522 lines
16 KiB
Python
522 lines
16 KiB
Python
"""
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===========================================
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临时图谱检索方案 - QuickGraphSearch
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===========================================
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功能：直接用用户问题embedding混合检索找最匹配节点
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获取该节点的一跳邻居，用大模型选择最合适的
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只输出入口节点和选中节点
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===========================================
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"""
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import json
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import os
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import logging
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import time
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import re
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from typing import Any, Dict, List, Optional
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from dotenv import load_dotenv
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from neo4j import AsyncGraphDatabase, GraphDatabase
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load_dotenv()
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logger = logging.getLogger(__name__)
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NEO4J_CONFIG = {
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"uri": os.getenv("NEO4J_URI", "bolt://192.168.0.46:57687"),
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"user": os.getenv("NEO4J_USER", "neo4j"),
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"password": os.getenv("NEO4J_PASSWORD", "zdht123@"),
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}
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class EmbeddingWrapper:
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"""嵌入模型包装器"""
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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"""批量获取嵌入向量（一次性处理所有文本）"""
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from modelsAPI.model_api import OpenaiAPI
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if not texts:
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return []
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try:
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result = OpenaiAPI.batch_embeddings(texts)
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if result and isinstance(result, list):
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embedding_list = [item.get('embedding') if isinstance(item, dict) else item for item in result]
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if embedding_list and len(embedding_list) == len(texts):
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return embedding_list
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else:
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logger.error(f"EmbeddingWrapper: 批量向量化返回数量不匹配，期望: {len(texts)}, 实际: {len(embedding_list) if embedding_list else 0}")
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return [None] * len(texts)
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else:
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logger.error(f"EmbeddingWrapper: 批量向量化返回格式异常")
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return [None] * len(texts)
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except Exception as e:
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logger.error(f"EmbeddingWrapper: 批量向量化失败: {e}", exc_info=True)
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return [None] * len(texts)
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async def _search_all_nodes_with_hybrid(
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session,
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query: str,
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top_k: int = 20
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) -> List[Dict[str, Any]]:
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"""
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直接对所有节点进行混合检索（embedding+全文），找最匹配的节点
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使用 NodeRetrieval 替代 HybridRetriever，避免兼容性问题
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Args:
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session: Neo4j 异步会话
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query: 用户查询
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top_k: 返回节点数量
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Returns:
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List[Dict]: 匹配到的节点列表
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"""
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from graph_search.node_retrieval import NodeRetrieval
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uri = NEO4J_CONFIG.get("uri")
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user = NEO4J_CONFIG.get("user")
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password = NEO4J_CONFIG.get("password")
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sync_driver = GraphDatabase.driver(uri, auth=(user, password))
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try:
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# 获取所有节点标签
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all_labels = ["设备","系统","子系统","故障现象"]
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logger.info(f"[混合检索] 所有节点标签: {all_labels}")
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# 使用 NodeRetrieval 进行检索
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embeddings = EmbeddingWrapper()
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node_retrieval = NodeRetrieval(sync_driver, embeddings)
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# 为每个标签构造路由结果
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route_res = [{"label": label, "entity": query} for label in all_labels]
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# 执行检索
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retrieved_nodes = await node_retrieval.retrieve_nodes(route_res, top_k=top_k)
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# 汇总结果
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all_results = []
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for label, nodes in retrieved_nodes.items():
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for node in nodes:
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all_results.append({
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"name": node.get("name"),
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"score": node.get("score", 0.0),
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"node": node.get("node", {}),
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"label": label
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})
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# 按score排序，取top_k
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all_results.sort(key=lambda x: x.get("score", 0), reverse=True)
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all_results = all_results[:top_k]
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logger.info(f"[混合检索] 找到 {len(all_results)} 个候选节点")
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# 获取完整节点信息
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final_nodes = []
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for item in all_results:
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node_data = item["node"]
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label = item["label"]
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name = item["name"]
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get_node_query = f"""
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MATCH (n:{label})
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WHERE n.名称 = $name OR n.name = $name
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RETURN elementId(n) AS id, n.名称 AS name, labels(n) AS labels, properties(n) AS props
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LIMIT 1
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"""
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result = await session.run(get_node_query, name=name)
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record = await result.single()
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if record:
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final_nodes.append({
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"id": record.get("id"),
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"name": record.get("name"),
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"labels": record.get("labels"),
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"props": record.get("props", {}),
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"score": item.get("score", 0.0)
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})
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return final_nodes
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finally:
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sync_driver.close()
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async def _rerank_nodes(
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query,
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nodes
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):
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"""
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使用 rerank 对节点进行重排序
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Args:
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query: 用户查询
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nodes: 节点列表
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Returns:
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List[Dict]: 重排序后的节点列表
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"""
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if not nodes:
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return []
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from modelsAPI.model_api import OpenaiAPI
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# 格式化节点文本
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documents = []
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for node in nodes:
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props = node.get("props", {})
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name = node.get("name", "")
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labels = ", ".join(node.get("labels", []))
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doc_text = f"名称: {name}; 类型: {labels}"
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documents.append(doc_text)
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# Rerank
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try:
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rerank_results = OpenaiAPI.rerank_query(query, documents, top_n=len(documents))
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# 按score排序
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sorted_rerank = sorted(rerank_results, key=lambda x: x.get('scores', 0), reverse=True)
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# 重新组织节点
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reranked_nodes = []
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for rr in sorted_rerank:
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idx = rr.get('index', 0)
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if 0 <= idx < len(nodes):
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node = nodes[idx].copy()
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node['rerank_score'] = rr.get('scores', 0)
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reranked_nodes.append(node)
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return reranked_nodes
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except Exception as e:
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logger.error(f"Rerank失败: {e}", exc_info=True)
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# 失败时返回原顺序
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for node in nodes:
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node['rerank_score'] = 0
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return nodes
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async def _get_one_hop_neighbors(
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session,
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node_id: str
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) -> List[Dict[str, Any]]:
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"""
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获取指定节点的一跳邻居节点
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Args:
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session: Neo4j 异步会话
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node_id: 节点ID
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Returns:
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List[Dict]: 一跳邻居节点列表
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"""
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exclude_keys = {
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"last_updated",
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"created_at",
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"knowledge_source",
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"fulltext",
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"embedding",
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}
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query = """
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MATCH (n)
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WHERE elementId(n) = $node_id
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MATCH (n)-[r]-(m)
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RETURN
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elementId(m) AS id,
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m.名称 AS name,
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labels(m) AS labels,
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properties(m) AS props,
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elementId(r) AS rel_id,
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type(r) AS rel_type,
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properties(r) AS rel_props,
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elementId(n) AS source_id,
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n.名称 AS source_name,
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labels(n) AS source_labels
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LIMIT 100
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"""
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result = await session.run(query, node_id=node_id)
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records = await result.data()
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neighbors = []
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for record in records:
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props = record.get("props", {})
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props_filtered = {k: v for k, v in props.items() if k not in exclude_keys}
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rel_props = record.get("rel_props", {})
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rel_props_filtered = {k: v for k, v in rel_props.items() if k not in exclude_keys}
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neighbors.append({
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"id": record.get("id"),
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"name": record.get("name"),
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"labels": record.get("labels", []),
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"properties": props_filtered,
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"relationship": {
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"id": record.get("rel_id"),
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"type": record.get("rel_type"),
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"properties": rel_props_filtered,
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"source_id": record.get("source_id"),
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"source_name": record.get("source_name"),
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"source_labels": record.get("source_labels", [])
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}
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})
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return neighbors
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def _format_node_for_llm(node: Dict[str, Any]) -> str:
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"""
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将节点信息格式化为供LLM理解的字符串（仅包含标签和中文名称）
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Args:
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node: 节点信息字典
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Returns:
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str: 格式化后的字符串
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"""
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name = node.get("name", "未知")
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labels = ", ".join(node.get("labels", []))
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rel = node.get("relationship", {})
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parts = [f"节点名称: {name}"]
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if labels:
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parts.append(f"节点类型: {labels}")
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if rel:
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parts.append(f"通过关系: {rel.get('type', '未知')} 连接自: {rel.get('source_name', '未知')}")
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return "\n".join(parts)
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async def _select_best_node_with_llm(
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query: str,
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neighbors: List[Dict[str, Any]]
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) -> Optional[Dict[str, Any]]:
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"""
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使用大模型从一跳邻居中选择最合适的节点
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Args:
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query: 用户查询
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neighbors: 一跳邻居节点列表
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Returns:
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Optional[Dict]: 最合适的节点信息，None表示没有合适的
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"""
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if not neighbors:
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return None
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from modelsAPI.model_api import OpenaiAPI
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# 格式化邻居节点信息供LLM参考
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neighbors_text = []
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for i, node in enumerate(neighbors, 1):
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neighbors_text.append(f"【候选节点 {i}】\n{_format_node_for_llm(node)}\n")
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prompt = f"""你是一个智能图谱检索助手，需要根据用户的查询，从提供的候选节点中选择最相关的一个节点。
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用户查询: {query}
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候选节点列表:
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{''.join(neighbors_text)}
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请仔细阅读用户查询和所有候选节点信息，选择与用户查询最相关的一个节点。
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输出要求：
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1. 请仅输出你选择的候选节点编号（1, 2, 3...），不要输出任何其他内容
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2. 如果没有任何相关的节点，请输出"0"
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3. 不要解释原因，只输出数字编号
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你的选择:"""
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try:
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model = os.getenv("OPENAI_MODEL")
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response = await OpenaiAPI.open_api_chat_async(prompt, model, temperature=0)
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# 提取数字编号
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match = re.search(r'\d+', response.strip())
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if match:
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selected_index = int(match.group()) - 1
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if 0 <= selected_index < len(neighbors):
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logger.info(f"LLM选择了候选节点 {selected_index + 1}: {neighbors[selected_index].get('name', '未知')}")
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return neighbors[selected_index]
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logger.warning(f"LLM未选择有效节点，原始响应: {response}")
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return None
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except Exception as e:
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logger.error(f"使用LLM选择节点失败: {e}", exc_info=True)
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return None
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def _build_graph_result(
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entry_node: Optional[Dict[str, Any]],
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best_node: Optional[Dict[str, Any]],
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query: str
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) -> Dict[str, Any]:
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"""
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构建与operate方法一致的输出格式（只包含入口节点和选中节点）
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Args:
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entry_node: 入口节点
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best_node: 选择的最佳节点
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query: 用户查询
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Returns:
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Dict: 格式化后的结果
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"""
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exclude_keys = {
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"last_updated",
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"created_at",
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"knowledge_source",
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"fulltext",
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"embedding",
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}
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nodes_g: List[Dict[str, Any]] = []
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links_g: List[Dict[str, Any]] = []
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node_id_set = set()
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# 添加入口节点
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if entry_node:
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nid = entry_node.get("id")
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if nid and nid not in node_id_set:
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node_id_set.add(nid)
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props = entry_node.get("props", {})
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props_filtered = {k: v for k, v in props.items() if k not in exclude_keys}
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name = props.get("名称") or props.get("name") or str(nid)
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labels = entry_node.get("labels", [])
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nodes_g.append({
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"id": nid,
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"name": name,
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"labels": labels,
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"properties": props_filtered,
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})
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# 添加最佳节点和关系
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if best_node:
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# 添加最佳节点
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best_nid = best_node.get("id")
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if best_nid and best_nid not in node_id_set:
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node_id_set.add(best_nid)
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props = best_node.get("properties", {})
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name = best_node.get("name") or str(best_nid)
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labels = best_node.get("labels", [])
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nodes_g.append({
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"id": best_nid,
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"name": name,
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"labels": labels,
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"properties": props,
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})
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# 添加关系
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rel = best_node.get("relationship", {})
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if rel:
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rel_id = rel.get("id")
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src_id = rel.get("source_id")
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tgt_id = best_nid
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if src_id in node_id_set and tgt_id in node_id_set:
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links_g.append({
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"id": rel_id,
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"label": rel.get("type", ""),
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"source": src_id,
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"target": tgt_id,
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"properties": rel.get("properties", {}),
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})
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# 生成 results 文本（优先用best_node，其次用entry_node）
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results_text = ""
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if best_node:
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best_props = best_node.get("properties", {})
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best_info = {k: v for k, v in best_props.items() if k not in exclude_keys and v}
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results_text = json.dumps(best_info, ensure_ascii=False) if best_info else ""
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elif entry_node:
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entry_props = entry_node.get("props", {})
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entry_info = {k: v for k, v in entry_props.items() if k not in exclude_keys and v}
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results_text = json.dumps(entry_info, ensure_ascii=False) if entry_info else ""
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graph_item = {"nodes": nodes_g, "links": links_g, "results": results_text}
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return {"success": True, "xxxx.graph": [graph_item]}
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async def quick_graph_search(
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query: str,
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route_res: Optional[List[Dict[str, str]]] = None,
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top_k: int = 10,
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) -> Dict[str, Any]:
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"""
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临时图谱检索接口
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Args:
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query: 用户查询
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route_res: 路由结果列表（本方案不使用）
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top_k: 检索返回的节点数量上限
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Returns:
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Dict: 检索结果，包含 success, xxxx.graph 等字段
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"""
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start_time = time.time()
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if not query or not str(query).strip():
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return {"success": False, "error": "查询不能为空"}
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uri = NEO4J_CONFIG.get("uri")
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user = NEO4J_CONFIG.get("user")
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password = NEO4J_CONFIG.get("password")
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if not uri or not user or not password:
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return {"success": False, "error": "Neo4j 配置不完整，请检查 NEO4J_CONFIG"}
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try:
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async with AsyncGraphDatabase.driver(uri, auth=(user, password)) as driver:
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async with driver.session() as session:
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logger.info(f"[QuickGraphSearch] 开始直接混合检索，查询: '{query}'")
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# 1. 直接用用户问题混合检索所有节点
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candidate_nodes = await _search_all_nodes_with_hybrid(session, query, top_k=20)
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if not candidate_nodes:
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return {"success": False, "error": "未找到任何匹配节点"}
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logger.info(f"[QuickGraphSearch] 混合检索找到 {len(candidate_nodes)} 个候选节点")
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# 2. Rerank 重排序
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reranked_nodes = await _rerank_nodes(query, candidate_nodes)
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if not reranked_nodes:
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return {"success": False, "error": "Rerank失败"}
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# 3. 取最匹配的节点作为入口
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entry_node = reranked_nodes[0]
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entry_id = entry_node.get("id")
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logger.info(f"[QuickGraphSearch] 选择入口节点: {entry_node.get('name', '未知')}, score={entry_node.get('rerank_score', 0)}")
|
||
|
||
# 4. 获取一跳邻居
|
||
neighbors = await _get_one_hop_neighbors(session, entry_id)
|
||
|
||
logger.info(f"[QuickGraphSearch] 获取到 {len(neighbors)} 个一跳邻居")
|
||
|
||
if not neighbors:
|
||
# 如果没有邻居节点，只返回入口节点
|
||
return _build_graph_result(entry_node, None, query)
|
||
|
||
# 5. 用LLM选择最合适的节点
|
||
best_node = await _select_best_node_with_llm(query, neighbors)
|
||
|
||
# 6. 构建结果（只包含入口节点和选中节点）
|
||
result = _build_graph_result(entry_node, best_node, query)
|
||
|
||
elapsed_ms = (time.time() - start_time) * 1000
|
||
logger.info(f"[QuickGraphSearch] 完成，耗时: {elapsed_ms:.2f}ms")
|
||
|
||
return result
|
||
|
||
except Exception as e:
|
||
logger.error(f"QuickGraphSearch 失败: {e}", exc_info=True)
|
||
return {"success": False, "error": f"检索失败: {str(e)}"}
|
||
|