76 lines
2.5 KiB
Python
Executable File
76 lines
2.5 KiB
Python
Executable File
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import asyncio
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import json
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import requests
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import codecs
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from typing import Dict, Any
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from mars_toolkit.core.llm_tools import llm_tool
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from mars_toolkit.core.config import config
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@llm_tool(
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name="retrieval_from_knowledge_base",
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description="Retrieve information from local materials science literature knowledge base"
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)
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async def retrieval_from_knowledge_base(query: str, topk: int = 3) -> str:
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"""
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检索本地材料科学文献知识库中的相关信息
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Args:
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query: 查询字符串，如材料名称"CsPbBr3"
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topk: 返回结果数量，默认3条
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Returns:
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包含文档ID、标题和相关性分数的字典
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"""
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# 设置Dify API的URL端点
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url = f'{config.DIFY_ROOT_URL}/v1/chat-messages'
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# 配置请求头，包含API密钥和内容类型
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headers = {
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'Authorization': f'Bearer {config.DIFY_API_KEY}',
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'Content-Type': 'application/json'
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}
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# 准备请求数据
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data = {
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"inputs": {"topK": topk}, # 设置返回的最大结果数量
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"query": query, # 设置查询字符串
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"response_mode": "blocking", # 使用阻塞模式，等待并获取完整响应
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"conversation_id": "", # 不使用会话ID，每次都是独立查询
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"user": "abc-123" # 用户标识符
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}
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try:
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# 发送POST请求到Dify API并获取响应
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# 设置较长的超时时间(1111秒)以处理可能的长时间响应
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response = requests.post(url, headers=headers, json=data, timeout=1111)
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# 获取响应文本
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response_text = response.text
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# 解码响应文本中的Unicode转义序列
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response_text = codecs.decode(response_text, 'unicode_escape')
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# 将响应文本解析为JSON对象
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result_json = json.loads(response_text)
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# 从响应中提取元数据
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metadata = result_json.get("metadata", {})
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# 构建包含关键信息的结果字典
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useful_info = {
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"id": metadata.get("document_id"), # 文档ID
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"title": result_json.get("title"), # 文档标题
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"content": result_json.get("answer", ""), # 内容字段，使用'answer'字段存储内容
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"score": metadata.get("score") # 相关性分数
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}
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# 返回提取的有用信息
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return json.dumps(useful_info, ensure_ascii=False, indent=2)
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except Exception as e:
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# 捕获并处理所有可能的异常，返回错误信息
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return f"错误: {str(e)}"
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