feat: 增强工具调用代理功能，支持多工具调用和消息历史转换
主要改进: - 新增 convert_tool_calls_to_content 函数，将消息历史中的 tool_calls 转换为 LLM 可理解的 XML 格式 - 修复 response_parser 支持同时解析多个 tool_calls - 优化响应解析逻辑，支持 content 和 tool_calls 同时存在 - 添加完整的测试覆盖，包括多工具调用、消息转换和混合响应 技术细节: - services.py: 实现工具调用历史到 content 的转换 - response_parser.py: 使用非贪婪匹配支持多个 tool_calls 解析 - main.py: 集成消息转换功能，确保消息历史正确传递给 LLM 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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Vertex-AI-Step-Builder
154
test_multiple_tool_calls.py
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154
test_multiple_tool_calls.py
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#!/usr/bin/env python3
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"""
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测试多个 tool_calls 的完整流程
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"""
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import sys
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import os
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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from app.services import convert_tool_calls_to_content
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from app.response_parser import ResponseParser
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from app.models import ChatMessage
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def test_multiple_tool_calls():
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"""测试多个 tool_calls 的完整流程"""
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print("=" * 60)
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print("测试场景：消息历史中有多个 tool_calls")
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print("=" * 60)
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# 模拟对话场景
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# 用户问北京和上海的天气，assistant 调用了两个工具
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messages = [
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ChatMessage(
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role="user",
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content="帮我查一下北京和上海的天气"
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),
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ChatMessage(
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role="assistant",
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tool_calls=[
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{
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"id": "call_1",
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"type": "function",
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"function": {
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"name": "get_weather",
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"arguments": '{"location": "北京", "unit": "celsius"}'
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}
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},
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{
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"id": "call_2",
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"type": "function",
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"function": {
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"name": "get_weather",
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"arguments": '{"location": "上海", "unit": "celsius"}'
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}
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}
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]
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),
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ChatMessage(
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role="user",
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content="结果怎么样？"
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)
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]
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print("\n1. 原始消息:")
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for i, msg in enumerate(messages):
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print(f" 消息 {i+1}: {msg.role}")
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if msg.content:
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print(f" 内容: {msg.content}")
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if msg.tool_calls:
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print(f" 工具调用: {len(msg.tool_calls)} 个")
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for j, tc in enumerate(msg.tool_calls):
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print(f" {j+1}. {tc['function']['name']}")
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# 转换 tool_calls 到 content
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print("\n2. 转换后的消息（发送给 LLM）:")
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converted = convert_tool_calls_to_content(messages)
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for i, msg in enumerate(converted):
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print(f" 消息 {i+1}: {msg.role}")
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if msg.content:
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# 只显示前 150 个字符
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content_preview = msg.content[:150] + "..." if len(msg.content) > 150 else msg.content
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print(f" 内容: {content_preview}")
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# 验证转换
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assert "<invoke>" in converted[1].content
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assert converted[1].content.count("<invoke>") == 2
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print("\n ✓ 转换成功！两个 tool_calls 都被转换成 XML 标签格式")
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# 模拟 LLM 返回新的响应（也包含多个 tool_calls）
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print("\n3. 模拟 LLM 响应（包含多个 tool_calls）:")
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llm_response = '''好的，我来帮你查一下其他城市的天气。
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<invoke>{"name": "get_weather", "arguments": {"location": "广州"}}</invoke>
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<invoke>{"name": "get_weather", "arguments": {"location": "深圳"}}</invoke>
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请稍等。'''
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print(f" {llm_response}")
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# 解析 LLM 响应
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print("\n4. 解析 LLM 响应:")
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parser = ResponseParser()
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parsed = parser.parse(llm_response)
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print(f" Content: {parsed.content}")
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print(f" Tool calls 数量: {len(parsed.tool_calls) if parsed.tool_calls else 0}")
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if parsed.tool_calls:
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for i, tc in enumerate(parsed.tool_calls):
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import json
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args = json.loads(tc.function.arguments)
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print(f" {i+1}. {tc.function.name}(location={args['location']})")
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# 验证解析
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assert parsed.tool_calls is not None
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assert len(parsed.tool_calls) == 2
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assert parsed.tool_calls[0].function.name == "get_weather"
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assert parsed.tool_calls[1].function.name == "get_weather"
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print("\n ✓ 解析成功！两个 tool_calls 都被正确提取")
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# 测试场景 2：单个 tool_call（向后兼容）
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print("\n" + "=" * 60)
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print("测试场景：单个 tool_call（向后兼容性）")
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print("=" * 60)
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single_response = '''我来帮你查询。
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<invoke>{"name": "search", "arguments": {"query": "今天天气"}}</invoke>'''
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parsed_single = parser.parse(single_response)
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print(f"Content: {parsed_single.content}")
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print(f"Tool calls 数量: {len(parsed_single.tool_calls) if parsed_single.tool_calls else 0}")
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assert parsed_single.tool_calls is not None
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assert len(parsed_single.tool_calls) == 1
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assert parsed_single.tool_calls[0].function.name == "search"
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print("✓ 单个 tool_call 解析正常")
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# 测试场景 3：没有 tool_call
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print("\n" + "=" * 60)
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print("测试场景：没有 tool_call")
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print("=" * 60)
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no_tool_response = "你好！有什么可以帮助你的吗？"
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parsed_no_tool = parser.parse(no_tool_response)
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print(f"Content: {parsed_no_tool.content}")
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print(f"Tool calls: {parsed_no_tool.tool_calls}")
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assert parsed_no_tool.content == no_tool_response
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assert parsed_no_tool.tool_calls is None
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print("✓ 普通文本响应解析正常")
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print("\n" + "=" * 60)
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print("所有测试通过! ✓")
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print("=" * 60)
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print("\n总结：")
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print("- 消息历史中的多个 tool_calls 可以正确转换为 XML 格式")
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print("- LLM 响应中的多个 tool_calls 可以正确解析")
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print("- 向后兼容单个 tool_call 和普通文本响应")
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if __name__ == "__main__":
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test_multiple_tool_calls()
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