Reference

Programmatic access to Nexus workspaces, contexts, sources, curation, and KnowQL queries.

Authenticate to the Nexus API with your Pinecone API key, sent directly or exchanged for a short-lived session token.

Configure an existing index.

Create an index for vectors created with an external embedding model.

Delete an existing index.

This operation returns a list of all indexes in a project.

The delete operation deletes vectors, by id, from a single namespace.

Search a namespace with a query vector or record ID and return the IDs of the most similar records, along with their similarity scores.

This operation returns a list of all indexes in a project.

Rerank query results according to their relevance to a query. This endpoint uses Pinecone Inference.

Rerank query results according to their relevance to a query. This endpoint uses Pinecone Inference.

Create an index with integrated embedding.

List all indexes in a project.

Get a description of a model hosted by Pinecone.

Retrieve context snippets from an assistant to use as part of RAG or any agentic flow.

Upload a file to the specified assistant.

Configure an existing index. For guidance and examples, see Manage indexes.

Create an index with integrated embedding.

Create a namespace in a serverless index.

Return statistics about the contents of an index, including the vector count per namespace, the number of dimensions, and the index fullness.

Describe a namespace in a serverless index, including the total number of vectors in the namespace.

Look up and return vectors by metadata filter from a single namespace. The returned vectors include the vector data and/or metadata.

List all namespaces in a serverless index.

Search a namespace using a query vector. It retrieves the ids of the most similar items in a namespace, along with their similarity scores.

Search a namespace with a query text, query vector, or record ID and return the most similar records, along with their similarity scores. Optionally, rerank the initial results based on their relevance to the query.

Update a vector in a namespace. If a value is included, it will overwrite the previous value. If a set_metadata is included, the values of the fields specified in it will be added or overwrite the previous value.

Get an API key's details, excluding its secret.

List all API keys in a project.

Chat with an assistant. This endpoint is based on the OpenAI Chat Completion API, a commonly used and adopted API.

Retrieve context snippets from an assistant to use as part of RAG or any agentic flow.

Delete an existing assistant.

Delete an uploaded file from an assistant.

Get the status of an assistant.

Get the current status and metadata of a file uploaded to an assistant.

Get the status of an operation.

List of all assistants in a project.

List all files in an assistant, with an option to filter files with metadata.

List all operations for an assistant. Returns operations that are in progress, as well as recently completed or failed operations.

Evaluate the correctness and completeness of a response from an assistant or a RAG system. The correctness and completeness are evaluated based on the precision and recall of the generated answer with respect to the ground truth answer facts. Alignment is the harmonic mean of correctness and completeness.

Update an existing assistant. You can modify the assistant's instructions.

Upload a file to the specified assistant.

Create or replace a file in the specified assistant. If a file with the given assistant_file_id already exists, it will be replaced with the new file. If it doesn't exist, a new file will be created with that identifier.