feat: 新增 core/agents 模块和 nanobot
- 新增 agents 模块，包含 agent、api、skills 等子模块 - 新增 nanobot 项目，支持多渠道集成 - 添加启动脚本 start-all.bat 和 start-all.sh Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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core/agents/providers/anthropic_provider.py
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241
core/agents/providers/anthropic_provider.py
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"""Anthropic LLM provider implementation."""
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import json
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import secrets
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import string
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from typing import Any
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import aiohttp
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from loguru import logger
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from agents.providers.base import LLMProvider, LLMResponse, ToolCallRequest
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_ALNUM = string.ascii_letters + string.digits
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def _short_tool_id() -> str:
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"""Generate a 9-char alphanumeric ID for tool calls."""
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return "".join(secrets.choice(_ALNUM) for _ in range(9))
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class AnthropicProvider(LLMProvider):
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"""Anthropic LLM provider using Claude API."""
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def __init__(
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self,
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api_key: str | None = None,
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api_base: str | None = None,
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default_model: str = "claude-sonnet-4-20250514",
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):
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super().__init__(api_key, api_base)
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self.default_model = default_model
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self._session: aiohttp.ClientSession | None = None
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async def _get_session(self) -> aiohttp.ClientSession:
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"""Get or create aiohttp session."""
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if self._session is None or self._session.closed:
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self._session = aiohttp.ClientSession()
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return self._session
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async def close(self):
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"""Close the HTTP session."""
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if self._session and not self._session.closed:
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await self._session.close()
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def _convert_messages_to_anthropic(self, messages: list[dict[str, Any]]) -> list[dict[str, Any]]:
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"""Convert messages to Anthropic API format."""
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converted = []
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for msg in messages:
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role = msg.get("role")
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content = msg.get("content")
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# Handle tool calls in assistant messages
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if role == "assistant" and msg.get("tool_calls"):
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# Anthropic doesn't support tool_calls in the same way, convert to text
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tool_calls_text = "\n".join([
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f"Tool call: {tc.get('name')}({json.dumps(tc.get('arguments', {}))})"
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for tc in msg["tool_calls"]
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])
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if content:
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content = f"{content}\n\n{tool_calls_text}"
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else:
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content = tool_calls_text
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# Handle tool results
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if role == "tool":
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# Convert tool result to Anthropic format
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tool_use_id = msg.get("tool_call_id", _short_tool_id())
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converted.append({
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"type": "tool_result",
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"tool_use_id": tool_use_id,
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"content": content or "(empty)",
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})
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continue
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# Skip system messages - they'll be handled separately
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if role == "system":
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continue
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# Convert content to Anthropic format
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if isinstance(content, str):
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converted.append({
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"role": role,
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"content": content,
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})
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elif isinstance(content, list):
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# Handle list content
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text_parts = []
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for item in content:
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if isinstance(item, dict):
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if item.get("type") == "text":
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text_parts.append(item.get("text", ""))
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elif item.get("type") == "tool_use":
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# This shouldn't happen in input, but handle it
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text_parts.append(f"[tool_use: {item.get('name')}]")
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elif item.get("type") == "tool_result":
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text_parts.append(item.get("content", ""))
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converted.append({
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"role": role,
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"content": "\n".join(text_parts),
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})
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else:
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converted.append({
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"role": role,
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"content": str(content) if content else "(empty)",
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})
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return converted
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def _get_system_message(self, messages: list[dict[str, Any]]) -> str | None:
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"""Extract system message from messages."""
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for msg in messages:
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if msg.get("role") == "system":
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return msg.get("content")
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return None
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async def chat(
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self,
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messages: list[dict[str, Any]],
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tools: list[dict[str, Any]] | None = None,
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model: str | None = None,
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max_tokens: int = 4096,
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temperature: float = 0.7,
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) -> LLMResponse:
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"""Send a chat completion request to Anthropic API."""
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model = model or self.default_model
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api_base = self.api_base or "https://api.anthropic.com"
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url = f"{api_base}/v1/messages"
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headers = {
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"Content-Type": "application/json",
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"anthropic-version": "2023-06-01",
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}
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if self.api_key:
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headers["x-api-key"] = self.api_key
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# Get system message and convert other messages
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system = self._get_system_message(messages)
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anthropic_messages = self._convert_messages_to_anthropic(messages)
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payload: dict[str, Any] = {
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"model": model,
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"messages": anthropic_messages,
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"max_tokens": max_tokens,
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"temperature": temperature,
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}
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if system:
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payload["system"] = system
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# Convert tools to Anthropic format if provided
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if tools:
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anthropic_tools = self._convert_tools(tools)
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payload["tools"] = anthropic_tools
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try:
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session = await self._get_session()
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async with session.post(url, json=payload, headers=headers) as resp:
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if resp.status != 200:
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error_text = await resp.text()
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try:
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error_json = json.loads(error_text)
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error_msg = error_json.get("error", {}).get("message", error_text)
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except json.JSONDecodeError:
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error_msg = error_text
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return LLMResponse(
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content=f"Anthropic API error (status {resp.status}): {error_msg}",
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finish_reason="error",
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)
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data = await resp.json()
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return self._parse_response(data, tools is not None)
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except aiohttp.ClientError as e:
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return LLMResponse(
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content=f"Anthropic API connection error: {str(e)}",
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finish_reason="error",
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)
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except Exception as e:
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return LLMResponse(
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content=f"Error calling Anthropic: {str(e)}",
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finish_reason="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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"""Convert OpenAI-style tools to Anthropic format."""
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anthropic_tools = []
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for tool in tools:
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func = tool.get("function", {})
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anthropic_tools.append({
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"name": func.get("name", ""),
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"description": func.get("description", ""),
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"input_schema": func.get("parameters", {"type": "object", "properties": {}}),
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})
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return anthropic_tools
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def _parse_response(self, data: dict[str, Any], has_tools: bool = False) -> LLMResponse:
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"""Parse Anthropic API response into our standard format."""
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content = data.get("content", [])
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# Extract text content
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text_content = ""
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tool_calls = []
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for block in content:
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if block.get("type") == "text":
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text_content += block.get("text", "")
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elif block.get("type") == "tool_use" and has_tools:
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# Convert Anthropic tool_use to our format
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args = block.get("input", {})
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tool_calls.append(ToolCallRequest(
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id=block.get("id", _short_tool_id()),
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name=block.get("name", ""),
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arguments=args,
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))
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# Determine finish reason
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stop_reason = data.get("stop_reason", "end_turn")
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if stop_reason == "tool_use":
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finish_reason = "tool_calls"
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elif stop_reason == "max_tokens":
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finish_reason = "length"
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else:
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finish_reason = "stop"
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# Parse usage
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usage = data.get("usage", {})
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usage_dict = {
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"prompt_tokens": usage.get("input_tokens", 0),
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"completion_tokens": usage.get("output_tokens", 0),
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"total_tokens": usage.get("input_tokens", 0) + usage.get("output_tokens", 0),
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}
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return LLMResponse(
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content=text_content if text_content else None,
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tool_calls=tool_calls,
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finish_reason=finish_reason,
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usage=usage_dict,
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)
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def get_default_model(self) -> str:
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"""Get the default model."""
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return self.default_model
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