ChromaDB

You can implement semantic search by creating a ChromaDB collection, ingesting your documents with auto-generated embeddings, and querying them with text or embedding vectors. This approach finds information based on meaning rather than just keywords.

Managing vector collections for semantic data retrieval involves creating, getting, listing, and deleting groups of document embeddings. You can add documents with pre-computed vectors or upsert existing records to keep your search database updated.

Yes, you can ingest documents using pre-computed vectors instead of relying on auto-generated embeddings. This allows you to upsert existing records and integrate your own custom embedding pipelines into the database.

ChromaDB supports powerful metadata and document content filtering for semantic searches. This allows you to narrow down your vector queries to specific subsets of data before retrieving the most relevant results.

You do not need an HTTP client to run a vector database for embeddings in-memory. ChromaDB supports in-memory, persistent, and HTTP clients, allowing you to choose the setup that best fits your application scale and deployment environment.

ChromaDB works with embedding functions like SentenceTransformers and OpenAI for generating document vectors. These functions automatically process your ingested text into embeddings for semantic search.