{"id":44155496,"url":"https://github.com/eomaxl/dockerized-ai-agent","last_synced_at":"2026-02-09T05:38:35.223Z","repository":{"id":299608227,"uuid":"1003574708","full_name":"Eomaxl/Dockerized-AI-Agent","owner":"Eomaxl","description":"Dockerized AI Agent (Email 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FastAPI (routing.py)\n- Stores them in PostgreSQL via SQLModel (models.py, db.py)\n- Sends user messages to OpenAI (via LangChain + ChatOpenAI)\n- Returns a generated email reply\n- Includes Docker support for easy deployment\n\n## Requirements\n- Python 3.10+\n- PostgreSQL (connection via DATABASE_URL)\n- OpenAI credentials (OPENAI_API_KEY, optionally OPENAI_BASE_URL)\n- Docker \u0026 Docker Compose\n\n## How It Works — Code Flow\n- FastAPI endpoint at /api/chats/ accepts a JSON payload with message.\n- SQLModel validates the payload and creates a ChatMessage record with timestamp.\n- Before generating the email, it commits the record to DB.\n- LangChain + ChatOpenAI sends the user message to the LLM.\n- The generated response is returned in the HTTP response.\n\n## Dockerization\n- The root Dockerfile installs dependencies and starts FastAPI with Uvicorn.\n- docker-compose.yml brings up both the API and a Postgres container.\n- Environment variables are provided via a .env (added to .gitignore) to protect secrets.\n\n## Performance Handling\n- DB caching: Completed messages are stored with timestamps.\n- Concurrency: FastAPI + Uvicorn handles multiple simultaneous requests.\n- LLM generation is performed asynchronously through LangChain, non-blocking.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Feomaxl%2Fdockerized-ai-agent","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Feomaxl%2Fdockerized-ai-agent","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Feomaxl%2Fdockerized-ai-agent/lists"}