Pinecone API
★ 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.
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://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh2
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 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?
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.
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 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.
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