Files
bot/skills/hanzo-search/SKILL.md
zooqueenandHanzo Dev d25a117d97 fix: update skill scripts to use live API endpoints
- Search: use search.hanzo.ai (Meilisearch native API)
- Crawl: use crawl.hanzo.ai (Crawl4AI native API)
- Vector: use vector.hanzo.ai on standard HTTPS (443 via ingress)
- Add User-Agent header to bypass Cloudflare bot protection
- Update SKILL.md docs to match live API contracts

Co-authored-by: Hanzo Dev <[email protected]>
2026-03-02 16:31:38 -08:00

4.6 KiB

name, description, metadata
name description metadata
hanzo-search Search indexed documentation and knowledge bases via Hanzo Search (Meilisearch). Supports fulltext search with faceted filtering and ranking. Use when a bot needs to search docs, knowledge bases, or indexed content, or when a user asks to find information across indexed sources.
bot
requires primaryEnv emoji
bins
python3
HANZO_API_KEY 🔍

Hanzo Search -- Meilisearch Document Search API

Search indexed documentation and knowledge bases with fulltext retrieval and faceted filtering, powered by Meilisearch.

API Endpoints

Base URL: https://search.hanzo.ai

Endpoint Method Purpose
/indexes/{index}/search POST Search documents in an index
/indexes GET List all indexes
/indexes/{index} GET Get index details
/indexes/{index}/documents GET Browse documents in an index

Authentication

All requests require a Bearer token in the Authorization header. Use the MEILI_MASTER_KEY via the HANZO_API_KEY env var.

Authorization: Bearer <token>

Search Documents

python3 {baseDir}/scripts/search.py --query "how to deploy" --store my-docs

Request Body (/indexes/{index}/search)

{
  "q": "how to deploy to kubernetes",
  "limit": 10,
  "offset": 0,
  "filter": "category = 'deployment'"
}

Fields

  • q (required): Search query string
  • limit (optional): Max results (default 10, max 100)
  • offset (optional): Pagination offset (default 0)
  • filter (optional): Meilisearch filter expression (e.g. "category = 'deployment'")
  • sort (optional): Array of sort rules (e.g. ["updated_at:desc"])
  • attributesToRetrieve (optional): Array of attributes to return
  • attributesToHighlight (optional): Array of attributes to highlight

Response

{
  "hits": [
    {
      "id": "doc-123",
      "title": "Kubernetes Deployment Guide",
      "url": "https://docs.example.com/deploy",
      "content": "To deploy your application to Kubernetes...",
      "category": "deployment",
      "updated_at": "2026-01-15"
    }
  ],
  "query": "how to deploy to kubernetes",
  "processingTimeMs": 12,
  "estimatedTotalHits": 42,
  "limit": 10,
  "offset": 0
}

RAG Chat over Search Results

RAG chat requires the Hanzo Cloud API layer on top of Meilisearch. The chat.py script calls the /api/chat-docs endpoint which retrieves documents via Meilisearch and generates an LLM-grounded response.

python3 {baseDir}/scripts/chat.py --query "explain the deployment process" --store my-docs

Request Body (/api/chat-docs)

{
  "query": "explain the deployment process step by step",
  "store": "my-docs",
  "mode": "hybrid",
  "limit": 5,
  "stream": true
}

Fields

  • query (required): Chat question
  • store (required): Search store to ground answers in
  • mode (optional): Search mode for retrieval ("hybrid", "fulltext", "vector")
  • limit (optional): Number of source documents to retrieve (default 5)
  • stream (optional): Stream response chunks (default true)
  • model (optional): LLM model for chat generation
  • system_prompt (optional): Override system prompt for the chat

Streaming Response

Each line is a JSON chunk:

{"type": "source", "data": {"id": "doc-123", "title": "...", "url": "...", "score": 0.95}}
{"type": "chunk", "data": {"text": "The deployment process"}}
{"type": "chunk", "data": {"text": " involves three steps..."}}
{"type": "done", "data": {"sources_count": 5, "tokens_used": 342}}

Scripts

scripts/search.py

Search documents from the command line via Meilisearch.

python3 {baseDir}/scripts/search.py \
  --query "search terms" \
  --store "index-name" \
  --limit 10 \
  --token "$HANZO_API_KEY"

scripts/chat.py

RAG chat over search results (requires Hanzo Cloud API layer).

python3 {baseDir}/scripts/chat.py \
  --query "your question" \
  --store "store-name" \
  --limit 5 \
  --token "$HANZO_API_KEY"

Billing

Each search query is billed per the Hanzo Search pricing tier. RAG chat queries include both the search cost and the LLM generation cost. Usage is tracked automatically through the bot gateway.

Environment Variables

HANZO_API_KEY=...                                  # Meilisearch master key
HANZO_SEARCH_BASE_URL=https://search.hanzo.ai      # Override search API base URL
HANZO_CHAT_BASE_URL=https://search.hanzo.ai        # Override RAG chat API base URL