via “vector embedding and storage with pluggable backends”
Opiniated RAG for integrating GenAI in your apps 🧠 Focus on your product rather than the RAG. Easy integration in existing products with customisation! Any LLM: GPT4, Groq, Llama. Any Vectorstore: PGVector, Faiss. Any Files. Anyway you want.
Unique: Implements a configuration-driven vector store abstraction that decouples embedding generation from storage backend, allowing seamless switching between PGVector and FAISS without code changes — achieved through a unified VectorStore interface that normalizes backend-specific APIs
vs others: More flexible than LangChain's vector store integrations because it treats vector storage as a first-class configurable component rather than an afterthought, enabling production teams to optimize storage independently from retrieval logic