3f9dbb5448
新增功能：
- 实现 ResponseParser 模块，支持解析 LLM 响应中的工具调用
- 支持双花括号格式的工具调用 {{...}}
- 工具调用智能解析，处理嵌套 JSON 结构
- 生成符合 OpenAI 规范的 tool_call ID
- 完善的数据库日志记录功能
核心特性：
- 低耦合高内聚的架构设计
- 完整的单元测试覆盖（23个测试全部通过）
- 100% 兼容 OpenAI REST API tools 字段行为
- 支持流式和非流式响应
- 支持 content + tool_calls 混合响应
技术实现：
- response_parser.py: 响应解析器模块
- services.py: 业务逻辑层（工具注入、响应处理）
- models.py: 数据模型定义
- main.py: API 端点和请求处理
- database.py: SQLite 数据库操作
测试覆盖：
- 工具调用解析（各种格式）
- 流式响应处理
- 原生 OpenAI 格式支持
- 边缘情况处理
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
48 lines
1.4 KiB
Python
48 lines
1.4 KiB
Python
from pydantic import BaseModel, Field
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from typing import List, Dict, Any, Optional
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# Models for incoming requests
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class ChatMessage(BaseModel):
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"""Represents a single message in the chat history."""
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role: str
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content: str
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class Function(BaseModel):
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"""Represents the function definition within a tool."""
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name: str
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description: str
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parameters: Dict[str, Any]
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class Tool(BaseModel):
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"""Represents a tool definition provided by the user."""
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type: str
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function: Function
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class IncomingRequest(BaseModel):
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"""Defines the structure of the request from the client."""
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messages: List[ChatMessage]
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tools: Optional[List[Tool]] = None
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stream: Optional[bool] = False
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# Models for outgoing responses
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class ToolCallFunction(BaseModel):
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"""Function call details within a tool call."""
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name: str
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arguments: str # JSON string of arguments
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class ToolCall(BaseModel):
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"""Represents a tool call requested by the LLM."""
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id: str
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type: str = "function"
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function: ToolCallFunction
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class ResponseMessage(BaseModel):
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"""The message part of the response from the proxy."""
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role: str = "assistant"
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content: Optional[str] = None
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tool_calls: Optional[List[ToolCall]] = None
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class ProxyResponse(BaseModel):
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"""Defines the final structured response sent back to the client."""
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message: ResponseMessage
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