langchain-pinecone

What it is and what it does

langchain-pinecone is a bridge between LangChain's vector store abstraction and Pinecone's managed vector database service. It wraps Pinecone's Python SDK to provide a standardized interface for storing embeddings, managing documents, and performing semantic similarity searches within LangChain workflows.

The package handles the mechanics of connecting to Pinecone indexes, adding and deleting documents with metadata, executing similarity searches with optional filtering, and converting vector stores into retrievers for use in LangChain chains and agents. It depends on langchain-core for document types, pinecone for the underlying vector database client, and optionally langchain-openai for embedding generation. The integration supports both synchronous and asynchronous operations, and can dynamically list supported embedding and reranking models available in Pinecone.

Use it for:

• Build retrieval-augmented generation (RAG) pipelines where documents are stored in Pinecone and retrieved by semantic similarity to answer user queries
• Implement semantic search over a corpus of documents by embedding them and querying with natural language
• Create LangChain agents that retrieve relevant context from a Pinecone index before generating responses
• Store and manage document embeddings with metadata filtering for multi-tenant or categorized knowledge bases
• Convert a vector store into a retriever for use in LangChain chains with configurable similarity thresholds

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

Connects LangChain applications to Pinecone vector databases for semantic search, document storage, and retrieval-augmented generation workflows.

Yes. The package is actively maintained, has no known vulnerabilities, installs with low friction, and is widely used (top 5000 downloads). Install it if you are building a LangChain application that needs persistent semantic search over documents via Pinecone. Skip it if you are not using LangChain or prefer direct Pinecone SDK calls without the abstraction layer.

Install

langchain-pinecone on PyPI

pip install langchain-pinecone

uv add langchain-pinecone

poetry add langchain-pinecone

Installing langchain-pinecone

Before you install

Low friction install with a pure-Python wheel. The package is actively maintained with a recent commit history and sits in the top 5000 PyPI packages by download volume, suggesting stable ongoing support.

License in practice

MIT license permits unrestricted use, modification, and distribution in both open-source and commercial contexts with minimal obligations.

Quickstart

pip install langchain-pinecone

from langchain_pinecone import PineconeVectorStore
from langchain_openai import OpenAIEmbeddings
from pinecone import ServerlessSpec

embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
vector_store = PineconeVectorStore(index=index, embedding=embeddings)
vector_store.add_documents(documents=docs, ids=ids)
results = vector_store.similarity_search("query text", k=2)

Requires PINECONE_API_KEY environment variable set; OPENAI_API_KEY needed for OpenAI embeddings. Python 3.9 or later.

Verify before relying

• Whether the package supports sparse vector embeddings or only dense vectors by default
• Performance characteristics when working with large-scale indexes (millions of documents)
• Latency overhead introduced by the LangChain abstraction layer versus direct Pinecone SDK calls

Package facts

Evidence: langchain_pinecone-0.2.13-py3-none-any.whl

Tags

More Artificial Intelligence packages

Further reading