There isn't a best universal choice for all situations. If you're already using Postgres and all you want is to add vector search, pgvector might be good enough

There isn't a best universal choice for all situations. If you're already using Postgres and all you want is to add vector search, pgvector might be good enough. txtai (https://github.com/neuml/txtai https://github.com/neuml/txtai) sets out to be an all-in-one embeddings database. This is more than just being a vector database with semantic search. It can embed text into vectors, run LLM workflows, has components for sparse/keyword indexing and graph based search. It also has a relational layer built-in for metadata filtering. txtai currently supports SQLite/DuckDB for relational data but can be extended. For example, relational data could be stored in Postgres, sparse/dense vectors in Elasticsearch/Opensearch and graph data in Neo4j. I believe modular solutions like this where internal components can be swapped in and out are the best option but given I'm the author of txtai, I'm a bit biased. This setup enables the scaling and reliability of existing solutions balanced with someone being able get started quickly with a POC to evaluate the use case.