chroma

To perform semantic search, you initialize a vector database client to store document embeddings alongside metadata, then execute similarity searches against the collection to retrieve relevant documents quickly.

Metadata filtering is fully supported for document retrieval. You can attach metadata to stored embeddings and apply nuanced filters during your similarity search to restrict results to specific document categories or attributes.

Building a RAG application involves storing your corpus embeddings and metadata in a vector database. During generation, query the database to retrieve contextually similar documents and feed them to your language model.

Yes, sentence-transformers is a required dependency for generating document embeddings. It handles the conversion of text into vector representations that the vector database stores and queries for similarity search.

A vector database is highly suitable for enterprise document management. It streamlines storing and querying large volumes of case documents, enabling rapid retrieval of similar files for legal or internal review workflows.

Irrelevant semantic search results often occur when document embeddings lack sufficient metadata context. Applying strict metadata filtering during retrieval ensures the similarity search respects specific document boundaries and attributes.