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>
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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.prodfrom.env.prod.exampleplaceholders. - 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/inputanddata/workfrom 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_events → OCR_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:
-
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.
-
Shared-secret API key (defence in depth). Set
API_KEYto a long random value (openssl rand -hex 32). Every request toAPP_API_PREFIXexcept/healthmust then carry either:X-API-Key: <key>or:
Authorization: Bearer <key>/healthis intentionally exempt so external probes do not need the secret.In production this is required (
docker-compose.prod.ymlfails the stack ifAPI_KEYis empty). In development the key is optional and the default empty value disables the middleware entirely.The frontend reads
VITE_API_KEYand injects the header on every Axios request. For SSO deployments leaveVITE_API_KEYempty and let the reverse proxy inject the header server-side. -
Trusted-header identity (gateway/SSO, the target user-auth mode). Behind the platform gateway set
AUTH_REQUIRE_IDENTITY=true. The gateway injectsX-TeamHub-Actor,-Actor-Id,-App,-Role,-Roles,-Scopes,-Entitlements-Version;app/integrations/identity.pyvalidates them and maps roles/scopes to module permissions. LegacyHUB performs no own login and never queries AD/LDAP/HRHUB/staffdirectly. These headers are trusted only because the module is unreachable except via the gateway (see below). TheX-API-Keylayer 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-onlylegacyhub_dbnetwork plusedge(so the published ports keep working); the prod overlay drops theedgeattachment. Theapicontainer carries a docker healthcheck against/api/v1/health. Direct:8050access 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 theapito the sharedteamhub_net, stops publishing the api port (the gateway reaches it overteamhub_net), and setsAUTH_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. SpoofingX-TeamHub-*is prevented by this isolation; without it the trusted headers are forgeable.
Verification gates (per change)
python -m pytest tests/ -q— full unit suite (19+ tests).python -m compileall -q app scripts tests.docker compose config --quiet.- Frontend:
npx tsc --noEmit && npm run build. /api/v1/healthreturns{"status":"ok"}.- One smoke ingest of a known PDF; verify
/searchreturns a result.
Rollback
- Capture deployed commit SHA before deploy (
git rev-parse HEAD). - 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 - 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:
- Lower
RERANK_CANDIDATES(default 40 — reducing to 20 roughly halves work). - Increase
RERANKER_BATCH_SIZE(memory permitting). - Switch
RERANKER_DEVICE=cudaand use a GPU-capable image. - Disable reranker (
RERANKER_ENABLED=false) and accept raw RRF order — the API still returns useful results; thererankedfield 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=cudaon 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;"