# Cursor Chat: ai-agent-book

## Metadata
- **Project**: ai-agent-book
- **Path**: `/Users/boj`
- **Date**: 2025-09-18 15:26:21
- **Session ID**: `45b88e0f-66d7-43ca-945e-f81dc76aeb24`

## Conversation

### 👤 You

In project/week3/sparse-embedding, create an educational sparse vector search engine, which is an HTTP service, with interfaces to index documents and search a textual query. We should use the inverted index and BM25 algorithm to generate the index. For simplicity, the index should be purely in memory. The algorithm implementation should be fully functional, not just a demo. The system should have interfaces to show the structure of the index for educational purposes. Because this is an educational sparse vector search engine, it should show extensive logs during indexing and searching.

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### 🤖 Cursor Assistant

I'll create an educational sparse vector search engine with inverted index and BM25 algorithm. Let me start by creating the directory structure and implementing the system.

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### 🤖 Cursor Assistant

Perfect! I've successfully created an educational sparse vector search engine with inverted index and BM25 algorithm in `/Users/boj/ai-agent-book/projects/week3/sparse-embedding`. 

## Summary of What Was Created

### Core Features Implemented:
1. **Full BM25 Algorithm**: Complete implementation with configurable k1 and b parameters
2. **Inverted Index**: Efficient data structure for term-to-document mapping
3. **HTTP API Service**: FastAPI-based server with RESTful endpoints
4. **Interactive Web UI**: Beautiful browser interface for indexing and searching
5. **Extensive Educational Logging**: Detailed logs showing every step of indexing and searching
6. **Index Visualization**: Endpoints to inspect the internal structure of the index

### Files Created:
- `bm25_engine.py` - Core search engine implementation with BM25 and inverted index
- `server.py` - FastAPI HTTP server with web UI
- `demo.py` - Demonstration script with sample documents
- `quickstart.py` - Interactive command-line interface
- `test_engine.py` - Comprehensive test suite
- `requirements.txt` - Python dependencies
- `README.md` - Complete documentation

### Key Educational Features:
- **Detailed Logging**: Shows IDF calculations, term frequencies, scoring steps
- **Index Structure Visualization**: View inverted index, document statistics, term distributions
- **Debug Information**: Each search result includes matched terms, document lengths, term contributions
- **Interactive Examples**: Pre-loaded sample documents about programming topics

### How to Use:

1. **Install dependencies**:
   ```bash
   pip install -r requirements.txt
   ```

2. **Run the HTTP server**:
   ```bash
   python server.py
   ```
   Then visit http://localhost:8000 for the web interface

3. **Try the interactive demo**:
   ```bash
   python quickstart.py
   ```

4. **Run the full demonstration**:
   ```bash
   python demo.py
   ```

5. **Run tests**:
   ```bash
   python test_engine.py
   ```

The system is fully functional and demonstrates how sparse vector search engines work, with extensive logging at every step for educational purposes. The BM25 implementation is complete with proper term frequency saturation and document length normalization. The inverted index efficiently maps terms to documents, and the web interface makes it easy to experiment with different queries and see how the scoring works.

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