SiphonClaw

Domain-agnostic document intelligence pipeline. Ingest PDFs, images, and spreadsheets into a searchable knowledge base with dual-track retrieval (text + visual), OCR, confidence scoring, and field capture.

Built for field service engineers, researchers, mechanics, and anyone who needs fast answers from large document collections.

What SiphonClaw Does

• Ingest documents (PDF, Excel, images, screenshots) into a local vector database with text and visual embeddings
• Search using triple hybrid retrieval: BM25 keyword matching + semantic text vectors + visual page embeddings, fused with RRF and reranked with a cross-encoder
• Identify equipment, parts, or components from photos using vision models, then search the local knowledge base
• Capture field fixes and repair notes as first-class knowledge base entries for future retrieval
• Score every response with composite confidence (retrieval + faithfulness + relevance + coverage) and footnote-style source citations

MCP Tools

SiphonClaw exposes five tools via MCP for integration with agents and other MCP-compatible clients.

siphonclaw_search

Search the knowledge base using triple hybrid retrieval (text + visual + keyword).

Parameters:

Returns:

{
  "results": [
    {
      "content": "Extracted text from the matching chunk or page",
      "source": "ServiceManual_ModelA.pdf",
      "page": 42,
      "section": "4.3 Transformer Replacement",
      "score": 0.92,
      "match_type": "hybrid"
    }
  ],
  "confidence": 0.87,
  "confidence_tier": "Confident - verify part number",
  "keywords_used": ["low voltage supply", "assembly mount", "ModelA"],
  "citations": ["[1] ServiceManual_ModelA, page 42", "[2] Parts Catalog PC-1102, page 15"]
}

siphonclaw_ingest

Add a document or photo to the knowledge base. Supports PDF, Excel, images (JPG/PNG), and screenshots.

Parameters:

Returns:

{
  "status": "ingested",
  "file": "ServiceManual_ModelA.pdf",
  "pages_processed": 127,
  "chunks_created": 843,
  "visual_pages_indexed": 127,
  "ocr_pages": 12,
  "duration_seconds": 45.2
}

siphonclaw_field_note

Save a field fix or repair note as a first-class knowledge base entry. These are indexed and retrievable in future searches, forming a learning loop.

Parameters:

Returns:

{
  "status": "saved",
  "field_note_id": "fn-2026-02-09-001",
  "indexed": true,
  "model": "ModelA",
  "parts_cross_referenced": ["12345"],
  "retrievable": true
}

siphonclaw_identify

Send a photo of equipment, a part, a label, or an error screen. SiphonClaw uses vision models to identify what it sees, then searches the local knowledge base for relevant documentation. Falls back to web search if local confidence is low.

Parameters:

Returns:

{
  "identification": "Industrial power supply board, Model PSU-200",
  "visual_features": ["green PCB", "3 large capacitors", "manufacturer logo visible", "part label partially obscured"],
  "ocr_text": "PSU-200 REV C  SN: 4829103",
  "search_results": [
    {
      "content": "PSU-200 replacement procedure...",
      "source": "ServiceManual_ModelA.pdf",
      "page": 67,
      "score": 0.94
    }
  ],
  "confidence": 0.91,
  "web_search_used": false
}

siphonclaw_status

Get pipeline health, ingestion statistics, model availability, and cost tracking.

Parameters:

Returns:

{
  "status": "healthy",
  "knowledge_base": {
    "total_documents": 3164,
    "total_chunks": 656000,
    "visual_pages_indexed": 31200,
    "last_ingestion": "2026-02-09T14:30:00Z"
  },
  "models": {
    "ocr": {"model": "qwen3-vl:latest", "provider": "ollama", "available": true},
    "text_embedding": {"model": "bge-m3:latest", "provider": "ollama", "available": true},
    "visual_embedding": {"model": "qwen3-vl-embed:2b", "provider": "ollama", "available": true},
    "generation": {"model": "MiniMax-M2.5", "provider": "openrouter", "available": true},
    "reasoning": {"model": "kimi-k2.5", "provider": "openrouter", "available": true},
    "fallback": {"model": "glm-4.7-flash:latest", "provider": "ollama", "available": true}
  },
  "costs": {
    "today": "$0.12",
    "this_month": "$2.45",
    "daily_budget": "$5.00",
    "budget_remaining": "$4.88"
  },
  "dead_letter_queue": {
    "pending_retry": 2,
    "permanently_failed": 1
  }
}

MCP Server

SiphonClaw runs as an MCP server that any MCP-compatible client (OpenClaw agents, Claude Desktop, etc.) can connect to.

# Start the MCP server (stdio transport - default for OpenClaw)
python mcp_server.py

# Start with SSE transport (for HTTP-based clients)
python mcp_server.py --sse --port 8000

OpenClaw agent config (~/.openclaw/openclaw.json):

{
  "mcpServers": {
    "siphonclaw": {
      "command": "python",
      "args": ["mcp_server.py"],
      "cwd": "/path/to/siphonclaw"
    }
  }
}

Claude Desktop config (claude_desktop_config.json):

{
  "mcpServers": {
    "siphonclaw": {
      "command": "python",
      "args": ["/path/to/siphonclaw/mcp_server.py"]
    }
  }
}

Setup

Mode A: Hybrid Local + Cloud (Recommended)

Local models handle ingestion (OCR + embeddings) for free. Cloud APIs handle intelligence (generation + reasoning) for pennies per query.

Monthly cost: ~$0.50-5/mo for typical use.

# 1. Install SiphonClaw
git clone https://github.com/curtisgc1/siphonclaw.git && cd siphonclaw
pip install -r requirements.txt

# 2. Install Ollama and pull local models (~10 GB total)
curl -fsSL https://ollama.com/install.sh | sh
ollama pull qwen3-vl:latest          # 6.1 GB - OCR
ollama pull bge-m3:latest             # ~1.5 GB - text embeddings
ollama pull qwen3-vl-embed:2b        # ~2 GB - visual embeddings

# 3. Get OpenRouter API key (ONE key for all intelligence models)
#    Visit: https://openrouter.ai -> Sign up -> Copy API key
siphonclaw config set openrouter_key sk-or-v1-xxxxx

# 4. (Optional) Get Brave Search API key for web search fallback
#    Visit: https://brave.com/search/api -> Sign up -> Free tier: 2,000 queries/mo
siphonclaw config set brave_key BSA-xxxxx

# 5. Point to your documents and ingest
siphonclaw config set docs_path /path/to/my/docs
siphonclaw ingest

# 6. Search
siphonclaw search "part number for compressor valve"

Mode B: Full Cloud

Everything runs via OpenRouter. Simpler setup (no Ollama needed), but ingestion of large document sets costs $50-100+ in API tokens.

First month: ~$50-105. After that: ~$0.50/mo.

# 1. Install SiphonClaw
pip install siphonclaw

# 2. Get OpenRouter API key
siphonclaw config set openrouter_key sk-or-v1-xxxxx

# 3. Set ingestion mode to cloud
siphonclaw config set ingestion_mode cloud

# 4. (Optional) Get Brave Search API key
siphonclaw config set brave_key BSA-xxxxx

# 5. Point to your documents and ingest
siphonclaw config set docs_path /path/to/my/docs
siphonclaw ingest

# 6. Search
siphonclaw search "part number for compressor valve"

Cost Comparison

Configuration Reference

SiphonClaw reads configuration from config/models.yaml and environment variables.

Environment variables (via .env or shell):

Agent config example (config.json):

{
  "skills": {
    "entries": {
      "siphonclaw": {
        "openrouter_key": "sk-or-v1-xxxxx",
        "brave_key": "BSA-xxxxx",
        "docs_path": "/path/to/docs",
        "ingestion_mode": "local",
        "ollama_url": "http://127.0.0.1:11434"
      }
    }
  }
}

Model configuration: See config/models.yaml for full model tier configuration with ingestion and intelligence settings.