rag_server/applications/utils/neo4j/graph_expansion.py

import json
from typing import List, Optional, Dict, Any


class AsyncGraphExpansion:
    def __init__(self, driver, database: str = "neo4j"):
        self.driver = driver
        self.database = database

    # ========== 共现 co-occurrence ==========
    async def co_occurrence(
        self,
        seed_name: str,
        seed_label: str,
        other_names: Optional[List[str]] = None,
        limit: int = 500,
    ) -> List[str]:
        """
        找与种子要素共现的 chunk
        :param seed_name: 种子名称
        :param seed_label: 种子标签 ('Entity', 'Concept', 'Topic')
        :param other_names: 指定要扩展的其它要素名称列表（若为 None，先查共现TopN再扩展）
        :param limit: 返回数量上限
        """
        async with self.driver.session(database=self.database) as session:
            if not other_names:
                # 先统计高频共现要素
                query_top = f"""
                MATCH (seed:`{seed_label}` {{name:$seed_name}})
                      <-[:HAS_ENTITY|:HAS_CONCEPT|:HAS_TOPIC]-(gc:GraphChunk)
                MATCH (gc)-[:HAS_ENTITY|:HAS_CONCEPT|:HAS_TOPIC]->(other)
                WHERE other <> seed
                RETURN other.name AS name, count(*) AS co_freq
                ORDER BY co_freq DESC
                LIMIT 20
                """
                records = await session.run(query_top, {"seed_name": seed_name})
                other_names = [r["name"] async for r in records]
                print(other_names)

            # 根据共现要素回捞 chunk
            query_expand = f"""
            MATCH (seed:`{seed_label}` {{name:$seed_name}})
                  <-[:HAS_ENTITY|:HAS_CONCEPT|:HAS_TOPIC]-(gc:GraphChunk)
            MATCH (gc)-[:HAS_ENTITY|:HAS_CONCEPT|:HAS_TOPIC]->(o)
            WHERE o.name IN $other_names
            RETURN DISTINCT gc.milvus_id AS milvus_id
            LIMIT $limit
            """
            records = await session.run(
                query_expand,
                {"seed_name": seed_name, "other_names": other_names, "limit": limit},
            )
            return [str(r["milvus_id"]) async for r in records]

    # ========== 多种子（每个 seed 自带标签）共现（性能优化版） ==========
    async def co_occurrence_multi_mixed_labels(
        self,
        seeds: List[
            Dict[str, str]
        ],  # [{"name": "...", "label": "Entity|Concept|Topic"}, ...]
        other_names: Optional[List[str]] = None,
        limit: int = 500,
        top_k: int = 20,
        min_support: int = 1,  # 至少与多少个不同 seed 共现
    ) -> List[str]:
        if not seeds:
            return []

        # 1) 标签白名单 + 去重分桶（减少 Cypher 分支中过滤与扫描）
        allowed = {"Entity", "Concept", "Topic"}
        seed_entities = sorted({s["name"] for s in seeds if s.get("label") == "Entity"})
        seed_concepts = sorted(
            {s["name"] for s in seeds if s.get("label") == "Concept"}
        )
        seed_topics = sorted({s["name"] for s in seeds if s.get("label") == "Topic"})
        if not (set(x.get("label") for x in seeds) <= allowed):
            raise ValueError("Seed label must be one of Entity/Concept/Topic")

        async with self.driver.session(database=self.database) as session:
            # 2) 统计跨 seeds 的共现 TopN（当未显式提供 other_names 时）
            if not other_names:
                query_top = """
                // ========== 先收敛到与 seeds 共现的 gc + sname ==========
                CALL {
                  WITH $seed_entities AS se
                  UNWIND se AS sname
                  MATCH (:Entity {name:sname})<-[:HAS_ENTITY]-(gc:GraphChunk)
                  RETURN gc, sname
                  UNION
                  WITH $seed_concepts AS sc
                  UNWIND sc AS sname
                  MATCH (:Concept {name:sname})<-[:HAS_CONCEPT]-(gc:GraphChunk)
                  RETURN gc, sname
                  UNION
                  WITH $seed_topics AS st
                  UNWIND st AS sname
                  MATCH (:Topic {name:sname})<-[:HAS_TOPIC]-(gc:GraphChunk)
                  RETURN gc, sname
                }

                // ========== 在这些 gc 上一次性发散到 other ==========
                MATCH (gc)-[:HAS_ENTITY|:HAS_CONCEPT|:HAS_TOPIC]->(other)
                // 排除把种子本身当作 other 的情况（基于标签分桶，仍然命中索引）
                WHERE NOT (
                  (other:Entity  AND other.name IN $seed_entities) OR
                  (other:Concept AND other.name IN $seed_concepts) OR
                  (other:Topic   AND other.name IN $seed_topics)
                )
                WITH other.name AS name, gc, sname
                // 跨 gc 汇总频次；跨 sname 汇总支持度（命中的不同 seed 数）
                WITH name,
                     count(DISTINCT gc)    AS co_freq,
                     count(DISTINCT sname) AS seed_support
                WHERE seed_support >= $min_support
                ORDER BY seed_support DESC, co_freq DESC
                LIMIT $top_k
                RETURN name
                """

                params_top = {
                    "seed_entities": seed_entities,
                    "seed_concepts": seed_concepts,
                    "seed_topics": seed_topics,
                    "top_k": top_k,
                    "min_support": min_support,
                }
                recs = await session.run(query_top, params_top)
                other_names = [r["name"] async for r in recs]
                if not other_names:
                    return []

            # 3) 回捞阶段：先确定“与 seeds 相连的 gc”，再用 other_names 索引命中
            #    这里用 UNWIND onames + 按标签逐类 MATCH，确保使用 name 索引，
            #    避免 `o.name IN $list` 走全表扫描/低效 plan。
            query_expand = """
            // 先生成候选 gc（包含任一 seed）
            CALL {
              WITH $seed_entities AS se
              UNWIND se AS sname
              MATCH (:Entity {name:sname})<-[:HAS_ENTITY]-(gc:GraphChunk)
              RETURN DISTINCT gc
              UNION
              WITH $seed_concepts AS sc
              UNWIND sc AS sname
              MATCH (:Concept {name:sname})<-[:HAS_CONCEPT]-(gc:GraphChunk)
              RETURN DISTINCT gc
              UNION
              WITH $seed_topics AS st
              UNWIND st AS sname
              MATCH (:Topic {name:sname})<-[:HAS_TOPIC]-(gc:GraphChunk)
              RETURN DISTINCT gc
            }

            // 用索引命中 other（UNWIND 列表，让等值匹配可走索引）
            UNWIND $other_names AS oname
            CALL {
              WITH oname
              MATCH (o:Entity {name:oname})  RETURN o
              UNION
              WITH oname
              MATCH (o:Concept {name:oname}) RETURN o
              UNION
              WITH oname
              MATCH (o:Topic {name:oname})   RETURN o
            }

            MATCH (gc)-[:HAS_ENTITY|:HAS_CONCEPT|:HAS_TOPIC]->(o)
            RETURN DISTINCT gc.milvus_id AS milvus_id
            LIMIT $limit
            """

            params_expand = {
                "seed_entities": seed_entities,
                "seed_concepts": seed_concepts,
                "seed_topics": seed_topics,
                "other_names": other_names,
                "limit": limit,
            }
            recs2 = await session.run(query_expand, params_expand)
            return [r["milvus_id"] async for r in recs2]

    # ========== 路径 Path ==========
    async def shortest_path_chunks(
        self,
        a_name: str,
        a_label: str,
        b_name: str,
        b_label: str,
        max_len: int = 4,
        limit: int = 200,
    ) -> List[str]:
        """
        找到两个要素之间的最短路径，并返回路径上的 chunk
        """
        query = f"""
        MATCH (a:`{a_label}` {{name:$a_name}}), (b:`{b_label}` {{name:$b_name}})
        CALL {{
          WITH a,b
          MATCH p = shortestPath(
            (a)-[:HAS_ENTITY|:HAS_CONCEPT|:HAS_TOPIC|:BELONGS_TO*..{max_len}]-(b)
          )
          RETURN p LIMIT 1
        }}
        WITH p
        UNWIND [n IN nodes(p) WHERE n:GraphChunk | n] AS gc
        RETURN DISTINCT gc.milvus_id AS milvus_id
        LIMIT ${limit}
        """
        async with self.driver.session(database=self.database) as session:
            records = await session.run(query, {"a_name": a_name, "b_name": b_name})
            return [str(r["milvus_id"]) async for r in records]

    # ========== 扩展 Expansion ==========
    async def expand_candidates(
        self, seed_ids: List[int], k_per_relation: int = 200, limit: int = 1000
    ) -> List[Dict[str, Any]]:
        """
        基于候选 milvus_id 做 1-hop 扩展（实体/概念/主题），并按权重汇总
        """
        query = """
          CALL {
          WITH $seed_ids AS seed_ids, $k_per_relation AS k
        
          // 同实体
          MATCH (gc0:GraphChunk) WHERE gc0.milvus_id IN seed_ids
          MATCH (gc0)-[:HAS_ENTITY]->(:Entity)<-[:HAS_ENTITY]-(cand:GraphChunk)
          WHERE cand <> gc0 AND NOT cand.milvus_id IN seed_ids
          WITH DISTINCT cand, 1.0 AS w
          LIMIT k
          RETURN cand AS gc, w
        
          UNION
        
          // 同概念
          WITH seed_ids, k
          MATCH (gc0:GraphChunk) WHERE gc0.milvus_id IN seed_ids
          MATCH (gc0)-[:HAS_CONCEPT]->(:Concept)<-[:HAS_CONCEPT]-(cand:GraphChunk)
          WHERE cand <> gc0 AND NOT cand.milvus_id IN seed_ids
          WITH DISTINCT cand, 0.7 AS w
          LIMIT k
          RETURN cand AS gc, w
        
          UNION
        
          // 同主题
          WITH seed_ids, k
          MATCH (gc0:GraphChunk) WHERE gc0.milvus_id IN seed_ids
          MATCH (gc0)-[:HAS_TOPIC]->(:Topic)<-[:HAS_TOPIC]-(cand:GraphChunk)
          WHERE cand <> gc0 AND NOT cand.milvus_id IN seed_ids
          WITH DISTINCT cand, 0.6 AS w
          LIMIT k
          RETURN cand AS gc, w
        }
        WITH gc, sum(w) AS score
        RETURN
          gc.milvus_id AS milvus_id,
          gc.chunk_id  AS chunk_id,   // 若你的属性名不同，请改成实际字段
          gc.doc_id    AS doc_id,     // 同上
          score
        ORDER BY score DESC
        LIMIT $limit
        """
        async with self.driver.session(database=self.database) as session:
            records = await session.run(
                query,
                {
                    "seed_ids": seed_ids,
                    "k_per_relation": k_per_relation,
                    "limit": limit,
                },
            )
            return [
                {
                    "milvus_id": r["milvus_id"],
                    "chunk_id": r["chunk_id"],
                    "doc_id": r["doc_id"],
                    "score": r["score"],
                }
                async for r in records
            ]