Merge pull request #10 from akashmadisetty/main

Fixed mkdocs build issue

Author: adithya-s-k

docs/index.md

@@ -2,7 +2,7 @@
 Vision Augmented Retrieval and Generation
 
 
-| ![VARAG](./docs/assets/llama.png)| VARAG (Vision-Augmented Retrieval and Generation) is a vision-first RAG engine that emphasizes vision-based retrieval techniques. It enhances traditional Retrieval-Augmented Generation (RAG) systems by integrating both visual and textual data through Vision-Language models. |
+| ![VARAG](assets/llama.png)| VARAG (Vision-Augmented Retrieval and Generation) is a vision-first RAG engine that emphasizes vision-based retrieval techniques. It enhances traditional Retrieval-Augmented Generation (RAG) systems by integrating both visual and textual data through Vision-Language models. |
 |:--:|:--|
 
 [![GitHub Stars](https://img.shields.io/github/stars/adithya-s-k/VARAG?style=social)](https://github.com/adithya-s-k/VARAG/stargazers)
@@ -11,6 +11,41 @@ Vision Augmented Retrieval and Generation
 [![GitHub Pull Requests](https://img.shields.io/github/issues-pr/adithya-s-k/VARAG)](https://github.com/adithya-s-k/VARAG/pulls)
 [![License](https://img.shields.io/github/license/adithya-s-k/VARAG)](https://github.com/adithya-s-k/VARAG/blob/main/LICENSE)
 
+## 🌟 Key Features
+
+VARAG offers a comprehensive set of features designed for vision-augmented document retrieval and generation:
+
+### 🔍 **Multiple Retrieval Strategies**
+- **Simple RAG**: Text extraction with OCR using Docling
+- **Vision RAG**: Cross-modal retrieval with JinaCLIP embeddings
+- **ColPali RAG**: Direct document page embeddings with late interaction
+- **Hybrid ColPali RAG**: Combined image embeddings and ColPali re-ranking
+
+### 🤖 **Multi-Provider LLM/VLM Support**
+- **OpenAI Integration**: Support for GPT-4o, GPT-4o-mini and other vision models
+- **LiteLLM Provider**: Unified interface for 100+ LLM providers including:
+  - Anthropic Claude
+  - Google Gemini
+  - Groq
+  - And many more through LiteLLM
+
+### 📊 **Advanced Interpretability**
+- **ColPali Similarity Maps**: Visual heatmaps showing attention patterns
+- **Token-level Analysis**: Understand model focus on document regions
+- **Comparative Analysis**: Compare different ColPali model performances
+- **Interactive Visualizations**: Explore retrieval results with similarity overlays
+
+### 🔧 **Flexible Architecture**
+- **Modular Design**: Mix and match components easily
+- **LanceDB Integration**: High-performance vector storage
+- **Custom Chunking**: Configurable text splitting strategies
+- **Environment Management**: Support for multiple Python environments
+
+### 🎯 **Interactive Demo**
+- **Gradio Interface**: Web-based demo for testing different RAG approaches
+- **Real-time Comparison**: Side-by-side evaluation of retrieval techniques
+- **Progress Tracking**: Monitor ingestion and retrieval progress
+- **API Key Management**: Secure handling of different provider credentials
 
 
 ### Supported Retrieval Techniques
@@ -31,6 +66,26 @@ Hybrid ColPali RAG further enhances retrieval performance by combining the stren
 
 </details>
 
+## 🏗️ Architecture Overview
+
+VARAG follows a modular architecture that separates concerns and enables easy experimentation:
+
+```
+VARAG/
+├── varag/
+│   ├── rag/           # Core RAG implementations
+│   ├── llms/          # Language model providers
+│   ├── vlms/          # Vision-language model providers
+│   ├── models/        # Model utilities and configurations
+│   ├── chunking/      # Text chunking strategies
+│   └── utils.py       # Utility functions and similarity mapping
+├── examples/          # Usage examples and demos
+│   ├── base_implementation/  # Base embedding implementations
+│   └── inference_colpali/   # ColPali interpretation tools
+├── docs/             # Documentation and notebooks
+└── demo.py          # Interactive Gradio demo
+```
+
 ---
 
 
@@ -71,6 +126,17 @@ To install OCR dependencies:
 ```bash
 pip install -e .["ocr"]
 ```
+### 4. Set Up API Keys
+
+Create a `.env` file in the project root:
+
+```bash
+# For OpenAI
+OPENAI_API_KEY="your-openai-api-key"
+
+# For Google Gemini (via LiteLLM)
+GEMINI_API_KEY="your-gemini-api-key"
+```
 
 ---
 
@@ -119,15 +185,42 @@ The abstraction is designed to simplify the process of experimenting with differ
 This paradigm is inspired by the [Byaldi](https://github.com/AnswerDotAI/byaldi) repo by Answer.ai.
 
 ---
+### ColPali Similarity Analysis
+
+```python
+from varag.utils import create_similarity_mapper, analyze_multiple_images
+
+# Create similarity mapper
+similarity_mapper = create_similarity_mapper(colpali_model, colpali_processor)
+
+# Analyze single image
+result = similarity_mapper.analyze_image_with_query(
+    image_path, 
+    "What are the key findings?"
+)
+
+# Analyze multiple images
+results = analyze_multiple_images(
+    similarity_mapper,
+    [image1, image2, image3],
+    "Compare the results across these documents"
+)
+```
+
+### ColPali Interpretability Tools
+Advanced analysis tools in `examples/inference_colpali/`:
+- **Similarity Heatmaps**: Visual attention maps for query-document matching
+- **Model Comparison**: Compare different ColPali model versions
+- **Token-level Analysis**: Understand fine-grained model behavior
 
 ### Techniques and Notebooks
 
 | **Technique**        | **Notebook**                                                                                                                                                                        | **Demo**                                                                                                                   |
 |----------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------|
-| **Simple RAG**        | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/adithya-s-k/VARAG/blob/main/docs/simpleRAG.ipynb)            | [simpleRAG.py](examples/textDemo.py)                                                                                       |
-| **Vision RAG**        | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/adithya-s-k/VARAG/blob/main/docs/visionRAG.ipynb)            | [visionDemo.py](examples/visionDemo.py)                                                                                   |
-| **Colpali RAG**       | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/adithya-s-k/VARAG/blob/main/docs/colpaliRAG.ipynb)            | [colpaliDemo.py](examples/colpaliDemo.py)                                                                                  |
-| **Hybrid Colpali RAG**| [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/adithya-s-k/VARAG/blob/main/docs/hybridColpaliRAG.ipynb)     | [hybridColpaliDemo.py](examples/hybridColpaliDemo.py)                                                                      |
+| **Simple RAG**        | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/adithya-s-k/VARAG/blob/main/docs/simpleRAG.ipynb)            | [simpleRAG.py](https://github.com/adithya-s-k/VARAG/blob/main/examples/textDemo.py)                                                                                       |
+| **Vision RAG**        | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/adithya-s-k/VARAG/blob/main/docs/visionRAG.ipynb)            | [visionDemo.py](https://github.com/adithya-s-k/VARAG/blob/main/examples/visionDemo.py)                                                                                   |
+| **Colpali RAG**       | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/adithya-s-k/VARAG/blob/main/docs/colpaliRAG.ipynb)            | [colpaliDemo.py](https://github.com/adithya-s-k/VARAG/blob/main/examples/colpaliDemo.py)                                                                                  |
+| **Hybrid Colpali RAG**| [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/adithya-s-k/VARAG/blob/main/docs/hybridColpaliRAG.ipynb)     | [hybridColpaliDemo.py](https://github.com/adithya-s-k/VARAG/blob/main/examples/hybridColpaliDemo.py)                                                                      |
 
 ---