nuvraxis/rag-chatbot-worker repository overview

⁠rag-chatbot-worker

The ingestion worker for the multi-tenant RAG chatbot platform. It takes documents uploaded through the API, extracts their text, splits it into passages, embeds those passages and writes the vectors — so that an upload returns immediately and the slow work happens off the request path.

Source and full documentation: https://github.com/Nuvraxis/nuvrag-platform⁠

⁠Tags

Pre-releases such as 1.2.3-rc.1 publish under that exact version only, and never move latest, 1.2 or 1.

Run the same version as the API. This image is built directly from nuvraxis/rag-chatbot-api⁠ with a different command, and the two share a database schema.

⁠What it needs

The same PostgreSQL + pgvector and Redis as the API, the same environment, and the same document storage. If STORAGE_BACKEND=local, the API and this worker must share that directory as a real volume — otherwise the worker cannot read what the API just wrote, and every document fails to ingest.

It is a Celery worker: it takes jobs from Redis and needs no inbound port and no ingress.

⁠Configuration

Identical to the API — see nuvraxis/rag-chatbot-api⁠ — plus two of its own:

AI_CREDENTIALS_ENCRYPTION_KEY must be the same value as the API's. The worker decrypts each tenant's embedding provider credentials with it; a different key means every ingestion job fails.

Optional malware scanning: set INGESTION_CLAMAV_HOST and a ClamAV daemon becomes mandatory — a scanner that cannot be reached fails the job rather than letting the file through.

⁠Image facts

• Port: none, and none needed
• User: non-root, UID/GID 1001
• Restarts: --max-tasks-per-child=200, so a leaky parser cannot accumulate
• Base: the API image of the same version

⁠Scaling

Worker load is queue depth, not CPU, so a CPU-based autoscaler will scale down while a backlog is still draining. The Helm chart at oci://registry-1.docker.io/nuvraxis/rag-platform supports KEDA on Redis list length for exactly this reason.

⁠Licence and security

See the repository. Report security issues privately through GitHub rather than in a public issue.