pinecone-research

name pinecone-research description Agent RAG and long-term memory with Pinecone. version 1.0.0 author immuhammadfurqan license MIT dependencies ["pinecone-client","langchain-pinecone"] platforms ["linux","macos","windows"] metadata {"hermes":{"tags":["RAG","Pinecone","Memory","Research","Vector Database","Agent","Retrieval"]}} Pinecone Research — Agent RAG & Long-Term Memory Use Pinecone as a retrieval-augmented generation (RAG) backend for agent conversations: persist embeddings, retrieve relevant context from past sessions, and build long-term memory. When to use this skill Use when: Building agent RAG pipelines with Pinecone as the vector store Need persistent long-term memory across agent sessions Combining retrieval with agent tool use Researching or prototyping semantic search workflows Use the mlops/pinecone skill instead when: Need a general Pinecone reference (index management, CRUD, hybrid search) Working on production infrastructure without agent integration Quick start Setup pip install pinecone-client langchain-pinecone langchain-openai Set your API key: export PINECONE_API_KEY= "your-api-key" Basic RAG pipeline from pinecone import Pinecone, ServerlessSpec from langchain_pinecone import PineconeVectorStore from langchain_openai import OpenAIEmbeddings # Initialize Pinecone pc = Pinecone(api_key=os.environ[ "PINECONE_API_KEY" ]) # Create or connect to index index_name = "agent-memory" if index_name not in [i.name for i in pc.list_indexes()]: pc.create_index( name=index_name, dimension= 1536 , metric= "cosine" , spec=ServerlessSpec(cloud= "aws" , region= "us-east-1" ), ) # Build vector store vectorstore = PineconeVectorStore.from_documents( documents=docs, embedding=OpenAIEmbeddings(), index_name=index_name, ) # Retrieve relevant context retriever = vectorstore.as_retriever(search_kwargs={ "k" : 5 }) results = retriever.invoke( "What did the agent discuss yesterday?" ) Namespace-based session memory # Store per-session memory vectorstore = PineconeVectorStore( index=pc.Index(index_name), embedding=OpenAIEmbeddings(), namespace= f"session- {session_id} " , ) # Query across all sessions (no namespace filter) all_memory = PineconeVectorStore( index=pc.Index(index_name), embedding=OpenAIEmbeddings(), ) results = all_memory.similarity_search( "relevant query" , k= 10 ) Best practices Namespace by session or user — isolate data for multi-tenant agents Batch upserts — 100–200 vectors per batch for efficiency Metadata filtering — tag vectors with session ID, timestamp, topic Prune old memory — delete stale namespaces to control costs Use serverless — auto-scaling, pay-per-use pricing Resources Pinecone Docs : https://docs.pinecone.io LangChain Integration : https://python.langchain.com/docs/integrations/vectorstores/pinecone Free Tier : 1 index, 100K vectors (1536 dimensions)