pinecone-rag
Build production RAG pipelines and persistent agent memory using Pinecone as the vector database backend. ALWAYS USE THIS SKILL when the user mentions Pinecone, wants to index documents for semantic search, build a retrieval-augmented generation system, store agent memory across sessions, implement hybrid search, or connect an LLM to a searchable knowledge base — even if they don't say "Pinecone" explicitly. Also use when the user asks about vector databases for RAG, namespace isolation for multi-tenant agents, embedding pipelines, or scaling a knowledge base beyond what local storage can hand
Profile →npx skills add github/awesome-copilot --skill pinecone-rag --agent copilot
Same command for any agent — swap --agent for claude-code, codex, cursor.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Pinecone RAG Skill This skill guides you through building a production RAG pipeline or persistent agent memory system using Pinecone. Follow the workflow from start to finish — don't skip steps or jump to code before understanding what the user actually needs. ## Before you start — ask one question Before writing any code, identify which of these two use cases applies: **A — RAG over documents**: User wants to index a corpus (PDFs, docs, code, web pages) and retrieve relevant chunks to ground LLM responses. **B — Agent memory**: User wants an agent to remember facts, decisions, or context across sessions or across multiple agents sharing a knowledge base. The setup is similar but the namespace strategy and retrieval patterns differ. If the user hasn't said, ask: *"Is this for document retrieval, agent memory, or both?"* Then follow the relevant workflow below. --- ## Step 1 — Choose your index configuration Pick the index type before writing any code. Getting this wrong means re-creating the index later. **Serverless (recommended for most cases)** ```python from pinecone import Pinecone, ServerlessSpec pc = Pinecone(api_key="PINECONE_API_KEY") if "my-index" not in pc.list_indexes
- Before you start — ask one question
- Step 1 — Choose your index configuration
- Step 2 — Embed and upsert documents
- Step 3 — Choose retrieval strategy
- Dense (semantic) search — use for most cases
- Hybrid search (semantic + BM25 keyword) — use when corpus has exact terminology
- Metadata filtering — use to scope results before semantic ranking
- Step 4A — Full RAG pipeline (document use case)
- Step 4B — Agent memory (memory use case)
- Step 5 — Wire it together and test end to end
- Common pitfalls — fix these before they become bugs
- When NOT to use this skill
What does the pinecone-rag skill do?
Build production RAG pipelines and persistent agent memory using Pinecone as the vector database backend. ALWAYS USE THIS SKILL when the user mentions Pinecone, wants to index documents for semantic search, build a retrieval-augmented generation system, store agent memory across sessions, implement hybrid search, or connect an LLM to a searchable knowledge base — even if they don't say "Pinecone" explicitly. Also use when the user asks about vector databases for RAG, namespace isolation for multi-tenant agents, embedding pipelines, or scaling a knowledge base beyond what local storage can hand
How do I install it?
Run `npx skills add github/awesome-copilot --skill pinecone-rag --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From github/awesome-copilot, a repository with 37,432 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.