Structured Indexing: RAPTOR & GraphRAG / 结构化索引：RAPTOR 与 GraphRAG

Companion material for AI Agents in Depth, Chapter 3 — Experiment 3-8: hierarchical RAPTOR trees vs GraphRAG knowledge graphs, plus offline structured-vs-flat demo.
配套《深入理解 AI Agent》第 3 章 实验 3-8：RAPTOR 层次树 vs GraphRAG 知识图谱，含离线「结构化 vs 扁平」演示。

← Chapter 3 index / 返回第 3 章目录

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English

Overview

Two advanced approaches for large technical documents (e.g. Intel® SDM-style manuals):

1. RAPTOR — hierarchical tree with recursive abstractive summarization
2. GraphRAG — entities, relations, communities, multi-hop traversal

Features

RAPTOR: multi-level abstraction; recursive summaries; leaf→root search; GMM clustering; UMAP.

GraphRAG: LLM entity/relation extract; community detection; community summaries; multi-strategy search; GraphRAGIndexer.multi_hop_search for “how is A connected to B” questions flat vector search cannot express.

HTTP API: build/query, uploads, async large docs, hybrid search, stats.

Installation

# From the repository root: use the shared Chapter 3 environment
uv sync --locked --python 3.12 --extra ch3

# Activate it before changing directories:
# macOS/Linux:
source .venv/bin/activate
# Windows PowerShell: .venv\Scripts\Activate.ps1
# Windows cmd: .venv\Scripts\activate.bat

# pip fallback when uv is not installed:
# python -m pip install -e ".[ch3]"

cd chapter3/structured-index

# Exact legacy parity path, including optional RAPTOR/GraphRAG/Azure packages:
# python -m pip install -r requirements.txt

cp env.example .env
# API keys and preferences

CLI

Chinese --help on all subcommands: python main.py --help, python main.py demo --help, etc.

usage: main.py [-h] {build,query,demo,serve} ...
  build   Build structured indexes (needs OPENAI_API_KEY)
  query   Query existing indexes (needs key + built indexes)
  demo    Offline structured vs flat compare (no API key)
  serve   Start HTTP API

0. Offline demo (no API key — recommended first)

Hand-curated small Intel x86 SIMD knowledge base; three query types: multi-hop, cross-node synthesis, multi-level navigation.

python main.py demo
python main.py demo --query "VADDPS 用到哪个寄存器"
python main.py demo --output demo_result.json

Example (multi-hop; flat fails, graph succeeds):

【查询 1｜多跳关系推理】运行 ADDPS 指令前，操作系统必须把哪个控制寄存器位置 1？
-- 扁平检索（按词面相似度返回独立片段）--
  1. [control-bit] CR4.OSFXSR  (score=0.459)
  ...
  ✗ 只能召回词面相近的孤立片段，无法把 ADDPS 与某个控制位「连」起来。
-- 结构化图检索（沿关系边多跳遍历）--
  ADDPS --属于--> SSE --需要启用--> CR4.OSFXSR
  ✓ 答案：CR4.OSFXSR（从 ADDPS 经 2 跳可达）

build / query need real indexes (LLM for entities/summaries) → OPENAI_API_KEY (embeddings: local SentenceTransformers). demo uses hand-authored structure so readers see the point without keys.

1. Build (needs OPENAI_API_KEY)

python main.py build path/to/document.pdf
python main.py build path/to/document.pdf --type raptor
python main.py build path/to/document.pdf --type graphrag
python main.py build path/to/document.pdf --output stats.json

2. Query

python main.py query "What are the MOV instruction variants?"
python main.py query "explain SSE instructions" --type raptor --top-k 10
python main.py query "SSE registers" --type graphrag --multi-hop 2
python main.py query "control registers" --output result.json

3. Serve

python main.py serve
# http://localhost:4242

HTTP API examples

curl -X POST "http://localhost:4242/upload" \
  -F "file=@path/to/intel_manual.pdf" \
  -F "index_type=both"

curl -X POST "http://localhost:4242/build" \
  -H "Content-Type: application/json" \
  -d '{"file_path": "/path/to/document.pdf", "index_type": "both", "force_rebuild": false}'

curl -X POST "http://localhost:4242/query" \
  -H "Content-Type: application/json" \
  -d '{"query": "What are vector instructions?", "index_type": "hybrid", "top_k": 5}'

curl http://localhost:4242/status
curl http://localhost:4242/statistics

Endpoint	Method	Description
/	GET	API info
/build	POST	Build from text/file
/upload	POST	Upload + build
/query	POST	Query indexes
/status	GET	Status
/statistics	GET	Stats
/indexes	DELETE	Clear

Project structure

structured-index/
├── config.py, raptor_indexer.py, graphrag_indexer.py
├── document_processor.py, api_service.py
├── structured_vs_flat_demo.py   # offline demo
├── main.py, requirements.txt, env.example
├── indexes/{raptor,graphrag}/, cache/

How it works

RAPTOR: chunk → embed → leaves → GMM cluster → parent summaries → multi-level tree → multi-level search.

GraphRAG: entity extract → relations → NetworkX graph → communities → summaries → hierarchical merge → entity/community search (+ multi-hop).

Advanced params (see config.py)

RAPTOR: chunk_size, chunk_overlap, tree_depth, summarization_length.
GraphRAG: chunk_size, max_knowledge_triples, community algorithm, summarization model.

Performance / troubleshooting

Large manuals take time; watch API rate limits and memory. Cache speeds re-queries. OOM → smaller chunks; check keys; start with smaller models for tests.

Integration

Backend for agentic-rag style projects; see related chapter labs.

References

• RAPTOR
• GraphRAG
• Intel SDM

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中文

概述

面向大型技术文档的两种结构化索引：

1. RAPTOR — 递归摘要的层次树
2. GraphRAG — 实体/关系/社区与多跳遍历

功能

RAPTOR： 多层抽象、递归摘要、自叶到根检索、GMM 聚类、UMAP。
GraphRAG： LLM 抽实体关系、社区发现、社区摘要、多策略检索、多跳关系遍历（扁平向量难以表达的「A 与 B 如何相连」）。
HTTP API： 构建/查询、上传、异步大文档、混合检索、状态统计。

安装

# 在仓库根目录使用统一的第 3 章环境
uv sync --locked --python 3.12 --extra ch3

# 切换目录前先激活环境：
# macOS/Linux：
source .venv/bin/activate
# Windows PowerShell：.venv\Scripts\Activate.ps1
# Windows cmd：.venv\Scripts\activate.bat

# 未安装 uv 时可用 pip 兜底：
# python -m pip install -e ".[ch3]"

cd chapter3/structured-index

# 精确复现旧版单项目环境，含可选 RAPTOR/GraphRAG/Azure 依赖：
# python -m pip install -r requirements.txt

cp env.example .env

命令行

所有子命令有中文 --help：

usage: main.py [-h] {build,query,demo,serve} ...
  build   从文档构建结构化索引（需要 OPENAI_API_KEY）
  query   查询已构建的索引
  demo    离线对比：结构化 vs 扁平（无需 API Key）
  serve   启动 HTTP API

0. 离线对比演示（推荐先跑）

python main.py demo
python main.py demo --query "VADDPS 用到哪个寄存器"
python main.py demo --output demo_result.json

示例输出见 English 节：扁平只能召回词面片段；图检索可经 ADDPS → SSE → CR4.OSFXSR 多跳得到答案。

1–3. 构建 / 查询 / 服务

python main.py build path/to/document.pdf
python main.py build path/to/document.pdf --type raptor
python main.py build path/to/document.pdf --type graphrag
python main.py build path/to/document.pdf --output stats.json

python main.py query "What are the MOV instruction variants?"
python main.py query "explain SSE instructions" --type raptor --top-k 10
python main.py query "SSE registers" --type graphrag --multi-hop 2
python main.py query "control registers" --output result.json

python main.py serve

HTTP 示例与端点表与 English 节相同。

项目结构

structured-index/
├── config.py, raptor_indexer.py, graphrag_indexer.py
├── document_processor.py, api_service.py
├── structured_vs_flat_demo.py
├── main.py, requirements.txt, env.example
├── indexes/{raptor,graphrag}/, cache/

工作原理

RAPTOR： 分块 → 嵌入 → 叶节点 → 聚类 → 父节点摘要 → 多层树 → 多层检索。
GraphRAG： 实体 → 关系 → 图 → 社区 → 摘要 → 层次聚合 → 实体/社区检索（+ 多跳）。

性能与排错

大文档耗时；注意限流与内存。OOM 减小 chunk；检查 API Key。

参考

RAPTOR · GraphRAG · Intel SDM

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Notes / 说明

OpenRouter 通用回退 / Universal OpenRouter fallback

Chat LLM for RAPTOR summarization and GraphRAG entity extraction can use OpenRouter when OPENROUTER_API_KEY is set. Embeddings stay local SentenceTransformers (all-MiniLM-L6-v2) and are unaffected.