GET api update for image vector

app/api/routers/search.py

@@ -23,7 +23,13 @@ from app.infrastructure.settings import (
 )
 from app.services import image_embedder
 from app.services.catalog_search import search_catalog
-from app.services.image_match import ImageSearchResult, InvalidVectorError, search_by_vector
+from app.services.image_match import (
+    ImageSearchResult,
+    InvalidVectorError,
+    normalise_vector,
+    parse_vector_param,
+    search_by_vector,
+)
 
 router = APIRouter(tags=["search"])
 
@@ -117,6 +123,39 @@ def image_vector_search_endpoint(body: ImageVectorSearchRequest) -> ImageSearchO
     return _to_image_search_out(result)
 
 
+@router.get("/search/image-vector", response_model=ImageSearchOut)
+def image_vector_search_get_endpoint(
+    vector: str = Query(
+        ..., min_length=1, max_length=16_000,
+        description="The 1024-float embedding: comma-separated decimals, or urlsafe base64 of "
+                    "1024 little-endian float32 (recommended - 5.5 KB instead of 8-10 KB)",
+    ),
+    text: Optional[str] = Query(None, max_length=500, description="OCR text read off the label"),
+    brand: Optional[str] = Query(None, max_length=120, description="Restrict to one brand (no fallback)"),
+    category: Optional[str] = Query(None, max_length=120),
+    top_k: int = Query(IMAGE_SEARCH_DEFAULT_TOP_K, ge=1, le=IMAGE_SEARCH_MAX_TOP_K),
+    min_score: float = Query(IMAGE_SEARCH_DEFAULT_MIN_SCORE, ge=-1.0, le=1.0),
+) -> ImageSearchOut:
+    """The POST above as a GET, for clients that can only pass a query string.
+
+    Same ranking, same response. The vector is 1024 floats, so the URL is
+    5.5 KB as base64 or 8-10 KB comma-separated: fine through the API host
+    (Traefik -> uvicorn, which serve.py gives 64 KB of request-line room),
+    but the comma form exceeds the 8 KB nginx allows on the app domain. Use
+    base64, or the POST, for anything that has to work everywhere.
+    """
+    try:
+        # Validated here, not only inside the service, so this route rejects a
+        # short / NaN / zero vector exactly as the POST's schema does.
+        values = normalise_vector(parse_vector_param(vector))
+        result = search_by_vector(
+            values, text=text, brand=brand, category=category, top_k=top_k, min_score=min_score,
+        )
+    except InvalidVectorError as exc:
+        raise HTTPException(status_code=422, detail=str(exc))
+    return _to_image_search_out(result)
+
+
 @router.post("/search/image", response_model=ImageSearchOut)
 async def image_search_endpoint(
     file: UploadFile = File(..., description="The product photo (JPEG/PNG/WebP), ideally cropped to the pack"),