Pinecone API

Name: Pinecone API API
Brand: Pinecone API
Availability: InStock

★ Only Publicly Available OpenAPI DocumentAI/MLEmbeddings SearchapiKey15 EndpointsREST

For Agents

Store, query, and manage high-dimensional vectors for similarity search, recommendation systems, and retrieval-augmented generation (RAG) applications.

Quickstart

Get started with Pinecone API in minutes using your preferred integration method.

# Add to your MCP client config (Claude Desktop, Cursor, Windsurf)
{
  "jentic": {
    "url": "https://api.jentic.com/mcp",
    "auth": "oauth"
  }
}

# Then ask your agent:
"query vectors by similarity for semantic search"

# → Jentic returns the GET /events tool with parameter schema, agent executes.

Capabilities

What an agent can do with Pinecone API API.

Upsert vectors with metadata into managed indexes for similarity search

Query indexes by vector similarity to find nearest neighbors

Create and configure vector indexes with custom dimensions and metrics

Manage collections as point-in-time snapshots of index data

GET STARTED

Start building with Pinecone API API

Explore with Jentic

View OpenAPI Document

Jentic publishes the only available OpenAPI document for Pinecone API, keeping it validated and agent-ready.

Pinecone API provides a managed vector database for similarity search and AI applications. The API enables creating and managing vector indexes, upserting high-dimensional vectors with metadata, querying by vector similarity, and managing collections for index snapshots. It supports 7 cloud regions and handles index scaling, replication, and infrastructure management automatically across 15 endpoints.

Use Cases

Patterns agents use Pinecone API API for, with concrete tasks.

★ Retrieval-Augmented Generation (RAG)

Store document embeddings in Pinecone and retrieve relevant context for LLM prompts. The /query endpoint accepts a vector and returns the most similar stored vectors with their metadata, enabling AI applications to ground responses in factual content from a knowledge base without fine-tuning.

Upsert 100 document chunk embeddings with source metadata into an index, then query with a user question embedding to retrieve the top 5 most relevant chunks

Semantic Search Engine

Build semantic search by indexing content embeddings and querying with natural language. Pinecone returns results ranked by vector similarity rather than keyword matching, enabling search that understands meaning. The API handles index scaling automatically as the corpus grows to millions of vectors.

Create a 1536-dimension index, upsert product description embeddings, and query with a natural language search embedding to find matching products

Recommendation System

Power recommendations by storing item embeddings and querying for similar items. When a user interacts with content, query Pinecone with that item's vector to find similar items the user might like. Metadata filters allow constraining results by category, price range, or availability.

Query the index with a product embedding vector and filter results to items in the same category with price under $50

AI Agent Knowledge Retrieval

AI agents use the Pinecone API through Jentic to store and retrieve knowledge vectors without managing infrastructure. Agents search for vector operations by intent, load the endpoint schema, and execute calls with Jentic handling API key authentication and region routing.

Search Jentic for 'query vector similarity search', load the Pinecone /query endpoint schema, and find the top 10 similar documents for a query embedding

Key Endpoints

15 endpoints — pinecone api provides a managed vector database for similarity search and ai applications.

METHOD

PATH

DESCRIPTION

POST

/vectors/upsert

Insert or update vectors in an index

POST

/query

Query an index with a vector for nearest neighbors

GET

/vectors/fetch

Fetch vectors by ID

POST

/vectors/delete

Delete vectors by ID or filter

POST

/vectors/update

Update a vector's values or metadata

POST

/describe_index_stats

Get index statistics

POST

/databases

Create a new index

GET

/databases

List all indexes

POST

/vectors/upsert

Insert or update vectors in an index

POST

/query

Query an index with a vector for nearest neighbors

GET

/vectors/fetch

Fetch vectors by ID

POST

/vectors/delete

Delete vectors by ID or filter

POST

/vectors/update

Update a vector's values or metadata

Why though Jentic?

Three things that make agents converge on Jentic-routed access.

Credential isolation

Pinecone API keys are stored encrypted in the Jentic vault (MAXsystem). Agents receive scoped access tokens so raw API keys never enter the agent context. Region routing is handled automatically.

Intent-based discovery

Agents search by intent (e.g., 'store embeddings for semantic search') and Jentic returns matching Pinecone operations with their input schemas, so the agent can call the right endpoint without navigating multiple environments.

Time to first call

Direct Pinecone integration: 1-2 days for environment setup, index creation, and query tuning. Through Jentic: under 1 hour — search, load schema, execute.

Related APIs

Alternatives and complements available in the Jentic catalogue.

Alternative

Chroma API

Chroma is an open-source embedding database that can run locally without cloud infrastructure

Use Chroma when you need a local/self-hosted vector database for development, smaller datasets, or when you want to avoid cloud vendor dependency.

Complementary

Algolia Search API

Algolia provides keyword search and faceted filtering that complements Pinecone's vector similarity search

Use Algolia when you need traditional keyword search, typo tolerance, faceted navigation, or search analytics alongside vector similarity.

Complementary

Apify API

Apify scrapes and extracts web content that can be embedded and stored in Pinecone for RAG

Use Apify when you need to crawl and extract web content to generate embeddings for indexing in Pinecone.

FAQs

Specific to using Pinecone API API through Jentic.

What authentication does the Pinecone API use?

The Pinecone API uses an API key passed in the request header. Each key is scoped to a project and environment. Through Jentic, this key is stored encrypted in the MAXsystem vault and injected automatically into requests.

Can I use Pinecone for retrieval-augmented generation (RAG)?

Yes. Upsert document chunk embeddings via /vectors/upsert with source metadata, then query with a user question embedding via /query to retrieve the most relevant chunks. The returned metadata provides the context for LLM grounding.

What vector dimensions does Pinecone support?

Pinecone supports vectors up to 20,000 dimensions. Common configurations use 1536 dimensions for OpenAI embeddings or 768 dimensions for sentence-transformers models. You specify the dimension when creating an index via POST /databases.

How do I query for similar vectors through the Pinecone API using Jentic?

Search Jentic for 'query vector similarity search', load the /query operation schema, and execute with your query vector, top_k count, and optional metadata filters. Jentic handles API key injection and region routing.

What are the rate limits for the Pinecone API?

Rate limits depend on your plan and pod type. The free tier allows 100 vectors per upsert call and limited queries per second. Paid plans scale based on the number of pods and replicas configured for each index.

Can I filter query results by metadata?

Yes. The /query endpoint accepts a filter parameter with metadata conditions. You can filter by exact match, range, or set membership on any metadata field attached to your vectors, combining similarity search with structured filtering.