Pinecone API

Name: Pinecone 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.

Install Jentic One Beta

Connect the Pinecone API to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Pinecone API, or any other public or private API you need. You set the rules, the agent never sees your credentials, and every call is logged.

Two steps, two machines. Install the instance in a safe environment, then register your agent from wherever it runs.

Step 1: Jentic One Host machine

# On the machine that will host your Jentic One instance:
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh

Step 2: Agent machine

# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh
jentic register       # connects your agent to your Jentic One instance

Jentic One is in public beta. The setup above keeps your agent separate from the instance, which is what you want before using real credentials: an agent running as the same OS user as Jentic One can read its stored keys directly. Just evaluating? A single local install is fine to start. See the secure deployment guide for the tiers.

Capabilities

What an agent can do with Pinecone 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

GET STARTED

Start building with Pinecone API

Explore with Jentic

View OpenAPI Document

Use Cases

Patterns agents use Pinecone 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 Jentic?

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

Credential isolation

Your Pinecone API key is stored once, encrypted, by your own Jentic One instance and injected at execution time, with the regional host resolved for you. It never enters the agent's prompt, logs, or context.

Intent-based discovery

Agents search Jentic by intent such as 'store embeddings for semantic search' or 'query the nearest vectors', and Jentic returns the matching Pinecone operation with its input schema so the agent calls the right endpoint without navigating multiple regional environments.

Time to first call

FAQs

Specific to using Pinecone 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 your Jentic One instance 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.

Can I limit what my agent is allowed to do with the Pinecone API?

Yes. Because Jentic One is self-hosted by you, your own rules decide which Pinecone operations and credentials the agent may use. Pinecone passes vector and index details in the request body rather than a resource id in the path, so you scope access by operation: grant only the calls the agent needs, such as /vectors/upsert and /query for semantic search. Destructive operations like /vectors/delete or deleting an index stay off the list unless you explicitly add them.

Pinecone API

Connect the Pinecone API to your agent

Capabilities

Start building with Pinecone API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs