{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/4"}],"enrichment":{"capability":"Connects LangChain applications to Pinecone vector databases for semantic search, document storage, and retrieval-augmented generation workflows.","skillfed_tags":["vector-database","rag","embeddings"],"use_cases":["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"],"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.\n\nThe 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.","worth_installing":"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."},"id":"langchain-pinecone","links":{"html":"https://skillfed.io/packages/langchain-pinecone","md":"https://skillfed.io/packages/langchain-pinecone.md","pypi":"https://pypi.org/project/langchain-pinecone/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2025-11-02","license_spdx":null,"license_treatment":"permissive","name":"langchain-pinecone","python_support":"supports_current","summary":"An integration package connecting Pinecone and LangChain"},"popularity":{"monthly_downloads":1037408,"position":4460,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.2.13"}