Initial commit

.gitignore

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+venv/

Dockerfile

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+FROM python:3.9
+WORKDIR /app
+COPY app/ /app/
+RUN pip install -r requirements.txt
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

README.md

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+---
+title: Embedding Fastapi
+emoji: 🏆
+colorFrom: pink
+colorTo: yellow
+sdk: docker
+pinned: false
+---

main.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+from typing import List, Tuple
+import numpy as np
+from sentence_transformers import SentenceTransformer
+
+# Load the pre-trained model
+model = SentenceTransformer('Alibaba-NLP/gte-base-en-v1.5', trust_remote_code=True)
+
+# Define request model
+class MessageRequest(BaseModel):
+    messages: List[str]
+
+# Define response model
+class EmbeddingResponse(BaseModel):
+    dimensions: int  # Only return embedding size
+    numeric_values: List[List[float]]
+
+# Initialize FastAPI app
+app = FastAPI()
+
+@app.get("/")
+def home ():
+    return {"Message":"Welcome to homepage, kindly proceed by giving /docs in the URL" }
+
+@app.post("/embed", response_model=EmbeddingResponse)
+def embed(request: MessageRequest):
+    new_embeddings = model.encode(request.messages, convert_to_tensor=True)
+    return EmbeddingResponse(
+        dimensions=new_embeddings.shape[1],  # Return only the embedding dimension
+        numeric_values=new_embeddings.tolist()
+    )

requirements.txt

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+fastapi
+uvicorn
+pandas
+scikit-learn
+sentence-transformers
+numpy