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DocsGPT/deployment/k8s/deployments/docsgpt-deploy.yaml
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Alex 00be2c05ad fix: make worker-delegated embedding survive the shipped deployments
Query embedding moved to the Celery worker, but nothing that ships was
updated to consume the queue it dispatches to.

- Add `embeddings` to every worker `-Q` list (compose x3, k8s, devcontainer,
  sandbox README). Without it a search blocked for EMBEDDINGS_DELEGATE_TIMEOUT
  and then answered with no retrieved context, because classic_rag swallows the
  dispatch error and skips the source -- bad answers, not an error.

- Skip the task_postrun heap reclaim for the embed task. The full gc.collect()
  was written for docling/torch parses; on a worker holding the ONNX model it
  measured ~86ms against ~8ms for the embed itself, a 9x slowdown of the round
  trip for a task that allocates a few kilobytes.

- Resolve the installation pin in the re-embed script. It never imports
  application.app, so an install pinned in app_metadata with no EMBEDDINGS_NAME
  set -- every stock k8s deployment, whose manifests carry no embedding config
  -- would rewrite its whole index with the legacy default and stamp
  sources.model to match, then be told by the boot warning to run it again.

- Fail fast for 30s after a failed dispatch. fanout.embed_questions falls back
  to letting each store embed its own query, so one dead-worker retrieval paid
  the timeout once in the fan-out and again per source.

- Forget the task result. Nothing reads it back: the key is per-dispatch UUID,
  not content-addressed, so a repeated query mints another. Left alone every
  search leaked ~17KB for result_expires (7 days) into the Redis the broker
  shares -- on the bundled k8s manifest (1Gi, no maxmemory policy) that is an
  OOMKill that takes the broker with it.

- Release the model ensure_vector_schema loads to read the width of an
  unregistered model, in a process that delegates and would never call it.
  The width still comes from the model, not the table, so the mismatch check
  the hook exists for keeps working.

- Correct the docs that said otherwise: embeddings.md claimed the standard
  deployment worked unchanged, upgrading.mdx said no action was needed, and
  the settings table listed none of the three delegation settings.
2026-08-28 14:31:19 +01:00

150 lines
4.0 KiB
YAML

apiVersion: apps/v1
kind: Deployment
metadata:
name: docsgpt-api
spec:
replicas: 1
selector:
matchLabels:
app: docsgpt-api
template:
metadata:
labels:
app: docsgpt-api
spec:
initContainers:
# Block pod start until Postgres accepts connections. The `postgres-init`
# Job is responsible for running alembic migrations; this container only
# waits for the server to be reachable.
- name: wait-for-postgres
image: postgres:16-alpine
command:
- sh
- -c
- |
until pg_isready -h postgres -p 5432 -U docsgpt -d docsgpt; do
echo "Waiting for postgres..."; sleep 2;
done
containers:
- name: docsgpt-api
image: arc53/docsgpt
ports:
- containerPort: 7091
resources:
limits:
memory: "4Gi"
cpu: "2"
requests:
memory: "2Gi"
cpu: "1"
envFrom:
- secretRef:
name: docsgpt-secrets
env:
- name: FLASK_APP
value: "application/app.py"
- name: DEPLOYMENT_TYPE
value: "cloud"
- name: POSTGRES_URI
valueFrom:
secretKeyRef:
name: docsgpt-secrets
key: POSTGRES_URI
# Disable in-app auto-bootstrap. The `postgres-init` Job under
# deployment/k8s/jobs/ owns schema creation and Alembic migrations,
# so application pods must not race with it on rollout.
- name: AUTO_MIGRATE
value: "false"
- name: AUTO_CREATE_DB
value: "false"
---
apiVersion: apps/v1
kind: Deployment
metadata:
name: docsgpt-worker
spec:
replicas: 1
selector:
matchLabels:
app: docsgpt-worker
template:
metadata:
labels:
app: docsgpt-worker
spec:
initContainers:
- name: wait-for-postgres
image: postgres:16-alpine
command:
- sh
- -c
- |
until pg_isready -h postgres -p 5432 -U docsgpt -d docsgpt; do
echo "Waiting for postgres..."; sleep 2;
done
containers:
- name: docsgpt-worker
image: arc53/docsgpt
# `parsing` queue carries read_document/parse_document; required for its await to resolve.
# For heavy/OCR parsing, run a separate deployment with `-Q parsing` (and GPU env).
command: ["celery", "-A", "application.app.celery", "worker", "-l", "INFO", "-n", "worker.%h", "-Q", "docsgpt,parsing,embeddings"]
resources:
limits:
memory: "4Gi"
cpu: "2"
requests:
memory: "2Gi"
cpu: "1"
envFrom:
- secretRef:
name: docsgpt-secrets
env:
- name: DEPLOYMENT_TYPE
value: "cloud"
- name: API_URL
value: "http://<your-api-endpoint>"
- name: POSTGRES_URI
valueFrom:
secretKeyRef:
name: docsgpt-secrets
key: POSTGRES_URI
# Disable in-app auto-bootstrap. The `postgres-init` Job under
# deployment/k8s/jobs/ owns schema creation and Alembic migrations,
# so application pods must not race with it on rollout.
- name: AUTO_MIGRATE
value: "false"
- name: AUTO_CREATE_DB
value: "false"
---
apiVersion: apps/v1
kind: Deployment
metadata:
name: docsgpt-frontend
spec:
replicas: 1
selector:
matchLabels:
app: docsgpt-frontend
template:
metadata:
labels:
app: docsgpt-frontend
spec:
containers:
- name: docsgpt-frontend
image: arc53/docsgpt-fe
ports:
- containerPort: 5173
resources:
limits:
memory: "1Gi"
cpu: "1"
requests:
memory: "256Mi"
cpu: "100m"
env:
- name: VITE_API_HOST
value: "http://<your-api-endpoint>"
- name: VITE_API_STREAMING
value: "true"