Pinecone API
Community OpenAPI document · agent-readyAI/MLEmbeddings SearchapiKey15 EndpointsREST
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For Agents
Store, query, and manage high-dimensional vectors for similarity search, recommendation systems, and retrieval-augmented generation (RAG) applications.
Use for: I need to store embedding vectors for semantic search, I want to query similar vectors by cosine similarity, Create a new vector index with 1536 dimensions, Retrieve vectors by their IDs from an index
Not supported: Does not handle embedding generation, text processing, or model inference - use for vector storage and similarity search only.
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.
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.
1
Step 1: Jentic One Host machine
# On the machine that will host your Jentic One instance:
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Fpinecone.io%2Fpinecone" | sh2
Step 2: Agent machine
# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Fpinecone.io%2Fpinecone" | sh
jentic register # connects your agent to your Jentic One instanceJentic 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
Manage collections as point-in-time snapshots of index data
Fetch specific vectors by ID for exact retrieval
Delete vectors by ID or metadata filter for index maintenance
Retrieve index statistics including vector counts and dimension info
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
POST
/describe_index_stats
Get index statistics
POST
/databases
Create a new index
GET
/databases
List all indexes
Why Jentic?
What agents get from Jentic-routed access to this vendor.
Setup
Wiring the Pinecone API by hand means setting up its API-key header auth and resolving the controller host for the region your key belongs to before coding each upsert, query, and index call. Through Jentic you install once, import Pinecone from the API Directory, store the API key once, and your agent calls it.
Permission scoping
Pinecone takes vector and index details in the request body rather than a resource id in the path, so scoping is by operation: you limit the agent to the operations it needs, such as upsert and query for semantic search, and leave the rest out. You choose that set, so destructive ones like deleting vectors or an index are not included unless you add them.
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.
Related APIs
Alternatives and complements available in the Jentic catalogue.
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.
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