pinecone-research

# 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 ```bash pip install pinecone-client langchain-pinecone langchain-openai ``` Set your API key: ```bash export PINECONE_API_KEY="your-api-key" ``` ### Basic RAG pipeline ```python 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 ```python # 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 1. **Namespace by session or user** — isolate data for multi-tenant agents 2. **Batch upserts** — 100–200 vectors per batch for efficiency 3. **Metadata filtering** — tag vectors with session ID, timestamp, topic 4. **Prune old memory** — delete stale namespaces to control costs 5. **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)