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https://github.com/BerriAI/litellm.git
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remove init AnthropicClient for completion calls
This commit is contained in:
ishaan-jaff
parent
2dc1c35a05
commit
bc065f08df
2 changed files with 119 additions and 136 deletions
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@ -1,16 +1,15 @@
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import os, json
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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
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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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@ -19,132 +18,120 @@ class AnthropicError(Exception):
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self.message
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) # Call the base class constructor with the parameters it needs
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class AnthropicLLM:
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def __init__(
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self, encoding, default_max_tokens_to_sample, logging_obj, api_key=None
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):
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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.api_key = api_key
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self.logging_obj = logging_obj
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self.validate_environment(api_key=api_key)
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def validate_environment(
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self, api_key
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): # 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(
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"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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)
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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(
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self,
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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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optional_params=None,
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litellm_params=None,
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logger_fn=None,
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): # 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 += (
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f"{AnthropicConstants.HUMAN_PROMPT.value}{message['content']}"
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)
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else:
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prompt += (
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f"{AnthropicConstants.AI_PROMPT.value}{message['content']}"
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)
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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(
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"inf"
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):
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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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self.logging_obj.pre_call(
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input=prompt,
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api_key=self.api_key,
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additional_args={"complete_input_dict": data},
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# makes headers for API call
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def validate_environment(api_key):
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if api_key is None:
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raise ValueError(
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"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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)
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## COMPLETION CALL
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if "stream" in optional_params and optional_params["stream"] == True:
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response = requests.post(
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self.completion_url,
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headers=self.headers,
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data=json.dumps(data),
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stream=optional_params["stream"],
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)
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return response.iter_lines()
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else:
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response = requests.post(
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self.completion_url, headers=self.headers, data=json.dumps(data)
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)
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## LOGGING
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self.logging_obj.post_call(
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input=prompt,
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api_key=self.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 AnthropicError(message=response.text, status_code=response.status_code)
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if "error" in completion_response:
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raise AnthropicError(
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message=str(completion_response["error"]),
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status_code=response.status_code,
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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": api_key,
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}
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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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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 += (
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f"{AnthropicConstants.HUMAN_PROMPT.value}{message['content']}"
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)
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else:
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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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prompt += (
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f"{AnthropicConstants.AI_PROMPT.value}{message['content']}"
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)
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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 = 256 # required anthropic param, default to 256 if user does not provide an input
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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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## CALCULATING USAGE
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prompt_tokens = len(
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self.encoding.encode(prompt)
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) ##[TODO] use the anthropic tokenizer here
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completion_tokens = len(
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self.encoding.encode(model_response["choices"][0]["message"]["content"])
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) ##[TODO] use the anthropic tokenizer here
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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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if "stream" in optional_params and optional_params["stream"] == True:
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response = requests.post(
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"https://api.anthropic.com/v1/complete",
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headers=headers,
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data=json.dumps(data),
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stream=optional_params["stream"],
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)
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return response.iter_lines()
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else:
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response = requests.post(
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"https://api.anthropic.com/v1/complete", headers=headers, data=json.dumps(data)
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)
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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 AnthropicError(
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message=response.text, status_code=response.status_code
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)
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if "error" in completion_response:
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raise AnthropicError(
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message=str(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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model_response["choices"][0]["message"]["content"] = completion_response[
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"completion"
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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"] = {
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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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## CALCULATING USAGE
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prompt_tokens = len(
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encoding.encode(prompt)
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) ##[TODO] use the anthropic tokenizer here
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completion_tokens = len(
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encoding.encode(model_response["choices"][0]["message"]["content"])
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) ##[TODO] use the anthropic tokenizer here
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def embedding(
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self,
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): # logic for parsing in - calling - parsing out model embedding calls
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pass
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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():
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# logic for parsing in - calling - parsing out model embedding calls
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pass
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