Vector Databases
Explore vector database options for building efficient Retrieval-Augmented Generation systems. Learn to evaluate local and managed vector stores like FAISS, Chroma, Pinecone, and Weaviate based on persistence, filtering, and scaling needs. Understand metadata schema importance and operational considerations to maintain reliable, scalable semantic search in AI applications.
We need fast semantic lookup over a few thousand to a few million chunks of our internal docs, queries must support metadata filters, results must persist across restarts, and setup must stay simple on a laptop today while leaving a path to managed scaling later. The similarity math stays the same across all options, but the backend changes what gets indexed, what gets stored, which filters run at query time, and who owns the operational work when something fails.
The decision comes down to a few dimensions that we can check in code and in deployment.
A useful way to verify a choice is to pick one query we care about, run it end to end, restart the process, and run it again with the same filter. If results disappear, filters break, or index build time dominates startup, the backend is not matching the constraint set.
Local-first choices behave differently
With local-first storage, our process ...