devjas1
commited on
Commit
Β·
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Parent(s):
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(UPDATE): [docs]: add ABOUT.md for project overview and installation instructions; update copyright year in index.html
Browse files- ABOUT.md +228 -0
- README.md +2 -0
- index.html +2 -1
ABOUT.md
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# CodeMind
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A CLI tool for intelligent document analysis and commit message generation using EmbeddingGemma-300m for embeddings, FAISS for vector storage, and Phi-2 for text generation.
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## Features
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- **Document Indexing**: Embed and index documents for semantic search
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- **Semantic Search**: Find relevant documents using natural language queries
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- **Smart Commit Messages**: Generate meaningful commit messages from staged git changes
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- **RAG (Retrieval-Augmented Generation)**: Answer questions using indexed document context
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## Setup
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### Prerequisites
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- Windows 11
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- Conda environment
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- Git
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### Installation
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1. **Create a Conda environment:**
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```bash
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conda create -n codemind python=3.9
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conda activate codemind
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```
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2. **Clone the repository:**
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```bash
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git clone https://github.com/devjas1/codemind.git
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cd codemind
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```
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3. **Install dependencies:**
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```bash
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pip install -r requirements.txt
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```
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4. **Download models:**
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**Embedding Model (EmbeddingGemma-300m):**
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- Download from Hugging Face: `google/embeddinggemma-300m`
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- Place in `./models/embeddinggemma-300m/` directory
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**Generation Model (Phi-2 GGUF):**
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- Download the quantized Phi-2 model: `phi-2.Q4_0.gguf`
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- Place in `./models/` directory
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- Download from: [Microsoft Phi-2 GGUF](https://huggingface.co/microsoft/phi-2-gguf) or similar quantized versions
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### Directory Structure
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```
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CodeMind/
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βββ cli.py # Main CLI entry point
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βββ config.yaml # Configuration file
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βββ requirements.txt # Python dependencies
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βββ models/ # Model storage
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β βββ embeddinggemma-300m/ # Embedding model directory
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β βββ phi-2.Q4_0.gguf # Phi-2 quantized model file
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βββ src/ # Core modules
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β βββ config_loader.py # Configuration management
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β βββ embedder.py # Document embedding
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β βββ retriever.py # Semantic search
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β βββ generator.py # Text generation
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β βββ diff_analyzer.py # Git diff analysis
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βββ docs/ # Documentation
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βββ vector_cache/ # FAISS index storage (auto-created)
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```
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## Usage
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### Initialize Document Index
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Index documents from a directory for semantic search:
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```bash
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python cli.py init ./docs/
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```
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This will:
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- Embed all documents in the specified directory
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- Create a FAISS index in `vector_cache/`
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- Save metadata for retrieval
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### Semantic Search
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Search for relevant documents using natural language:
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```bash
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python cli.py search "how to configure the model"
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```
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Returns ranked results with similarity scores.
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### Ask Questions (RAG)
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Get answers based on your indexed documents:
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```bash
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python cli.py ask "What are the configuration options?"
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```
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Uses retrieval-augmented generation to provide contextual answers.
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### Git Commit Message Generation
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Generate intelligent commit messages from staged changes:
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```bash
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# Preview commit message without applying
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python cli.py commit --preview
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# Show staged files and analysis without generating message
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python cli.py commit --dry-run
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# Generate and apply commit message
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python cli.py commit --apply
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```
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### Start API Server (Future Feature)
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```bash
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python cli.py serve --port 8000
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```
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_Note: API server functionality is planned for future releases._
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## Configuration
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Edit `config.yaml` to customize behavior:
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```yaml
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embedding:
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model_path: "./models/embeddinggemma-300m"
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dim: 768
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truncate_to: 128
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generator:
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model_path: "./models/phi-2.Q4_0.gguf"
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quantization: "Q4_0"
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max_tokens: 512
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n_ctx: 2048
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retrieval:
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vector_store: "faiss"
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top_k: 5
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similarity_threshold: 0.75
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commit:
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tone: "imperative"
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style: "conventional"
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max_length: 72
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logging:
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verbose: true
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telemetry: false
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```
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### Configuration Options
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- **embedding.model_path**: Path to the EmbeddingGemma-300m model
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- **generator.model_path**: Path to the Phi-2 GGUF model file
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- **retrieval.top_k**: Number of documents to retrieve for context
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- **retrieval.similarity_threshold**: Minimum similarity score for results
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- **generator.max_tokens**: Maximum tokens for generation
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- **generator.n_ctx**: Context window size for Phi-2
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## Dependencies
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- `sentence-transformers>=2.2.2` - Document embedding
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- `faiss-cpu>=1.7.4` - Vector similarity search
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- `llama-cpp-python>=0.2.23` - Phi-2 model inference (Windows compatible)
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- `typer>=0.9.0` - CLI framework
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- `PyYAML>=6.0` - Configuration file parsing
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## Troubleshooting
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### Model Loading Issues
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If you encounter model loading errors:
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1. **Embedding Model**: Ensure `embeddinggemma-300m` is a directory containing all model files
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2. **Phi-2 Model**: Ensure `phi-2.Q4_0.gguf` is a single GGUF file
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3. **Paths**: All paths in `config.yaml` should be relative to the project root
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### Memory Issues
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For systems with limited RAM:
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- Use Q4_0 quantization for Phi-2 (already configured)
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- Reduce `n_ctx` in config.yaml if needed
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- Process documents in smaller batches
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### Windows-Specific Issues
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- Ensure `llama-cpp-python` version supports Windows
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- Use PowerShell or Command Prompt for CLI commands
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- Check file path separators in configuration
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## Development
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To test the modules:
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```bash
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python -c "from src import *; print('All modules imported successfully')"
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```
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To run in development mode:
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```bash
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python cli.py --help
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```
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## Contributing
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Contributions to CodeMind are welcome! Please feel free to submit pull requests, create issues, or suggest new features.
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## License
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This project is licensed under the terms of the LICENSE file included in the repository.
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Β© 2025 CodeMind. All rights reserved.
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README.md
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@@ -341,3 +341,5 @@ Contributions to CodeMind are welcome! Please feel free to submit pull requests,
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## License
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This project is licensed under the terms of the LICENSE file included in the repository.
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## License
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This project is licensed under the terms of the LICENSE file included in the repository.
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Β© 2025 CodeMind. All rights reserved.
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index.html
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@@ -275,7 +275,8 @@
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<li><a href="#setup">Setup</a></li>
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</ul>
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-
<p>©
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</div>
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</footer>
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</body>
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<li><a href="#setup">Setup</a></li>
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</ul>
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<p>© 2025 CodeMind. All rights reserved.
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</p>
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</div>
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</footer>
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</body>
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