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.

stacks/vllm/.env.example

@@ -12,23 +12,15 @@
 VLLM_VERSION=v0.24.0
 
 # Host ports (container always listens on 8000 internally)
-EMBED_PORT=8001
-# 8013 — the reranker moved here 2026-08-20 when bge-v2-m3 (the R43 winner, which
-# had been running as a throwaway `docker run` on this port) was promoted into
-# this stack and the Qwen incumbent on :8002 was retired.
-RERANK_PORT=8013
+# (EMBED_PORT 8001 / RERANK_PORT 8013 retired 2026-09-25 with their seats —
+# embedding + reranking moved to TEI on esh-ml1, stacks/embed-rerank.)
 REWARD_PORT=8003
 
 # GPU assignment — all services share this GPU
 # (ana-ml2 has 0 and 1; default 1 keeps 0 free for heavy LLM work in llama-swap)
 GPU_ID=1
 
-# Models — reference by full repo name in API requests
-EMBED_MODEL=Qwen/Qwen3-Embedding-0.6B
-# bge-reranker-v2-m3 — the R43 bake-off winner, replacing Qwen3-Reranker-0.6B
-# (measured HARMING 80/90 fleet queries). Multilingual cross-encoder; needs no
-# --hf-overrides, unlike the Qwen reranker it displaced.
-RERANK_MODEL=BAAI/bge-reranker-v2-m3
+# Models
 # Skywork is a local-path AWQ output, not from HF Hub. Bind-mounted into the
 # reward container at /local-models — see compose.yaml. No env var here for
 # the model path itself since it's hard-coded in the compose command.
@@ -62,16 +54,12 @@ RERANK_MODEL=BAAI/bge-reranker-v2-m3
 # each at 0.05 (mostly util-reservation waste); 0.03 (~3.6 GB) fits weights +
 # CUDA context with room, freeing ~4 GB back to granite. Recreate them ONE AT A
 # TIME — concurrent recreate races the memory-profiling assertion.
-EMBED_GPU_MEM_UTIL=0.03
-RERANK_GPU_MEM_UTIL=0.03
 REWARD_GPU_MEM_UTIL=0.10
 
 # Context length caps — lower these if VRAM is tight.
 # Qwen3-Embedding supports up to 32k; reranker up to 32k.
 # Skywork capped at 16k server-side as defense-in-depth; JudgeClient also
 # enforces the cap at dispatch time per spec.
-EMBED_MAX_MODEL_LEN=8192
-RERANK_MAX_MODEL_LEN=8192
 REWARD_MAX_MODEL_LEN=16384
 
 # Optional API key — leave blank for no auth (fine on the internal network).