chaoscypher-cortex 0.1.0

Chaos Cypher Cortex - Full-featured knowledge graph platform (processing center)

ChaosCypher Cortex

Full-featured knowledge graph backend API - Processing center

Cortex is the main backend API for ChaosCypher, providing comprehensive CRUD operations, workflow execution, document source processing, AI-powered chat, and knowledge graph management using Vertical Slice Architecture (VSA).

Features

• 📊 Knowledge Graph Management: Full CRUD for nodes, edges, and templates
• 💬 AI Chat: Conversational interface with RAG and tool use
• 📁 Document Source Processing: Process PDFs, text files, CSVs into knowledge graphs
• 🔄 Workflow Engine: Execute multi-step AI research workflows with triggers
• 🔍 Search: FTS5 full-text + sqlite-vec vector search; multi-hop GraphRAG retrieval
• 🔌 MCP Server: Expose graph operations as Model Context Protocol tools
• ⚙️ Settings Management: Configure LLM providers, databases, and system settings
• 🔐 Single-User Auth: nginx auth_request gates every API call (no admin/user split)
• 🗄️ Multi-Database: Isolated workspaces with independent graphs

Architecture

Cortex is part of the ChaosCypher neural architecture:

• Core - Brain (business logic)
• Cortex - Processing center (full backend) 👈 You are here
• Neuron - Worker cells (background processing)
• Interface - Interaction layer (UI)

Vertical Slice Architecture (VSA)

Cortex uses VSA with self-contained feature slices. Each slice contains its own routes, service logic, and Pydantic models. The authoritative list is the set of directories under packages/cortex/src/chaoscypher_cortex/features/:

packages/cortex/src/chaoscypher_cortex/
├── features/                 # VSA slices (chats, sources, nodes, edges,
│                             # templates, search, llm, queue, settings,
│                             # settings_public, workflows, triggers, tools,
│                             # dashboard, graph, graph_snapshot, mcp, lexicon,
│                             # backup, export, quality, counts, health,
│                             # diagnostics, logs, pause, upgrade, edition,
│                             # databases, local_auth, admin_plugins)
├── shared/                   # Shared infrastructure
│   ├── api/                 # Auth dependencies, error handling, pagination
│   ├── database/            # Database session
│   ├── llm/                 # LLM factory
│   └── queue/               # Queue utilities
└── main.py                   # FastAPI application + router registration

Each feature slice contains:

• models.py - Pydantic DTOs (Request/Response)
• repository.py - Data access layer
• service.py - Business logic
• api.py - REST endpoints + DI factory
• __init__.py - Barrel exports

Installation

# From source (workspace sync — installs core + cortex + all dev tools)
uv sync --all-packages --extra dev

# Single-package mode (cortex + its core dep only)
uv sync --package chaoscypher-cortex

The repo uses uv workspaces (see pyproject.toml [tool.uv.workspace]); pip install -e is no longer the supported install path. Install uv via the official installer before running these commands.

Usage

Standalone

# Start Cortex server
cc-cortex start

# Custom host/port
cc-cortex start --host 0.0.0.0 --port 8080

# With environment variables
QUEUE_HOST=localhost QUEUE_PORT=6379 cc-cortex start

Docker

# Development
docker compose -f packages/docker/multi-container/docker-compose.dev.yml up cortex

# Production
docker run -p 8080:8080 -e QUEUE_HOST=valkey -e QUEUE_PORT=6379 chaoscypher-cortex

Programmatic

from chaoscypher_cortex.main import create_app

app = create_app()

# Run with uvicorn
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8080)

Configuration

Configure via environment variables or packages/docker/data/settings.yaml:

# Queue (Valkey)
QUEUE_HOST=localhost
QUEUE_PORT=6379

# Database
CHAOSCYPHER_DATA_DIR=~/.local/share/chaoscypher
CHAOSCYPHER_CONFIG_DIR=~/.config/chaoscypher

# Logging
LOG_LEVEL=INFO
USE_JSON_LOGGING=false

# LLM Provider
LLM_CHAT_PROVIDER=ollama
LLM_CHAT_MODEL=llama3.2
LLM_EMBEDDING_PROVIDER=ollama
LLM_EMBEDDING_MODEL=snowflake-arctic-embed2

# API Keys (if using cloud providers)
OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=sk-ant-...
GOOGLE_API_KEY=...

API Endpoints

Routes live under /api/v1/ and are gated by nginx auth_request (no register/login flow inside Cortex). The full surface includes:

• Knowledge graph — /nodes, /edges, /templates, /graph
• Sources — /sources (upload, list, extract, commit, citations, chunks). SourceResponse exposes user upload-time choices via the nested upload_options object (auto_analyze, enable_normalization, enable_vision, content_filtering, filtering_mode, extraction_depth, forced_domain), per-stage drop / merge counters via quality_metrics (45 typed counters + companion fields like loader_encoding_used), and search-index health via quality_metrics.vector_indexing_status (pending / indexed / degraded / failed). New persisted upload settings must round-trip through upload_options, never as siblings on SourceResponse — see packages/docs/docs/reference/api/sources.md.
• Search — /search (FTS5, vector, hybrid, GraphRAG)
• Chat — /chats, /chats/{id}/messages
• Workflows — /workflows, /workflows/{id}/execute, /triggers, /tools
• Settings — /settings, /settings/reset (plus scoped /settings/reset/{scope} variants)
• Queue — /queue/tasks, /queue/status
• Operations — /llm/instances, /databases, /exports, /backup
• Diagnostics — /health, /diagnostics, /logs, /pause, /edition
• MCP — /mcp/sse, /mcp/messages

The complete reference (request/response shapes, query params, error envelopes) is in packages/docs/docs/reference/api/ and at the live OpenAPI page: http://localhost:8080/docs (when running).

Development

Project Structure

packages/cortex/
├── src/chaoscypher_cortex/
│   ├── features/           # VSA feature slices
│   ├── shared/             # Shared infrastructure
│   └── main.py             # FastAPI app
├── tests/                  # Test suite
├── Dockerfile              # Production image
├── Dockerfile.dev          # Development image
└── pyproject.toml          # Package configuration

Adding New Features

1. Create directory: features/{feature}/
2. Define DTOs: {feature}/models.py
3. Create repository: {feature}/repository.py
4. Create service: {feature}/service.py
5. Create API + factory: {feature}/api.py
6. Export: {feature}/__init__.py
7. Register router in main.py

Example:

# features/my_feature/models.py
from pydantic import BaseModel

class MyFeatureRequest(BaseModel):
    name: str

class MyFeatureResponse(BaseModel):
    id: str
    name: str

# features/my_feature/service.py
class MyFeatureService:
    def create(self, data: dict) -> dict:
        # Business logic
        return {"id": "123", "name": data["name"]}

# features/my_feature/api.py
from fastapi import APIRouter, Depends

router = APIRouter(prefix="/api/v1/my-feature", tags=["My Feature"])

def get_service() -> MyFeatureService:
    return MyFeatureService()

@router.post("/", response_model=MyFeatureResponse)
def create_item(
    request: MyFeatureRequest,
    service: Annotated[MyFeatureService, Depends(get_service)]
):
    return service.create(request.model_dump())

Testing

# Run all tests
pytest

# Unit tests only
pytest -m unit

# Integration tests
pytest -m integration

# With coverage
pytest --cov=chaoscypher_cortex --cov-report=html

Hot-Reload Development

# Using watchdog
watchmedo auto-restart -d src -p '*.py' -- cc-cortex start

# Using Docker
docker compose -f packages/docker/multi-container/docker-compose.dev.yml up cortex

Dependencies

• Core: chaoscypher-core - Business logic
• FastAPI: Web framework
• SQLModel: Database ORM
• ARQ: Background task queue
• Structlog: Structured logging
• Pydantic: Data validation

License

AGPL-3.0 License - See LICENSE file for details