This repository is a home for code related to fetching and generating embeddings for semantic document search and testing the effectiveness of the embeddings. It is designed to explore, develop, and evaluate methodologies for embedding-based semantic search systems, ensuring high-quality and efficient information retrieval.
Embedding Generation: Tools and utilities for generating embeddings from various document types.
Semantic Search: Implementation of algorithms and frameworks for semantic document retrieval.
Effectiveness Testing: Scripts and methodologies to evaluate the quality and effectiveness of generated embeddings.
Extensibility: Designed to easily integrate with different embedding models and search engines.
git clone https://github.com/tcollins2011/chatgxy-materials.git cd chatgxy-materials
pip install -r requirements.txt
Generate Embeddings:
python generate_embeddings.py --input_path /path/to/documents --output_path /path/to/embeddings
Run Semantic Search:
python semantic_search.py --query "your search query" --embeddings_path /path/to/embeddings
Evaluate Effectiveness:
python evaluate_embeddings.py --embeddings_path /path/to/embeddings --ground_truth /path/to/ground_truth
This project is licensed under the MIT License. See the LICENSE file for more details.
We welcome contributions to this project! Please follow these steps:
Fork the repository.
Create a feature branch.
Commit your changes.
Open a pull request.
If you encounter any issues or have questions, feel free to open an issue on the GitHub Issues page.
Thanks to the open-source community for the tools and libraries that make this project possible.