A centralized Retrieval-Augmented Generation (RAG) system that provides shared semantic memory and document querying across multiple AI agents.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install shared-pinecone-rag
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install shared-pinecone-rag using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Shared Pinecone RAG?
The Shared Pinecone RAG skill acts as a unified knowledge repository for all agents within a workspace, leveraging Pinecone as a vector database. Instead of each agent maintaining its own isolated data, this skill enables a shared infrastructure where agents can ingest markdown and text documents into a central index and query them for semantic context. It is a core component for developers building complex workflows within Openclaw Skills, ensuring that every agent has access to the same updated documentation and research data.
By positioning this skill alongside health monitoring tools, developers can create a robust Persistent Memory skill stack. This architecture minimizes data redundancy, lowers API overhead, and ensures that the retrieval layer remains consistent across the entire agent lifecycle, providing a significantly more coherent user experience.
Shared Pinecone RAG Use Cases
- Creating a centralized knowledge base for technical documentation that all coding agents can reference.
- Implementing long-term memory for AI agents to recall previous project contexts or architectural decisions.
- Reducing token usage by retrieving only relevant document snippets via semantic search.
- Standardizing research data access across a team of specialized agents in the Openclaw Skills environment.
How Shared Pinecone RAG Works
- Source documents in Markdown (.md) or text (.txt) format are placed into the designated docs directory within the shared workspace.
- The ingestion script is triggered to process these files, convert them into vector embeddings, and upload them to the pulse-rag Pinecone index.
- When an agent requires information, it executes a query script passing a natural language question as an argument.
- The system performs a semantic lookup within the default namespace of the vector index.
- Relevant context is returned to the agent, allowing it to generate accurate, data-backed responses.
Shared Pinecone RAG Setup
To get started with this component of Openclaw Skills, follow these installation steps:
- Ensure you have a Pinecone account and an API key.
- Navigate to the project directory:
cd /home/Mike/.openclaw/workspace/rag-pinecone-starter
- Configure your environment by adding your
PINECONE_API_KEYto the.envfile. - Verify that a Python virtual environment exists at
.venv. - To ingest your initial documents, place them in the
docs/folder and run:
bash scripts/ingest-shared-rag.sh
Shared Pinecone RAG Data Schema & Taxonomy
The skill organizes data within a centralized directory structure to maintain consistency across the Openclaw Skills ecosystem:
| Property | Value | Description |
|---|---|---|
| Index Name | pulse-rag | The primary Pinecone index used for vector storage. |
| Namespace | default | The logical grouping used for all document retrievals. |
| Doc Path | /docs/ | The directory where source .md and .txt files are stored. |
| Scripts | /scripts/ | Contains bash utilities for ingestion and querying. |
| Environment | .env | Stores sensitive credentials like the Pinecone API key. |
Shared Pinecone RAG Advanced Features
- Persistent Memory Stack: Integrates seamlessly with hybrid-db-health to provide a reliable, self-healing retrieval layer.
- Multi-Agent Concurrency: Designed to handle simultaneous query requests from different agents without index locking.
- Centralized Ingestion: Prevents document duplication by maintaining a single source of truth for the entire workspace.
- Seamless Integration: Optimized for the Openclaw Skills framework to allow for rapid semantic search implementation in any agent workflow.
SKILL.md
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