AI & Machine Learningpinecone.io &nearr;

Pinecone API for AI Agents

Vector database for AI applications

Pinecone provides a managed vector database for storing and querying embeddings at scale. AI agents can use Pinecone for semantic search, recommendation systems, and retrieval-augmented generation (RAG) workflows.

What AI agents can do with Pinecone

Structured actions an AI agent can execute through the Pinecone API

Action

Description

Inputs

Outputs

upsert

Insert or update vectors

vectors[], namespace

upserted_count

query

Search for similar vectors

vector, top_k, namespace, filter

matches[], namespace

deleteVectors

Delete vectors by ID or filter

ids[], namespace, filter

describeIndex

Get index statistics

index_name

dimension, total_vector_count, namespaces

Use cases for Pinecone + AI agents

Semantic search for knowledge bases
RAG pipelines for grounded AI responses
Recommendation systems
Duplicate detection
Long-term agent memory storage

How to connect Pinecone to an AI agent

1Get your Pinecone API key and create an index
2Generate an AgentSpec for vector operations
3Define upsert, query, and delete actions
4Publish for agent discovery
5Pair with an embedding model (OpenAI, Cohere)

Best practices

✓Use namespaces to separate different data types

✓Match embedding dimensions to your model output

✓Include metadata for filtering and context

✓Batch upserts for efficiency (up to 100 vectors per request)

✓Use hybrid search (dense + sparse) for better results

Frequently asked questions

How do AI agents use Pinecone?+

Agents generate embeddings from text, store them in Pinecone via upsert, then query for similar content. This powers RAG, semantic search, and long-term memory for agents.

What embedding model should I use with Pinecone?+

OpenAI text-embedding-3-small (1536 dimensions) is popular and cost-effective. Cohere embed-v3 and sentence-transformers also work well. Match your index dimensions to the model output.

Can Pinecone be used for agent memory?+

Yes. Store conversation summaries, user preferences, and past decisions as vectors. Query by semantic similarity to retrieve relevant context for new interactions.