🌌 Langent — Personal Ontology & 3D Knowledge Nebula

Langent is a RAG (Retrieval-Augmented Generation) framework that transforms your local workspace into a 3D cosmic nebula of knowledge. It combines vector embeddings (ChromaDB) with knowledge graphs (Neo4j) to provide a deeply connected AI experience.

📺 Watch the Demo

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✨ Key Features

• 📂 Auto-Ingestion: Automatically scans and indexes MD, PDF, TXT, CSV, JSON, and YAML files.
• 🧠 Hybrid RAG: Merges semantic vector search with graph-based relationship traversal for superior context.
• 🌌 3D Nebula Visualizer: Explore your knowledge base in an interactive Three.js 3D environment.
• 🔗 Knowledge Linking: Automatically discovers and creates relationships between your documents and entities.
• 🤖 MCP Integration: Built-in support for Model Context Protocol (MCP) to plug into Claude Desktop, Antigravity, and more.

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🚀 Installation & Quick Start

1. Install via Pip

git clone https://github.com/yourusername/langent.git
cd langent
pip install -e .

2. Configure Environment

cp .env.example .env

Edit .env to set your LANGENT_WORKSPACE (the folder containing your documents).

3. Ingest & Serve

langent ingest --path ./samples  # Start with our sample data!
langent serve                    # Open http://localhost:8000

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� Data Workflow: From Workspace to 3D Nebula

Langent automates the complex journey from raw raw files to an interactive 3D knowledge universe.

1. Gather Data (Workspace): Drop your "data lumps" (PDFs, Markdown notes, CSV spreadsheets, Research papers) into your connected LANGENT_WORKSPACE folder.
2. Chunking: Langent automatically breaks these large files into smaller, semantically meaningful chunks (300-500 tokens).
3. Vectorization (ChromaDB):
   • Using local embedding models (e.g., all-MiniLM-L6-v2), each chunk is transformed into a high-dimensional vector.
   • These vectors are stored in ChromaDB for lightning-fast semantic retrieval.
4. 3D Projection:
   • Langent uses advanced dimensionality reduction (UMAP) to project these high-dimensional vectors into a 3D Point Cloud.
   • Points that are semantically similar "cluster" together in space, forming the constellations of your knowledge base.

Result: Your messy folder becomes a beautiful, searchable, and navigable 3D cosmic map.

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�🛠️ Advanced Setup

1. Connecting to Neo4j (Graph Store)

To enable the Knowledge Graph features (searching relationships, linking entities), you need a Neo4j instance.

• Option A: Docker (Recommended)

  docker run \
      --name langent-neo4j \
      -p 7474:7474 -p 7687:7687 \
      -e NEO4J_AUTH=neo4j/your_password \
      neo4j:latest

• Option B: Neo4j Desktop Install Neo4j Desktop, create a local project, and update your .env:

  NEO4J_URI="bolt://localhost:7687"
  NEO4J_USER="neo4j"
  NEO4J_PASSWORD="your_password"

2. MCP Integration (Claude & Antigravity)

Langent acts as an MCP (Model Context Protocol) server, allowing AI agents like Claude or Antigravity to use your workspace as their long-term memory.

• For Antigravity / Claude Desktop: Add the following to your mcp_config.json:

  "langent": {
    "command": "python",
    "args": ["-m", "langent.server.mcp_server"],
    "env": {
      "LANGENT_WORKSPACE": "/path/to/your/workspace"
    }
  }

🤖 Using Langent with AI Agents (Antigravity, Claude Code)

Once connected via MCP, you can talk to your workspace as if it's an intelligent entity.

• Data Ingestion:

  "Langent의 mcp 도구를 사용해서 내 워크스페이스에 있는 새로운 문서들을 인덱싱해줘."

• Semantic Search:

  "내 워크스페이스에서 'AI 미래 전략'과 관련된 내용을 네뷸라에서 검색해서 요약해줘."

• Graph Insight:

  "내 연구 주제인 'AI 에이전트'와 가장 많이 연결된 핵심 키워드들을 그래프로 분석해서 보고서로 만들어줘."

Langent provides the AI with specific tools (ingest_workspace, search_nebula, query_graph) allowing it to act as a 3D Knowledge Librarian.

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🌌 How to use Nebula 3D Visualizer

Once you run langent serve, navigate to http://localhost:8000.

• Points (Star Dust): Each point represents a chunk of your documents.
  • 🎨 Lavender: Regular markdown/text files.
  • 💖 Hot Pink: Important entities like "Alex AI" or yourself.
• Nodes (Planets): These are entities extracted into the Neo4j Graph.
• Lines (Cosmic Strings):
  • Weak Lines: Relationships between graph entities.
  • Dashed Lines: Semantic links between vector points and graph nodes.
• Controls:
  • Left Click: Select a point to see its original content.
  • Right Click / Drag: Rotate the universe.
  • Scroll: Zoom in/out of the knowledge cluster.
  • Search Bar: Type a keyword (e.g., "Suseo") to highlight matching stars in white.

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📄 License

This project is licensed under the Apache License 2.0.

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Created with ❤️ by Alex AI