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BytesAgain › Skills › Memora - Personal Knowledge Base (RAG)
🦀 ClawHub

Memora - Personal Knowledge Base (RAG)

by @zzlzzlzzl15

Memora — A self-hosted RAG (Retrieval-Augmented Generation) personal knowledge base. Built with FastAPI + Qdrant + DashScope/OpenAI Embedding + DeepSeek/Open...

Versionv1.2.0
Downloads1,144
Stars⭐ 2
#legal
TERMINAL
clawhub install memora-knowledge-base

📖 About This Skill

name: personal-knowledge-base description: > Memora — A self-hosted RAG (Retrieval-Augmented Generation) personal knowledge base. Built with FastAPI + Qdrant + DashScope/OpenAI Embedding + DeepSeek/OpenAI LLM. Supports semantic vector search, AI-powered Q&A with source citations, hybrid retrieval (dense + BM42 sparse + rerank), and full document management (upload PDF/DOCX/TXT/MD, create, list, detail). Use when: user asks about stored documents, wants to search/upload/create documents, or needs AI-organized answers from their knowledge base. NOT for: general chat, real-time news, tasks unrelated to the knowledge base. metadata: openclaw: requires: env: - KB_API_BASE

Memora — Personal Knowledge Base (RAG)

A self-hosted Retrieval-Augmented Generation (RAG) personal knowledge base that lets your AI assistant search, query, and manage your private documents.

Tech Stack

  • Backend: FastAPI (Python)
  • Vector Database: Qdrant (dense + sparse vectors)
  • Embedding: DashScope text-embedding-v4 / OpenAI compatible
  • LLM: DeepSeek / OpenAI compatible
  • Retrieval: Hybrid search (dense vectors + BM42 sparse vectors + Qwen3 Rerank)
  • Metadata Store: MySQL
  • Skill Client: Zero-dependency Python (stdlib only — urllib, json)

    • Features

  • Semantic Search — Find documents by meaning using vector similarity, not just keywords
  • AI-Powered Q&A — Ask a question, get an LLM-generated answer grounded in your documents with source citations
  • Hybrid Retrieval — Dense embedding + BM42 sparse vectors + reranking for optimal recall and precision
  • Document Upload — Ingest PDF, DOCX, TXT, and Markdown files with automatic chunking and vectorization
  • Document Creation — Create text documents directly from the agent
  • Document Management — List, view details, and organize your knowledge base

    • When to Run

  • User asks a question that may be answered by stored documents
  • User wants to search the knowledge base
  • User wants to list documents or view document details
  • User wants to upload a file or create a new document
  • User needs AI-organized answers on a topic from their personal knowledge

    • Workflow

    Upload a File

    1. Get the file path and title from the user 2. Run: 

       python scripts/kb_api.py upload "{absolute_file_path}" "{document_title}"
    3. Supported formats: .txt .pdf .docx .md 4. Returns upload result with document_id

    Create a Text Document

    1. Get the title and text content from the user 2. Run: 

       python scripts/kb_api.py create "{title}" "{content}"
    3. Returns creation result with document_id

    Search with AI Answer (RAG)

    1. Extract the user's query 2. Run: 

       python scripts/kb_api.py search_answer "{query}"
    3. Parse the returned JSON: extract answer and source documents from sources 4. Present the answer with source citations

    Search Documents Only

    1. Extract the user's search keywords 2. Run: 

       python scripts/kb_api.py search "{keywords}"
    3. Parse and display the ranked search results

    List All Documents

    1. Run: 

       python scripts/kb_api.py list
    2. Display the document list

    View Document Details

    1. Get the document ID 2. Run: 

       python scripts/kb_api.py detail "{document_id}"
    3. Display the document content

    Output Format

    Upload / Create:

    Document "{title}" has been added to the knowledge base (ID: {document_id})

    Search with AI Answer:

    Knowledge Base Query Result

    {AI-generated answer based on retrieved documents}

    Sources:

  • {document_title} (relevance: {score})

    • List Documents:

    Documents ({n} total) 1. {title} — {created_at} 2. ...

    Configuration

    Set the environment variable KB_API_BASE to point to the Memora backend. Default: http://127.0.0.1:8080

    Source code & setup guide: https://github.com/zzlzzlzzl15/Memora

    ⚙️ Configuration

    Set the environment variable KB_API_BASE to point to the Memora backend. Default: http://127.0.0.1:8080

    Source code & setup guide: https://github.com/zzlzzlzzl15/Memora