Memora

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