Climate Chatbot Backend

A Retrieval-Augmented Generation API for the Climate Academy student book. Built with Flask, Celery, Redis, ChromaDB, Sentence Transformers, and Ollama.

Stack

• Flask + Gunicorn — REST API
• Celery + Redis — async task queue and session store
• ChromaDB — vector database
• all-MiniLM-L6-v2 — local embeddings
• Ollama (Llama 3.1 8B) — local LLM inference

Setup

1. Install dependencies

uv sync

2. Configure environment

cp .env.example .env
# Edit .env if needed

3. Pull the LLM model

ollama pull llama3.1:8b

4. Run ingestion

uv run python ingest.py

5. Start services

Terminal 1 — Flask:

uv run python run.py

Terminal 2 — Celery:

uv run celery -A app.tasks worker --concurrency=2 --loglevel=info

API Endpoints

Method	Endpoint	Description
GET	/health	Health check
POST	/session	Create a new session
DELETE	/session/	Clear a session
POST	/chat	Send a message (returns task_id)
GET	/result/<task_id>	Poll for result