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Memory, Research, Vector Database, Agent, Retrieval]\n\n---\n\n# Pinecone Research — Agent RAG & Long-Term Memory\n\nUse Pinecone as a retrieval-augmented generation (RAG) backend for agent\nconversations: persist embeddings, retrieve relevant context from past\nsessions, and build long-term memory.\n\n## When to use this skill\n\n**Use when:**\n- Building agent RAG pipelines with Pinecone as the vector store\n- Need persistent long-term memory across agent sessions\n- Combining retrieval with agent tool use\n- Researching or prototyping semantic search workflows\n\n**Use the mlops\u002Fpinecone skill instead when:**\n- Need a general Pinecone reference (index management, CRUD, hybrid search)\n- Working on production infrastructure without agent integration\n\n## Quick start\n\n### Setup\n\n```bash\npip install pinecone-client langchain-pinecone langchain-openai\n```\n\nSet your API key:\n```bash\nexport PINECONE_API_KEY=\"your-api-key\"\n```\n\n### Basic RAG pipeline\n\n```python\nfrom pinecone import Pinecone, ServerlessSpec\nfrom langchain_pinecone import PineconeVectorStore\nfrom langchain_openai import OpenAIEmbeddings\n\n# Initialize Pinecone\npc = Pinecone(api_key=os.environ[\"PINECONE_API_KEY\"])\n\n# Create or connect to index\nindex_name = \"agent-memory\"\nif index_name not in [i.name for i in pc.list_indexes()]:\n    pc.create_index(\n        name=index_name,\n        dimension=1536,\n        metric=\"cosine\",\n        spec=ServerlessSpec(cloud=\"aws\", region=\"us-east-1\"),\n    )\n\n# Build vector store\nvectorstore = PineconeVectorStore.from_documents(\n    documents=docs,\n    embedding=OpenAIEmbeddings(),\n    index_name=index_name,\n)\n\n# Retrieve relevant context\nretriever = vectorstore.as_retriever(search_kwargs={\"k\": 5})\nresults = retriever.invoke(\"What did the agent discuss yesterday?\")\n```\n\n### Namespace-based session memory\n\n```python\n# Store per-session memory\nvectorstore = PineconeVectorStore(\n    index=pc.Index(index_name),\n    embedding=OpenAIEmbeddings(),\n    namespace=f\"session-{session_id}\",\n)\n\n# Query across all sessions (no namespace filter)\nall_memory = PineconeVectorStore(\n    index=pc.Index(index_name),\n    embedding=OpenAIEmbeddings(),\n)\nresults = all_memory.similarity_search(\"relevant query\", k=10)\n```\n\n## Best practices\n\n1. **Namespace by session or user** — isolate data for multi-tenant agents\n2. **Batch upserts** — 100–200 vectors per batch for efficiency\n3. **Metadata filtering** — tag vectors with session ID, timestamp, topic\n4. **Prune old memory** — delete stale namespaces to control costs\n5. **Use serverless** — auto-scaling, pay-per-use pricing\n\n## Resources\n\n- **Pinecone Docs**: https:\u002F\u002Fdocs.pinecone.io\n- **LangChain Integration**: https:\u002F\u002Fpython.langchain.com\u002Fdocs\u002Fintegrations\u002Fvectorstores\u002Fpinecone\n- **Free Tier**: 1 index, 100K vectors (1536 dimensions)\n","# Pinecone Research — Agent RAG & Long-Term Memory\n\nUse Pinecone as a retrieval-augmented generation (RAG) backend for agent\nconversations: persist embeddings, retrieve relevant context from past\nsessions, and build long-term memory.\n\n## When to use this skill\n\n**Use when:**\n- Building agent RAG pipelines with Pinecone as the vector store\n- Need persistent long-term memory across agent sessions\n- Combining retrieval with agent tool use\n- Researching or prototyping semantic search workflows\n\n**Use the mlops\u002Fpinecone skill instead when:**\n- Need a general Pinecone reference (index management, CRUD, hybrid search)\n- Working on production infrastructure without agent integration\n\n## Quick start\n\n### Setup\n\n```bash\npip install pinecone-client langchain-pinecone langchain-openai\n```\n\nSet your API key:\n```bash\nexport PINECONE_API_KEY=\"your-api-key\"\n```\n\n### Basic RAG pipeline\n\n```python\nfrom pinecone import Pinecone, ServerlessSpec\nfrom langchain_pinecone import PineconeVectorStore\nfrom langchain_openai import OpenAIEmbeddings\n\n# Initialize Pinecone\npc = Pinecone(api_key=os.environ[\"PINECONE_API_KEY\"])\n\n# Create or connect to index\nindex_name = \"agent-memory\"\nif index_name not in [i.name for i in pc.list_indexes()]:\n    pc.create_index(\n        name=index_name,\n        dimension=1536,\n        metric=\"cosine\",\n        spec=ServerlessSpec(cloud=\"aws\", region=\"us-east-1\"),\n    )\n\n# Build vector store\nvectorstore = PineconeVectorStore.from_documents(\n    documents=docs,\n    embedding=OpenAIEmbeddings(),\n    index_name=index_name,\n)\n\n# Retrieve relevant context\nretriever = vectorstore.as_retriever(search_kwargs={\"k\": 5})\nresults = retriever.invoke(\"What did the agent discuss yesterday?\")\n```\n\n### Namespace-based session memory\n\n```python\n# Store per-session memory\nvectorstore = PineconeVectorStore(\n    index=pc.Index(index_name),\n    embedding=OpenAIEmbeddings(),\n    namespace=f\"session-{session_id}\",\n)\n\n# Query across all sessions (no namespace filter)\nall_memory = PineconeVectorStore(\n    index=pc.Index(index_name),\n    embedding=OpenAIEmbeddings(),\n)\nresults = all_memory.similarity_search(\"relevant query\", k=10)\n```\n\n## Best practices\n\n1. **Namespace by session or user** — isolate data for multi-tenant agents\n2. **Batch upserts** — 100–200 vectors per batch for efficiency\n3. **Metadata filtering** — tag vectors with session ID, timestamp, topic\n4. **Prune old memory** — delete stale namespaces to control costs\n5. **Use serverless** — auto-scaling, pay-per-use pricing\n\n## Resources\n\n- **Pinecone Docs**: https:\u002F\u002Fdocs.pinecone.io\n- **LangChain Integration**: https:\u002F\u002Fpython.langchain.com\u002Fdocs\u002Fintegrations\u002Fvectorstores\u002Fpinecone\n- **Free Tier**: 1 index, 100K vectors (1536 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