langchain-pinecone
An integration package connecting Pinecone and LangChain
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires PINECONE_API_KEY environment variable set; OPENAI_API_KEY needed for OpenAI embeddings.
- Python 3.9 or later.
- Low friction install with a pure-Python wheel.
License · maintenance · safety
MIT (permissive) — MIT license permits unrestricted use, modification, and distribution in both open-source and commercial contexts with minimal obligations.
last release 2025-11-02 (285 days) · last repo commit 2026-08-14 · 144,267 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,037,408 downloads/mo, #4,460 on PyPI
Alternatives
Verify before relying
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)- 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
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 on it.
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
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.
Requires PINECONE_API_KEY environment variable set; OPENAI_API_KEY needed for OpenAI embeddings. Python 3.9 or later.
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)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
| License | MIT permissive |
| Python support | Supports the current Python release <3.14,>=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 7 packageslangchain-corepineconenumpylangchain-openaihttpxsimsimdpydantic |
| Maintenance | Actively maintained 285 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,037,408 / month, #4,460 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: langchain_pinecone-0.2.13-py3-none-any.whl
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See also llama-index-vector-stores-pinecone · pinecone-text · pinecone-plugin-inference · langchain-mongodb · pinecone · langchain-postgres · langchain-milvus · langchain-elasticsearch · pinecone-plugin-assistant · langchain-qdrant