go

main.py

@@ -175,25 +175,6 @@ async def create_embeddings(request: EmbedRequest):
         logger.error(f"Inference error: {e}")
         raise HTTPException(status_code=500, detail=str(e))
 
-@app.post("/deembed", dependencies=[Depends(verify_token)])
-async def de_embed_vector(request: DeEmbedRequest):
-    """
-    Experimental: Reverse vector to text.
-    
-    NOTE: Mathematically, standard embedding models (BERT, BGE) are NOT reversible 
-    because they are lossy compression algorithms. 
-    
-    To retrieve text from a vector, you must use a Vector Database (retrieval), 
-    not a direct model inversion.
-    """
-    # In a real scenario, this would look like:
-    # result = vector_db.search(vector=request.vector, top_k=1)
-    # return {"text": result.text}
-    
-    raise HTTPException(
-        status_code=501, 
-        detail=(
-            "De-embedding (Vector-to-Text) is not possible with standalone embedding models. "
-            "This endpoint requires a connected Vector Database to perform a similarity search."
-        )
-    )
+@app.get("/ping")
+async def ping():
+    return {"message": "embed-api is alive!"}