Cortex/docker-compose.yml at main · Remskill/Cortex · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
# ============================================
# CORTEX - AI Memory System
# ============================================
# Standalone semantic search and knowledge base for ANY codebase
# Provides vector embeddings via MCP server integration
#
# This is a portable, self-contained system that can be dropped
# into any project to provide AI memory capabilities via Claude Code.
#
# Architecture:
#   - PostgreSQL with pgvector extension (vector database)
#   - Ollama (embedding service using nomic-embed-text model)
#   - MCP Server runs locally via .mcp.json (STDIO transport)
#
# Quick Start:
#   1. Copy this entire cortex/ folder to your project
#   2. Create .env file (copy from .env.example)
#   3. Set WORKSPACE_ROOT to your project path in .env
#   4. docker-compose up -d
#   5. npm install && npm run setup
#
# MCP Integration (.mcp.json in your project root):
#   {
#     "mcpServers": {
#       "cortex": {
#         "command": "npx",
#         "args": ["tsx", "cortex/src/server.ts"],
#         "env": {
#           "DATABASE_URL": "postgres://cortex:cortex-password@localhost:5433/cortex",
#           "OLLAMA_URL": "http://localhost:11434",
#           "WORKSPACE_ROOT": "/absolute/path/to/your/project"
#         }
#       }
#     }
#   }
#
# Health Checks:
#   curl http://localhost:5433          # PostgreSQL
#   curl http://localhost:11434         # Ollama API
#
# Environment Variables (set in .env file):
#   CORTEX_POSTGRES_PASSWORD - Database password
#   WORKSPACE_ROOT - Absolute path to your codebase (for .mcp.json)
#   OLLAMA_URL - Ollama API endpoint

services:
  # ===========================================
  # POSTGRESQL - Vector Database with pgvector
  # ===========================================
  cortex-postgres:
    image: pgvector/pgvector:pg16
    container_name: cortex-postgres
    ports:
      - "5433:5432"  # 5433 to avoid conflict with system PostgreSQL
    environment:
      - POSTGRES_DB=cortex
      - POSTGRES_USER=cortex
      - POSTGRES_PASSWORD=${CORTEX_POSTGRES_PASSWORD:-cortex-dev-pass-123}
    volumes:
      - cortex_postgres_data:/var/lib/postgresql/data
    restart: unless-stopped
    healthcheck:
      test: ["CMD-SHELL", "pg_isready -U cortex -d cortex"]
      interval: 10s
      timeout: 5s
      retries: 5
    networks:
      - cortex-network

  # ===========================================
  # OLLAMA - Embedding Service
  # ===========================================
  # Uses nomic-embed-text model (768 dimensions)
  # Automatically pulls model on first startup
  cortex-ollama:
    image: ollama/ollama:latest
    container_name: cortex-ollama
    ports:
      - "11434:11434"  # Ollama API
    volumes:
      - cortex_ollama_data:/root/.ollama
    restart: unless-stopped
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:11434/api/tags"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 60s
    networks:
      - cortex-network
    # Pull nomic-embed-text model on startup
    entrypoint: ["/bin/sh", "-c"]
    command:
      - |
        /bin/ollama serve &
        OLLAMA_PID=$$!
        sleep 10
        echo "Pulling nomic-embed-text model..."
        ollama pull nomic-embed-text
        echo "Model ready!"
        wait $$OLLAMA_PID

volumes:
  cortex_postgres_data:
    driver: local
  cortex_ollama_data:
    driver: local

networks:
  cortex-network:
    driver: bridge