Update apis/chat_api.py
Browse files- apis/chat_api.py +20 -22
apis/chat_api.py
CHANGED
@@ -188,36 +188,34 @@ class ChatAPIApp:
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data_response = streamer.chat_return_dict(stream_response)
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return data_response
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async def
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api_url = f"https://api-inference.huggingface.co/pipeline/feature-extraction/{model_name}"
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headers = {"Authorization": f"Bearer {api_key}"}
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if isinstance(result, list) and len(result) > 0 and isinstance(result[0], list):
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return [item for sublist in result for item in sublist] # Flatten list of lists
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else:
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logging.error(f"Unexpected response format: {result}")
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raise RuntimeError("Unexpected response format.")
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async def embedding(self, request: QueryRequest, api_key: str = Depends(extract_api_key)):
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try:
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embeddings = await self.chat_embedding(request.input, request.model, api_key)
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data = [{"object": "embedding", "index": i, "embedding": embedding} for i, embedding in enumerate(embeddings)]
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return EmbeddingResponse(
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object="list",
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data=data,
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model=request.
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usage={"prompt_tokens": len(request.
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)
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except Exception as e:
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raise HTTPException(status_code=500, detail=str(e))
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def setup_routes(self):
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data_response = streamer.chat_return_dict(stream_response)
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return data_response
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async def embedding(request: QueryRequest, api_key: str = Depends(extract_api_key)):
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api_url = f"https://api-inference.huggingface.co/pipeline/feature-extraction/{request.model_name}"
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headers = {"Authorization": f"Bearer {api_key}"}
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try:
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response = requests.post(api_url, headers=headers, json={"inputs": request.input_text})
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result = response.json()
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if "error" in result:
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logging.error(f"Error from Hugging Face API: {result.get('error', 'No detailed error message provided.')}")
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raise HTTPException(status_code=503, detail="The model is currently loading, please re-run the query.")
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if not (isinstance(result, list) and len(result) > 0 and isinstance(result[0], list)):
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logging.error(f"Unexpected response format: {result}")
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raise HTTPException(status_code=500, detail="Unexpected response format.")
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# Assuming each embedding is a list of lists of floats, flatten it
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flattened_embeddings = [sum(embedding, []) for embedding in result]
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data = [{"object": "embedding", "index": i, "embedding": embedding} for i, embedding in enumerate(flattened_embeddings)]
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return EmbeddingResponse(
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object="list",
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data=data,
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model=request.model_name,
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usage={"prompt_tokens": len(request.input_text), "total_tokens": len(request.input_text)}
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)
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except Exception as e:
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logging.error(f"An error occurred: {str(e)}")
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raise HTTPException(status_code=500, detail=str(e))
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def setup_routes(self):
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