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Dockerfile

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+FROM python:3.12-slim
+
+# Set the working directory to /code
+WORKDIR /code
+
+# Copy the requirements.txt file into the container
+COPY requirements.txt .
+
+# Install the dependencies
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+# Set up a new user named "user" with user ID 1000
+RUN useradd -m -u 1000 user
+
+# Switch to the "user" user
+USER user
+
+# Set home to the user's home directory
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH
+
+# Set the working directory to the user's home directory
+WORKDIR $HOME/app
+
+# Copy the current directory contents into the container at $HOME/app
+COPY --chown=user . $HOME/app
+
+# Expose the port FastAPI will run on
+EXPOSE 7860
+
+# Start the FastAPI app on port 7860 (HF Spaces default)
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

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("Gaykar/all-MiniLM-L6-medical-rag")
+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}
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=8001)

requirements.txt

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+sentence-transformers==5.0.0
+fastapi==0.118.1
+uvicorn==0.40.0