Boss AI

[HooJohn]

Gemini LangGraph 集成方案详细设计（无权限认证）

1. LangGraph核心组件修改

1.1 Agent定义

from langgraph.agent import Agent

class EnterpriseSearchAgent(Agent):
    def __init__(self):
        self.opensearch_client = OpenSearchClient()
        self.tools = {
            "search": self.search_content,
            "aggregate": self.aggregate_results,
            "analyze": self.analyze_content
        }
    
    async def search_content(self, query: str):
        # 调用OpenSearch API进行检索
        results = await self.opensearch_client.search({
            "query": {
                "multi_match": {
                    "query": query,
                    "fields": ["content", "title", "metadata"]
                }
            }
        })
        return results

    async def aggregate_results(self, search_results: list):
        # 聚合搜索结果
        pass

    async def analyze_content(self, content: str):
        # 内容分析
        pass

1.2 工具链定义

from langgraph.tools import Tool

class OpenSearchTool(Tool):
    def __init__(self, client):
        self.client = client
        
    async def search(self, index: str, query: dict):
        return await self.client.search(index=index, body=query)
        
    async def index(self, index: str, document: dict):
        return await self.client.index(index=index, body=document)

2. OpenSearch API集成

2.1 OpenSearch客户端封装

from opensearchpy import AsyncOpenSearch

class OpenSearchClient:
    def __init__(self, hosts, auth):
        self.client = AsyncOpenSearch(
            hosts=hosts,
            http_auth=auth,
            use_ssl=True,
            verify_certs=True
        )
    
    async def search(self, query):
        return await self.client.search(body=query)
    
    async def bulk_index(self, documents):
        # 批量索引文档
        pass
        
    async def update_mapping(self, index, mapping):
        # 更新索引映射
        pass

2.2 索引管理

class IndexManager:
    def __init__(self, client):
        self.client = client
    
    async def create_index(self, name, settings):
        # 创建索引
        pass
        
    async def update_settings(self, name, settings):
        # 更新索引设置
        pass

3. 工作流定义

3.1 搜索工作流

from langgraph.graph import Graph

def create_search_workflow():
    graph = Graph()
    
    # 定义节点
    nodes = {
        "query_understanding": query_understanding_node,
        "search": search_node,
        "result_processing": result_processing_node,
        "response_generation": response_generation_node
    }
    
    # 定义边
    graph.add_edge("query_understanding", "search")
    graph.add_edge("search", "result_processing")
    graph.add_edge("result_processing", "response_generation")
    
    return graph

3.2 数据同步工作流

def create_sync_workflow():
    graph = Graph()
    
    # 定义数据同步节点
    nodes = {
        "data_extraction": data_extraction_node,
        "transformation": transformation_node,
        "indexing": indexing_node
    }
    
    # 定义同步流程
    graph.add_edge("data_extraction", "transformation")
    graph.add_edge("transformation", "indexing")
    
    return graph

4. 配置管理

4.1 OpenSearch配置

opensearch:
  hosts:
    - https://localhost:9200
  auth:
    username: admin
    password: admin
  indices:
    documents:
      settings:
        number_of_shards: 3
        number_of_replicas: 1
      mappings:
        properties:
          content:
            type: text
          metadata:
            type: object

4.2 Gemini配置

gemini:
  api_key: YOUR_API_KEY
  model: gemini-pro
  temperature: 0.7
  max_tokens: 1000

5. 数据处理管道

5.1 文本处理

class TextProcessor:
    def __init__(self):
        self.nlp = spacy.load("zh_core_web_sm")
    
    def process(self, text):
        # 文本预处理
        doc = self.nlp(text)
        return {
            "tokens": [token.text for token in doc],
            "entities": [{"text": ent.text, "label": ent.label_} for ent in doc.ents]
        }

5.2 数据转换器

class DataTransformer:
    def transform_to_opensearch(self, data):
        # 转换数据为OpenSearch文档格式
        pass

6. 监控与日志

6.1 性能监控

class PerformanceMonitor:
    def __init__(self):
        self.metrics = {}
    
    async def record_latency(self, operation, duration):
        # 记录操作延迟
        pass
    
    async def record_error(self, operation, error):
        # 记录错误
        pass

6.2 日志管理

import logging

def setup_logging():
    logging.basicConfig(
        level=logging.INFO,
        format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
    )

后续步骤

1. 部署基础设施
2. 实现核心组件
3. 进行集成测试
4. 性能优化
5. 安全审计
6. 文档完善

注意事项

1. 确保所有异步操作正确处理
2. 实现适当的错误处理和重试机制
3. 添加详细的日志记录
4. 定期备份索引数据
5. 监控系统性能指标