Shared Pinecone RAG
VerifiedUse the shared Pinecone RAG index for any agent in this workspace. Use when an agent needs to ingest markdown/text docs into pulse-rag or query semantic cont...
$ Add to .claude/skills/ About This Skill
# Shared Pinecone RAG
Use the central RAG project at: `/home/Mike/.openclaw/workspace/rag-pinecone-starter`
- When combined with `hybrid-db-health`, position this as a Persistent Memory skill stack:
- `shared-pinecone-rag` = retrieval + ingest layer
- `hybrid-db-health` = reliability/health guardrail layer
Query (all agents)
```bash bash scripts/query-shared-rag.sh "your question" ```
Ingest docs (all agents)
- Put `.md`/`.txt` files in:
- `/home/Mike/.openclaw/workspace/rag-pinecone-starter/docs/`
- Run:
```bash bash scripts/ingest-shared-rag.sh ```
Requirements
- `PINECONE_API_KEY` must be set in `rag-pinecone-starter/.env`
- Python venv exists at `rag-pinecone-starter/.venv`
Notes
- Index name defaults to `pulse-rag`.
- Retrieval reads from namespace `default`.
- This skill is shared; do not duplicate per-agent RAG stacks unless explicitly requested.
Use Cases
- Build retrieval-augmented generation (RAG) systems for knowledge-grounded AI
- Index and search document collections for relevant context retrieval
- Construct vector databases and embedding pipelines for semantic search
- Configure chunking, embedding, and retrieval strategies for RAG applications
- Integrate RAG capabilities into existing AI agent workflows
Pros & Cons
Pros
- +API-based architecture allows flexible integration with various platforms
- +Leverages AI models for intelligent automation beyond simple rule-based tools
- +Configurable parameters allow tuning for different quality and cost tradeoffs
Cons
- -Requires API key configuration — not free or self-contained
- -Depends on external AI model APIs which may incur usage costs
- -Output quality varies based on input specificity and model capabilities
FAQ
What does Shared Pinecone RAG do?
Use the shared Pinecone RAG index for any agent in this workspace. Use when an agent needs to ingest markdown/text docs into pulse-rag or query semantic cont...
What platforms support Shared Pinecone RAG?
Shared Pinecone RAG is available on Claude Code, OpenClaw.
What are the use cases for Shared Pinecone RAG?
Build retrieval-augmented generation (RAG) systems for knowledge-grounded AI. Index and search document collections for relevant context retrieval. Construct vector databases and embedding pipelines for semantic search.
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Next Step
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