Files
LegacyHUB/RUNBOOK.md
Vadim Malanov 221cdc4d0f feat(compose): internal db network and api healthcheck
Add the internal-only legacyhub_db network (same name the teamhub
federation overlay already uses) plus an edge network to the base
compose. Dev keeps host-published ports via edge; the prod overlay
pins data services to legacyhub_db only, closing the module-contract
gap (DB/broker on internal networks). Add a curl liveness healthcheck
for the api container against /api/v1/health.

Verified: docker compose config for dev, prod and prod+teamhub
stacks; per-service network/port/healthcheck matrix inspected via
config --format json.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-07-07 09:50:31 +03:00

11 KiB
Raw Blame History

LegacyHUB — Operational Runbook

Quick boot (dev)

cp .env.example .env
docker compose up -d --build
docker compose exec api python scripts/init_db.py
docker compose exec api python scripts/init_opensearch.py
docker compose exec api python scripts/init_qdrant.py
docker compose exec api python scripts/smoke_test.py

Verify:

curl -fsS http://localhost:8050/api/v1/health | jq .

Frontend dev:

cd frontend && cp .env.example .env && npm install && npm run dev
# http://localhost:5273

Production deploy

Production overlay enables OpenSearch security plugin, removes default ports, pins data services to the internal-only legacyhub_db network (no host access, no egress), forces externally-supplied credentials, and disables debug routes.

# 1. Ensure secrets exist
cp .env.prod.example .env.prod
$EDITOR .env.prod          # rotate every credential, never commit

# 2. Build + recreate
docker compose \
  -f docker-compose.yml -f docker-compose.prod.yml \
  --env-file .env.prod \
  up -d --build --force-recreate api worker

# 3. Migrations
docker compose -f docker-compose.yml -f docker-compose.prod.yml \
  --env-file .env.prod exec api python scripts/init_db.py

# 4. Health gate
docker compose -f docker-compose.yml -f docker-compose.prod.yml \
  --env-file .env.prod exec api python scripts/smoke_test.py
curl -fsS https://<host>/api/v1/health | jq -e '.status == "ok"'

Hardening notes (mandatory for prod):

  • Rotate every credential in .env.prod from .env.prod.example placeholders.
  • Put OpenSearch behind TLS and admin password. Remove DISABLE_SECURITY_PLUGIN=true (handled by overlay).
  • Front the API with a reverse proxy that performs auth + TLS termination.
  • Restrict CORS via CORS_ALLOWED_ORIGINS (comma-separated) — never * in prod.
  • MinIO root key/secret in prod must come from a secret store, not the repo.
  • Mount data/input and data/work from durable storage, not the workstation.

Ingestion

POST /api/v1/ingest/folder is deprecated and disabled by default (ENABLE_FOLDER_INGEST=false, returns 410 Gone). TeamHUB integrations ingest via POST /api/v1/knowledge-ingest with an AssetManifest. Enable folder ingest only for local bulk loading.

# deprecated local bulk load (requires ENABLE_FOLDER_INGEST=true + X-API-Key)
curl -X POST http://localhost:8050/api/v1/ingest/folder \
  -H "Content-Type: application/json" \
  -d '{"path":"/data/input","recursive":true,"force":false}'

# or inline (no Celery)
docker compose exec api python scripts/ingest_folder.py \
  --path /data/input --recursive --mode inline

# re-index a single doc
docker compose exec api python scripts/reindex_document.py \
  --document-id <uuid>

Failure handling

Each stage emits a row to processing_events with level and data. Inspect:

docker compose exec postgres psql -U legacyhub -d legacyhub -c \
  "SELECT created_at, stage, level, message FROM processing_events
   ORDER BY created_at DESC LIMIT 50;"
Failure Where to look Fix
OCR_FAILED processing_eventsOCR_STARTED then error Confirm tesseract-ocr-rus package; rerun scripts/reindex_document.py
EXTRACTION_FAILED processing_events → Docling stage Check timeout; verify Docling version pin
Indexing stuck OpenSearch + Qdrant health scripts/init_opensearch.py, scripts/init_qdrant.py
Reranker disabled API logs → reranker.disabled Ensure RERANKER_ENABLED=true; HF cache mounted

API authentication

Two mechanisms layered together:

  1. Reverse proxy / SSO (preferred). Front the API with nginx, Traefik, or an OAuth gateway. The reverse proxy terminates TLS and authenticates the caller; LegacyHUB never sees a raw user identity.

  2. Shared-secret API key (defence in depth). Set API_KEY to a long random value (openssl rand -hex 32). Every request to APP_API_PREFIX except /health must then carry either:

    X-API-Key: <key>
    

    or:

    Authorization: Bearer <key>
    

    /health is intentionally exempt so external probes do not need the secret.

    In production this is required (docker-compose.prod.yml fails the stack if API_KEY is empty). In development the key is optional and the default empty value disables the middleware entirely.

    The frontend reads VITE_API_KEY and injects the header on every Axios request. For SSO deployments leave VITE_API_KEY empty and let the reverse proxy inject the header server-side.

  3. Trusted-header identity (gateway/SSO, the target user-auth mode). Behind the platform gateway set AUTH_REQUIRE_IDENTITY=true. The gateway injects X-TeamHub-Actor, -Actor-Id, -App, -Role, -Roles, -Scopes, -Entitlements-Version; app/integrations/identity.py validates them and maps roles/scopes to module permissions. LegacyHUB performs no own login and never queries AD/LDAP/HRHUB/staff directly. These headers are trusted only because the module is unreachable except via the gateway (see below). The X-API-Key layer is a separate machine-to-machine concern.

Network model & firewall (federation)

  • Standalone dev (docker-compose.yml): runs the full stack and publishes ports on localhost for convenience. Data services sit on the internal-only legacyhub_db network plus edge (so the published ports keep working); the prod overlay drops the edge attachment. The api container carries a docker healthcheck against /api/v1/health. Direct :8050 access bypasses the gateway and must never be exposed beyond the workstation.

  • Federated / prod: add docker-compose.teamhub.yml. It moves every backing service onto an internal-only network (no host ports), attaches the api to the shared teamhub_net, stops publishing the api port (the gateway reaches it over teamhub_net), and sets AUTH_REQUIRE_IDENTITY=true.

    docker network create teamhub_net      # or bring up TeamHUB-Platform infra
    docker compose -f docker-compose.yml -f docker-compose.prod.yml \
      -f docker-compose.teamhub.yml --env-file .env.prod up -d --build
    
  • Firewall (host): open only the gateway's public port. Do not expose the api port, PostgreSQL (5440), MinIO (9000/9001), OpenSearch (9200/9600), Qdrant (6333/6334) or Redis (6379) to any address other than the gateway or loopback. Spoofing X-TeamHub-* is prevented by this isolation; without it the trusted headers are forgeable.

Verification gates (per change)

  1. python -m pytest tests/ -q — full unit suite (19+ tests).
  2. python -m compileall -q app scripts tests.
  3. docker compose config --quiet.
  4. Frontend: npx tsc --noEmit && npm run build.
  5. /api/v1/health returns {"status":"ok"}.
  6. One smoke ingest of a known PDF; verify /search returns a result.

Rollback

  1. Capture deployed commit SHA before deploy (git rev-parse HEAD).
  2. To roll back the API/worker image only:
    docker compose -f docker-compose.yml -f docker-compose.prod.yml \
      --env-file .env.prod up -d --build --force-recreate api worker \
      --no-deps  # keep PG/MinIO/OS/Qdrant intact
    
  3. Data services (PostgreSQL, MinIO, OpenSearch, Qdrant) are stateful and should not be rolled back casually. Restore from backup via the standard TeamHUB Suite backup runbook.

Reranker benchmark

The reranker is the latency-defining stage of the hybrid search path. Run the benchmark on every hardware change (CPU vs GPU, instance type, batch size) before promoting the configuration.

# synthetic warmup + 32 queries x 40 candidates, ~700-char passages
docker compose exec api python scripts/benchmark_reranker.py \
  --queries 32 --candidates 40 --warmup 4

# real corpus sample (after some documents are indexed)
docker compose exec api python scripts/benchmark_reranker.py \
  --source opensearch --query "ГОСТ 21.501-93" --candidates 40

Target SLOs (subject to revision once staging numbers land):

Metric CPU target GPU target
p95 latency / query < 700 ms < 120 ms
Throughput > 60 pair/s > 600 pair/s

If the measured p95 exceeds the budget, options in order of preference:

  1. Lower RERANK_CANDIDATES (default 40 — reducing to 20 roughly halves work).
  2. Increase RERANKER_BATCH_SIZE (memory permitting).
  3. Switch RERANKER_DEVICE=cuda and use a GPU-capable image.
  4. Disable reranker (RERANKER_ENABLED=false) and accept raw RRF order — the API still returns useful results; the reranked field reports the truth.

Passages are clipped to 2048 chars before being fed to the cross-encoder so a runaway chunk cannot starve the budget.

Load testing

Two complementary harnesses live under scripts/:

Ingest load

# Generate synthetic PDFs (~3 KB each, real PDF/1.4 with embedded text)
docker compose exec api python scripts/generate_synthetic_pdfs.py \
  --count 10000 --out /data/input/load

# Trigger ingest, sample status every 10 s, dump JSON history
docker compose exec api python scripts/load_ingest.py \
  --path /data/input/load \
  --api-url http://localhost:8050/api/v1 \
  --watch-seconds 1800 \
  --report-file /data/work/load_report.json

Target SLOs at the 70k-document scale (subject to refinement once measured):

Metric CPU target GPU target
Sustained throughput (docs/min) > 30 > 200
Failure rate < 1 % < 0.5 %
p95 per-document wall time < 90 s < 25 s

Search load

pip install locust  # one-time

locust -f scripts/locustfile_search.py \
       --host http://localhost:8050 \
       --headless --users 100 --spawn-rate 10 --run-time 10m \
       --html load_search.html

Target SLOs for hybrid mode with the reranker enabled:

Percentile CPU GPU
p50 600 ms 120 ms
p95 1500 ms 300 ms
p99 3500 ms 700 ms

If staging numbers miss the budget, walk the reranker remediation ladder above before chasing index sharding.

Scaling notes (~70k PDFs)

  • Workers horizontally scale: docker compose up -d --scale worker=8.
  • Set EMBEDDING_DEVICE=cuda on a GPU-capable worker image for ~10× embedding throughput.
  • OpenSearch single shard suffices to ~10M chunks; increase shards and add replicas in prod.
  • Qdrant single-node OK for ~5M vectors; switch to cluster build beyond that.

Common one-liners

# count indexed chunks in OpenSearch
curl 'http://localhost:9200/legacy_chunks/_count' | jq .

# inspect Qdrant collection
curl 'http://localhost:6333/collections/legacy_chunks' | jq .

# list MinIO buckets
docker compose exec minio mc alias set local http://localhost:9000 \
  "$MINIO_ACCESS_KEY" "$MINIO_SECRET_KEY"
docker compose exec minio mc ls local

# how many docs reached INDEXING_COMPLETED
docker compose exec postgres psql -U legacyhub -d legacyhub -c \
  "SELECT status, COUNT(*) FROM documents GROUP BY status;"