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(feat) maintain support to Anthropic text completion
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ishaan-jaff
parent
bd37b38ece
commit
9094be7fbd
2 changed files with 224 additions and 1 deletions
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@ -573,6 +573,7 @@ from .utils import (
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)
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from .llms.huggingface_restapi import HuggingfaceConfig
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from .llms.anthropic import AnthropicConfig
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from .llms.anthropic_text import AnthropicTextConfig
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from .llms.replicate import ReplicateConfig
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from .llms.cohere import CohereConfig
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from .llms.ai21 import AI21Config
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@ -594,7 +595,7 @@ from .llms.bedrock import (
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AmazonCohereConfig,
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AmazonLlamaConfig,
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AmazonStabilityConfig,
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AmazonMistralConfig
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AmazonMistralConfig,
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)
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from .llms.openai import OpenAIConfig, OpenAITextCompletionConfig
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from .llms.azure import AzureOpenAIConfig, AzureOpenAIError
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222
litellm/llms/anthropic_text.py
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222
litellm/llms/anthropic_text.py
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@ -0,0 +1,222 @@
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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(
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method="POST", url="https://api.anthropic.com/v1/complete"
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)
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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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) # Call the base class constructor with the parameters it needs
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class AnthropicTextConfig:
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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] = (
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litellm.max_tokens
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) # anthropic requires a default
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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__(
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self,
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max_tokens_to_sample: Optional[int] = 256, # anthropic requires a default
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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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) -> 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 {
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k: v
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for k, v in cls.__dict__.items()
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if not k.startswith("__")
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and not isinstance(
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v,
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(
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types.FunctionType,
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types.BuiltinFunctionType,
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classmethod,
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staticmethod,
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),
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)
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and v is not None
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}
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# makes headers for API call
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def validate_environment(api_key, user_headers):
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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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if user_headers is not None and isinstance(user_headers, dict):
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headers = {**headers, **user_headers}
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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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headers={},
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):
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headers = validate_environment(api_key, headers)
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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(
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model=model, messages=messages, custom_llm_provider="anthropic"
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)
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## Load Config
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config = litellm.AnthropicTextConfig.get_config()
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for k, v in config.items():
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if (
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k not in optional_params
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): # completion(top_k=3) > anthropic_config(top_k=3) <- allows for dynamic variables to be passed in
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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
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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={
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"complete_input_dict": data,
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"api_base": api_base,
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"headers": headers,
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},
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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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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(
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status_code=response.status_code, message=response.text
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)
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return response.iter_lines()
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else:
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response = requests.post(api_base, headers=headers, data=json.dumps(data))
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if response.status_code != 200:
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raise AnthropicError(
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status_code=response.status_code, message=response.text
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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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if len(completion_response["completion"]) > 0:
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model_response["choices"][0]["message"]["content"] = (
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completion_response["completion"]
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)
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model_response.choices[0].finish_reason = completion_response["stop_reason"]
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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"].get("content", ""))
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) ##[TODO] use the anthropic tokenizer here
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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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# logic for parsing in - calling - parsing out model embedding calls
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pass
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