Import AITURK IDE 1.0.0-beta.1 from Hermes 63279301; preserve MIT license

website/docs/user-guide/skills/optional/research/research-pinecone-research.md

@@ -0,0 +1,125 @@
+---
+title: "Pinecone Research — Agent RAG and long-term memory with Pinecone"
+sidebar_label: "Pinecone Research"
+description: "Agent RAG and long-term memory with Pinecone"
+---
+
+{/* This page is auto-generated from the skill's SKILL.md by website/scripts/generate-skill-docs.py. Edit the source SKILL.md, not this page. */}
+
+# Pinecone Research
+
+Agent RAG and long-term memory with Pinecone.
+
+## Skill metadata
+
+| | |
+|---|---|
+| Source | Optional — install with `hermes skills install official/research/pinecone-research` |
+| Path | `optional-skills/research\pinecone-research` |
+| Version | `1.0.0` |
+| Author | immuhammadfurqan |
+| License | MIT |
+| Dependencies | `pinecone-client`, `langchain-pinecone` |
+| Platforms | linux, macos, windows |
+| Tags | `RAG`, `Pinecone`, `Memory`, `Research`, `Vector Database`, `Agent`, `Retrieval` |
+
+## Reference: full SKILL.md
+
+:::info
+The following is the complete skill definition that Hermes loads when this skill is triggered. This is what the agent sees as instructions when the skill is active.
+:::
+
+# 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)