Tutorial: Setting up a local multi-agent RAG pipeline for company documents

Enterprise data is sensitive. If you want to use AI to query your company's internal documents, sending that data to a third-party API isn't just a security risk - it's often a hard "no" from compliance teams.

The solution is Retrieval-Augmented Generation (RAG) running entirely on your local infrastructure. In this tutorial, we will set up a local multi-agent RAG pipeline using OpenClaw.

The Architecture

Our pipeline consists of three main components:

1. Document Ingestion: A script that crawls your local folders, extracts text, and chunks it.
2. Vector Store: A local vector database (like ChromaDB or FAISS) to store embeddings.
3. Retrieval Agent: An OpenClaw agent that retrieves relevant chunks based on user questions and passes them to the LLM for synthesis.

Step 1: Document Ingestion and Embeddings

First, we need to convert your documents into a searchable format.

# Simple embedding pipeline example
python3 embed_docs.py --source ./company_docs/ --destination ./vector_store/

This script will read your PDFs, Markdown, and text files, break them into manageable chunks, and create vector embeddings using a local model like nomic-embed-text.

Step 2: The Retrieval Agent

Configure your OpenClaw agent to use the local vector store as a tool.

# agent-config.yaml
tools:
  - name: "document_search"
    description: "Search internal company documentation."
    path: "/path/to/vector_store"

The agent's instruction: "When a user asks a question, use document_search to find relevant information, then summarize the answer based only on the provided context."

Why Local RAG for Enterprise?

1. Security: Data never leaves your premises.
2. Performance: Low latency, as there's no network round-trip to an external API.
3. Control: You can update documents in real-time, and the agent sees them immediately.

Conclusion

Building a local RAG pipeline is the gold standard for enterprise AI implementation. It ensures data privacy while providing all the benefits of intelligent, context-aware AI agents.

Have you built a RAG pipeline for your data yet? Let's discuss your architecture.