Infrastructure

  +--------------------+      +--------------------+
  |   GitHub Repo 1    |      |   GitHub Repo 2    |
  |   ACP-PROJECT      |      |   ACP-PROMPTS      |
  +--------+-----------+      +--------+-----------+
           |                           |
           | GitHub Actions            | Raw URL fetch
           | CI/CD                     |
           v                           v
  +--------------------+      +--------------------+
  |   Vercel           |      |   Cloudflare       |
  |   Frontend         |<-----|   Worker API       |
  |   axiomprotocol.org|      |   Edge Functions   |
  +--------------------+      +--------+-----------+
                                       |
                              +--------+----------+
                              |                   |
                              v                   v
                      +---------------+   +---------------+
                      |  Vectorize    |   | KV Namespaces |
                      |  (3 indexes)  |   | - Cache       |
                      |  - axioms     |   | - Datasets    |
                      |  - queries    |   | - Rate limits |
                      |  - results    |   +---------------+
                      +-------+-------+
                              |
                      +-------+--------+
                      |  GitHub Repo 3 |
                      |  ACP-DATASETS  |
                      +----------------+

{
  "query": "What is the fastest sorting algorithm?",
  "models": [
    "openai/gpt-5.4",
    "anthropic/claude-sonnet-4-6",
    "google/gemini-2.5-flash"
  ],
  "structure": "sonata",
  "max_iterations": 7
}

{
  "consensus_reached": true,
  "final_answer": "QuickSort has O(n log n) average complexity...",
  "final_D": 0.08,
  "iterations_used": 3,
  "convergence_path": [0.35, 0.18, 0.08],
  "axioms_used": [
    "acp-comp-quicksort-avg-v1",
    "acp-comp-timsort-python-v1"
  ],
  "proof": "Verified via MathOracle + WikidataOracle",
  "structure_used": "sonata",
  "timestamp": "2026-04-09T12:00:00Z"
}

// 1. Generate embedding for the user query
const embedding = await env.AI.run(
  '@cf/baai/bge-base-en-v1.5',
  { text: [query] }
);

// 2. Query Vectorize with level filtering
const results = await env.VECTORIZE_AXIOMS.query(
  embedding.data[0],
  {
    topK: 5,
    filter: { level: [4, 5, 6, 7] },
    returnMetadata: true
  }
);

// 3. Return axioms with similarity scores
return results.matches.map(match => ({
  id: match.id,
  statement: match.metadata.statement,
  level: match.metadata.level,
  domain: match.metadata.domain,
  confidence: match.metadata.confidence,
  similarity: match.score
}));

# Install dependencies
pip install -r requirements.txt

# Start the API server
uvicorn main:app --reload

# Server runs at http://localhost:8000
# API docs at http://localhost:8000/docs (Swagger)
# Alternative docs at http://localhost:8000/redoc

  git push to main
        |
        v
  +---------------------+
  |   GitHub Actions     |
  |                     |
  | 1. Lint (ruff,black)|
  | 2. Type check (mypy)|
  | 3. Tests (pytest)   |
  | 4. Build check      |
  +----------+----------+
             |
     +-------+-------+
     |               |
     v               v
  +----------+  +-----------+
  |  Vercel  |  | Cloudflare|
  | Frontend |  |  Worker   |
  | Deploy   |  |  Deploy   |
  +----------+  +-----------+

services:
  acp-backend:
    image: acp/backend:latest
    ports:
      - "8000:8000"
    environment:
      - OPENROUTER_API_KEY=${OPENROUTER_API_KEY}
      - DATABASE_URL=${DATABASE_URL}
      - REDIS_URL=${REDIS_URL}
    depends_on:
      - postgres
      - redis

  postgres:
    image: postgres:15
    environment:
      - POSTGRES_DB=acp
      - POSTGRES_USER=acp
      - POSTGRES_PASSWORD=${POSTGRES_PASSWORD}
    volumes:
      - pgdata:/var/lib/postgresql/data

  redis:
    image: redis:7-alpine
    ports:
      - "6379:6379"

volumes:
  pgdata: