# Cursor Chat: ai-agent-book

## Metadata
- **Project**: ai-agent-book
- **Path**: `/Users/boj`
- **Date**: 2025-09-24 20:58:20
- **Session ID**: `12ef5ad9-6f63-4def-8fa0-b0cbd498e70c`

## Conversation

### 👤 You

In agentic-rag project, add a script to chunk and index all laws in @https://github.com/ZixinxinWang/Legal-Eagle-InternLM/tree/main/Retrieval-Augmented%20Generation/laws 

In contextual-retrieval project, also add the script to chunk and index all laws in it, using the contextual retrieval.

In both projects, do NOT chunk each paragraph as a chunk. You should set a character soft limit (e.g. 4096 characters) and hard limit (e.g. 8192 chars) and aware paragraph boundary when chunking (accumulate until exceeding soft limit, but cut off paragraph if reach hard limit).

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### 🤖 Cursor Assistant

I'll create scripts for both the `agentic-rag` and `contextual-retrieval` projects to chunk and index legal documents from the specified repository. Let me first examine the existing structure of both projects to understand their implementation patterns.

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### 🤖 Cursor Assistant

Now I'll create scripts for both projects to download, chunk, and index the legal documents. Let me start with the `agentic-rag` project first.

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### 👤 You

Do NOT use crawler. The laws folder is already present locally. You should first clean up existing indexes before creating the new index.

agentic-rag % ls laws
1-宪法	2-宪法相关法  3-民法典	3-民法商法  4-行政法  5-经济法	6-社会法  7-刑法  8-诉讼与非诉讼程序法

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### 🤖 Cursor Assistant

I see the laws are already available locally. Let me create scripts for both projects that will:
1. Clean up existing indexes first
2. Read the local legal documents from the laws directory
3. Apply smart chunking with proper paragraph boundaries
4. Index them appropriately

Let me start with the `agentic-rag` project:

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### 🤖 Cursor Assistant

Now let me create the script for the `contextual-retrieval` project that uses contextual chunking:

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### 🤖 Cursor Assistant

Let me check if the laws directory exists in the contextual-retrieval project and create a symlink if needed:

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### 🤖 Cursor Assistant

Now let me create documentation for both scripts:

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### 🤖 Cursor Assistant

## ✅ Completed: Legal Document Indexing Scripts

I've created two comprehensive scripts for chunking and indexing the local legal documents (288 files across 9 categories):

### 1. **Agentic-RAG Project** (`index_local_laws.py`)
- Standard chunking with smart paragraph boundaries
- Soft limit: 4096 chars, Hard limit: 8192 chars  
- Cleans existing indexes before processing
- Category-based organization
- Progress tracking and verification tests

### 2. **Contextual-Retrieval Project** (`index_local_laws_contextual.py`)
- Implements Anthropic's Contextual Retrieval approach
- Generates LLM-based context for each chunk
- Significantly improves retrieval accuracy
- Includes caching to reduce API costs
- Comparison mode to test with/without context

## Key Features

Both scripts implement:
- **Smart Chunking Strategy**: Accumulates paragraphs until soft limit, respects boundaries up to hard limit
- **Automatic Cleanup**: Clears existing indexes before processing
- **Category Support**: Process all or specific legal categories
- **Progress Monitoring**: Real-time updates with detailed statistics
- **Verification Tests**: Built-in search queries to validate indexing

## Usage Examples

**Agentic-RAG (Standard):**
```bash
# Index all documents
python index_local_laws.py

# Index specific categories with verification
python index_local_laws.py --categories "宪法" "民法典" --verify
```

**Contextual-Retrieval (Enhanced):**
```bash
# Index with contextual enhancement
python index_local_laws_contextual.py --compare

# Compare with non-contextual mode
python index_local_laws_contextual.py --no-contextual --compare
```

## Documentation

Created comprehensive README files for both scripts:
- `agentic-rag/README_LEGAL_INDEXING.md`
- `contextual-retrieval/README_LEGAL_INDEXING.md`

The contextual-retrieval version includes detailed explanation of the context generation process, cost estimation, and performance optimization strategies.

The scripts are ready to use with the existing 288 legal documents in the `laws` directory, properly organized by category (宪法, 民法典, 刑法, etc.).

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