How to Implement Semantic Search in OpenClaw

Semantic search in OpenClaw finds files by meaning, powered by Fast.io's built-in vector embeddings. When Intelligence Mode is enabled on a Fast.io workspace, uploaded documents get automatically indexed. OpenClaw agents access this through the Fast.io ClawHub skill, using tools like workspace-search or ai-chat-create for RAG queries.

Traditional keyword search matches exact terms. Semantic search matches on context. Query "show recent quarter sales reports with high revenue" and it retrieves relevant docs even without those exact words.

Fast.io handles indexing, so OpenClaw focuses on querying. Supports PDFs, code, text files up to 1GB. Results include citations to pages or sections.

Fast.io generates embeddings using transformer models, converting document chunks into numerical vectors that capture semantic meaning. Queries are embedded similarly, and cosine similarity measures the angle between vectors to rank results on a scale from -1 to 1, where 1 indicates identical meaning.

This setup lets OpenClaw agents handle complex tasks like legal document review or codebase search.

Advantages for agents:

• Cited RAG reduces hallucinations
• Handles synonyms ("payment terms" matches "billing")
• Real-time updates as files change
• Scoped to folders or metadata

Before setup, prepare these:

• OpenClaw installed (https://openclaw.ai)
• Node.js for ClawHub (npm/npx)
• Free Fast.io agent account (50GB storage, 5000 credits/month). No credit card needed.
• Basic knowledge of OpenClaw skills and MCP tools

Create account at https://go.fast.io/signup (agent flow).

Run this in terminal:

npx clawhub@latest install dbalve/fast-io ([Fast.io ClawHub skill](https://fast.io/storage-for-openclaw/))

Registers 14 tools: auth, org, workspace, share, storage, upload, ai, event, member, comment, download, prompts, resources, metadata.

First tool call opens browser for Fast.io auth (PKCE, one-time). The skill provides 14 MCP-compatible tools mirroring Fast.io UI capabilities, from upload to AI chat.

Test installation: Prompt your OpenClaw "List available Fast.io tools." It should enumerate auth, workspace-search, ai-chat-create, etc.

Common issue: if npx fails, make sure you have recent Node.js with an updated npm.

Use OpenClaw agent to run:

1. Authenticate: auth signin or signup for agent account.

2. Create org: org create (auto agent plan).

3. Create workspace: org create-workspace name="project-docs" intelligence=true.

intelligence=true enables automatic indexing of uploads for semantic search.

Verify: workspace details workspace_id={id} check intelligence: true. Intelligence Mode auto-processes uploads: text extraction, chunking (typically 512 tokens), embedding generation.

Processing time varies by document size and complexity; monitor via storage details.

Pro Tip: Name workspaces descriptively and organize folders early for better scoping.

Code example (OpenClaw prompt):

Create a Fast.io workspace called "docs-search" with intelligence enabled for semantic search.

Upload files:

• Direct upload: upload create-session workspace={id} path="/docs/" filename="report.pdf" then stream content.

• URL import: web-upload workspace={id} url="https://example.com/doc.pdf" path="/docs/".

Wait for ai_state: "ready" via storage details node_id={id}. For large files, use chunked upload: create-session then upload-chunk calls. Supports resumable up to 1GB.

URL import pulls from Drive/Box via OAuth, no agent local I/O needed. Great for cloud-native workflows.

Example prompt: "Upload these PDFs to /contracts/ in docs-search workspace."

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Query with workspace-search:

workspace-search workspace={id} query="contracts with indemnity clauses"

Returns JSON: files, similarity scores, preview snippets. Use higher thresholds for more precise results.

ai-chat-create streams SSE for long responses. folders_scope limits context size/cost, e.g. "root:top" for top levels.

RAG chat: ai chat-create context_type=workspace type=chat_with_files folders_scope=root:top query_text="Summarize indemnity terms across contracts".

Citations include page numbers.

Prompt example: "Search my docs for recent quarter revenue reports with high numbers and summarize findings."

• Scope queries: Use folders_scope="folderId:abc" to limit search.

• Batch uploads: Chunk large sets.

• Monitor credits: auth status shows usage.

• Multi-agent: Use file locks storage lock for concurrent access.

• Human handoff: org transfer-token-create generate claim link.

In multi-agent setups, coordinate access with file locks via storage lock-acquire and storage lock-release. Locks prevent concurrent modifications.

Use webhooks (webhook create) for event-driven workflows: notify agents when new files arrive for indexing.

Combine with ownership transfer: agents build shared knowledge bases, transfer to human teams for ongoing use.

Define clear tool contracts and fallback behavior so agents fail safely when dependencies are unavailable. This improves reliability in production workflows.

Indexing not starting: Verify intelligence=true on workspace. Re-upload files to trigger.

No search results: Check ai_state="ready" with storage details. Start with broad queries.

Slow responses: Scope with folders_scope or metadata. Monitor credits via auth status.

Auth errors: Run auth signin again. Clear ClawHub cache if persistent.

Tool unavailable: Reinstall npx clawhub@latest install dbalve/fast-io.

Legal Document Review: Query "contracts with termination clauses". Agent summarizes risks across numerous files.

Codebase Search: "functions for user authentication in src/". Retrieves relevant modules with snippets.

Financial Analysis: "quarterly reports showing growth". Chains to chart generation.

Multi-Agent Workflow: Index agent uploads, search agent queries, summary agent reports, human reviews via share.