RAGGENIE: An open-source, low-code platform to build custom Retrieval-Augmented Generation (RAG) Copilets with your own data. Simplify AI development with ease!
Description #137
This PR introduces a new feature to integrate a VectorDB into the bot configuration, enhancing the bot's ability to store and retrieve vector-based data efficiently. This integration supports improved query performance and precise matching capabilities for use cases.
Key Changes:
Added backend support for MongoDB and ChromaDB as VectorDBs, including test credentials for both to facilitate development.
Implemented logic to load vector data from the configured VectorDB if available; otherwise, defaults to the standard configuration.
VectorDB Setup: Added configuration options for initializing and connecting to the VectorDB, supporting ChromaDB and MongoDB.
ChromaDB: Connection configured via path parameter.
MongoDB: Connection configured via uri parameter.
Test Credentials: Users can test database credentials before finalizing the setup. Upon successful testing, users can add VectorDB (ChromaDB or MongoDB) to the bot’s configuration.
Description #137 This PR introduces a new feature to integrate a VectorDB into the bot configuration, enhancing the bot's ability to store and retrieve vector-based data efficiently. This integration supports improved query performance and precise matching capabilities for use cases.
Key Changes: