| name | moorcheh-cookbooks |
| description | Use this skill when the user wants to build AI applications with Moorcheh. Contains blueprints and implementation guides for knowledge base RAG, customer support chatbots, semantic search applications, AI Q&A systems, llm-wiki, knowledge base, personal wiki, karpathy, and optional frontend integration. Each cookbook includes architecture, code examples, setup instructions, and deployment guidance. |
Moorcheh Cookbooks
Overview
This skill provides an index of implementation guides and foundational requirements for building Moorcheh-powered AI applications. Use the references to quickly scaffold full-stack applications with best practices for data management, semantic search, and RAG.
Moorcheh Account
If the user does not have an account yet, direct them to the cloud console to register and create a free account.
Create a Moorcheh account at console.moorcheh.ai.
Before Building Any Cookbook
Follow these shared guidelines before generating any cookbook app:
Code Generation Rules
- No Unicode emoji in output. Do not use ✅ ❌ 📁 ⏳ 🎉 or any emoji in
print() or log messages. They crash on Windows cp1252 terminals. Use [OK], [ERROR], [WAIT] instead.
- Use snake_case everywhere. Moorcheh JSON (REST and Python SDK) uses snake_case for answer/search payloads as of platform 1.5.10. Do not use legacy camelCase field names.
Then proceed to the specific cookbook reference below.
Cookbook Index
- Knowledge Base RAG: Build a document Q&A system that ingests documents into Moorcheh and generates AI-powered answers with source citations using RAG.
- Customer Support Bot: Build a customer support chatbot that answers questions from your FAQ and documentation using conversational RAG with chat history.
- Semantic Search App: Build a semantic search application with ITS scoring, metadata filtering, and relevance-labeled results.
- AI Q&A System: Build a question-answering system with structured output, custom prompts, and multi-namespace search.
- LLM Wiki- Self-maintaining personal knowledge base using Karpathy's LLM Wiki
pattern extended with Moorcheh ITS search. See references/llm_wiki.md.
- Deep Ingest: Ingest large documents (>200K chars) or binary files (PDF, DOCX, XLSX) by uploading to a Moorcheh staging namespace. No local extraction needed- Moorcheh handles parsing, chunking, and indexing.
Integrations
LLM Wiki (Karpathy Pattern + Moorcheh)
Build a self-maintaining personal knowledge base where an AI agent reads your
documents once, builds a structured wiki of interlinked markdown pages, and
uploads them to Moorcheh for persistent ITS-powered semantic search.
When to use this cookbook:
- You are accumulating knowledge across many sources over time (research, product, competitive intel)
- You want your AI agent to build and maintain a wiki automatically
- You need semantic search + metadata filtering across a large collection of notes
- You want knowledge to compound- every source and every query enriches the base
Key components:
raw/- immutable source documents (agent reads only)wiki/- agent-generated markdown pages, interlinked and growingCLAUDE.md / AGENTS.md- the schema that tells the agent how to maintain the wiki- Moorcheh namespace- persistent ITS-indexed copy of all wiki pages
Full implementation guide: references/llm_wiki.md
Starter repo: https://github.com/moorcheh-ai/llm-wiki
Interface (Optional)
Use this when the user explicitly asks for a frontend for their Moorcheh backend:
- Frontend Interface: Build a Next.js frontend to interact with the Moorcheh backend, including chat UI and search interfaces.
