behnamoh

Can anyone please suggest a good stack for the following:

- calculating text embeddings using open-source/local methods (not OpenAI)

- storing them in a vector database. I'm confused by the myriad of options like Chromadb, Pinecone, etc.

- running vector similarity search using open-source/local methods.

Also, how granular should the text chunks be? Too short and we'll end up with a huge database, too long and we'll probably miss some relevant information in some chunks.

Has anyone been able to achieve reliable results from these? Preferably w/o using Langchain.

liliuleo93
Hi, if you're looking into vector storage and querying, there are several things to consider. For lightweight usage, you can directly use FAISS without any database overhead. For heavy usage, Milvus/Zilliz is the most production-ready solution.

Also, here's a benchmark that allows you to easily test their performance differences through a user-friendly interface. This includes both cloud solutions and open-source options. If you prefer to view pre-tested results, there are standard ones available as well. Check it out here: VectorDBBench. https://github.com/zilliztech/VectorDBBench