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Overview

Chaos Cypher is an AI-powered knowledge graph engine that transforms unstructured documents into structured, queryable knowledge. Upload PDFs, Word documents, web pages, or plain text — Chaos Cypher automatically indexes them for search, extracts entities and relationships into a knowledge graph, and lets you ask questions with retrieval-augmented generation (RAG).

Who is it for?​

  • Researchers managing large document collections who need to find connections across sources
  • Knowledge workers building structured knowledge bases from unstructured content
  • Developers wanting a self-hosted, multi-LLM knowledge platform with a clean API
  • Teams needing a shared knowledge graph with document processing pipelines

Key Capabilities​

Document Processing — Upload files or URLs. Chaos Cypher chunks, embeds, and indexes content automatically. Optionally extract entities and relationships into a knowledge graph using AI.

Knowledge Graph — Explore extracted entities and relationships through an interactive graph canvas. Create, edit, and connect nodes manually or through AI extraction. Apply templates for consistent entity types.

AI Chat with RAG — Ask questions about your documents. Responses are grounded in your actual content with citations. Scope conversations to specific sources or search the entire database.

Semantic Search — Find information using keyword, semantic (vector), or hybrid search. GraphRAG fuses graph traversal with vector search for multi-hop questions. Re-ranking ensures the most relevant results surface first.

MCP Server — Built-in Model Context Protocol server lets AI assistants like Claude Desktop, Cursor, and ChatGPT query and build your knowledge graph directly.

Multi-LLM Support — Works with Ollama (fully local), OpenAI, Anthropic, and Gemini. Switch providers with a single configuration change. Embeddings run locally on the CPU — no API keys needed.

Quick Code Preview​

from chaoscypher_core import ChaosCypher

# Extract entities from any document — one line
result = ChaosCypher.extract_sync("paper.pdf")
print(f"{len(result.entities)} entities found")
from chaoscypher_core import Engine

# Build a knowledge graph with persistent storage
with Engine("./data/databases/demo") as engine:
    alice = engine.add_node("Person", "Alice", properties={"role": "Engineer"})
    bob = engine.add_node("Person", "Bob", properties={"role": "Designer"})
    engine.add_edge("knows", alice, bob)
    print(f"Graph: {engine.get_stats().nodes} nodes")

Developer Quick Start

Neural Architecture​

Chaos Cypher is organized using a brain-inspired metaphor:

  • Core — The brain. Framework-agnostic business logic using hexagonal architecture.
  • Cortex — The processing center. FastAPI backend with vertical slice architecture.
  • Neuron — Worker cells. Background task processing for LLM calls and operations.
  • Interface — The interaction layer. React + TypeScript web UI.

This separation means each component can be used independently — the CLI uses Core directly without the web stack, and workers process jobs without the API server.

Install Chaos Cypher