Created app.py

app.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+from typing import List
+from sentence_transformers import SentenceTransformer
+import uvicorn
+
+app = FastAPI(title="Medical Embedding Service")
+
+# Load model ONCE at startup
+print("Loading Medical RAG Model... this may take a moment.")
+model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
+print("Model loaded successfully!")
+
+class QueryRequest(BaseModel):
+    text: str
+
+class DocumentRequest(BaseModel):
+    texts: List[str]
+
+@app.post("/embed_query")
+async def embed_query(request: QueryRequest):
+    # Uses specialized encode_query for IR tasks
+    embedding = model.encode_query(request.text).tolist()
+    return {"embedding": embedding}
+
+@app.post("/embed_docs")
+async def embed_docs(request: DocumentRequest):
+    # Uses specialized encode_document for IR tasks
+    embeddings = model.encode_document(request.texts).tolist()
+    return {"embeddings": embeddings}