feat: Add ColQwen multimodal PDF retrieval integration
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aakash
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COLQWEN_GUIDE.md
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COLQWEN_GUIDE.md
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# ColQwen Integration Guide
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Easy-to-use multimodal PDF retrieval with ColQwen2/ColPali models.
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## Quick Start
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> **🍎 Mac Users**: ColQwen is optimized for Apple Silicon with MPS acceleration for faster inference!
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### 1. Install Dependencies
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```bash
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uv pip install colpali_engine pdf2image pillow matplotlib qwen_vl_utils einops seaborn
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brew install poppler # macOS only, for PDF processing
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```
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### 2. Basic Usage
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```bash
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# Build index from PDFs
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python -m apps.colqwen_rag build --pdfs ./my_papers/ --index research_papers
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# Search with text queries
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python -m apps.colqwen_rag search research_papers "How does attention mechanism work?"
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# Interactive Q&A
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python -m apps.colqwen_rag ask research_papers --interactive
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```
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## Commands
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### Build Index
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```bash
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python -m apps.colqwen_rag build \
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--pdfs ./pdf_directory/ \
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--index my_index \
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--model colqwen2 \
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--pages-dir ./page_images/ # Optional: save page images
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```
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**Options:**
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- `--pdfs`: Directory containing PDF files (or single PDF path)
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- `--index`: Name for the index (required)
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- `--model`: `colqwen2` (default) or `colpali`
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- `--pages-dir`: Directory to save page images (optional)
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### Search Index
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```bash
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python -m apps.colqwen_rag search my_index "your question here" --top-k 5
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```
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**Options:**
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- `--top-k`: Number of results to return (default: 5)
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- `--model`: Model used for search (should match build model)
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### Interactive Q&A
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```bash
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python -m apps.colqwen_rag ask my_index --interactive
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```
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**Commands in interactive mode:**
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- Type your questions naturally
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- `help`: Show available commands
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- `quit`/`exit`/`q`: Exit interactive mode
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## 🧪 Test & Reproduce Results
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Run the reproduction test for issue #119:
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```bash
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python test_colqwen_reproduction.py
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```
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This will:
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1. ✅ Check dependencies
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2. 📥 Download sample PDF (Attention Is All You Need paper)
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3. 🏗️ Build test index
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4. 🔍 Run sample queries
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5. 📊 Show how to generate similarity maps
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## 🎨 Advanced: Similarity Maps
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For visual similarity analysis, use the existing advanced script:
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```bash
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cd apps/multimodal/vision-based-pdf-multi-vector/
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python multi-vector-leann-similarity-map.py
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```
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Edit the script to customize:
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- `QUERY`: Your question
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- `MODEL`: "colqwen2" or "colpali"
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- `USE_HF_DATASET`: Use HuggingFace dataset or local PDFs
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- `SIMILARITY_MAP`: Generate heatmaps
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- `ANSWER`: Enable Qwen-VL answer generation
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## 🔧 How It Works
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### ColQwen2 vs ColPali
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- **ColQwen2** (`vidore/colqwen2-v1.0`): Latest vision-language model
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- **ColPali** (`vidore/colpali-v1.2`): Proven multimodal retriever
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### Architecture
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1. **PDF → Images**: Convert PDF pages to images (150 DPI)
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2. **Vision Encoding**: Process images with ColQwen2/ColPali
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3. **Multi-Vector Index**: Build LEANN HNSW index with multiple embeddings per page
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4. **Query Processing**: Encode text queries with same model
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5. **Similarity Search**: Find most relevant pages/regions
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6. **Visual Maps**: Generate attention heatmaps (optional)
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### Device Support
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- **CUDA**: Best performance with GPU acceleration
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- **MPS**: Apple Silicon Mac support
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- **CPU**: Fallback for any system (slower)
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Auto-detection: CUDA > MPS > CPU
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## 📊 Performance Tips
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### For Best Performance:
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```bash
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# Use ColQwen2 for latest features
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--model colqwen2
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# Save page images for reuse
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--pages-dir ./cached_pages/
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# Adjust batch size based on GPU memory
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# (automatically handled)
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```
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### For Large Document Sets:
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- Process PDFs in batches
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- Use SSD storage for index files
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- Consider using CUDA if available
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## 🔗 Related Resources
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- **Fast-PLAID**: https://github.com/lightonai/fast-plaid
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- **Pylate**: https://github.com/lightonai/pylate
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- **ColBERT**: https://github.com/stanford-futuredata/ColBERT
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- **ColPali Paper**: Vision-Language Models for Document Retrieval
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- **Issue #119**: https://github.com/yichuan-w/LEANN/issues/119
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## 🐛 Troubleshooting
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### PDF Conversion Issues (macOS)
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```bash
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# Install poppler
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brew install poppler
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which pdfinfo && pdfinfo -v
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```
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### Memory Issues
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- Reduce batch size (automatically handled)
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- Use CPU instead of GPU: `export CUDA_VISIBLE_DEVICES=""`
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- Process fewer PDFs at once
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### Model Download Issues
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- Ensure internet connection for first run
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- Models are cached after first download
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- Use HuggingFace mirrors if needed
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### Import Errors
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```bash
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# Ensure all dependencies installed
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uv pip install colpali_engine pdf2image pillow matplotlib qwen_vl_utils einops seaborn
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# Check PyTorch installation
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python -c "import torch; print(torch.__version__)"
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```
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## 💡 Examples
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### Research Paper Analysis
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```bash
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# Index your research papers
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python -m apps.colqwen_rag build --pdfs ~/Papers/AI/ --index ai_papers
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# Ask research questions
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python -m apps.colqwen_rag search ai_papers "What are the limitations of transformer models?"
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python -m apps.colqwen_rag search ai_papers "How does BERT compare to GPT?"
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```
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### Document Q&A
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```bash
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# Index business documents
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python -m apps.colqwen_rag build --pdfs ~/Documents/Reports/ --index reports
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# Interactive analysis
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python -m apps.colqwen_rag ask reports --interactive
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```
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### Visual Analysis
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```bash
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# Generate similarity maps for specific queries
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cd apps/multimodal/vision-based-pdf-multi-vector/
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# Edit multi-vector-leann-similarity-map.py with your query
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python multi-vector-leann-similarity-map.py
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# Check ./figures/ for generated heatmaps
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```
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---
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**🎯 This integration makes ColQwen as easy to use as other LEANN features while maintaining the full power of multimodal document understanding!**
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@@ -60,20 +60,6 @@ python -m apps.colqwen_rag ask my_index --interactive
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- `help`: Show available commands
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- `quit`/`exit`/`q`: Exit interactive mode
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## 🧪 Test & Reproduce Results
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Run the reproduction test for issue #119:
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```bash
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python test_colqwen_reproduction.py
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```
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This will:
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1. ✅ Check dependencies
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2. 📥 Download sample PDF (Attention Is All You Need paper)
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3. 🏗️ Build test index
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4. 🔍 Run sample queries
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5. 📊 Show how to generate similarity maps
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## 🎨 Advanced: Similarity Maps
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For visual similarity analysis, use the existing advanced script:
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@@ -1,162 +0,0 @@
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#!/usr/bin/env python3
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"""
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Test script to reproduce ColQwen results from issue #119
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https://github.com/yichuan-w/LEANN/issues/119
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This script demonstrates the ColQwen workflow:
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1. Download sample PDF
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2. Convert to images
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3. Build multimodal index
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4. Run test queries
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5. Generate similarity maps
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"""
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import importlib.util
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import os
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from pathlib import Path
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def main():
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print("🧪 ColQwen Reproduction Test - Issue #119")
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print("=" * 50)
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# Check if we're in the right directory
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repo_root = Path.cwd()
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if not (repo_root / "apps" / "colqwen_rag.py").exists():
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print("❌ Please run this script from the LEANN repository root")
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print(" cd /path/to/LEANN && python test_colqwen_reproduction.py")
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return
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print("✅ Repository structure looks good")
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# Step 1: Check dependencies
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print("\n📦 Checking dependencies...")
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try:
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import torch
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# Check if pdf2image is available
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if importlib.util.find_spec("pdf2image") is None:
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raise ImportError("pdf2image not found")
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# Check if colpali_engine is available
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if importlib.util.find_spec("colpali_engine") is None:
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raise ImportError("colpali_engine not found")
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print("✅ Core dependencies available")
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print(f" - PyTorch: {torch.__version__}")
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print(f" - CUDA available: {torch.cuda.is_available()}")
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print(
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f" - MPS available: {hasattr(torch.backends, 'mps') and torch.backends.mps.is_available()}"
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)
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except ImportError as e:
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print(f"❌ Missing dependency: {e}")
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print("\n📥 Install missing dependencies:")
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print(
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" uv pip install colpali_engine pdf2image pillow matplotlib qwen_vl_utils einops seaborn"
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)
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return
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# Step 2: Download sample PDF
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print("\n📄 Setting up sample PDF...")
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pdf_dir = repo_root / "test_pdfs"
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pdf_dir.mkdir(exist_ok=True)
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sample_pdf = pdf_dir / "attention_paper.pdf"
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if not sample_pdf.exists():
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print("📥 Downloading sample paper (Attention Is All You Need)...")
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import urllib.request
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try:
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urllib.request.urlretrieve("https://arxiv.org/pdf/1706.03762.pdf", sample_pdf)
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print(f"✅ Downloaded: {sample_pdf}")
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except Exception as e:
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print(f"❌ Download failed: {e}")
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print(" Please manually download a PDF to test_pdfs/attention_paper.pdf")
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return
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else:
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print(f"✅ Using existing PDF: {sample_pdf}")
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# Step 3: Test ColQwen RAG
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print("\n🚀 Testing ColQwen RAG...")
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# Build index
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print("\n1️⃣ Building multimodal index...")
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build_cmd = f"python -m apps.colqwen_rag build --pdfs {pdf_dir} --index test_attention --model colqwen2 --pages-dir test_pages"
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print(f" Command: {build_cmd}")
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try:
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result = os.system(build_cmd)
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if result == 0:
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print("✅ Index built successfully!")
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else:
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print("❌ Index building failed")
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return
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except Exception as e:
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print(f"❌ Error building index: {e}")
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return
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# Test search
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print("\n2️⃣ Testing search...")
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test_queries = [
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"How does attention mechanism work?",
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"What is the transformer architecture?",
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"How do you compute self-attention?",
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]
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for query in test_queries:
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print(f"\n🔍 Query: '{query}'")
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search_cmd = f'python -m apps.colqwen_rag search test_attention "{query}" --top-k 3'
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print(f" Command: {search_cmd}")
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try:
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result = os.system(search_cmd)
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if result == 0:
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print("✅ Search completed")
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else:
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print("❌ Search failed")
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except Exception as e:
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print(f"❌ Search error: {e}")
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# Test interactive mode (briefly)
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print("\n3️⃣ Testing interactive mode...")
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print(" You can test interactive mode with:")
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print(" python -m apps.colqwen_rag ask test_attention --interactive")
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# Step 4: Test similarity maps (using existing script)
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print("\n4️⃣ Testing similarity maps...")
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similarity_script = (
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repo_root
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/ "apps"
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/ "multimodal"
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/ "vision-based-pdf-multi-vector"
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/ "multi-vector-leann-similarity-map.py"
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)
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if similarity_script.exists():
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print(" You can generate similarity maps with:")
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print(f" cd {similarity_script.parent}")
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print(" python multi-vector-leann-similarity-map.py")
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print(" (Edit the script to use your local PDF)")
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print("\n🎉 ColQwen reproduction test completed!")
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print("\n📋 Summary:")
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print(" ✅ Dependencies checked")
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print(" ✅ Sample PDF prepared")
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print(" ✅ Index building tested")
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print(" ✅ Search functionality tested")
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print(" ✅ Interactive mode available")
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print(" ✅ Similarity maps available")
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print("\n🔗 Related repositories to check:")
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print(" - https://github.com/lightonai/fast-plaid")
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print(" - https://github.com/lightonai/pylate")
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print(" - https://github.com/stanford-futuredata/ColBERT")
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print("\n📝 Next steps:")
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print(" 1. Test with your own PDFs")
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print(" 2. Experiment with different queries")
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print(" 3. Generate similarity maps for visual analysis")
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print(" 4. Compare ColQwen2 vs ColPali performance")
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if __name__ == "__main__":
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main()
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Reference in New Issue
Block a user