A powerful RAG and knowledge management platform that uses tree-based document indexing for precise semantic search and navigation.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install orchata
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install orchata using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Orchata RAG?
Orchata RAG is a sophisticated knowledge management platform designed to organize documents into hierarchical tree structures. Unlike traditional RAG systems that treat documents as flat chunks, Orchata parses files into sections, summaries, and page ranges, allowing for highly granular retrieval. By integrating these Openclaw Skills, developers can interact with Spaces—logical containers for related content—to perform semantic searches that understand the structural context of complex documents.
This platform supports a wide variety of formats including PDF, Word, Excel, PowerPoint, and Markdown. It exposes specialized tools that allow AI agents to not only search but also manage the lifecycle of a knowledge base, from creation and document ingestion to advanced tree-based browsing.
Orchata RAG Use Cases
- Navigating large technical manuals with hierarchical section awareness.
- Managing cross-document knowledge bases within distinct, searchable Spaces.
- Automating document ingestion and indexing for AI-driven research workflows.
- Discovering relevant knowledge areas via LLM-powered smart queries across multiple repositories.
- Extracting specific data points from processed documents while maintaining page-level context.
How Orchata RAG Works
- Organize content into Spaces which serve as semantic folders with descriptions used for discovery.
- Upload documents which are automatically parsed into hierarchical Document Trees containing titles, summaries, and page ranges.
- Use smart_query to leverage LLM reasoning to identify which Spaces are most relevant to a specific question.
- Execute semantic searches via query_spaces to retrieve detailed facts, numbers, or content chunks.
- Navigate document structures using tree-visibility tools to drill down into specific sections without reading the entire file.
Orchata RAG Setup
To begin using these Openclaw Skills, you must interact with the Orchata MCP server. Start by listing your existing spaces or creating a new one for your content.
# List all active knowledge spaces
list_spaces with status="active"
# Create a new space for your technical documentation
manage_space with action="create" name="Engineering Wiki" description="Core technical specs"
# Upload a document to the space
save_document with spaceId="your_space_id" filename="architecture.md" content="# System Design..."
Orchata RAG Data Schema & Taxonomy
Orchata organizes data through a multi-layered taxonomy to ensure high retrieval precision:
| Entity | Properties | Description |
|---|---|---|
| Space | name, description, icon | A logical container for related documents. |
| Document | status, filename, metadata | Individual files with processing states (PENDING, PROCESSING, COMPLETED, FAILED). |
| Tree Node | title, summary, pages, content | Hierarchical segments of a document for granular navigation. |
| Query Result | content, source, score | Semantic matches returned with optional compact or detailed metadata. |
Orchata RAG Advanced Features
- Hierarchical tree-based indexing for navigating deep document structures with section summaries.
- Smart discovery logic that identifies relevant knowledge silos before performing deep searches.
- Multi-space semantic search capabilities with tunable result density (topK).
- Asynchronous document processing pipeline with comprehensive status monitoring.
- Compact mode support for broad discovery without heavy token consumption in Openclaw Skills.
- Support for multiple document formats including OCR-processed scanned PDFs and structured spreadsheets.
SKILL.md
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