Langent v3 — 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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What's New in v3

• Security: Removed all hardcoded credentials, added Cypher injection prevention, optional API key auth
• Logging: Replaced all print() with structured logging module
• Testing: Comprehensive pytest suite with 40+ test cases
• CI/CD: GitHub Actions for lint + test across Python 3.10/3.11/3.12
• Tooling: ruff (lint), mypy (type check), Dockerfile for containerized deployment
• API: Pydantic request models, FastAPI dependency injection, health check endpoint
• Bug fixes: Fixed ${} interpolation bug in workflows, fixed MCP server method call error

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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.
• LangGraph Workflows: Agent state machines with RAG + Graph reasoning pipeline.

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Installation & Quick Start

1. Install via Pip

git clone https://github.com/AlexAI-MCP/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 --workspace ./samples  # Start with sample data
langent serve                         # Open http://localhost:8000

4. Docker (Alternative)

docker build -t langent .
docker run -p 8000:8000 -v ./workspace:/app/workspace langent

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Development

# Install with dev dependencies
pip install -e ".[dev]"

# Run tests
pytest tests/ -v

# Lint
ruff check langent/ tests/

# Type check
mypy langent/

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Data Workflow: From Workspace to 3D Nebula

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

1. Gather Data (Workspace): Drop your PDFs, Markdown notes, CSV spreadsheets, and research papers into your 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 UMAP to project high-dimensional vectors into a 3D Point Cloud.
   • Semantically similar points cluster together, forming knowledge constellations.

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 Knowledge Graph features, 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. API Authentication

Set an API key in your .env to protect write endpoints:

API_KEY="your-secret-api-key"

Then pass it via the X-API-Key header for protected endpoints (/api/ingest, /api/link, /api/graph).

3. MCP Integration (Claude & Antigravity)

Langent acts as an MCP server, allowing AI agents like Claude to use your workspace as long-term memory.

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."

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Nebula 3D Visualizer

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

• Points (Star Dust): Each point represents a chunk of your documents.
• Nodes (Planets): Entities extracted into the Neo4j Graph.
• Lines (Cosmic Strings): Relationships between graph entities and semantic links.
• 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 to highlight matching stars.

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License

This project is licensed under the Apache License 2.0.

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Created by Alex AI