bge-m3's sparse and colbert come from linear heads (sparse_linear.pt /
colbert_linear.pt) applied to the last hidden state -- a transformers forward
pass, not an embedding server's output. Two consequences that were both silent:

A DENSE-ONLY BACKEND NOW SAYS SO. `embedding_types` was simply ignored outside
the native path: a caller asking for sparse got a normal-looking response with
only `embedding` in it, its hybrid retrieval then ran on half the signal, and
the result was merely a worse score -- which reads as a model problem, not a
configuration one. Now a 400 that names the cause, where it DOES work
(coderai-embeddings), and why the vLLM image cannot: that image is LIGHT core
with no torch in its main venv BY DESIGN, so _load_bge_m3 could not even import.
Dense-only requests are untouched.

MULTI-VECTOR LONG INPUTS ARE WINDOWED. 8192 tokens is ~6000 words, so this bites
on whole documents rather than passages -- and when it bit, the tokenizer's
`truncation=True` kept the opening and said nothing. Each type is merged the way
the encoder already merges WITHIN a window, which is what makes these choices
defensible rather than invented:

  dense    mean, RE-NORMALIZED. It is an L2-normalized CLS vector and a mean of
           unit vectors is not one; cosine similarity on a non-unit vector is
           quietly wrong.
  sparse   MAX per token id -- the encoder max-pools per token id already
           (scatter_reduce amax), and a term's weight should be its strongest
           occurrence, not an average diluted by the pages that never mention
           it. A test guards against someone later tidying max into mean.
  colbert  CONCATENATED: max-sim scores each query token against ALL document
           token vectors, so every content token of every window belongs.

`embed_long_input=truncate` keeps the old behaviour explicitly, and `first`
keeps only the opening window, for an operator who would rather have that than a
merge.

Also removes three customer references I had put in shipped profile comments,
two of which went out in 0.3.69. A capability must be scoped and justified by
what it does, never by one install's needs, and a customer name does not belong
in a released image. The measurements keep their force without the attribution:
"hybrid retrieval wants dense and sparse from one pass", "a production fleet",
"a GPU-less orchestrator".

39 tests. Suite at the 25 pre-existing failures.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01HXRMe9ZPUQCCCtBai9uqt3