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https://github.com/BerriAI/litellm.git
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anthropic fixes
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parent
65941644dc
commit
70b323e0f5
1 changed files with 31 additions and 17 deletions
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@ -1,4 +1,4 @@
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import json
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import os, 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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@ -15,11 +15,15 @@ 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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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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class AnthropicLLM:
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def __init__(self, encoding, default_max_tokens_to_sample, logging_obj, api_key=None):
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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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@ -27,13 +31,13 @@ class AnthropicLLM:
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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(self, api_key): # set up the environment required to run the model
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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 is None:
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if self.api_key == None:
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raise ValueError(
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"Missing Anthropic API Key -"
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+ " A call is being made to anthropic but no key is set either"
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+ " in the environment variables or via params"
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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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@ -58,13 +62,19 @@ class AnthropicLLM:
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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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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 += f"{AnthropicConstants.AI_PROMPT.value}{message['content']}"
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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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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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@ -75,7 +85,7 @@ class AnthropicLLM:
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**optional_params,
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}
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# LOGGING
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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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@ -99,7 +109,7 @@ class AnthropicLLM:
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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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## 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(
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@ -107,13 +117,17 @@ class AnthropicLLM:
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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["completion"]
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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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# CALCULATING USAGE
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prompt_tokens = len(self.encoding.encode(prompt)) # [TODO] use the anthropic tokenizer here
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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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) ##[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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