Update README.md

README.md

@@ -1,344 +1,355 @@
-# CodeMind
-
-**CodeMind** is a AI-powered development assistant that runs entirely on your local machine for intelligent document analysis and commit message generation. It leverages modern machine learning models for: helping you understand your codebase through semantic search and generates meaningful commit messages using locally hosted language models, ensuring complete privacy and no cloud dependencies.
-
-- **Efficient Knowledge Retrieval**: Makes searching and querying documentation more power☺ful by using semantic embeddings rather than keyword search.
-- **Smarter Git Workflow**: Automates the creation of meaningful commit messages by analyzing git diffs and using an LLM to summarize changes.
-- **AI-Powered Documentation**: Enables you to ask questions about your project, using your own docs/context rather than just generic answers.
-
-**Check it out on Hugging Face Spaces:**  
-[![HuggingFace](https://img.shields.io/badge/-HuggingFace-FDEE21?style=for-the-badge&logo=HuggingFace&logoColor=black)](https://huggingface.co/spaces/dev-jas/CodeMind)
-
----
-
-## Features
-
-- **Document Embedding** (using [EmbeddingGemma-300m](https://huggingface.co/google/embeddinggemma-300m))
-- **Semantic Search** (using [FAISS](https://github.com/facebookresearch/faiss) for vector similarity search)
-- **Commit Message Generation** (using [Phi-2](https://huggingface.co/microsoft/phi-2-gguf) for text generation): Automatically generate descriptive commit messages based on your changes
-- **Retrieval-Augmented Generation (RAG)**: Answers questions using indexed document context
-- **Local Processing**: All AI processing happens on your machine with no data sent to cloud services
-- **Flexible Configuration**: Customize models and parameters to suit your specific needs
-- **FAISS Integration**: Efficient vector similarity search for fast retrieval
-- **Multiple Model Support**: Compatible with GGUF and SentenceTransformers models
-
-## Prerequisites
-
-- **Python 3.8 or higher**
-- **8GB+ RAM** recommended (for running language models)
-- **4GB+ disk space** for model files
-- **Git** for repository cloning
-
-### Platform Recommendations
-
-- **Linux** (Recommended for best compatibility)
-- **macOS** (Good compatibility)
-- **Windows** (May require additional setup for some dependencies)
-
-## Installation
-
-### 1. Clone the Repository
-
-```bash
-git clone https://github.com/devjas1/codemind.git
-cd codemind
-```
-
-### 2. Set Up Python Environment
-
-Create and activate a virtual environment:
-
-```bash
-
-# Create virtual environment
-python -m venv venv
-
-# Activate on macOS/Linux
-source venv/bin/activate
-
-# Activate on Windows
-venv\Scripts\activate
-```
-
-### 3. Install Dependencies
-
-```bash
-pip install -r requirements.txt
-```
-
-**Note**: If you encounter installation errors related to C++/PyTorch/FAISS:
-
-- Ensure you have Python development tools installed
-- Linux/macOS are preferred for FAISS compatibility
-- On Windows, you may need to install Visual Studio Build Tools
-
-## Model Setup
-
-### Directory Structure
-
-Create the following directory structure for model files:
-
-```text
-models/
-  ├── phi-2.Q4_0.gguf              # For commit message generation (Phi-2 model)
-  └── embeddinggemma-300m/         # For document embedding (EmbeddingGemma model)
-      └── [model files here]
-```
-
-### Downloading Models
-
-1. **Phi-2 Model** (for commit message generation):
-
-   - Download `phi-2.Q4_0.gguf` from a trusted source
-   - Place it in the `models/` directory
-
-2. **EmbeddingGemma Model** (for document embedding):
-
-   - Download the EmbeddingGemma-300m model files
-   - Place all files in the `models/embeddinggemma-300m/` directory
-
-> **Note**: The specific process for obtaining these models may vary. Check the documentation in each model folder for detailed instructions.
-
-## Configuration
-
-Edit the `config.yaml` file to match your local setup:
-
-```yaml
-# Model configuration for commit message generation
-generator:
-  model_path: "./models/phi-2.Q4_0.gguf"
-  quantization: "Q4_0"
-  max_tokens: 512
-  n_ctx: 2048
-
-# Model configuration for document embedding
-embedding:
-  model_path: "./models/embeddinggemma-300m"
-
-# Retrieval configuration for semantic search
-retrieval:
-  vector_store: "faiss"
-  top_k: 5 # Number of results to return
-  similarity_threshold: 0.7 # Minimum similarity score (0.0 to 1.0)
-```
-
-### Configuration Tips
-
-- Adjust `top_k` to control how many results are returned for each query
-- Modify `similarity_threshold` to filter results by relevance
-- Ensure all file paths are correct for your system
-- For larger codebases, you may need to increase `max_tokens`
-
-## Indexing Documents
-
-To enable semantic search over your documentation or codebase, you need to create a FAISS index:
-
-```bash
-# Basic usage
-python src/embedder.py path/to/your/documents config.yaml
-
-# Example with docs directory
-python src/embedder.py ./docs config.yaml
-
-# Example with specific code directory
-python src/embedder.py ./src config.yaml
-```
-
-This process:
-
-1. Reads all documents from the specified directory
-2. Generates embeddings using the configured model
-3. Creates a FAISS index in the `vector_cache/` directory
-4. Enables fast semantic search capabilities
-
-> **Note**: The indexing process may take several minutes depending on the size of your codebase and your hardware capabilities.
-
-## Usage
-
-### Command Line Interface
-
-Run the main CLI interface:
-
-```bash
-python cli.py
-```
-
-### Available Commands
-
-#### Get Help
-
-```bash
-python cli.py --help
-```
-
-#### Ask Questions About Your Codebase
-
-```bash
-python cli.py ask "How does this repository work?"
-python cli.py ask "Where is the main configuration handled?"
-python cli.py ask "Show me examples of API usage"
-```
-
-#### Generate Commit Messages
-
-```bash
-# Preview a generated commit message
-python cli.py commit --preview
-
-# Generate commit message without preview
-python cli.py commit
-```
-
-#### API Server (Placeholder)
-
-```bash
-python cli.py serve --port 8000
-```
-
-> **Note**: The API server functionality is not yet implemented. This command will display: "API server functionality not implemented yet."
-
-### Advanced Usage
-
-For more advanced usage, you can modify the configuration to:
-
-- Use different models for specific tasks
-- Adjust the context window size for larger documents
-- Customize the similarity threshold for retrieval
-- Use different vector stores (though FAISS is currently the only supported option)
-
-## Troubleshooting
-
-### Common Issues
-
-#### Model Errors
-
-**Problem**: Model files not found or inaccessible  
-**Solution**:
-
-- Verify model files are in the correct locations
-- Check file permissions
-- Ensure the paths in `config.yaml` are correct
-
-#### FAISS Errors
-
-**Problem**: "No FAISS index found" error  
-**Solution**:
-
-- Run the embedder script to create the index
-- Ensure the `vector_cache/` directory has write permissions
-
-```bash
-python src/embedder.py path/to/documents config.yaml
-```
-
-#### SentenceTransformers Issues
-
-**Problem**: Compatibility errors with SentenceTransformers  
-**Solution**:
-
-- Check that the model format is compatible with SentenceTransformers
-- Verify the version in requirements.txt
-- Ensure all model files are present in the model directory
-
-#### Performance Issues
-
-**Problem**: Slow response times  
-**Solution**:
-
-- Ensure you have adequate RAM
-- Consider using smaller quantized models
-- Close other memory-intensive applications
-
-#### Platform-Specific Issues
-
-**Windows-specific issues**:
-
-- FAISS may require additional compilation
-- Path separators may need adjustment in configuration
-
-**macOS/Linux**:
-
-- Generally fewer compatibility issues
-- Ensure you have write permissions for all directories
-
-### Validation Checklist
-
-- All model files present in correct directories
-- FAISS index built in `vector_cache/`
-- `config.yaml` paths match your local setup
-- Python environment activated
-- All dependencies installed
-- Adequate disk space available
-- Sufficient RAM available
-
-### Getting Detailed Error Information
-
-For specific errors, run commands with verbose output:
-
-```bash
-# Add debug flags if available
-python cli.py --verbose ask "Your question"
-```
-
-## Project Structure
-
-```text
-codemind/
-├── models/                 # AI model files
-│   ├── phi-2.Q4_0.gguf    # Phi-2 model for generation
-│   └── embeddinggemma-300m/ # Embedding model
-│       └── [model files]
-├── src/                   # Source code
-│   └── embedder.py        # Document embedding script
-├── vector_cache/          # FAISS vector store (auto-generated)
-├── config.yaml           # Configuration file
-├── requirements.txt      # Python dependencies
-├── cli.py               # Command-line interface
-└── README.md            # This file
-```
-
-## FAQ
-
-> **Q:** **Can I use different models?**  
-> **A:** Yes, you can use any GGUF-compatible model for generation and any SentenceTransformers-compatible model for embeddings. Update the paths in `config.yaml` accordingly.
-
----
-
-> **Q:** **How much RAM do I need?**  
-> **A**: For the Phi-2 Q4_0 model, 8GB RAM is recommended. Larger models will require more memory.
-
----
-
-> **Q:** **Can I index multiple directories?**  
-> **A**: Yes, you can run the embedder script multiple times with different directories, or combine your documents into one directory before indexing.
-
----
-
-> **Q:** **Is my data sent to the cloud?**  
-> **A**: No, all processing happens locally on your machine. No code or data is sent to external services.
-
----
-
-> **Q:** **How often should I re-index my documents?**  
-> **A**: Re-index whenever your documentation or codebase changes significantly to keep search results relevant.
-
-## Support
-
-If you encounter issues:
-
-1. Check the troubleshooting section above
-2. Verify all model files are in correct locations
-3. Confirm Python and library versions match requirements
-4. Ensure proper directory permissions
-
-For specific errors, please include the full traceback when seeking assistance.
-
-## Contributing
-
-Contributions to CodeMind are welcome! Please feel free to submit pull requests, create issues, or suggest new features.
-
-## License
-
-This project is licensed under the terms of the LICENSE file included in the repository.
-
-© 2025 CodeMind. All rights reserved.
+---
+title: CodeMind
+emoji: 🔧
+colorFrom: purple
+colorTo: indigo
+sdk: static
+pinned: false
+license: apache-2.0
+short_description: AI-powered development assistant CLI Tool
+---
+
+# CodeMind
+
+**CodeMind** is a AI-powered development assistant that runs entirely on your local machine for intelligent document analysis and commit message generation. It leverages modern machine learning models for: helping you understand your codebase through semantic search and generates meaningful commit messages using locally hosted language models, ensuring complete privacy and no cloud dependencies.
+
+- **Efficient Knowledge Retrieval**: Makes searching and querying documentation more power☺ful by using semantic embeddings rather than keyword search.
+- **Smarter Git Workflow**: Automates the creation of meaningful commit messages by analyzing git diffs and using an LLM to summarize changes.
+- **AI-Powered Documentation**: Enables you to ask questions about your project, using your own docs/context rather than just generic answers.
+
+**Check it out on Hugging Face Spaces:**  
+[![HuggingFace](https://img.shields.io/badge/-HuggingFace-FDEE21?style=for-the-badge&logo=HuggingFace&logoColor=black)](https://huggingface.co/spaces/dev-jas/CodeMind)
+
+---
+
+## Features
+
+- **Document Embedding** (using [EmbeddingGemma-300m](https://huggingface.co/google/embeddinggemma-300m))
+- **Semantic Search** (using [FAISS](https://github.com/facebookresearch/faiss) for vector similarity search)
+- **Commit Message Generation** (using [Phi-2](https://huggingface.co/microsoft/phi-2-gguf) for text generation): Automatically generate descriptive commit messages based on your changes
+- **Retrieval-Augmented Generation (RAG)**: Answers questions using indexed document context
+- **Local Processing**: All AI processing happens on your machine with no data sent to cloud services
+- **Flexible Configuration**: Customize models and parameters to suit your specific needs
+- **FAISS Integration**: Efficient vector similarity search for fast retrieval
+- **Multiple Model Support**: Compatible with GGUF and SentenceTransformers models
+
+## Prerequisites
+
+- **Python 3.8 or higher**
+- **8GB+ RAM** recommended (for running language models)
+- **4GB+ disk space** for model files
+- **Git** for repository cloning
+
+### Platform Recommendations
+
+- **Linux** (Recommended for best compatibility)
+- **macOS** (Good compatibility)
+- **Windows** (May require additional setup for some dependencies)
+
+## Installation
+
+### 1. Clone the Repository
+
+```bash
+git clone https://github.com/devjas1/codemind.git
+cd codemind
+```
+
+### 2. Set Up Python Environment
+
+Create and activate a virtual environment:
+
+```bash
+
+# Create virtual environment
+python -m venv venv
+
+# Activate on macOS/Linux
+source venv/bin/activate
+
+# Activate on Windows
+venv\Scripts\activate
+```
+
+### 3. Install Dependencies
+
+```bash
+pip install -r requirements.txt
+```
+
+**Note**: If you encounter installation errors related to C++/PyTorch/FAISS:
+
+- Ensure you have Python development tools installed
+- Linux/macOS are preferred for FAISS compatibility
+- On Windows, you may need to install Visual Studio Build Tools
+
+## Model Setup
+
+### Directory Structure
+
+Create the following directory structure for model files:
+
+```text
+models/
+  ├── phi-2.Q4_0.gguf              # For commit message generation (Phi-2 model)
+  └── embeddinggemma-300m/         # For document embedding (EmbeddingGemma model)
+      └── [model files here]
+```
+
+### Downloading Models
+
+1. **Phi-2 Model** (for commit message generation):
+
+   - Download `phi-2.Q4_0.gguf` from a trusted source
+   - Place it in the `models/` directory
+
+2. **EmbeddingGemma Model** (for document embedding):
+
+   - Download the EmbeddingGemma-300m model files
+   - Place all files in the `models/embeddinggemma-300m/` directory
+
+> **Note**: The specific process for obtaining these models may vary. Check the documentation in each model folder for detailed instructions.
+
+## Configuration
+
+Edit the `config.yaml` file to match your local setup:
+
+```yaml
+# Model configuration for commit message generation
+generator:
+  model_path: "./models/phi-2.Q4_0.gguf"
+  quantization: "Q4_0"
+  max_tokens: 512
+  n_ctx: 2048
+
+# Model configuration for document embedding
+embedding:
+  model_path: "./models/embeddinggemma-300m"
+
+# Retrieval configuration for semantic search
+retrieval:
+  vector_store: "faiss"
+  top_k: 5 # Number of results to return
+  similarity_threshold: 0.7 # Minimum similarity score (0.0 to 1.0)
+```
+
+### Configuration Tips
+
+- Adjust `top_k` to control how many results are returned for each query
+- Modify `similarity_threshold` to filter results by relevance
+- Ensure all file paths are correct for your system
+- For larger codebases, you may need to increase `max_tokens`
+
+## Indexing Documents
+
+To enable semantic search over your documentation or codebase, you need to create a FAISS index:
+
+```bash
+# Basic usage
+python src/embedder.py path/to/your/documents config.yaml
+
+# Example with docs directory
+python src/embedder.py ./docs config.yaml
+
+# Example with specific code directory
+python src/embedder.py ./src config.yaml
+```
+
+This process:
+
+1. Reads all documents from the specified directory
+2. Generates embeddings using the configured model
+3. Creates a FAISS index in the `vector_cache/` directory
+4. Enables fast semantic search capabilities
+
+> **Note**: The indexing process may take several minutes depending on the size of your codebase and your hardware capabilities.
+
+## Usage
+
+### Command Line Interface
+
+Run the main CLI interface:
+
+```bash
+python cli.py
+```
+
+### Available Commands
+
+#### Get Help
+
+```bash
+python cli.py --help
+```
+
+#### Ask Questions About Your Codebase
+
+```bash
+python cli.py ask "How does this repository work?"
+python cli.py ask "Where is the main configuration handled?"
+python cli.py ask "Show me examples of API usage"
+```
+
+#### Generate Commit Messages
+
+```bash
+# Preview a generated commit message
+python cli.py commit --preview
+
+# Generate commit message without preview
+python cli.py commit
+```
+
+#### API Server (Placeholder)
+
+```bash
+python cli.py serve --port 8000
+```
+
+> **Note**: The API server functionality is not yet implemented. This command will display: "API server functionality not implemented yet."
+
+### Advanced Usage
+
+For more advanced usage, you can modify the configuration to:
+
+- Use different models for specific tasks
+- Adjust the context window size for larger documents
+- Customize the similarity threshold for retrieval
+- Use different vector stores (though FAISS is currently the only supported option)
+
+## Troubleshooting
+
+### Common Issues
+
+#### Model Errors
+
+**Problem**: Model files not found or inaccessible  
+**Solution**:
+
+- Verify model files are in the correct locations
+- Check file permissions
+- Ensure the paths in `config.yaml` are correct
+
+#### FAISS Errors
+
+**Problem**: "No FAISS index found" error  
+**Solution**:
+
+- Run the embedder script to create the index
+- Ensure the `vector_cache/` directory has write permissions
+
+```bash
+python src/embedder.py path/to/documents config.yaml
+```
+
+#### SentenceTransformers Issues
+
+**Problem**: Compatibility errors with SentenceTransformers  
+**Solution**:
+
+- Check that the model format is compatible with SentenceTransformers
+- Verify the version in requirements.txt
+- Ensure all model files are present in the model directory
+
+#### Performance Issues
+
+**Problem**: Slow response times  
+**Solution**:
+
+- Ensure you have adequate RAM
+- Consider using smaller quantized models
+- Close other memory-intensive applications
+
+#### Platform-Specific Issues
+
+**Windows-specific issues**:
+
+- FAISS may require additional compilation
+- Path separators may need adjustment in configuration
+
+**macOS/Linux**:
+
+- Generally fewer compatibility issues
+- Ensure you have write permissions for all directories
+
+### Validation Checklist
+
+- All model files present in correct directories
+- FAISS index built in `vector_cache/`
+- `config.yaml` paths match your local setup
+- Python environment activated
+- All dependencies installed
+- Adequate disk space available
+- Sufficient RAM available
+
+### Getting Detailed Error Information
+
+For specific errors, run commands with verbose output:
+
+```bash
+# Add debug flags if available
+python cli.py --verbose ask "Your question"
+```
+
+## Project Structure
+
+```text
+codemind/
+├── models/                 # AI model files
+│   ├── phi-2.Q4_0.gguf    # Phi-2 model for generation
+│   └── embeddinggemma-300m/ # Embedding model
+│       └── [model files]
+├── src/                   # Source code
+│   └── embedder.py        # Document embedding script
+├── vector_cache/          # FAISS vector store (auto-generated)
+├── config.yaml           # Configuration file
+├── requirements.txt      # Python dependencies
+├── cli.py               # Command-line interface
+└── README.md            # This file
+```
+
+## FAQ
+
+> **Q:** **Can I use different models?**  
+> **A:** Yes, you can use any GGUF-compatible model for generation and any SentenceTransformers-compatible model for embeddings. Update the paths in `config.yaml` accordingly.
+
+---
+
+> **Q:** **How much RAM do I need?**  
+> **A**: For the Phi-2 Q4_0 model, 8GB RAM is recommended. Larger models will require more memory.
+
+---
+
+> **Q:** **Can I index multiple directories?**  
+> **A**: Yes, you can run the embedder script multiple times with different directories, or combine your documents into one directory before indexing.
+
+---
+
+> **Q:** **Is my data sent to the cloud?**  
+> **A**: No, all processing happens locally on your machine. No code or data is sent to external services.
+
+---
+
+> **Q:** **How often should I re-index my documents?**  
+> **A**: Re-index whenever your documentation or codebase changes significantly to keep search results relevant.
+
+## Support
+
+If you encounter issues:
+
+1. Check the troubleshooting section above
+2. Verify all model files are in correct locations
+3. Confirm Python and library versions match requirements
+4. Ensure proper directory permissions
+
+For specific errors, please include the full traceback when seeking assistance.
+
+## Contributing
+
+Contributions to CodeMind are welcome! Please feel free to submit pull requests, create issues, or suggest new features.
+
+## License
+
+This project is licensed under the terms of the LICENSE file included in the repository.
+
+© 2025 CodeMind. All rights reserved.