In the langchain wiki of FAISS, https://python.langchain.com/v0.2/docs/integrations/vectorstores/faiss/, it only talks about saving indexes to files.
db.save_local("faiss_index")
new_db = FAISS.load_local("faiss_index", embeddings)
docs = new_db.similarity_search(query)
How can I save the indexes to databases, such that we can organize and concurrently access multiple indexes?
Searched online but could not get much info on this. Can FAISS be used with any kind of distributed databases?
In fact, FAISS is considered as an in-memory database itself in order to vector search based on similarity that you can serialize and deserialize the indexes using functions like write_index and read_index within the FAISS interface directly or using save_local and load_local within the LangChain integration which typically uses the pickle for serialization.
If you need to store serialized files, you could manually save them in a NoSQL database like MongoDB as binary data, and then deserialize and retrieve them when needed, however, it is not the best practice!
If you are looking for a vector database that is not in-memory and capable in a scalable system, you might want to consider using Milvus which is designed for this purpose.