feat(): salary prediction

src/template_project/ml/routes/embed.py

@@ -0,0 +1,44 @@
+from dishka import FromDishka
+from dishka.integrations.fastapi import DishkaRoute
+from fastapi import APIRouter
+from pydantic import BaseModel, Field
+
+from template_project.application.common.embedding import Embedder
+
+router = APIRouter(route_class=DishkaRoute, tags=["Embedding"])
+
+
+class GetEmbeddingRequest(BaseModel):
+    text: str = Field(
+        ..., min_length=1, description="Text to encode", examples=["python backend developer with django"]
+    )
+
+    model_config = {"json_schema_extra": {"example": {"text": "python backend developer with django"}}}
+
+
+class GetEmbeddingResponse(BaseModel):
+    embedding: list[float] = Field(..., description="Embedding vector")
+
+    model_config = {
+        "json_schema_extra": {
+            "example": {
+                "embedding": [0.1, 0.2, 0.3, 0.4, 0.5],
+            }
+        }
+    }
+
+
+@router.post(
+    "/get_embedding",
+    summary="Get embedding",
+    description="Encode text into embedding vector",
+    responses={
+        200: {"description": "Embedding generated successfully", "model": GetEmbeddingResponse},
+    },
+)
+async def get_embedding(
+    request: GetEmbeddingRequest,
+    embedder: FromDishka[Embedder],
+) -> GetEmbeddingResponse:
+    embedding = await embedder.encode(request.text)
+    return GetEmbeddingResponse(embedding=embedding)