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optional-skills/research/pinecone-research/SKILL.md

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+---
+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
+
+```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)