of tool definitions in OpenAI format.
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**kwargs: Additional options.
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Returns:
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LLMResponse with content and/or tool_calls.
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"""
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if not self.api_key:
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raise ValueError("Anthropic API key is required")
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# Convert OpenAI-style messages to Anthropic format
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anthropic_messages = []
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system_content = None
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for msg in messages:
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role = msg.get("role", "user")
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if role == "system":
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system_content = msg.get("content", "")
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elif role == "assistant":
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# Handle assistant messages with tool calls
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if msg.get("tool_calls"):
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content = []
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if msg.get("content"):
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content.append({"type": "text", "text": msg["content"]})
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for tc in msg["tool_calls"]:
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content.append(
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{
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"type": "tool_use",
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"id": tc["id"],
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"name": tc["function"]["name"],
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"input": json.loads(tc["function"]["arguments"])
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if isinstance(tc["function"]["arguments"], str)
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else tc["function"]["arguments"],
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}
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)
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anthropic_messages.append({"role": "assistant", "content": content})
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else:
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anthropic_messages.append(
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{
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"role": "assistant",
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"content": msg.get("content", ""),
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}
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)
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elif role == "tool":
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# Tool response
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anthropic_messages.append(
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{
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"role": "user",
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"content": [
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{
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"type": "tool_result",
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"tool_use_id": msg.get("tool_call_id", ""),
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"content": msg.get("content", ""),
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}
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],
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}
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)
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else:
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anthropic_messages.append(
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{
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"role": "user",
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"content": msg.get("content", ""),
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}
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)
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# Convert OpenAI-style tools to Anthropic format
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anthropic_tools = None
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if tools:
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anthropic_tools = []
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for tool in tools:
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if tool.get("type") == "function":
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func = tool["function"]
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anthropic_tools.append(
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{
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"name": func["name"],
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"description": func.get("description", ""),
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"input_schema": func.get("parameters", {}),
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}
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)
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request_body = {
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"model": kwargs.get("model", self.model),
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"max_tokens": kwargs.get("max_tokens", self.max_tokens),
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"temperature": kwargs.get("temperature", self.temperature),
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"messages": anthropic_messages,
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}
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if system_content:
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request_body["system"] = system_content
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if anthropic_tools:
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request_body["tools"] = anthropic_tools
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response = requests.post(
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self.API_URL,
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headers={
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"x-api-key": self.api_key,
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"anthropic-version": self.API_VERSION,
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"Content-Type": "application/json",
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},
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json=request_body,
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timeout=120,
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)
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response.raise_for_status()
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data = response.json()
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# Parse response
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content = ""
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tool_calls = None
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for block in data.get("content", []):
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if block.get("type") == "text":
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content += block.get("text", "")
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elif block.get("type") == "tool_use":
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if tool_calls is None:
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tool_calls = []
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tool_calls.append(
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ToolCall(
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id=block.get("id", ""),
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name=block.get("name", ""),
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arguments=block.get("input", {}),
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)
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)
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return LLMResponse(
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content=content,
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model=data.get("model", self.model),
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provider="anthropic",
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tokens_used=data.get("usage", {}).get("input_tokens", 0)
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+ data.get("usage", {}).get("output_tokens", 0),
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finish_reason=data.get("stop_reason"),
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tool_calls=tool_calls,
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)
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