To start using DataBridge, users need to clone the repository, set up a Python environment, install dependencies, configure environment variables, and run a setup script to create necessary resources like the database and vector index. The server can be started locally, and users can access the OpenAPI documentation at `http://localhost:8000/docs`. The system supports extending its base components for document parsing, vector storage, embedding models, and storage, allowing for customization and scalability. The project is licensed under the MIT License, and contributions are welcome through issues or pull requests.
Key takeaways:
- DataBridge is an open-source document processing and retrieval system with a modular architecture for document parsing, embedding generation, and vector search.
- The system supports extensible architecture, vector search, JWT-based authentication, and includes components like Unstructured API, MongoDB Atlas, OpenAI, and AWS S3.
- To start the server, clone the repository, set up a Python environment, install dependencies, configure environment variables, and run the setup script.
- DataBridge provides a Python SDK for easy integration, allowing users to ingest and query documents using semantic search capabilities.