Glossary

This page defines concepts in Pinecone and how they relate to each other.

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Organization

A organization is a group of one or more projects that use the same billing. Organizations allow one or more users to control billing and permissions for all of the projects belonging to the organization.

For more information, see Understanding organizations.

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Project

A project belongs to an organization and contains one or more indexes. Each project belongs to exactly one organization, but only users who belong to the project can access the indexes in that project. API keys and Assistants are project-specific.

For more information, see Understanding projects.

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Index

An index is the highest-level organizational unit of data. It defines the dimension (i.e., number of values in a vector) of the vectors to be stored and the similarity metric to be used when querying them. Normally, you choose a dimension and similarity metric based on the embedding model used to create your vectors.

In Pinecone, there are two types of indexes: serverless and pod-based.

For more information, see Understanding indexes.

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Namespace

A namespace is a partition within an index. It divides records in an index into separate groups.

All upserts, queries, and other data operations always target one namespace:

For more information, see Use namespaces.

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Record

A record is a basic unit of data and consists of the following:

• Record ID
• Dense vector
• Metadata (optional)
• Sparse vector (optional)

For more information, see Upsert data.

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Record ID

A record ID is a record’s unique ID. Use ID prefixes to segment your data beyond namespaces.

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Dense vector

A dense vector, also referred to as a vector embedding or simply a vector, is the basic vector type in Pinecone. It is a series of numerical values that represent different dimensions of the data that are essential for understanding patterns, relationships, and underlying structures (i.e., its semantic information). A vector is a type of data representation that is generated by AI models, such as LLMs.

For more information, see What are vector embeddings?.

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Metadata

Metadata is additional information that can be attached to vector embeddings to provide more context and enable additional filtering capabilities. For example, the original text of the embeddings can be stored in the metadata.

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Sparse vector

A sparse vector, also referred to as a sparse vector embedding, has a large number of dimensions, but only a small proportion of those values are non-zero. Sparse vectors are often used to represent documents or queries in a way that captures keyword information. Each dimension in a sparse vector typically represents a word from a dictionary, and the non-zero values represent the importance of these words in the document.

For more information, see Understanding hybrid search.

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Other concepts

Although not represented in the diagram above, Pinecone also contains the following concepts:

• API key
• User
• Collection
• Pinecone Assistant
• Pinecone Inference

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API key

An API key is a unique token that authenticates and authorizes access to the Pinecone APIs. API keys are project-specific.

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User

A user is a member of organizations and projects. Users are assigned specific roles at the organization and project levels that determine the user’s permissions in the Pinecone console.

For more information, see Manage organization members and Manage project members.

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Backup or collection

A backup is a static copy of a serverless index.

A collection is a static copy of a pod-based index.

Both backups and collections only consume storage. They are non-queryable representations of a set of records. You can create a backup or collection from an index, and you can create a new index from that backup or collection. The new index configuration can differ from the original source index: for example, it can have a different name or a different number of pods or pod type. However, it must have the same number of dimensions and similarity metric as the source index.

For more information, see Understanding backups and collections.

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Pinecone Assistant

Pinecone Assistant is a service that allows you to upload documents, ask questions, and receive responses that reference your documents. This is known as retrieval-augmented generation (RAG).

For more information, see Understanding Pinecone Assistant.

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Pinecone Inference

Pinecone Inference is an API service that provides access to embedding models hosted on Pinecone’s infrastructure.

For more information, see Understanding Pinecone Inference.

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Learn more

• Vector database
• Pinecone APIs
• Approximate nearest neighbor (ANN) algorithms
• Retrieval augmented generation (RAG)
• Hybrid search
• Image search
• Tokenization
• Jupyter notebooks for machine learning and AI tasks