# Cursor Chat: ai-agent-book

## Metadata
- **Project**: ai-agent-book
- **Path**: `/Users/boj`
- **Date**: 2025-09-18 17:59:28
- **Session ID**: `a859075e-8e66-42a7-a0a7-164d13564035`

## Conversation

### 👤 You

In project/week3/structured-index, create an educational project making use of RAPTOR @https://github.com/parthsarthi03/raptor  and GraphRAG @https://github.com/microsoft/graphrag?tab=readme-ov-file  to index a very long document, for example, the 5000-page Intel® 64 and IA-32 Architectures
Software Developer’s Manual. These structured indexes using tree or graph leverage summarization to extract summarized information about the Intel instructions. We should include scripts to build the index using RAPTOR or GraphRAG, and include an HTTP-based API service to query the index.

Then we should add two backends for the week3/agentic-rag to use tree-based index or graph-based index of this project as alternatives of local retrieval pipeline and Dify API.

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### 🤖 Cursor Assistant

I'll create an educational project that demonstrates structured document indexing using RAPTOR (tree-based) and GraphRAG (graph-based) approaches, with an HTTP API for querying. Let me start by setting up the project structure and implementing the core components.

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### 🤖 Cursor Assistant

## Summary

I've successfully created an educational project in `projects/week3/structured-index` that demonstrates advanced document indexing techniques using RAPTOR and GraphRAG, with full integration into the agentic-rag system. Here's what was implemented:

### 📚 Structured Index Project (`week3/structured-index`)

1. **RAPTOR Tree-Based Indexing** [[memory:8629901]]
   - Hierarchical tree structure with recursive summarization
   - Multi-level abstraction from leaf nodes to root
   - Gaussian Mixture Model clustering for intelligent grouping
   - Cross-level search capability for comprehensive results

2. **GraphRAG Knowledge Graph Indexing**
   - LLM-powered entity and relationship extraction
   - Community detection using Leiden/Louvain algorithms
   - Hierarchical community summarization
   - Graph-based search supporting entities and communities

3. **Document Processing**
   - Support for PDF, TXT, Markdown, and HTML formats
   - Specialized handling for technical documentation
   - Intel x86/x64 instruction extraction
   - Table parsing and formatting

4. **HTTP API Service**
   - RESTful API using FastAPI
   - Endpoints for building, querying, and managing indexes
   - Asynchronous processing for large documents
   - Support for file uploads and batch operations

### 🔗 Agentic RAG Integration (`week3/agentic-rag`)

1. **New Knowledge Base Backends**
   - Added `RAPTOR` and `GRAPHRAG` as knowledge base types
   - Seamless integration with existing tool system
   - Unified search interface across all backends

2. **Enhanced Configuration**
   - Support for RAPTOR and GraphRAG API endpoints
   - Configurable search parameters for each backend
   - Flexible backend switching via environment variables

3. **Testing Infrastructure**
   - Comprehensive test script for both backends
   - Comparison utilities for evaluating different approaches
   - Sample Intel documentation and queries for testing

### 📖 Key Features

- **Tree-based retrieval** (RAPTOR): Best for hierarchical information and long documents
- **Graph-based retrieval** (GraphRAG): Best for complex relationships and entity-centric queries
- **Unified API**: Single interface for both indexing approaches
- **Production-ready**: Includes error handling, logging, and persistence
- **Educational focus**: Clear documentation and examples for learning

### 🚀 Usage

1. **Build indexes** from the Intel manual or other technical documentation:
   ```bash
   cd projects/week3/structured-index
   python main.py build sample_intel_manual.txt --type both
   ```

2. **Start the API server**:
   ```bash
   python main.py serve
   ```

3. **Use with Agentic RAG**:
   ```bash
   cd projects/week3/agentic-rag
   KB_TYPE=raptor python main.py --query "What are x86 registers?"
   KB_TYPE=graphrag python main.py --query "Show CPU instruction relationships"
   ```

The implementation provides a comprehensive educational example of how structured indexing approaches like RAPTOR and GraphRAG can enhance retrieval-augmented generation systems, especially for complex technical documentation like the Intel Architecture manuals.

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*Exported from [Cursor View](https://github.com/saharmor/cursor-view)*