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vh 7bdac80878 feat(embed-rerank): TEI is the fleet embed/rerank engine; esh-ml1 sole backend; retire fv-ml1 seats

Prime, 2026-09-25: TEI serves embedding + reranking for esh-ml1 and the fleet
from now on; fv-ml1 retires both once esh-ml1 is up.

- stacks/embed-rerank: vLLM -> TEI 1.9.4 (89- Ada build), same ports
  8001/8013, fail-closed truncation (--auto-truncate false; embed
  --max-batch-tokens 32768).
- litellm: qwen3-embedding -> esh-ml1 only (hosted_vllm/, unchanged address);
  reranker -> huggingface/ provider at :8013 (hosted_vllm/ 422s on TEI's
  `texts` body). DB alias reranker-a3-bge-v2-m3 patched to the same target.
- Verified via the gateway against the retiring fv-ml1 seats: embed cosine
  median 0.999927 (n=203); rerank top-1/top-3 29/30.
- stacks/vllm: vllm-embed and vllm-rerank-a3 removed (containers retired on
  fv-ml1, GPU 1 freed ~6.1 GB); reward + coder unchanged.
- Bake-off record moved to docs/pfi/embed-rerank-tei-vs-vllm-bakeoff.md;
  CLAUDE.md gains the TEI convention.

2026-09-25 08:30:53 -07:00

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embed-rerank

The fleet's embedding + reranking service, on esh-ml1 (CT 110 on esh-pve, RTX 2000E Ada), served by Hugging Face Text Embeddings Inference (TEI). Since 2026-09-25 it is the only backend behind the gateway names qwen3-embedding, reranker and reranker-a3-bge-v2-m3.

TEI is the fleet's embed/rerank engine (Prime, 2026-09-25). New embedding or reranking seats go on TEI, not vLLM. Why, and the measurements behind it: docs/pfi/embed-rerank-tei-vs-vllm-bakeoff.md.

container	model	port	endpoint
`tei-embed`	`Qwen/Qwen3-Embedding-0.6B`	8001	`/v1/embeddings` (OpenAI), `/embed`
`tei-rerank`	`BAAI/bge-reranker-v2-m3`	8013	`/rerank` — body `{"query", "texts"}`

Gateway wiring (stacks/litellm/conf/config.yaml)

qwen3-embedding → hosted_vllm/Qwen/Qwen3-Embedding-0.6B, api_base: http://10.0.50.80:8001/v1.
reranker → huggingface/BAAI/bge-reranker-v2-m3, api_base: http://10.0.50.80:8013 (no /v1). ⚠ hosted_vllm/ sends documents and TEI answers 422 "missing field texts".
reranker-a3-bge-v2-m3 is a DB-only alias (not in config.yaml) with the same target.

⚠ Invariants

Changing EMBED_MODEL invalidates every index built on it (Worldtree, nevermore, Open WebUI). An engine change is allowed only with a parity measurement against the current vectors. The TEI switch measured cosine median 0.999925 and old-index retrieval overlap 0.988.
Truncation is fail-closed (--auto-truncate false). TEI's default silently embeds a prefix of an over-length input and returns 200. Now: embed rejects more than 32,768 tokens and rerank more than 8,192, both with 422.
The image tag is GPU-generation specific: 89- is Ada. A different card needs a different prefix (TEI README image table).
nevermore thresholds rerank scores at 0.3. TEI scores differ from the old vLLM seat by up to 0.019, which produced 0 flips in 2,000 scores. Recheck thresholding consumers after any engine or version change.

Deploy

scripts/deploy-stack.sh esh-ml1 embed-rerank
ssh esh-ml1 'cd /opt/docker/compose/embed-rerank && cp -n .env.example .env && docker compose config -q && docker compose up -d'

Host prerequisites (driver, LXC, docker, toolkit) are in servers/esh-ml1/README.md.

Smoke test

curl -s http://10.0.50.80:8001/v1/embeddings -H 'content-type: application/json' \
  -d '{"model":"Qwen/Qwen3-Embedding-0.6B","input":"hello"}' | jq '.data[0].embedding | length'   # 1024
curl -s http://10.0.50.80:8013/rerank -H 'content-type: application/json' \
  -d '{"query":"cat","texts":["a cat","a car"]}'                      # index 0 scores ~0.9986

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