How to Build RAG Pipelines with OpenClaw

An OpenClaw RAG pipeline indexes documents into a vector store, retrieves relevant chunks based on a query using semantic similarity, and passes those chunks to an LLM prompt for grounded response generation.

OpenClaw agents can each handle part of it. One indexes, one retrieves, one synthesizes. Fastio provides the storage and indexing built in. No need for services like Pinecone or Weaviate.

Core steps:

• Ingestion and indexing: Split documents into chunks, generate embeddings, store in vector DB.
• Retrieval: Embed query, find top-k similar chunks (e.g., high cosine similarity).
• Generation: Stuff context into LLM prompt: "Use only this context: {chunks}. Answer: {query}"

RAG reduces hallucinations on your domain data, as Pinecone explains. Fastio workspaces index files for search once Intelligence is enabled for OpenClaw agents.

Fastio supports large-file uploads with chunked transfer. Indexed content becomes queryable via natural language.

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More on this subject: OpenClaw Setup and Guides (199 guides)

OpenClaw handles agent coordination well but lacks storage and built-in RAG. Fastio adds those with agent tools.

Benefits:

• No infrastructure needed: Intelligence Mode indexes uploads automatically. Queries include citations right away.
• Agent collaboration: Multiple OpenClaw instances work in one workspace with version history and audit logs.
• Usage-based plans: Usage-based pricing starting at $29/mo with a 14-day Business Trial.
• Scales to production: Handles book-length PDFs, multi-format support (PDF, DOCX, code).

LangChain requires setting up a vector store. Here, Fastio connects via remote MCP without separate vector infrastructure.

Get set up:

1. Install OpenClaw locally.
2. Create a Fastio account and start a 14-day Business Trial.
3. Connect to the remote MCP server at https://mcp.fast.io/mcp using a key from https://mcp.fast.io/mcp/key.

Auth happens once via browser. Test with: "Create workspace rag-demo".

Verify: Connect to the MCP server to access the consolidated MCP toolset.

Create Fastio workspace "rag-test" with intelligence enabled.

Workspaces act as your vector stores. Intelligence Mode enables auto-indexing.

MCP call:

org-create-workspace {"name": "rag-pipeline", "intelligence": true}

Confirm with workspace-details: "intelligence_enabled": true.

Uploaded files are indexed and searchable once Intelligence is enabled for the workspace.

Pro tip: Name workspaces by project, e.g., "q1-financials-rag".

Load your documents.

Local upload:

workspace-storage-add-file workspace="rag-pipeline" path="/docs/" filename="manual.pdf" content_base64="[base64]"

URL import:

web-import workspace="rag-pipeline" path="/docs/" url="https://example.com/data.pdf"

Pulls from Drive/Box via OAuth. No local storage hit.

Batch via agent loop:

docs = ["doc1.pdf", "doc2.txt"]
for doc in docs:
    tool("workspace-storage-add-file", {"workspace": "rag-pipeline", "path": "/corpus/", "filename": doc})

Poll storage-list: ai_state: "ready". Large files chunk automatically.

Core of RAG: fetch relevant context.

Semantic query:

workspace-search workspace="rag-pipeline" query="payment terms" folders_scope="root:abc"

Returns top chunks, scores, nodeIds.

Hybrid (keyword + semantic): Add search_type: "hybrid".

Agent code:

def retrieve(query):
    results = tool("workspace-search", {"workspace": "rag-pipeline", "query": query, "top_k": 5})
    return [r["content"] for r in results if r["score"] > 0.75]

Scope with folders_scope="folderId:abc" limits to subfolders.

Combine retrieval + LLM.

Direct AI chat (easiest):

ai-chat-create context_type="workspace" type="chat_with_files" folders_scope="root:abc" query_text="Summarize payment terms"

Polls until done and returns cited answers.

Custom LLM:

chunks = retrieve("indemnity clauses")
prompt = f"""Context: {' '.join(chunks)}
Question: What are indemnity clauses?
Answer using only context:"""
response = llm(prompt)

Post-process: re-rank chunks, compress context with an LLM.

Ready for Agentic RAG?

Connect OpenClaw agents to Fastio workspaces via the remote MCP server for persistent, indexed RAG storage.

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A single agent works for simple cases. Production setups use agent teams.

Three-agent research pipeline:

• Indexer agent: Uses web-import to pull sources and uploads files to the workspace, where version history cleanly records changes.
• Retriever agent: Polls the realtime activity feed or WebSocket events feed for new files, then performs workspace-search for relevant chunks.
• Synthesizer agent: Takes retrieved chunks, generates response using document chat or LLM prompt, outputs to notes with citations.

Indexer agent code example:

# Import external source directly into the workspace
tool("web-import", {"workspace": "rag-pipeline", "path": "/corpus/", "url": "https://example.com/source.pdf"})

Retriever agent event handler:

def handle_new_file(event):
    query = f"Key points from {event['filename']}"
    chunks = tool("workspace-search", {"workspace": "rag-pipeline", "query": query, "top_k": 5})
    synth_agent.run(chunks)

Conflict-free multi-agent access: File version history with restore support and an append-only audit log track all human and agent operations. Invite other agents with member-add.

Scale to production: Pair with human review in the UI. Transfer ownership to clients through claim links. Poll the activity feed to re-index on file changes. The setup works for many agents and large data amounts.

Build reliable pipelines.

• Chunking strategy: Fastio handles semantic boundary-aware chunking automatically, with chunk sizes optimized for retrieval.
• Top-k tuning: Begin with a small top-k set; evaluate using precision@K or NDCG metrics on a validation set.
• Scoped retrieval: Use folder-level scopes like folders_scope="root:abc" for modular pipelines.
• Version pinning: Cache reliable results by querying nodeId:versionId for reproducible retrieval.
• Monitoring ingestion: Poll the realtime activity feed for indexing status; alert on errors.
• Cost management: Monitor credit usage against plan allowances; batch operations to stay under limits.
• Reranking: Apply a cross-encoder model post-retrieval to boost precision.
• Query expansion: Rewrite queries with LLM for better recall in ambiguous cases.

Hybrid search combines semantic vectors with keyword matching to catch domain terms.

Evaluation framework: Create a dataset of query-ground_truth pairs. Compute recall@k, precision@k, and faithfulness score using tools like RAGAS or TruLens.

No results: Check ai_state: "ready", intelligence ON. Low scores: Increase top_k, check query embedding. Credit errors: Upgrade or transfer ownership. Version conflicts: Restore previous file versions from version history. Large files: Use chunked uploads for large document sets.

Logs: The append-only audit log records actor and event details.

Fastio eliminates DB setup, adds collab.