418 lines
14 KiB
Python
418 lines
14 KiB
Python
# neo4j-admin database load --from-path="neo4j.dump文件所在目录" --overwrite-destination=true neo4j --verbose
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import os
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import re
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import jieba
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import logging
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import numpy as np
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from concurrent.futures import ThreadPoolExecutor
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from neo4j import GraphDatabase
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from neo4j_graphrag.indexes import (
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create_vector_index,
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upsert_vectors,
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create_fulltext_index,
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)
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from modelsAPI.model_api import OpenaiAPI
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from dotenv import load_dotenv
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from typing import List, Set, Tuple, Dict, Optional
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from config import LLM_CONFIG, EMBEDDING_CONFIG, NEO4J_CONFIG, SEARCH_CONFIG
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# 加载 .env 文件中的环境变量
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load_dotenv()
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# 配置控制台日志
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logger = logging.getLogger("indexing")
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logger.setLevel(logging.INFO)
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if not logger.handlers:
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formatter = logging.Formatter("[%(levelname)s]%(asctime)s: %(message)s")
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handler = logging.StreamHandler()
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handler.setFormatter(formatter)
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logger.addHandler(handler)
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VECTOR_DIMENSION = SEARCH_CONFIG["vector_dimension"]
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vector_dim = SEARCH_CONFIG["vector_dimension"] # OpenAI bge-m3 模型的嵌入向量维度
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embed_batch_size = SEARCH_CONFIG["embed_batch_size"] # 嵌入向量计算批次大小
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EMBED_MAX_WORKERS = SEARCH_CONFIG["embed_thread_num"] # embedding 批次并行线程数（受下游 OpenaiAPI 限流约束，勿过大）
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NODE_FETCH_PAGE = SEARCH_CONFIG["node_fetch_page"] # 单次拉取待生成 embedding 节点的分页大小
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# 全局索引名称（覆盖所有节点标签）
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DEFAULT_URI = NEO4J_CONFIG["uri"]
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DEFAULT_AUTH = (
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NEO4J_CONFIG["username"],
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NEO4J_CONFIG["password"],
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)
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NAME_PROPERTY = SEARCH_CONFIG["name_property"]
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FULLTEXT_PROPERTY = SEARCH_CONFIG["fulltext_property"]
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EMBEDDING_PROPERTY = SEARCH_CONFIG["embedding_property"]
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SEARCH_LABEL = SEARCH_CONFIG["search_label"]
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VECTOR_INDEX_NAME = SEARCH_CONFIG["vector_index_name"]
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FULLTEXT_INDEX_NAME = SEARCH_CONFIG["fulltext_index_name"]
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FULLTEXT_FIELD_INDEX_NAME = SEARCH_CONFIG["fulltext_field_index_name"]
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EXCLUDED_BUSINESS_LABELS: Set[str] = set(SEARCH_CONFIG["excluded_business_labels"])
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OLD_INDEX_NAMES: List[str] = [
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VECTOR_INDEX_NAME,
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FULLTEXT_INDEX_NAME,
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FULLTEXT_FIELD_INDEX_NAME,
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]
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def get_all_labels(driver):
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"""
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获取知识图谱中所有的节点标签
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Args:
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driver: Neo4j driver
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Returns:
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list: 所有节点标签列表
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"""
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query = "CALL db.labels()"
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result = driver.execute_query(query)
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labels = [record["label"] for record in result.records]
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return labels
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def get_label_properties(driver, label):
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"""
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获取指定标签的所有属性键（通过采样多个节点获取所有可能的属性）
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Args:
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driver: Neo4j driver
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label: 节点标签
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Returns:
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list: 属性键列表（排除内部属性如 embedding, fulltext）
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"""
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# 采样多个节点来获取所有可能的属性（因为不同节点可能有不同的属性集）
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query = f"""
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MATCH (n:{label})
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WITH n LIMIT 100
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UNWIND keys(n) AS prop
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RETURN DISTINCT prop AS properties
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ORDER BY properties
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"""
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result = driver.execute_query(query)
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if result.records:
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properties = [record["properties"] for record in result.records]
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# 排除内部索引属性
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excluded_props = {"embedding", "fulltext", "elementId", "id"}
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properties = [prop for prop in properties if prop not in excluded_props]
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return properties
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else:
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logger.warning(f"标签 {label} 没有节点，无法获取属性列表")
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return []
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def build_text_from_all_properties(record_dict):
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"""
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从节点的所有属性构建文本（用于全文索引）
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Args:
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record_dict: 节点属性字典（已排除 id）
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Returns:
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str: 构建的文本字符串
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"""
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text_parts = []
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for key, value in record_dict.items():
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if value is not None:
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# 将值转换为字符串
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if isinstance(value, (list, dict)):
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value_str = str(value)
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else:
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value_str = str(value).strip()
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if value_str:
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text_parts.append(f"{key}：{value_str}")
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return " ".join(text_parts) if text_parts else None
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def _compute_embeddings_parallel(names: List[str]) -> List[list]:
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"""
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并行计算一批名称的 embedding。
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将 names 切成多个 batch，用线程池并发调用 OpenaiAPI.batch_embeddings，
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再按原始顺序拼回。相比串行可显著缩短大批量节点的 embedding 耗时。
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Args:
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names: 待计算 embedding 的文本列表
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Returns:
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list: 与 names 等长、顺序一致的 embedding 向量列表
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Raises:
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任一 batch 计算失败时向上抛出异常
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"""
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# 切分批次，保留批次索引以便按序拼回
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batches = [
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(idx, names[i:i + embed_batch_size])
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for idx, i in enumerate(range(0, len(names), embed_batch_size))
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]
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results: Dict[int, list] = {}
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def _run_batch(item):
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idx, batch_names = item
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batch_result = OpenaiAPI.batch_embeddings(batch_names)
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# batch_result 返回的是字典列表，每个字典包含 'embedding' 字段
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return idx, [row["embedding"] for row in batch_result]
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workers = min(EMBED_MAX_WORKERS, len(batches)) or 1
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with ThreadPoolExecutor(max_workers=workers) as executor:
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for idx, batch_embeddings in executor.map(_run_batch, batches):
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results[idx] = batch_embeddings
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# 按批次顺序拼回，保证与输入 names 对齐
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all_embeddings: List[list] = []
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for idx in range(len(batches)):
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all_embeddings.extend(results[idx])
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return all_embeddings
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def drop_constraint(driver):
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"""删除所有约束"""
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records = driver.execute_query("show constraints").records
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for record in records:
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driver.execute_query(f"drop constraint {record['name']} if exists")
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def drop_index_without_constraint(driver):
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"""删除所有没有约束的索引"""
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records = driver.execute_query("show index").records
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for record in records:
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if not record["owningConstraint"]:
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driver.execute_query(f"drop index {record['name']} if exists")
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def print_neo4j_version(driver) -> None:
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"""打印当前 Neo4j 实例的版本和版本类型。"""
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with driver.session() as session:
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rec = session.run(
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"CALL dbms.components() YIELD name, versions, edition "
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"WHERE name='Neo4j Kernel' RETURN versions[0] AS v, edition"
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).single()
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if rec:
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logger.info(f"Neo4j 版本: {rec['v']} ({rec['edition']})")
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def migrate_remove_entity_label(driver, dry_run: bool = False) -> None:
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"""
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从所有节点上移除 :Entity 标签。
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Parameters
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----------
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driver : Neo4j GraphDatabase driver
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dry_run : 若为 True，只统计数量，不执行移除
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"""
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with driver.session() as session:
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cnt = session.run("MATCH (n:Entity) RETURN count(n) AS c").single()["c"]
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logger.info(f"当前带 :Entity 标签的节点数: {cnt}")
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if cnt == 0:
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return
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if dry_run:
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logger.info("[dry_run] 未执行移除")
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return
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session.run("MATCH (n:Entity) REMOVE n:Entity")
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logger.info(f"已从 {cnt} 个节点上移除 :Entity")
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def tag_searchable_nodes(driver, excluded: Set[str]) -> int:
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"""
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为满足以下条件的节点打上 :Searchable 标签：
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- 拥有 `名称` 属性
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- 拥有 `embedding` 属性
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- 尚未持有 :Searchable 标签
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- 至少有一个非排除业务标签
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注意：排除标签列表通过查询参数传入，避免 Cypher 字符串注入。
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Returns
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-------
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int : 本次新打标签的节点数
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"""
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# 标签名无法参数化，但排除标签集合的“值”可以参数化，避免拼接注入
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query = f"""
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MATCH (n)
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WHERE n.`{NAME_PROPERTY}` IS NOT NULL
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AND n.{EMBEDDING_PROPERTY} IS NOT NULL
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AND NOT n:{SEARCH_LABEL}
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AND any(lbl IN labels(n) WHERE NOT lbl IN $excluded)
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SET n:{SEARCH_LABEL}
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RETURN count(n) AS added
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"""
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excluded_list = list(excluded)
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with driver.session() as session:
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added = session.run(query, excluded=excluded_list).single()["added"]
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logger.info(f"本次新打上 :{SEARCH_LABEL} 的节点数: {added}")
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with driver.session() as session:
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total = session.run(
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f"MATCH (n:{SEARCH_LABEL}) RETURN count(n) AS c"
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).single()["c"]
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logger.info(f"当前带 :{SEARCH_LABEL} 标签的节点总数: {total}")
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return added
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def drop_old_indexes(driver, names: List[str]) -> None:
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"""删除指定名称的索引（不存在时忽略）。"""
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with driver.session() as session:
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for name in names:
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try:
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session.run(f"DROP INDEX {name} IF EXISTS")
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logger.info(f"已删除旧索引: {name}")
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except Exception as e:
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logger.warning(f"删除索引 {name} 失败（可忽略）: {e}")
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def create_indexes(driver) -> None:
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"""
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创建三个索引：
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1. 向量索引 —— 用于语义相似度检索
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2. 名称全文索引 —— 用于节点名称关键词检索
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3. fulltext 字段全文索引 —— 用于节点 fulltext 属性关键词检索
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"""
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with driver.session() as session:
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# 1. 向量索引
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session.run(f"""
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CREATE VECTOR INDEX {VECTOR_INDEX_NAME} IF NOT EXISTS
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FOR (n:{SEARCH_LABEL}) ON (n.{EMBEDDING_PROPERTY})
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OPTIONS {{
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indexConfig: {{
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`vector.dimensions`: {VECTOR_DIMENSION},
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`vector.similarity_function`: 'cosine'
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}}
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}}
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""")
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logger.info(f"向量索引 '{VECTOR_INDEX_NAME}' 已就绪")
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# 2. 名称全文索引
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session.run(f"""
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CREATE FULLTEXT INDEX {FULLTEXT_INDEX_NAME} IF NOT EXISTS
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FOR (n:{SEARCH_LABEL}) ON EACH [n.`{NAME_PROPERTY}`]
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""")
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logger.info(f"全文索引（名称） '{FULLTEXT_INDEX_NAME}' 已就绪")
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# 3. fulltext 字段全文索引
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session.run(f"""
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CREATE FULLTEXT INDEX {FULLTEXT_FIELD_INDEX_NAME} IF NOT EXISTS
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FOR (n:{SEARCH_LABEL}) ON EACH [n.`{FULLTEXT_PROPERTY}`]
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""")
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logger.info(f"全文索引（fulltext 字段） '{FULLTEXT_FIELD_INDEX_NAME}' 已就绪")
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# ================== 连接解析工具 ==================
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def _resolve_connection(
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uri: Optional[str],
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user: Optional[str],
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password: Optional[str],
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) -> Tuple[str, Tuple[str, str]]:
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"""
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按优先级解析连接参数：传参 > 环境变量 > 默认常量。
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Returns
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-------
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(uri, (user, password))
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"""
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resolved_uri = uri or os.getenv("NEO4J_URI", DEFAULT_URI)
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resolved_user = user or os.getenv("NEO4J_USER", DEFAULT_AUTH[0])
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resolved_pw = password or os.getenv("NEO4J_PASSWORD", DEFAULT_AUTH[1])
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return resolved_uri, (resolved_user, resolved_pw)
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# ================== 主入口函数 ==================
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def build_hybrid_indexes(
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driver=None,
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*,
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uri: Optional[str] = None,
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user: Optional[str] = None,
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password: Optional[str] = None,
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remove_entity_label: bool = False,
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dry_run_entity: bool = False,
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drop_old: bool = True,
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) -> dict:
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"""
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完整的混合索引构建流程。
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连接参数优先级：传参 > 环境变量(NEO4J_URI/NEO4J_USER/NEO4J_PASSWORD) > 默认常量
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Parameters
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----------
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driver : 已有的 Neo4j driver（传入则直接使用，不再创建新连接）
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uri : Neo4j 连接地址，如 "bolt://host:7687"
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user : 数据库用户名
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password : 数据库密码
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remove_entity_label : 是否执行移除 :Entity 标签的迁移步骤
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dry_run_entity : remove_entity_label=True 时，是否仅统计而不实际移除
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drop_old : 是否在创建前先删除旧索引（建议保持 True）
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Returns
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-------
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dict : {"success": bool, "message": str, "searchable_added": int}
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失败时 success=False 且 message 含错误信息
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"""
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# 若未传入 driver，则根据参数/环境变量/默认值自动创建
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_owns_driver = driver is None
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if _owns_driver:
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resolved_uri, resolved_auth = _resolve_connection(uri, user, password)
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logger.info(f"连接 Neo4j: {resolved_uri} 用户: {resolved_auth[0]}")
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driver = GraphDatabase.driver(resolved_uri, auth=resolved_auth)
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try:
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logger.info("=" * 60)
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logger.info("开始构建混合索引")
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logger.info("=" * 60)
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print_neo4j_version(driver)
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if remove_entity_label:
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migrate_remove_entity_label(driver, dry_run=dry_run_entity)
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searchable_added = tag_searchable_nodes(driver, EXCLUDED_BUSINESS_LABELS)
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if drop_old:
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drop_old_indexes(driver, OLD_INDEX_NAMES)
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create_indexes(driver)
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return {
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"success": True,
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"message": "混合索引构建完成",
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"searchable_added": searchable_added,
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}
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except Exception as e:
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error_msg = f"构建混合索引过程中发生错误: {e}"
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logger.error(error_msg, exc_info=True)
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return {
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"success": False,
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"message": error_msg,
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"searchable_added": 0,
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}
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finally:
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# 仅关闭由本函数自己创建的 driver
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if _owns_driver:
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driver.close()
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# 向后兼容别名：保留旧拼写，避免其他模块仍引用 build_hyrid_indexes 时报错
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build_hyrid_indexes = build_hybrid_indexes
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if __name__ == "__main__":
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result = build_hybrid_indexes(
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drop_old=True
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)
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if result["success"]:
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logger.info("混合索引创建成功")
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else:
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logger.error(result["message"]) |