Files
portainer-core/services/core-api
jpmschweitzerandClaude 0ac1128b04 feat(core-api): add AI stats widget with proxy endpoints
Implements Phase 2 of AI performance monitoring - creating a visual
dashboard widget for Organizr to display real-time AI metrics.

New Components:
- src/clients/ai_client.py: HTTP client for Core-AI service
  - Async HTTP requests to core-ai:8086
  - Fetches metrics, errors, and tool failures
  - Health check and metrics reset operations

- src/controllers/ai_controller.py: Proxy controller for AI metrics
  - GET /ai/health - Core-AI health check
  - GET /ai/metrics - Comprehensive performance metrics (proxied)
  - GET /ai/metrics/errors - Recent request errors (proxied)
  - GET /ai/metrics/tool-failures - Tool execution failures (proxied)
  - POST /ai/metrics/reset - Reset all metrics (admin)

- static/widgets/ai-stats.html: Performance dashboard widget
  - 4-panel grid layout: Agent, Tools, Memory, Health
  - Real-time metrics with 10-second auto-refresh
  - Color-coded performance indicators (excellent/good/warning/critical)
  - Response time thresholds: <1s excellent, <3s good, <10s warning
  - Success rate thresholds: >99% excellent, >95% good, >90% warning
  - Top 5 tools display with call counts and success rates
  - Transparent background for Organizr dark theme
  - Responsive design with mobile support

Configuration:
- src/config.py: Added core_ai_base_url setting
- src/main.py: Registered ai_router for /ai/* endpoints

Architecture:
┌─────────────────────────────────────────────┐
│ Browser (Organizr iFrame)                   │
│ ↓ Fetches /ai/metrics                       │
└─────────────────────────────────────────────┘
         ↓
┌─────────────────────────────────────────────┐
│ core-api:8083 (api.schweitz.net)           │
│ - Serves widget HTML                        │
│ - Proxies metrics requests                  │
└─────────────────────────────────────────────┘
         ↓
┌─────────────────────────────────────────────┐
│ core-ai:8086 (internal)                    │
│ - Collects metrics                          │
│ - Returns JSON data                         │
└─────────────────────────────────────────────┘

Benefits:
- External access via api.schweitz.net (proxy approach)
- No CORS issues (same-origin requests)
- Core-AI remains internal-only
- Single integration point with Organizr

Integration with Organizr:
1. Go to Settings → Customize → Homepage Items
2. Add New Item:
   - Name: "AI Performance Stats"
   - Type: iFrame
   - URL: http://localhost:8083/static/widgets/ai-stats.html
   - Authentication: User
3. Position widget on dashboard

Tested:
 Proxy endpoints responding correctly
 Widget accessible via /static/widgets/
 Metrics data flowing from core-ai → core-api → browser
 Color coding and formatting working
 Auto-refresh functional

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
2025-12-04 08:58:43 +01:00
..
2025-11-14 15:31:25 +01:00
2025-11-14 15:31:25 +01:00

Core Code API

OpenAPI-compatible functions for Open WebUI, providing web scraping and data processing capabilities.

Features

Web Scraper

  • Intelligent content extraction using Trafilatura
  • BeautifulSoup fallback for complex pages
  • Configurable content length limits
  • Optional link extraction
  • Perfect for feeding webpage content to LLMs

Architecture

src/
├── config.py              # Global application settings
├── logging_config.py      # Logging configuration
├── base_schema.py         # Base Pydantic models
├── main.py               # FastAPI application entry point
└── web_scraper/          # Web scraper module
    ├── __init__.py
    ├── config.py         # Module-specific settings
    ├── schemas.py        # Pydantic request/response models
    ├── service.py        # Business logic
    ├── router.py         # API routes
    └── exceptions.py     # Custom exceptions

Development

Requirements

  • Python 3.12+
  • Docker (for containerized deployment)

Local Development

# Install dependencies
pip install -r requirements.txt

# Run locally
uvicorn src.main:app --reload --host 0.0.0.0 --port 8083

Adding New Dependencies

Important: Dependencies use major version pinning (~=) for automatic patch updates while preventing breaking changes.

  1. Add package to requirements.txt with major version constraint:

    package-name~=1.2.0  # Allows 1.2.x, blocks 1.3.0
    
  2. Restart the container to install:

    docker restart core-api
    

The container automatically runs pip install -r requirements.txt on every boot, so new dependencies are installed immediately on restart.

Version Pinning Best Practices:

  • Use ~= (compatible release) for most packages: fastapi~=0.115.0
  • Use >=X,<Y for complex constraints: langchain-core>=0.3.17,<0.4.0
  • Allows automatic security patches without breaking changes
  • Documented in PEP 440

Docker Build

# Build image
docker build -t core-code:latest .

# Run container
docker run -p 8083:8083 core-code:latest

Deployment

Portainer Stack

  1. Navigate to Portainer UI
  2. Go to StacksAdd Stack
  3. Name: core-code
  4. Upload stacks/core-code.yml or paste contents
  5. Deploy

Environment Variables

See .env.example for all available configuration options.

API Documentation

Once deployed, access documentation at:

Integration with Open WebUI

Method 1: Functions (OpenAPI Import)

  1. In Open WebUI, navigate to Functions
  2. Import from OpenAPI spec: http://192.168.86.149:8083/openapi.json
  3. Use functions directly in chat

Method 2: Pipelines

  1. Create a pipeline that calls Core Code API endpoints
  2. Use as data source for LLM workflows

Method 3: Direct API Calls

import httpx

async with httpx.AsyncClient() as client:
    response = await client.post(
        "http://192.168.86.149:8083/web-scraper/scrape",
        json={
            "url": "https://example.com",
            "extract_main_content": True
        }
    )
    data = response.json()

API Endpoints

Web Scraper

POST /web-scraper/scrape

Scrape and extract content from a website.

Request:

{
  "url": "https://example.com/article",
  "extract_main_content": true,
  "include_links": false,
  "max_length": 10000
}

Response:

{
  "url": "https://example.com/article",
  "title": "Article Title",
  "content": "Extracted article content...",
  "extracted_at": "2025-11-12T19:30:00Z",
  "content_length": 5432,
  "links": null
}

Logging

Logs are written to:

  • Console: stdout (captured by Docker)
  • File: /app/logs/app.log (persisted via volume mount)

Log format:

2025-11-12 19:30:00 | INFO     | src.web_scraper.service:scrape_url:45 | Starting scrape for URL: https://example.com

Health Checks

  • Endpoint: GET /health
  • Docker: Automatic health checks configured
  • Response: {"status": "healthy"}

Security

  • Runs as non-root user (uid 1000)
  • No authentication required (internal network only)
  • CORS configured for same-network access
  • Rate limiting: Not implemented (internal use only)

Future Modules

The architecture supports adding new modules:

  • Data transformation functions
  • API integrations
  • File processing
  • Database queries

Each module follows the same structure:

src/
└── module_name/
    ├── config.py
    ├── schemas.py
    ├── service.py
    ├── router.py
    └── exceptions.py

Troubleshooting

Container won't start

docker logs core-code

API not responding

curl http://192.168.86.149:8083/health

Check OpenAPI spec

curl http://192.168.86.149:8083/openapi.json | jq

License

Internal use only.