Merge skills branch with auth hardening and live API endpoints: - Hanzo Search (Meilisearch-powered): search, chat, index management - Hanzo Crawl (Crawl4AI-powered): web scraping with JS execution - Hanzo Vector (Qdrant-powered): vector database operations - Fail-fast auth validation across all skill scripts - SSRF risk documentation for js_code parameter
4.7 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. |
|
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. Set HANZO_SEARCH_KEY or HANZO_API_KEY.
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 stringlimit(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 returnattributesToHighlight(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 questionstore(required): Search store to ground answers inmode(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 generationsystem_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 Hanzo Search (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_SEARCH_KEY=... # Search API key (Meilisearch master key)
HANZO_API_KEY=... # Fallback API 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