EMBED_MODEL can be changed freely, but dims stays hardcoded at 384

[royalpinto007]

Desired outcome

Configuring a different embedding model fails loudly instead of silently producing wrong-width vectors.

Why it matters

app/embeddings.py hardcodes the dimension globally:

DIMS = 384  # all-MiniLM-L6-v2. If you change the model, change the schema's vector(384) too.

and LocalEmbedder claims it unconditionally:

class LocalEmbedder:
    dims = DIMS
    def __init__(self, model_name: str = "sentence-transformers/all-MiniLM-L6-v2") -> None:

but model_name is fully user-controlled. app/config.py exposes EMBED_MODEL as an env var, and app/cli.py passes it through at lines 43, 65, and 104. Set EMBED_MODEL to any other sentence-transformers model, say all-mpnet-base-v2 at 768 dimensions, and LocalEmbedder.dims still reports 384 while embed() returns 768-wide vectors. The failure surfaces far from the cause: an opaque pgvector error on insert against the vector(384) column in app/schema.sql, or, worse, a partially ingested corpus.

The comment already tells the reader what to keep in sync. Nothing enforces it.

Steps

1. In LocalEmbedder._load(), after constructing the SentenceTransformer, read the real width with model.get_sentence_embedding_dimension() and store it on the instance.
2. Raise a clear error when it does not match DIMS, naming the model, both dimensions, and the vector(384) column in app/schema.sql that must change too.
3. Make dims a property that returns the loaded value rather than the class constant, so callers cannot read a stale 384.
4. Add a note in README.md under configuration that changing EMBED_MODEL requires a schema change and a re-ingest.

Keep the lazy import: the module deliberately avoids pulling in torch at import time, and the check belongs inside _load() for that reason.

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