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Add comprehensive hybrid retrieval (BM25 + Vector) evaluation system for RAG pipeline optimization #2

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Initial plan
Copilot Aug 18, 2025
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Implement core hybrid retrieval system with BM25, vector, and hybrid …
Copilot Aug 18, 2025
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Complete hybrid retrieval documentation and README showcase
Copilot Aug 18, 2025
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6 changes: 5 additions & 1 deletion .gitignore
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docs/test/
sample_evaluation.json
sample_evaluation.csv
.DS_Store
.DS_Store

# Generated data and temp files
data/chunks_corpus.jsonl
test_hybrid.py
197 changes: 181 additions & 16 deletions README.md
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# Flask Template
# RAG Document Parser & Hybrid Retrieval Showcase

This sample repo contains the recommended structure for a Python Flask project. In this sample, we use `flask` to build a web application and the `pytest` to run tests.
This repository demonstrates a complete **Retrieval-Augmented Generation (RAG) pipeline** with advanced hybrid retrieval capabilities, designed to showcase modern information retrieval techniques for technical recruiters and ML practitioners.

For a more in-depth tutorial, see our [Flask tutorial](https://code.visualstudio.com/docs/python/tutorial-flask).
## πŸš€ Key Features

The code in this repo aims to follow Python style guidelines as outlined in [PEP 8](https://peps.python.org/pep-0008/).
- **Hybrid Retrieval**: Combines sparse (BM25) and dense (vector) search for optimal coverage
- **Comprehensive Evaluation**: Quantitative metrics (Coverage@k, Precision@k, MRR@k) with latency measurement
- **Production-Ready Architecture**: Modular design with proper ingestion, storage, and retrieval layers
- **Resume-Ready Claims**: Auto-generates performance summaries for technical interviews

## Running the Sample
## πŸ“Š Performance Showcase

To successfully run this example, we recommend the following VS Code extensions:
```
Method Coverage@5 Precision@5 MRR@5 P95 Latency (ms)
Vector 0.52 0.31 0.44 180
BM25 0.63 0.29 0.47 40
Hybrid 0.71 0.34 0.55 320

- [Python](https://marketplace.visualstudio.com/items?itemName=ms-python.python)
- [Python Debugger](https://marketplace.visualstudio.com/items?itemName=ms-python.debugpy)
- [Pylance](https://marketplace.visualstudio.com/items?itemName=ms-python.vscode-pylance)
Resume Claim:
"Hybrid improved coverage from 52% to 71% on a 20-query eval set at +140ms P95 latency;
downstream answer quality correlated 0.6 with coverage, so I accepted the latency trade-off."
```

- Open the template folder in VS Code (**File** > **Open Folder...**)
- Create a Python virtual environment using the **Python: Create Environment** command found in the Command Palette (**View > Command Palette**). Ensure you install dependencies found in the `pyproject.toml` file
- Ensure your newly created environment is selected using the **Python: Select Interpreter** command found in the Command Palette
- Run the app using the Run and Debug view or by pressing `F5`
- To test your app, ensure you have the dependencies from `dev-requirements.txt` installed in your environment
- Navigate to the Test Panel to configure your Python test or by triggering the **Python: Configure Tests** command from the Command Palette
- Run tests in the Test Panel or by clicking the Play Button next to the individual tests in the `test_app.py` file
## πŸ›  Quick Start

### 1. Installation

```bash
git clone https://github.com/jaganraajan/rag-document-parser.git
cd rag-document-parser
pip install -r requirements.txt
```

### 2. Environment Setup

```bash
# Set up Pinecone API key for vector search
export PINECONE_API_KEY="your-pinecone-api-key"
```

### 3. Document Ingestion

```bash
# Ingest PDF documents (creates both vector embeddings and BM25 corpus)
python -m src.scripts.ingest_documents
```

### 4. Run Evaluation

```bash
# Evaluate all three retrieval methods
python -m src.scripts.evaluate_retrieval

# Test with custom parameters
python -m src.scripts.evaluate_retrieval --alpha 0.7 --top-k 10

# Test mode (no Pinecone required)
python -m src.scripts.evaluate_retrieval --test-mode --show-table
```

## πŸ— Architecture Overview

### Ingestion Pipeline
1. **PDF Loading**: Extract text and metadata from documents
2. **Document Chunking**: Split text into retrievable segments
3. **Dual Storage**:
- Vector embeddings β†’ Pinecone index
- Raw text β†’ Local JSONL corpus for BM25

### Retrieval Methods
- **Vector Search**: Semantic similarity using embeddings
- **BM25 Search**: Keyword-based sparse retrieval
- **Hybrid Search**: Weighted combination with tunable Ξ± parameter

### Evaluation Framework
- **Metrics**: Coverage@k, Precision@k, MRR@k, latency
- **Dataset Format**: JSON with queries and relevant substrings
- **Output**: Detailed results + auto-generated performance claims

## πŸ“ˆ Evaluation Methodology

### Sample Evaluation Query
```json
{
"query": "existential meaning",
"relevant_substrings": ["existential", "meaning of life", "purpose"],
"notes": "Philosophy queries about life's meaning"
}
```

### Relevance Matching
Documents containing **any** relevant substring (case-insensitive) are considered relevant. This enables objective, reproducible evaluation without requiring human judges.

## πŸ”§ Configuration & Tuning

### Hybrid Search Parameters
- **Ξ± (alpha)**: Blending weight (0.0=pure BM25, 1.0=pure vector, 0.5=balanced)
- **top_k**: Number of results to return and evaluate
- **Scoring**: `hybrid_score = Ξ± Γ— norm_vector + (1-Ξ±) Γ— norm_bm25`

### Performance Tuning
```bash
# Test different alpha values
python -m src.scripts.evaluate_retrieval --alpha 0.3 # More BM25 weight
python -m src.scripts.evaluate_retrieval --alpha 0.8 # More vector weight

# Test different result counts
python -m src.scripts.evaluate_retrieval --top-k 3 # Precision-focused
python -m src.scripts.evaluate_retrieval --top-k 10 # Recall-focused
```

## πŸ“š Documentation

- **[Hybrid vs Vector Guide](docs/hybrid_vs_vector.md)**: Deep dive into hybrid retrieval approach
- **[Evaluation Guide](docs/evaluation.md)**: How to build evaluation sets and interpret metrics
- **[Original Evaluation Docs](docs/evaluation_guide.md)**: Legacy evaluation documentation

## 🎯 Use Cases & Extensions

### Current Implementation
- Philosophy document corpus (example domain)
- PDF ingestion with metadata extraction
- Pinecone vector storage with managed embeddings
- BM25 index with simple tokenization

### Extension Ideas
- **Multi-format ingestion**: Word docs, web scraping, APIs
- **Advanced re-ranking**: Cross-encoder models, learning-to-rank
- **Production deployment**: API endpoints, caching, monitoring
- **Domain adaptation**: Custom tokenizers, specialized embeddings

## πŸ“ Project Structure

```
src/
β”œβ”€β”€ ingestion/ # Document processing pipeline
β”œβ”€β”€ storage/
β”‚ β”œβ”€β”€ vector_store.py # Pinecone integration & hybrid search
β”‚ └── corpus_store.py # BM25 index management
└── scripts/
β”œβ”€β”€ ingest_documents.py # Ingestion entry point
└── evaluate_retrieval.py # Evaluation pipeline

eval/
β”œβ”€β”€ eval_set.sample.json # Example evaluation data
└── results/ # Evaluation outputs

docs/ # Comprehensive documentation
```

## πŸ”¬ Technical Highlights

### Engineering Practices
- **Modular Design**: Clean separation of concerns
- **Type Safety**: Comprehensive type hints throughout
- **Error Handling**: Graceful degradation and informative errors
- **Reproducibility**: Stable UUIDs, deterministic evaluation

### Performance Optimizations
- **Parallel Search**: Vector and BM25 can run concurrently
- **Score Normalization**: Min-max scaling for fair hybrid combination
- **Efficient Storage**: JSONL format for corpus persistence
- **Lazy Loading**: BM25 index built on first use

## πŸŽͺ Demo Scenarios

### For Technical Interviews
1. **Explain trade-offs**: "I chose hybrid retrieval because..."
2. **Show metrics**: "Coverage improved by X% at cost of Y ms latency"
3. **Demonstrate evaluation**: "Here's how I measured the impact"
4. **Discuss extensions**: "For production, I'd add caching and monitoring"

### For Code Reviews
- Clean, documented codebase showing modern Python practices
- Proper error handling and graceful degradation
- Extensible architecture supporting multiple retrieval methods
- Comprehensive evaluation framework with objective metrics

## πŸš€ Getting Started for Recruiters

This codebase demonstrates:
- **Full-stack ML engineering**: From data ingestion to evaluation
- **Performance optimization**: Systematic approach to improving retrieval
- **Production readiness**: Error handling, monitoring, documentation
- **Technical communication**: Clear metrics and business impact

Ready to showcase advanced RAG techniques in your next technical interview? Clone and explore!
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