forked from phoenix/litellm-mirror
121 lines
4 KiB
Python
121 lines
4 KiB
Python
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, Optional
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from litellm.utils import ModelResponse
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from .prompt_templates.factory import prompt_factory, custom_prompt
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class OobaboogaError(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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) # Call the base class constructor with the parameters it needs
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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"Token {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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api_base: Optional[str],
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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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custom_prompt_dict={},
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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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default_max_tokens_to_sample=None,
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):
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headers = validate_environment(api_key)
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if "https" in model:
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completion_url = model
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elif api_base:
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completion_url = api_base
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else:
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raise OobaboogaError(status_code=404, message="API Base not set. Set one via completion(..,api_base='your-api-url')")
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model = model
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if model in custom_prompt_dict:
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# check if the model has a registered custom prompt
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model_prompt_details = custom_prompt_dict[model]
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prompt = custom_prompt(
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role_dict=model_prompt_details["roles"],
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initial_prompt_value=model_prompt_details["initial_prompt_value"],
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final_prompt_value=model_prompt_details["final_prompt_value"],
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messages=messages
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)
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else:
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prompt = prompt_factory(model=model, messages=messages)
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completion_url = completion_url + "/api/v1/generate"
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data = {
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"prompt": prompt,
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**optional_params,
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}
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## LOGGING
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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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## COMPLETION CALL
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response = requests.post(
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completion_url, headers=headers, data=json.dumps(data), stream=optional_params["stream"] if "stream" in optional_params else False
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)
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if "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
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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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## RESPONSE OBJECT
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try:
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completion_response = response.json()
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except:
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raise OobaboogaError(message=response.text, status_code=response.status_code)
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if "error" in completion_response:
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raise OobaboogaError(
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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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try:
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model_response["choices"][0]["message"]["content"] = completion_response['results'][0]['text']
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except:
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raise OobaboogaError(message=json.dumps(completion_response), status_code=response.status_code)
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## CALCULATING USAGE
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prompt_tokens = len(
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encoding.encode(prompt)
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)
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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"] = time.time()
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model_response["model"] = model
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model_response.usage.completion_tokens = completion_tokens
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model_response.usage.prompt_tokens = prompt_tokens
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model_response.usage.total_tokens = prompt_tokens + completion_tokens
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return model_response
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def embedding():
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# logic for parsing in - calling - parsing out model embedding calls
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pass
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