Pinecone integrates AI inferencing with vector database
Pinecone supplies a vector embedding database to be used by AI language models when building responses to chatbot user requests. Vector embeddings are symbolic representations of multiple dimensions of text, image, audio, and video objects used in semantic search by large language models (LLMs) and small language models (SMLs). It says the database now includes fully managed embedding and reranking models, plus a “novel approach” to sparse embedding retrieval alongside its existing dense retrieval features.
Pinecone CEO Edo Liberty, a former research director at AWS and Yahoo, stated: “By adding built-in and fully managed inference capabilities directly into our vector database, as well as new retrieval functionality, we’re not only simplifying the development process but also dramatically improving the performance and accuracy of AI-powered solutions.”
Dense retrieval, employed by GenAI’s language models during semantic searches against vector databases, utilizes all relevant vectors. Sparse retrieval is a keyword search method where only specific words and terms are vectorized, while all other dimensions in the vector embeddings are assigned a zero value. The keywords can be represented as sparse vectors, with each keyword corresponding to a dimension in the vector space.
