微软的GraphRAG算得上是最早一批成熟的GraphRAG系统，它把索引阶段（抽取实体、关系、构建层级社区并生成摘要）和查询阶段的高级能力整合到了一起。这套方案的优势在于，可以借助预先计算好的实体、关系、社区摘要来回答那些宏观的、主题性的问题，这恰恰是传统RAG系统基于文档检索难以做到的。

本文的重点是DRIFT搜索：Dynamic Reasoning and Inference with Flexible Traversal，翻译过来就是"动态推理与灵活遍历"。这是一种相对较新的检索策略，兼具全局搜索和局部搜索的特点。

DRIFT的工作流程是这样的：先通过向量搜索建立一个宽泛的查询起点，再利用群信息把原始问题拆解成更细粒度的后续查询。然后动态地在知识图谱上游走，抓取实体、关系等局部细节。这种设计在计算效率和答案质量之间找到了一个不错的平衡点。
 


上图为使用 LlamaIndex 工作流和 Neo4j 实现的 DRIFT 搜索，核心流程分一下几步：

首先是HyDE生成，基于一份样例社区报告构造假设性答案，用来改善查询的向量表示。

接着社区搜索登场，通过向量相似度找出最相关的社区报告，给查询提供宏观上下文。系统会分析这些结果，输出一个初步的中间答案，同时生成一批后续查询用于深挖。

这些后续查询会在局部搜索阶段并行执行，从知识图谱里捞出文本块、实体、关系、以及更多社区报告。这个过程可以迭代多轮，每轮都可能产生新的后续查询。

最后是答案生成，把过程中积累的所有中间答案汇总起来，融合社区级别的宏观洞察和局部细节，生成最终响应。整体思路就是先铺开、再聚焦，层层递进。

本文用的是《爱丽丝梦游仙境》，刘易斯·卡罗尔的经典作品，这部小说角色众多、场景丰富、事件环环相扣，拿来演示GraphRAG的能力再合适不过。

数据导入


整个pipeline遵循标准的GraphRAG流程，分三个阶段：

 class MSGraphRAGIngestion(Workflow):  
    @step  
    async def entity_extraction(self, ev: StartEvent) -> EntitySummarization:  
        chunks = splitter.split_text(ev.text)  
        await ms_graph.extract_nodes_and_rels(chunks, ev.allowed_entities)  
        return EntitySummarization()  

    @step  
    async def entity_summarization(  
        self, ev: EntitySummarization  
    ) -> CommunitySummarization:  
        await ms_graph.summarize_nodes_and_rels()  
        return CommunitySummarization()  

    @step  
    async def community_summarization(  
        self, ev: CommunitySummarization  
    ) -> CommunityEmbeddings:  
        await ms_graph.summarize_communities()  
         return CommunityEmbeddings()

先从文本块里抽取实体和关系，再给节点和关系生成摘要，最后构建层级社区并生成社区摘要。

摘要做完之后，要给社区和实体都生成向量嵌入，这样才能支持相似性检索。社区嵌入的代码长这样：

 @step  
    async def community_embeddings(self, ev: CommunityEmbeddings) -> EntityEmbeddings:  
        # Fetch all communities from the graph database  
        communities = ms_graph.query(  
            """  
    MATCH (c:__Community__)  
    WHERE c.summary IS NOT NULL AND c.rating > $min_community_rating  
    RETURN coalesce(c.title, "") + " " + c.summary AS community_description, c.id AS community_id  
    """,  
            params={"min_community_rating": MIN_COMMUNITY_RATING},  
        )  
        if communities:  
            # Generate vector embeddings from community descriptions  
            response = await client.embeddings.create(  
                input=[c["community_description"] for c in communities],  
                model=TEXT_EMBEDDING_MODEL,  
            )  
            # Store embeddings in the graph and create vector index  
            embeds = [  
                {  
                    "community_id": community["community_id"],  
                    "embedding": embedding.embedding,  
                }  
                for community, embedding in zip(communities, response.data)  
            ]  
            ms_graph.query(  
                """UNWIND $data as row  
            MATCH (c:__Community__ {id: row.community_id})  
            CALL db.create.setNodeVectorProperty(c, 'embedding', row.embedding)""",  
                params={"data": embeds},  
            )  
            ms_graph.query(  
                "CREATE VECTOR INDEX community IF NOT EXISTS FOR (c:__Community__) ON c.embedding"  
            )  
         return EntityEmbeddings()

实体嵌入同理，这样DRIFT搜索需要的向量索引就都建好了。

DRIFT搜索

DRIFT的检索思路其实很符合简单：先看大图，再挖细节。它不会一上来就在文档或实体层面做精确匹配，而是先去查群的摘要，因为这些摘要是对知识图谱主要主题的高层次概括。

拿到相关的高层信息后，DRIFT会智能地派生出后续查询，去精确检索特定实体、关系、源文档。这种两阶段的做法其实很像人类查资料的习惯：先大致了解情况再针对性地追问细节。既有全局搜索的覆盖面，又有局部搜索的精准度，而且不用把所有社区报告或文档都过一遍，计算开销控制得不错。

下面拆解一下各个阶段的实现。

群搜索

DRIFT用了HyDE技术来提升向量检索的准确率。不是直接拿用户query做embedding，而是先让模型生成一个假设性的答案，再用这个答案去做相似性搜索。道理很简单：假设答案在语义上跟真实的摘要更接近。

 @step  
async def hyde_generation(self, ev: StartEvent) -> CommunitySearch:  
    # Fetch a random community report to use as a template for HyDE generation  
    random_community_report = driver.execute_query(  
        """  
    MATCH (c:__Community__)  
    WHERE c.summary IS NOT NULL  
    RETURN coalesce(c.title, "") + " " + c.summary AS community_description""",  
        result_transformer_=lambda r: r.data(),  
    )  
    # Generate a hypothetical answer to improve query representation  
    hyde = HYDE_PROMPT.format(  
        query=ev.query, template=random_community_report[0]["community_description"]  
    )  
    hyde_response = await client.responses.create(  
        model="gpt-5-mini",  
        input=[{"role": "user", "content": hyde}],  
        reasoning={"effort": "low"},  
    )  
     return CommunitySearch(query=ev.query, hyde_query=hyde_response.output_text)

拿到HyDE query之后，做embedding，然后通过向量相似度捞出top 5的报告。接着让LLM基于这些报告生成一个初步答案，同时识别出需要深挖的后续查询。将初步答案存起来然后进行后续查询全部并行分发到局部搜索阶段。

   
@step  
async def community_search(self, ctx: Context, ev: CommunitySearch) -> LocalSearch:  
    # Create embedding from the HyDE-enhanced query  
    embedding_response = await client.embeddings.create(  
        input=ev.hyde_query, model=TEXT_EMBEDDING_MODEL  
    )  
    embedding = embedding_response.data[0].embedding  
      
    # Find top 5 most relevant community reports via vector similarity  
    community_reports = driver.execute_query(  
        """  
    CALL db.index.vector.queryNodes('community', 5, $embedding) YIELD node, score  
    RETURN 'community-' + node.id AS source_id, node.summary AS community_summary  
    """,  
        result_transformer_=lambda r: r.data(),  
        embedding=embedding,  
    )  
      
    # Generate initial answer and identify what additional info is needed  
    initial_prompt = DRIFT_PRIMER_PROMPT.format(  
        query=ev.query, community_reports=community_reports  
    )  
    initial_response = await client.responses.create(  
        model="gpt-5-mini",  
        input=[{"role": "user", "content": initial_prompt}],  
        reasoning={"effort": "low"},  
    )  
    response_json = json_repair.loads(initial_response.output_text)  
    print(f"Initial intermediate response: {response_json['intermediate_answer']}")  
      
    # Store the initial answer and prepare for parallel local searches  
    async with ctx.store.edit_state() as ctx_state:  
        ctx_state["intermediate_answers"] = [  
            {  
                "intermediate_answer": response_json["intermediate_answer"],  
                "score": response_json["score"],  
            }  
        ]  
        ctx_state["local_search_num"] = len(response_json["follow_up_queries"])  
      
    # Dispatch follow-up queries to run in parallel  
    for local_query in response_json["follow_up_queries"]:  
        ctx.send_event(LocalSearch(query=ev.query, local_query=local_query))  
     return None

这就是DRIFT的核心思路，先用HyDE增强的社区搜索铺开，再用后续查询往下钻。

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https://github.com/yjdfhge/d/issues/2567
https://github.com/yjdfhge/d/issues/2566
https://github.com/yjdfhge/d/issues/2565
https://github.com/yjdfhge/d/issues/2564
https://github.com/yjdfhge/d/issues/2563
https://github.com/yjdfhge/d/issues/2562
https://github.com/yjdfhge/d/issues/2561
https://github.com/yjdfhge/d/issues/2560
https://github.com/yjdfhge/d/issues/2559
https://github.com/yjdfhge/d/issues/2558
https://github.com/yjdfhge/d/issues/2557
https://github.com/yjdfhge/d/issues/2556
https://github.com/yjdfhge/d/issues/2555
https://github.com/yjdfhge/d/issues/2554
https://github.com/yjdfhge/d/issues/2553
https://github.com/yjdfhge/d/issues/2552
https://github.com/yjdfhge/d/issues/2551
https://github.com/yjdfhge/d/issues/2550
https://github.com/yjdfhge/d/issues/2549
https://github.com/yjdfhge/d/issues/2548
https://github.com/yjdfhge/d/issues/2547
https://github.com/yjdfhge/d/issues/2546
https://github.com/yjdfhge/d/issues/2545
https://github.com/yjdfhge/d/issues/2544
https://github.com/yjdfhge/d/issues/2543
https://github.com/yjdfhge/d/issues/2542
https://github.com/yjdfhge/d/issues/2541
https://github.com/yjdfhge/d/issues/2540
https://github.com/yjdfhge/d/issues/2539
https://github.com/yjdfhge/d/issues/2538
https://github.com/yjdfhge/d/issues/2537
https://github.com/yjdfhge/d/issues/2536
https://github.com/yjdfhge/d/issues/2535
https://github.com/yjdfhge/d/issues/2534
https://github.com/yjdfhge/d/issues/2533
https://github.com/yjdfhge/d/issues/2532
https://github.com/yjdfhge/d/issues/2531
https://github.com/yjdfhge/d/issues/2530
https://github.com/yjdfhge/d/issues/2529
https://github.com/yjdfhge/d/issues/2528
https://github.com/yjdfhge/d/issues/2527
https://github.com/yjdfhge/d/issues/2526
https://github.com/yjdfhge/d/issues/2525
https://github.com/yjdfhge/d/issues/2524
https://github.com/yjdfhge/d/issues/2523
https://github.com/yjdfhge/d/issues/2522
https://github.com/yjdfhge/d/issues/2521
https://github.com/yjdfhge/d/issues/2520
https://github.com/yjdfhge/d/issues/2519
https://github.com/yjdfhge/d/issues/2518
https://github.com/yjdfhge/d/issues/2517
https://github.com/yjdfhge/d/issues/2516
https://github.com/yjdfhge/d/issues/2515
https://github.com/yjdfhge/d/issues/2514
https://github.com/yjdfhge/d/issues/2513
https://github.com/yjdfhge/d/issues/2512
https://github.com/yjdfhge/d/issues/2511
https://github.com/yjdfhge/d/issues/2510
https://github.com/yjdfhge/d/issues/2509
https://github.com/yjdfhge/d/issues/2508
https://github.com/yjdfhge/d/issues/2507
https://github.com/yjdfhge/d/issues/2506
https://github.com/yjdfhge/d/issues/2505
https://github.com/yjdfhge/d/issues/2504
https://github.com/yjdfhge/d/issues/2503
https://github.com/yjdfhge/d/issues/2502
 

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