GraphRAG——知识图谱 + LLM处理金融报告的多跳推理

日期: 2026-09-21 方向: AI系统工程 / RAG 阶段: Phase 3 - RAG高级模式 (Day 135-148) 标签: #GraphRAG #KG #Microsoft #Multihop #Neo4j #LlamaIndex

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今日目标

核心结论预告：在 多跳推理query 上（"BlackRock holdings里semiconductor公司前5大客户是谁？"），vanilla RAG Recall = 0.42，GraphRAG = 0.84。但代价是indexing成本3-5x，简单query上GraphRAG无显著优势。

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一、核心概念

1.1 Vanilla RAG的多跳极限

Q: "Of the AI chip companies in BlackRock's top 10 holdings,
    which ones were founded before 2000?"

需要的信息:
  Step 1: BlackRock top 10 holdings → [NVDA, AAPL, MSFT, ...]
  Step 2: 哪些是 AI chip公司 → [NVDA, AVGO, AMD]
  Step 3: founding date < 2000 → [NVDA (1993), AVGO (1991), AMD (1969)]

Vanilla RAG的问题:
  - "BlackRock holdings"和"AI chip公司"和"founding date"在不同文档
  - 一次retrieval拿不到三类信息的交集
  - 即使拿到，LLM需要在context里做JOIN

1.2 知识图谱的优势

                        [BlackRock]
                            │
                  holds_position
                            │
              ┌─────────────┼─────────────┐
              ▼             ▼             ▼
           [NVDA]        [AAPL]        [AVGO]
              │             │             │
        is_in_industry  is_in_industry  is_in_industry
              │             │             │
              ▼             ▼             ▼
         [Semiconductor]  [Tech]    [Semiconductor]
              │                          │
        founded_year                founded_year
              │                          │
              ▼                          ▼
            [1993]                     [1991]

KG天然支持多跳：用 graph traversal (Cypher / SPARQL) 直接查询。

1.3 Microsoft GraphRAG架构

2024年7月Microsoft Research发布

┌─────────────────────────────────────────────────────────────┐
│                     Indexing Phase                           │
│                                                              │
│  Documents                                                   │
│      ↓                                                       │
│  [Chunking] (parent-child如Day 142)                          │
│      ↓                                                       │
│  [Entity Extraction] ← LLM                                   │
│      ↓                                                       │
│  [Relation Extraction] ← LLM                                 │
│      ↓                                                       │
│  [KG Construction]                                          │
│      ↓                                                       │
│  [Community Detection] ← Leiden algorithm                   │
│      ↓                                                       │
│  [Community Summarization] ← LLM (per community)            │
│      ↓                                                       │
│  [Embed everything]                                         │
│                                                              │
└─────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────┐
│                     Query Phase                              │
│                                                              │
│  User Query                                                  │
│      ↓                                                       │
│  [Local Search]: 实体级精确查询                                │
│      ↓                                                       │
│  [Global Search]: 通过community summary回答跨文档/全局查询      │
│      ↓                                                       │
│  Answer                                                      │
└─────────────────────────────────────────────────────────────┘

1.4 Local vs Global Search

例子：

• Local: "What did Apple acquire in 2024?" → 找到 Apple node → 邻居 acquired_company
• Global: "What are common risks across tech 10-Ks?" → community summaries of all tech docs → aggregate

1.5 Community Detection (Leiden)

把graph分成 highly-connected sub-graphs：

Tech Sector Community:
  AAPL ─── partner ─── MSFT
   │                    │
   │                  competes
   │                    │
   ▼                    ▼
  NVDA ──── supplies ── (multiple companies)
  
Healthcare Community:
  PFE ─── acquires ─── BIIB
  ...

每个community由LLM生成summary，retrieve时可以直接拿summary而非raw chunks。

---

二、完整实现：graphrag_demo

2.1 项目结构

graphrag_demo/
├── graphrag_pipeline.py   # 主流水线
├── entity_extractor.py    # Entity / Relation extraction with Claude
├── neo4j_loader.py        # 写入Neo4j
├── community_detector.py  # Leiden algorithm
├── retriever.py           # Local + Global search
├── data/
│   ├── apple_10k_2024.txt
│   ├── tesla_10k_2024.txt
│   └── ...
└── output/
    ├── entities.json
    ├── relations.json
    └── communities.json

2.2 Entity & Relation Extraction

"""
entity_extractor.py — 用Claude抽取金融domain的entity & relation
"""
import json
from typing import List, Dict, Tuple
from anthropic import Anthropic

anthropic = Anthropic()

ENTITY_TYPES = [
    "COMPANY",        # Apple Inc.
    "PERSON",         # Tim Cook
    "PRODUCT",        # iPhone
    "LOCATION",       # Cupertino
    "DATE",           # Q4 2024
    "FINANCIAL_METRIC",  # Revenue, EPS
    "SECTOR",         # Semiconductor
    "REGULATION",     # MiFID II
    "SUBSIDIARY",     # Beats Electronics
]

RELATION_TYPES = [
    "ACQUIRED_BY", "ACQUIRED",
    "SUBSIDIARY_OF", "OWNS",
    "PARTNERED_WITH", "COMPETES_WITH",
    "SUPPLIES", "SUPPLIED_BY",
    "OPERATES_IN", "FOUNDED_IN",
    "CEO_OF", "CFO_OF",
    "REGULATED_BY", "MENTIONED_IN",
    "HAS_REVENUE", "HAS_METRIC",
]

PROMPT = """You are a financial knowledge graph extraction expert.

From the following text, extract:
1. Entities (with type from {entity_types})
2. Relations between entities (with type from {relation_types})

Output ONLY valid JSON in this format:
{{
  "entities": [
    {{"name": "Apple Inc.", "type": "COMPANY", "description": "..."}},
    ...
  ],
  "relations": [
    {{"source": "Apple Inc.", "target": "iPhone", "type": "OWNS",
      "evidence": "Apple sells iPhones..."}},
    ...
  ]
}}

Text:
{text}"""


def extract_entities_relations(text: str) -> Dict:
    msg = PROMPT.format(
        entity_types=", ".join(ENTITY_TYPES),
        relation_types=", ".join(RELATION_TYPES),
        text=text[:6000],   # 控制单次输入
    )
    resp = anthropic.messages.create(
        model="claude-sonnet-4-5-20250929",
        max_tokens=4096,
        messages=[{"role": "user", "content": msg}],
    )
    text_out = resp.content[0].text.strip()
    try:
        # 提取JSON部分
        start = text_out.index("{")
        end = text_out.rindex("}") + 1
        return json.loads(text_out[start:end])
    except Exception as e:
        return {"entities": [], "relations": [], "error": str(e)}


def process_document(text: str, doc_id: str,
                     chunk_size: int = 2000) -> Dict:
    """对长文档分块抽取，最后merge"""
    chunks = [text[i:i + chunk_size] for i in range(0, len(text), chunk_size)]
    all_entities = {}
    all_relations = []

    for i, chunk in enumerate(chunks):
        print(f"  Processing chunk {i+1}/{len(chunks)}...")
        result = extract_entities_relations(chunk)
        for e in result.get("entities", []):
            key = e["name"].lower()
            if key in all_entities:
                # merge descriptions
                all_entities[key]["description"] += " | " + e.get("description", "")
            else:
                e["doc_ids"] = [doc_id]
                e["chunk_ids"] = [f"{doc_id}_c{i}"]
                all_entities[key] = e

        for r in result.get("relations", []):
            r["doc_id"] = doc_id
            r["chunk_id"] = f"{doc_id}_c{i}"
            all_relations.append(r)

    return {
        "entities": list(all_entities.values()),
        "relations": all_relations,
    }

2.3 Neo4j Loader

"""
neo4j_loader.py — Load entities and relations into Neo4j
"""
from neo4j import GraphDatabase

class Neo4jLoader:
    def __init__(self, uri="bolt://localhost:7687",
                 user="neo4j", password="password"):
        self.driver = GraphDatabase.driver(uri, auth=(user, password))

    def clear(self):
        with self.driver.session() as s:
            s.run("MATCH (n) DETACH DELETE n")

    def upsert_entity(self, e):
        with self.driver.session() as s:
            s.run("""
                MERGE (n:Entity {name: $name})
                SET n.type = $type, n.description = $desc
            """, name=e["name"], type=e["type"], desc=e.get("description", ""))

    def upsert_relation(self, r):
        with self.driver.session() as s:
            s.run("""
                MATCH (a:Entity {name: $src}), (b:Entity {name: $tgt})
                MERGE (a)-[rel:RELATES {type: $rtype}]->(b)
                SET rel.evidence = $ev, rel.doc_id = $doc
            """, src=r["source"], tgt=r["target"],
                 rtype=r["type"], ev=r.get("evidence", ""),
                 doc=r.get("doc_id", ""))

    def query_neighbors(self, entity_name: str, depth: int = 2):
        with self.driver.session() as s:
            result = s.run(f"""
                MATCH path=(start:Entity {{name: $name}})-[*1..{depth}]-(neighbor)
                RETURN path
                LIMIT 50
            """, name=entity_name)
            return [r.data() for r in result]

2.4 Community Detection

"""
community_detector.py — Leiden community detection on KG
"""
import networkx as nx
import community as community_louvain   # python-louvain pip
from collections import defaultdict

def detect_communities(entities, relations) -> Dict[str, List[str]]:
    G = nx.Graph()
    for e in entities:
        G.add_node(e["name"])
    for r in relations:
        if G.has_node(r["source"]) and G.has_node(r["target"]):
            G.add_edge(r["source"], r["target"], rel=r["type"])

    # Louvain (近似Leiden)
    partition = community_louvain.best_partition(G, resolution=1.0)
    communities = defaultdict(list)
    for node, cid in partition.items():
        communities[f"community_{cid}"].append(node)
    return dict(communities)


def summarize_community(community_entities: List[str],
                         entity_descriptions: Dict[str, str],
                         relations: List[Dict]) -> str:
    """LLM summarize each community"""
    from anthropic import Anthropic
    client = Anthropic()

    relevant_relations = [
        r for r in relations
        if r["source"] in community_entities and r["target"] in community_entities
    ]

    context = "\n".join(
        f"- {e}: {entity_descriptions.get(e, '')}"
        for e in community_entities[:30]  # 控制
    ) + "\n\nRelations:\n" + "\n".join(
        f"- {r['source']} -[{r['type']}]-> {r['target']}: {r.get('evidence', '')}"
        for r in relevant_relations[:30]
    )

    resp = client.messages.create(
        model="claude-sonnet-4-5-20250929",
        max_tokens=512,
        messages=[{"role": "user", "content":
            f"Summarize this community of related financial entities in 3-5 sentences. "
            f"Focus on what they have in common and key relationships:\n\n{context}"
        }]
    )
    return resp.content[0].text.strip()

2.5 Retriever (Local + Global)

"""
retriever.py — Local and Global search on GraphRAG
"""
from typing import List, Dict
import json
from anthropic import Anthropic
from openai import OpenAI

anthropic = Anthropic()
openai_client = OpenAI()


def local_search(query: str, kg_loader, entity_index) -> str:
    """根据query找相关entities，traversal邻居，给LLM"""
    # Step 1: extract focal entity
    focal = anthropic.messages.create(
        model="claude-haiku-4-5-20250929",
        max_tokens=100,
        messages=[{"role": "user",
                   "content": f"From this question, what is the focal entity? "
                              f"Reply with just the entity name.\n\n{query}"}],
    ).content[0].text.strip()

    # Step 2: graph traversal
    neighbors = kg_loader.query_neighbors(focal, depth=2)

    # Step 3: assemble context
    context_parts = [f"FOCAL ENTITY: {focal}"]
    for path in neighbors[:30]:
        # path is a Neo4j path object
        context_parts.append(f"PATH: {json.dumps(path, default=str)[:300]}")
    context = "\n".join(context_parts)

    # Step 4: LLM answer
    resp = anthropic.messages.create(
        model="claude-sonnet-4-5-20250929",
        max_tokens=1024,
        messages=[{"role": "user",
                   "content": f"CONTEXT (knowledge graph):\n{context}\n\n"
                              f"QUESTION: {query}\n\n"
                              f"Answer using the graph context above."}]
    )
    return resp.content[0].text


def global_search(query: str, communities: Dict[str, str]) -> str:
    """让LLM在所有community summaries上回答"""
    # Step 1: 选最相关community summaries
    summaries = "\n\n".join(
        f"[{cid}]\n{summary}" for cid, summary in communities.items()
    )

    # Step 2: map step (per-community partial answers)
    map_prompt = f"""Given the question and a community summary, generate a
partial answer with key points relevant to the question. If unrelated, say
"NOT RELEVANT".

QUESTION: {query}

SUMMARIES:
{summaries[:6000]}

Output JSON list of {{"community": cid, "partial_answer": "..."}}"""

    map_resp = anthropic.messages.create(
        model="claude-sonnet-4-5-20250929",
        max_tokens=2048,
        messages=[{"role": "user", "content": map_prompt}],
    ).content[0].text

    # Step 3: reduce step (合并)
    reduce_prompt = f"""Combine the following partial answers into a coherent
final answer. Cite community IDs.

QUESTION: {query}

PARTIAL ANSWERS:
{map_resp}

Final answer:"""

    final = anthropic.messages.create(
        model="claude-sonnet-4-5-20250929",
        max_tokens=1024,
        messages=[{"role": "user", "content": reduce_prompt}],
    ).content[0].text
    return final


def graphrag_query(query: str, kg_loader, communities: Dict[str, str],
                   entity_index: Dict, mode: str = "auto") -> str:
    if mode == "auto":
        # 简单heuristic: query里提到具体entity → local; "across", "common", "all" → global
        is_global = any(k in query.lower() for k in
                        ["across", "common", "all", "average", "general", "summary"])
        mode = "global" if is_global else "local"

    if mode == "local":
        return local_search(query, kg_loader, entity_index)
    else:
        return global_search(query, communities)

2.6 Pipeline主程序

"""
graphrag_pipeline.py — End-to-end GraphRAG pipeline
"""
from entity_extractor import process_document
from neo4j_loader import Neo4jLoader
from community_detector import detect_communities, summarize_community
from retriever import graphrag_query
import json


def main():
    docs = {
        "apple10k": open("data/apple_10k_2024.txt").read(),
        "tesla10k": open("data/tesla_10k_2024.txt").read(),
        "br13f":    open("data/blackrock_13f.txt").read(),
    }

    # Phase 1: extract entities & relations
    all_entities = {}
    all_relations = []
    for doc_id, text in docs.items():
        print(f"Processing {doc_id}...")
        result = process_document(text, doc_id)
        for e in result["entities"]:
            key = e["name"].lower()
            if key in all_entities:
                all_entities[key]["doc_ids"].append(doc_id)
            else:
                all_entities[key] = e
        all_relations.extend(result["relations"])

    print(f"Total entities: {len(all_entities)}, relations: {len(all_relations)}")

    # Phase 2: load to Neo4j
    loader = Neo4jLoader()
    loader.clear()
    for e in all_entities.values():
        loader.upsert_entity(e)
    for r in all_relations:
        loader.upsert_relation(r)

    # Phase 3: community detection
    communities_dict = detect_communities(
        list(all_entities.values()), all_relations
    )
    print(f"Detected {len(communities_dict)} communities")

    # Phase 4: community summarization
    entity_descs = {e["name"]: e.get("description", "")
                     for e in all_entities.values()}
    community_summaries = {}
    for cid, members in communities_dict.items():
        community_summaries[cid] = summarize_community(
            members, entity_descs, all_relations
        )

    # Save outputs
    json.dump(all_entities, open("output/entities.json", "w"), indent=2, default=str)
    json.dump(all_relations, open("output/relations.json", "w"), indent=2, default=str)
    json.dump(community_summaries, open("output/communities.json", "w"), indent=2)

    # Phase 5: query examples
    queries = [
        "What did Apple acquire recently?",                    # local
        "Common risks across tech 10-Ks?",                     # global
        "Of BlackRock's top holdings, which compete with Apple?", # multi-hop
    ]
    for q in queries:
        ans = graphrag_query(q, loader, community_summaries,
                             all_entities, mode="auto")
        print(f"\nQ: {q}\nA: {ans[:500]}")


if __name__ == "__main__":
    main()

---

三、实测结果

3.1 多跳推理query benchmark

20对人工标注的多跳金融query：

观察：

• 多跳推理上GraphRAG显著优势 (+30-40%)
• 单点事实GraphRAG轻微落后vanilla（KG信息可能不全）
• Latency高3-4倍（Cypher查询 + LLM多轮）

3.2 真实多跳例子

Q: "Of the AI chip companies in BlackRock's top 10 holdings,
    which were founded before 2000?"

[Vanilla RAG v2]
Top chunks: BlackRock 13F第一页（提到holdings但没company details）
Answer: "BlackRock holds AI chip companies including NVIDIA. NVIDIA was 
founded in 1993, before 2000."
→ Misses AVGO, AMD; partial answer.

[GraphRAG local]
Step 1: Find "BlackRock" node in KG
Step 2: traverse "holds_position" edges → [NVDA, AAPL, MSFT, GOOGL, AVGO, ...]
Step 3: filter by industry=Semiconductor → [NVDA, AVGO]
Step 4: lookup founded_year → NVDA 1993, AVGO 1991 (Avago)
Answer: "Among AI chip companies in BlackRock's top 10 holdings:
- NVIDIA (NVDA): founded 1993
- Broadcom (AVGO): founded 1991 (as Avago Technologies)
Both are pre-2000 foundings. AMD is in top 30 but not top 10."
→ Complete and structured answer.

3.3 Indexing成本对比

巨大代价：GraphRAG indexing比vanilla贵 15-30x！

---

四、金融领域应用

4.1 投资关系网络

Microsoft GraphRAG 应用一例:
处理5年的 Apple 10-K + Tesla 10-K + 全 SEC 13F filings
得到的KG:
  - 25,000 entities (companies, people, products)
  - 80,000 relations
  - 自动detect出 35 communities：
    * Tech Giants
    * EV / Auto
    * Semiconductor
    * Cloud Infrastructure
    * etc.

Q: "How is the AI semiconductor supply chain structured?"
→ Global search retrieves "Semiconductor" community summary
→ Includes: NVDA→TSM, AMD→TSM, MSFT→NVDA, etc.
→ LLM构建 supply chain narrative

4.2 ESG / Sustainability报告

ESG报告的复杂关系：

• 公司 → carbon emissions reductions
• 公司 → suppliers → supply chain emissions
• 政府regulation → 公司compliance
• 第三方 audit firm → 公司

→ 多跳推理需求强烈，KG天然适合。

4.3 反洗钱 (AML)

实体关系网络是AML的核心：

• 客户 → 关联 → 其他客户
• 客户 → 转账 → 受益人 → 受益人是PEP
• 公司 → UBO → 制裁名单

在金融KG上做RAG对合规人员查询效率提升巨大。

---

五、生产经验

5.1 8个GraphRAG的坑

5.2 Schema约束的重要性

不约束relation types会得到：

"acquired", "bought", "purchased", "took over", "absorbed", ...

全是同义词的不同表达。

修复：在prompt里 白名单 relation types，强制LLM选择最近的。

5.3 Hybrid GraphRAG + Vanilla

实战推荐 混合方案：

[Query]
   │
   ▼
[Classifier]
  ├── single-hop fact → Vanilla RAG (cheap, fast)
  └── multi-hop / aggregate → GraphRAG (expensive, accurate)

按query类型路由，节约80%的GraphRAG成本。

---

六、Cost & Latency

6.1 月度账单（10K query/day）

10K queries/day × 30 = 300K queries:

• Vanilla: $6,600/月
• GraphRAG: $15,000-30,000/月

6.2 何时GraphRAG值这个钱？

• 多跳查询占 > 30%的query
• 用户是 专家（合规、研究员、分析师）
• 答错代价高 (>$100/error)
• 数据是关系密集型

---

七、关键速查表

7.1 选型决策

                  [问题类型]
                        │
        ┌───────────────┼───────────────┐
        ▼                                ▼
   单点事实查询                   多跳/aggregate
   ("X的revenue?")               ("X和Y有什么共同点")
        │                                │
        ▼                                ▼
   Vanilla RAG                       GraphRAG
   (cheap, 2s)                       (expensive, 5s)
        │                                │
        ▼                                ▼
   Hybrid + rerank                   Local + Global

7.2 GraphRAG构建checklist

• Entity types schema (10-20 types)
• Relation types schema (20-40 types)
• LLM prompt严格JSON output
• Entity disambiguation (name normalization)
• Confidence threshold for relations
• Community size limit (<200 entities)
• Time-stamping relations (for evolving data)

---

八、面试题

Q1: GraphRAG vs Vanilla RAG，何时选GraphRAG？

三个判断维度：(1) Query类型 —— 如果 30%+ 是多跳推理（"X和Y的关系"、"在A集合中满足B条件的"），GraphRAG明显优势；(2) 数据特性 —— 实体关系密集型（金融、生物医学、法律），KG有富矿；纯叙事文本则收益小；(3) 预算 —— GraphRAG indexing成本 15-30x，每query LLM cost 2-3x。当query准确率从0.5提升到0.85对业务带来的收入 > 额外成本时，值得。实战推荐：从vanilla起步，监控错例分析，发现多跳错误集中再上GraphRAG。

Q2: Microsoft GraphRAG的global search为什么用map-reduce模式？

跨community的query一次性把所有summaries送给LLM会超context（千个community × 500 tok = 500K tok，超Claude Sonnet）。Map-Reduce: (1) Map step: 每个community独立产生partial answer（可并行）; (2) Reduce step: 合并partial answers成final answer。这样每次LLM call都在context budget内，可scale到数千个communities。代价是latency高（多轮LLM call）和成本（每community一次map）。

Q3: Entity extraction的relation类型怎么schema化？

三步：(1) 领域专家访谈 确定核心关系类型（金融domain：acquired, partnered, competes, supplies, employees, regulated_by 等）；(2) LLM-assisted bootstrapping：先无约束跑10个文档，看LLM自然生成什么relation types，clustering合并同义；(3) white-list with descriptions：每个type附描述给LLM，限定choice范围。典型初版：20-40 relation types。后续根据extraction quality迭代。

Q4: 金融KG的"时间维度"怎么处理？

关系是 temporal 的（Apple 2014年收购Beats，但Beats现在已并入Apple ecosystem）。三种方案：(1) 简单：每个relation带 valid_from 和 valid_to 时间戳；(2) bitemporal：transaction time + valid time（适合审计场景）；(3) versioned KG：每个时间点snapshot，query时指定时间。Neo4j原生不支持，用 Neo4j Temporal Plugin 或 RDF+TimeML。生产推荐 方案(1)+ defaults to "current"。

Q5: GraphRAG的KG质量如何评估？

没有银弹但有标准做法：(1) Precision：人工抽查extracted relations，准确率应该 > 0.85; (2) Recall：人工标注一份小ground-truth KG（100 relations），看LLM recall %; (3) Downstream task quality：在multi-hop benchmark上的Recall@5，间接但实用; (4) Coverage：高频entity是否被识别（top 100 frequent terms in corpus，至少90%是entity）；(5) Schema adherence：所有relation types是否在schema内。常见baseline：Microsoft GraphRAG paper 报告 entity extraction P=0.86, R=0.71。

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九、明日预告

Day 144: Agentic RAG——RAG的下一个进化是 让LLM自主决定retrieval策略。Self-RAG让模型评估自己的answer是否需要更多context；CRAG (Corrective RAG) 在retrieval质量差时自动rewrite query或调用web search。明天我们实现一个完整的 self-correcting RAG pipeline，看在难query上是否能从0.85提升到0.92+。