mirror of
https://github.com/BerriAI/litellm.git
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93 lines
No EOL
4.3 KiB
Python
93 lines
No EOL
4.3 KiB
Python
import os, json
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from enum import Enum
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import requests
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from litellm import logging
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import time
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from typing import Callable
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class AnthropicConstants(Enum):
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HUMAN_PROMPT = "\n\nHuman:"
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AI_PROMPT = "\n\nAssistant:"
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class AnthropicError(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__(self.message) # Call the base class constructor with the parameters it needs
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class AnthropicLLM:
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def __init__(self, encoding, default_max_tokens_to_sample, api_key=None):
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self.encoding = encoding
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self.default_max_tokens_to_sample = default_max_tokens_to_sample
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self.completion_url = "https://api.anthropic.com/v1/complete"
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self.validate_environment(api_key=api_key)
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def validate_environment(self, api_key): # set up the environment required to run the model
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# set the api key
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if self.api_key == None:
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raise ValueError("Missing Anthropic API Key - A call is being made to anthropic but no key is set either in the environment variables or via params")
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self.api_key = api_key
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self.headers = {
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"accept": "application/json",
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"anthropic-version": "2023-06-01",
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"content-type": "application/json",
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"x-api-key": self.api_key
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}
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def completion(self, model: str, messages: list, model_response: dict, print_verbose: Callable, optional_params=None, litellm_params=None, logger_fn=None): # logic for parsing in - calling - parsing out model completion calls
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model = model
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prompt = f"{AnthropicConstants.HUMAN_PROMPT.value}"
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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"{AnthropicConstants.HUMAN_PROMPT.value}{message['content']}"
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else:
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prompt += f"{AnthropicConstants.AI_PROMPT.value}{message['content']}"
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else:
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prompt += f"{AnthropicConstants.HUMAN_PROMPT.value}{message['content']}"
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prompt += f"{AnthropicConstants.AI_PROMPT.value}"
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if "max_tokens" in optional_params and optional_params["max_tokens"] != float('inf'):
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max_tokens = optional_params["max_tokens"]
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else:
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max_tokens = self.default_max_tokens_to_sample
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data = {
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"model": model,
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"prompt": prompt,
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"max_tokens_to_sample": max_tokens,
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**optional_params
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}
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## LOGGING
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logging(model=model, input=prompt, additional_args={"litellm_params": litellm_params, "optional_params": optional_params}, logger_fn=logger_fn)
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## COMPLETION CALL
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response = requests.post(self.completion_url, headers=self.headers, data=json.dumps(data))
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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(model=model, input=prompt, additional_args={"litellm_params": litellm_params, "optional_params": optional_params, "original_response": response.text}, logger_fn=logger_fn)
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print_verbose(f"raw model_response: {response.text}")
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## RESPONSE OBJECT
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completion_response = response.json()
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if "error" in completion_response:
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raise AnthropicError(message=completion_response["error"], status_code=response.status_code)
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else:
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model_response["choices"][0]["message"]["content"] = completion_response["completion"]
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## CALCULATING USAGE
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prompt_tokens = len(self.encoding.encode(prompt)) ##[TODO] use the anthropic tokenizer here
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completion_tokens = len(self.encoding.encode(model_response["choices"][0]["message"]["content"])) ##[TODO] use the anthropic tokenizer here
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model_response["created"] = time.time()
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model_response["model"] = model
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model_response["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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return model_response
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def embedding(): # logic for parsing in - calling - parsing out model embedding calls
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pass |