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import os |
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import json |
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from enum import Enum |
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import requests |
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import time |
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from typing import Callable |
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from litellm.utils import ModelResponse, Usage |
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class BasetenError(Exception): |
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def __init__(self, status_code, message): |
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self.status_code = status_code |
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self.message = message |
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super().__init__( |
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self.message |
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) |
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def validate_environment(api_key): |
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headers = { |
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"accept": "application/json", |
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"content-type": "application/json", |
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} |
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if api_key: |
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headers["Authorization"] = f"Api-Key {api_key}" |
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return headers |
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def completion( |
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model: str, |
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messages: list, |
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model_response: ModelResponse, |
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print_verbose: Callable, |
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encoding, |
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api_key, |
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logging_obj, |
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optional_params=None, |
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litellm_params=None, |
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logger_fn=None, |
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): |
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headers = validate_environment(api_key) |
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completion_url_fragment_1 = "https://app.baseten.co/models/" |
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completion_url_fragment_2 = "/predict" |
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model = model |
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prompt = "" |
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for message in messages: |
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if "role" in message: |
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if message["role"] == "user": |
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prompt += f"{message['content']}" |
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else: |
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prompt += f"{message['content']}" |
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else: |
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prompt += f"{message['content']}" |
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data = { |
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"inputs": prompt, |
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"prompt": prompt, |
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"parameters": optional_params, |
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"stream": True if "stream" in optional_params and optional_params["stream"] == True else False |
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} |
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logging_obj.pre_call( |
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input=prompt, |
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api_key=api_key, |
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additional_args={"complete_input_dict": data}, |
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) |
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response = requests.post( |
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completion_url_fragment_1 + model + completion_url_fragment_2, |
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headers=headers, |
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data=json.dumps(data), |
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stream=True if "stream" in optional_params and optional_params["stream"] == True else False |
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) |
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if 'text/event-stream' in response.headers['Content-Type'] or ("stream" in optional_params and optional_params["stream"] == True): |
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return response.iter_lines() |
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else: |
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logging_obj.post_call( |
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input=prompt, |
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api_key=api_key, |
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original_response=response.text, |
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additional_args={"complete_input_dict": data}, |
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) |
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print_verbose(f"raw model_response: {response.text}") |
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completion_response = response.json() |
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if "error" in completion_response: |
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raise BasetenError( |
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message=completion_response["error"], |
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status_code=response.status_code, |
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) |
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else: |
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if "model_output" in completion_response: |
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if ( |
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isinstance(completion_response["model_output"], dict) |
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and "data" in completion_response["model_output"] |
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and isinstance( |
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completion_response["model_output"]["data"], list |
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) |
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): |
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model_response["choices"][0]["message"][ |
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"content" |
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] = completion_response["model_output"]["data"][0] |
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elif isinstance(completion_response["model_output"], str): |
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model_response["choices"][0]["message"][ |
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"content" |
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] = completion_response["model_output"] |
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elif "completion" in completion_response and isinstance( |
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completion_response["completion"], str |
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): |
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model_response["choices"][0]["message"][ |
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"content" |
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] = completion_response["completion"] |
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elif isinstance(completion_response, list) and len(completion_response) > 0: |
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if "generated_text" not in completion_response: |
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raise BasetenError( |
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message=f"Unable to parse response. Original response: {response.text}", |
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status_code=response.status_code |
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) |
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model_response["choices"][0]["message"]["content"] = completion_response[0]["generated_text"] |
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if "details" in completion_response[0] and "tokens" in completion_response[0]["details"]: |
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model_response.choices[0].finish_reason = completion_response[0]["details"]["finish_reason"] |
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sum_logprob = 0 |
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for token in completion_response[0]["details"]["tokens"]: |
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sum_logprob += token["logprob"] |
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model_response["choices"][0]["message"]._logprobs = sum_logprob |
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else: |
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raise BasetenError( |
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message=f"Unable to parse response. Original response: {response.text}", |
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status_code=response.status_code |
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) |
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prompt_tokens = len(encoding.encode(prompt)) |
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completion_tokens = len( |
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encoding.encode(model_response["choices"][0]["message"]["content"]) |
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) |
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model_response["created"] = int(time.time()) |
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model_response["model"] = model |
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usage = Usage( |
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prompt_tokens=prompt_tokens, |
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completion_tokens=completion_tokens, |
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total_tokens=prompt_tokens + completion_tokens |
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) |
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model_response.usage = usage |
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return model_response |
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def embedding(): |
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pass |
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