The vector database to build knowledgeable AI | Pinecone
Build knowledgeable AI. With its vector database at the core, Pinecone is the leading knowledge platform for building accurate, secure, and scalable AI applications.
Understanding Pinecone Assistant
How it works. When a document is uploaded, the assistant processes the content by chunking it into smaller parts and generating vector embeddings for each chunk. These embeddings are stored in an index, making them ready for retrieval.During a chat, the assistant processes the chat history to formulate relevant search queries, which are used to query the index and identify the …
Key features - Pinecone Docs
Embedding. Dense vector embeddings are the basic unit of data in Pinecone and what Pinecone was specially designed to store and work with. A dense vector embedding is a list of numbers that represents the semantics of data such as text, images, and audio recordings.
Understanding Pinecone Inference
pinecone-sparse-english-v0 is a sparse embedding model for converting text to sparse vectors for keyword or hybrid semantic/keyword search. Built on the innovations of the DeepImpact architecture, the model directly estimates the lexical importance of tokens by leveraging their context, unlike traditional retrieval models like BM25, which rely solely on term frequency.
The vector database to build knowledgeable AI | Pinecone
2024年12月2日 · First-of-its-kind Pinecone Knowledge Platform to Power Best-in-class Retrieval for Customers. Industry-leading vector database capabilities combined with proprietary AI models to help developers build up to 48% more accurate AI applications, faster and more easily.
Get index stats - Pinecone Docs
The `describe_index_stats` operation returns statistics about the contents of an index, including the vector count per namespace, the number of dimensions, and the index fullness. Serverless indexes scale automatically as needed, so index fullness is relevant only for pod-based indexes.
Pricing - Pinecone
How We Bill. For each existing index, hourly billing is determined by the per-hour-price Varies by plan, pod type, pod size, and cloud region. Try our pricing estimator below. of a pod multiplied by the number of pods Varies by plan, pod type, pod size, and cloud region. Try our pricing estimator below. the index uses. You will be sent an invoice at the end of the month for the total minutes ...
Create an index - Pinecone Docs
This operation deploys a Pinecone index. This is where you specify the measure of similarity, the dimension of vectors to be stored in the index, which cloud provider you would like to deploy with, and more.
Manage RAG documents - Pinecone Docs
In Retrieval Augmented Generation (RAG) use cases, it is best practice to chunk large documents into smaller segments, embed each chunk separately, and then store each embedded chunk as a distinct record in Pinecone. This page shows you how to model, store, and manage such records in serverless indexes.
Scale pod-based indexes - Pinecone Docs
This guidance applies to pod-based indexes only. With serverless indexes, you don’t configure any compute or storage resources, and you don’t manually manage those resources to meet demand, save on cost, or ensure high availability.Instead, serverless indexes scale automatically based on usage.