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import os, types |
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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, Usage |
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import litellm |
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from .prompt_templates.factory import prompt_factory, custom_prompt |
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import httpx |
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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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self.request = httpx.Request(method="POST", url="https://api.anthropic.com/v1/complete") |
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self.response = httpx.Response(status_code=status_code, request=self.request) |
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super().__init__( |
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self.message |
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) |
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class AnthropicConfig(): |
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""" |
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Reference: https://docs.anthropic.com/claude/reference/complete_post |
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to pass metadata to anthropic, it's {"user_id": "any-relevant-information"} |
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""" |
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max_tokens_to_sample: Optional[int]=litellm.max_tokens |
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stop_sequences: Optional[list]=None |
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temperature: Optional[int]=None |
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top_p: Optional[int]=None |
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top_k: Optional[int]=None |
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metadata: Optional[dict]=None |
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def __init__(self, |
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max_tokens_to_sample: Optional[int]=256, |
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stop_sequences: Optional[list]=None, |
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temperature: Optional[int]=None, |
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top_p: Optional[int]=None, |
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top_k: Optional[int]=None, |
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metadata: Optional[dict]=None) -> None: |
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locals_ = locals() |
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for key, value in locals_.items(): |
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if key != 'self' and value is not None: |
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setattr(self.__class__, key, value) |
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@classmethod |
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def get_config(cls): |
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return {k: v for k, v in cls.__dict__.items() |
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if not k.startswith('__') |
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and not isinstance(v, (types.FunctionType, types.BuiltinFunctionType, classmethod, staticmethod)) |
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and v is not None} |
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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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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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api_base: str, |
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custom_prompt_dict: dict, |
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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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if model in custom_prompt_dict: |
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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, custom_llm_provider="anthropic") |
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config = litellm.AnthropicConfig.get_config() |
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for k, v in config.items(): |
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if k not in optional_params: |
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optional_params[k] = v |
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data = { |
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"model": model, |
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"prompt": prompt, |
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**optional_params, |
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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, "api_base": api_base}, |
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) |
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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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api_base, |
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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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if response.status_code != 200: |
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raise AnthropicError(status_code=response.status_code, message=response.text) |
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return response.iter_lines() |
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else: |
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response = requests.post( |
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api_base, headers=headers, data=json.dumps(data) |
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) |
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if response.status_code != 200: |
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raise AnthropicError(status_code=response.status_code, message=response.text) |
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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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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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if len(completion_response["completion"]) > 0: |
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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.choices[0].finish_reason = completion_response["stop_reason"] |
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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"].get("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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