{"library":"llama-index-vector-stores-pinecone","type":"library","category":null,"description":"This library provides the integration for using Pinecone as a vector store backend within LlamaIndex applications. It enables storing and retrieving document embeddings in a Pinecone index for efficient semantic search and Retrieval-Augmented Generation (RAG). As of version 0.8.0, it supports LlamaIndex's modular architecture, requiring separate installation from the core LlamaIndex library. It follows a frequent release cadence, often aligning with LlamaIndex core updates.","language":"python","status":"active","version":"0.8.0","tags":["LlamaIndex","Pinecone","vector database","vector store","RAG","LLM","embeddings","AI"],"last_verified":"Tue May 26","install":[{"cmd":"pip install llama-index-vector-stores-pinecone","imports":["from llama_index.vector_stores.pinecone import PineconeVectorStore","from llama_index.core import VectorStoreIndex","from llama_index.core import SimpleDirectoryReader","from llama_index.core import StorageContext","from pinecone import Pinecone"]},{"cmd":"pip install llama-index llama-index-vector-stores-pinecone pinecone-client openai","imports":[]}],"homepage":"https://www.pinecone.io","github":null,"docs":null,"changelog":null,"pypi":"https://pypi.org/project/llama-index-vector-stores-pinecone/","npm":null,"openapi_spec":null,"status_page":null,"smithery":null,"compatibility":{"summary":{"python_range":"3.10–3.9","success_rate":100,"avg_install_s":20.6,"avg_import_s":5.94,"wheel_type":"wheel"},"url":"https://checklist.day/v1/registry/llama-index-vector-stores-pinecone/compatibility"}}