---
id: llama-index-vector-stores-pinecone
version: "0.8.1"
license: MIT
license_treatment: permissive
maintenance: active
---

# llama-index-vector-stores-pinecone — llama-index vector_stores pinecone integration

License: permissive · Maintenance: active · Downloads: 194.4K/mo

## What it is and what it does

This package is a bridge between LlamaIndex and Pinecone, enabling you to use Pinecone as the vector storage backend for retrieval-augmented generation (RAG) and semantic search workflows. It abstracts Pinecone's API behind LlamaIndex's standard vector store interface, so you can swap vector databases without rewriting your application logic.

The package handles the low-level details of connecting to Pinecone, storing embeddings, and retrieving similar vectors based on semantic similarity. It's designed for developers building LLM applications who want to leverage Pinecone's managed vector database without writing custom integration code. Python 3.10 or later is required.

Use it for:

- Build a RAG pipeline where documents are embedded and stored in Pinecone, then retrieved by semantic similarity to answer user queries.
- Implement semantic search over a large corpus by storing embeddings in Pinecone and querying through LlamaIndex.
- Swap Pinecone for another vector store in an existing LlamaIndex application by changing only the vector store initialization.
- Integrate Pinecone into a multi-step LLM workflow where context retrieval is one stage in a larger chain.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Integrates Pinecone vector database with LlamaIndex for storing and retrieving embeddings in RAG and semantic search applications.

Yes, if you are already using LlamaIndex and want to use Pinecone as your vector store. The package has low install friction, active maintenance, MIT licensing, and no known vulnerabilities. It is a straightforward integration layer with no hidden complexity—install it only if Pinecone is your chosen vector database.

## Install

pip install llama-index-vector-stores-pinecone
uv add llama-index-vector-stores-pinecone
poetry add llama-index-vector-stores-pinecone

## Installing llama-index-vector-stores-pinecone

Before you install: Low install friction with a pure Python wheel. Active maintenance as of release date. Depends on llama-index-core and pinecone, both established packages in the LLM ecosystem.

License in practice: MIT license permits commercial and private use with minimal restrictions, typical for ecosystem integration packages.

Quickstart:

pip install llama-index-vector-stores-pinecone

from llama_index.vector_stores.pinecone import PineconeVectorStore

vector_store = PineconeVectorStore(index_name="my-index")

Requires active Pinecone account and API credentials configured in environment or passed to the client.

Verify before relying:

- Whether Pinecone API credentials or account setup is required before the package becomes functional
- Compatibility guarantees with specific versions of llama-index-core or pinecone beyond what requires_python declares

## Package facts

- License: MIT (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 194.4K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

pinecone vector store integration, llama index pinecone, vector database embedding storage, semantic search with pinecone, rag vector store adapter, pinecone llm integration, embedding retrieval pinecone, vector-database, rag, llm-integration

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