vh
7bdac80878
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.
57 lines
2.8 KiB
Markdown
57 lines
2.8 KiB
Markdown
# embed-rerank
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**The fleet's embedding + reranking service**, on **esh-ml1** (CT 110 on esh-pve,
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RTX 2000E Ada), served by **Hugging Face Text Embeddings Inference (TEI)**.
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Since 2026-09-25 it is the only backend behind the gateway names
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`qwen3-embedding`, `reranker` and `reranker-a3-bge-v2-m3`.
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**TEI is the fleet's embed/rerank engine** (Prime, 2026-09-25). New embedding or
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reranking seats go on TEI, not vLLM. Why, and the measurements behind it:
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[`docs/pfi/embed-rerank-tei-vs-vllm-bakeoff.md`](../../docs/pfi/embed-rerank-tei-vs-vllm-bakeoff.md).
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| container | model | port | endpoint |
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|---|---|---|---|
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| `tei-embed` | `Qwen/Qwen3-Embedding-0.6B` | 8001 | `/v1/embeddings` (OpenAI), `/embed` |
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| `tei-rerank` | `BAAI/bge-reranker-v2-m3` | 8013 | `/rerank` — body `{"query", "texts"}` |
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## Gateway wiring (stacks/litellm/conf/config.yaml)
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- `qwen3-embedding` → `hosted_vllm/Qwen/Qwen3-Embedding-0.6B`, `api_base: http://10.0.50.80:8001/v1`.
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- `reranker` → **`huggingface/`**`BAAI/bge-reranker-v2-m3`, `api_base: http://10.0.50.80:8013` (**no `/v1`**).
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⚠ `hosted_vllm/` sends `documents` and TEI answers **422 "missing field texts"**.
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- `reranker-a3-bge-v2-m3` is a DB-only alias (not in config.yaml) with the same target.
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## ⚠ Invariants
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- **Changing `EMBED_MODEL` invalidates every index built on it** (Worldtree,
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nevermore, Open WebUI). An engine change is allowed only with a parity
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measurement against the current vectors. The TEI switch measured cosine median
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0.999925 and old-index retrieval overlap 0.988.
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- **Truncation is fail-closed** (`--auto-truncate false`). TEI's default silently
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embeds a prefix of an over-length input and returns 200. Now: embed rejects
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more than 32,768 tokens and rerank more than 8,192, both with 422.
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- **The image tag is GPU-generation specific**: `89-` is Ada. A different card
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needs a different prefix (TEI README image table).
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- nevermore thresholds rerank scores at 0.3. TEI scores differ from the old vLLM
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seat by up to 0.019, which produced 0 flips in 2,000 scores. Recheck
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thresholding consumers after any engine or version change.
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## Deploy
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```bash
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scripts/deploy-stack.sh esh-ml1 embed-rerank
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ssh esh-ml1 'cd /opt/docker/compose/embed-rerank && cp -n .env.example .env && docker compose config -q && docker compose up -d'
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```
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Host prerequisites (driver, LXC, docker, toolkit) are in
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[`servers/esh-ml1/README.md`](../../servers/esh-ml1/README.md).
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## Smoke test
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```bash
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curl -s http://10.0.50.80:8001/v1/embeddings -H 'content-type: application/json' \
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-d '{"model":"Qwen/Qwen3-Embedding-0.6B","input":"hello"}' | jq '.data[0].embedding | length' # 1024
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curl -s http://10.0.50.80:8013/rerank -H 'content-type: application/json' \
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-d '{"query":"cat","texts":["a cat","a car"]}' # index 0 scores ~0.9986
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```
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