Vectara

Vectara is a managed search and Retrieval Augmented Generation platform: you upload documents to corpora, retrieve relevant passages, and optionally generate grounded summaries. Its API handles much of the retrieval workflow, so teams do not need to assemble a separate embedding model, vector database, and language model for basic search. [Vectara REST API documentation](https://docs.vectara.com/docs/integrations/rest)

Yes, Vectara supports extracting text from PDFs and tables before indexing them into a corpus. That makes it useful for querying manuals, reports, and policy files instead of matching only exact keywords.

Its default query behavior uses neural or semantic retrieval, and Vectara also documents a hybrid mode that mixes keyword and neural results. Hybrid search is useful when exact product codes, legal phrases, or names matter alongside meaning.

Pinecone is primarily a vector database, while Vectara packages search, document handling, retrieval, and generated summaries in a higher-level service. That difference matters if you want a ready search API instead of building the retrieval and answer layers yourself.