Integrating a robust RAG system into my application was more challenging than I anticipated. While excellent databases and frameworks are available, the process was far from straightforward. Vector databases served as a strong foundation, but mapping vectors to corresponding resources became tedious unless I embedded all metadata into the vector storage - an approach I found less than ideal.

Frameworks like LangChain and LlamaIndex offered inspiration, but their documentation often felt overwhelming due to multiple tools and versions, which added complexity instead of clarity.

After experimenting, I settled on a hybrid approach: using MongoDB as the primary database to manage RAG resources and Pinecone as a plugin for vector search. This combination offered the flexibility and performance I needed for smooth integration.

To help others facing similar challenges, I’ve made this setup available to developers. I’d love to hear your thoughts!

Documentation: https://docs.onenode.ai