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import os, types, traceback |
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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, httpx |
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from typing import Callable, Optional |
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from litellm.utils import ModelResponse, Choices, Message |
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import litellm |
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class AI21Error(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.ai21.com/studio/v1/") |
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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 AI21Config(): |
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""" |
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Reference: https://docs.ai21.com/reference/j2-complete-ref |
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The class `AI21Config` provides configuration for the AI21's API interface. Below are the parameters: |
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- `numResults` (int32): Number of completions to sample and return. Optional, default is 1. If the temperature is greater than 0 (non-greedy decoding), a value greater than 1 can be meaningful. |
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- `maxTokens` (int32): The maximum number of tokens to generate per result. Optional, default is 16. If no `stopSequences` are given, generation stops after producing `maxTokens`. |
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- `minTokens` (int32): The minimum number of tokens to generate per result. Optional, default is 0. If `stopSequences` are given, they are ignored until `minTokens` are generated. |
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- `temperature` (float): Modifies the distribution from which tokens are sampled. Optional, default is 0.7. A value of 0 essentially disables sampling and results in greedy decoding. |
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- `topP` (float): Used for sampling tokens from the corresponding top percentile of probability mass. Optional, default is 1. For instance, a value of 0.9 considers only tokens comprising the top 90% probability mass. |
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- `stopSequences` (array of strings): Stops decoding if any of the input strings is generated. Optional. |
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- `topKReturn` (int32): Range between 0 to 10, including both. Optional, default is 0. Specifies the top-K alternative tokens to return. A non-zero value includes the string representations and log-probabilities for each of the top-K alternatives at each position. |
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- `frequencyPenalty` (object): Placeholder for frequency penalty object. |
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- `presencePenalty` (object): Placeholder for presence penalty object. |
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- `countPenalty` (object): Placeholder for count penalty object. |
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""" |
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numResults: Optional[int]=None |
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maxTokens: Optional[int]=None |
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minTokens: Optional[int]=None |
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temperature: Optional[float]=None |
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topP: Optional[float]=None |
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stopSequences: Optional[list]=None |
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topKReturn: Optional[int]=None |
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frequencePenalty: Optional[dict]=None |
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presencePenalty: Optional[dict]=None |
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countPenalty: Optional[dict]=None |
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def __init__(self, |
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numResults: Optional[int]=None, |
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maxTokens: Optional[int]=None, |
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minTokens: Optional[int]=None, |
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temperature: Optional[float]=None, |
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topP: Optional[float]=None, |
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stopSequences: Optional[list]=None, |
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topKReturn: Optional[int]=None, |
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frequencePenalty: Optional[dict]=None, |
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presencePenalty: Optional[dict]=None, |
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countPenalty: 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 AI21 API Key - A call is being made to ai21 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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"content-type": "application/json", |
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"Authorization": "Bearer " + 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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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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model = model |
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prompt = "" |
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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"{message['content']}" |
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) |
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else: |
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prompt += ( |
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f"{message['content']}" |
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) |
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else: |
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prompt += f"{message['content']}" |
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config = litellm.AI21Config.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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"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}, |
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) |
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response = requests.post( |
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api_base + model + "/complete", headers=headers, data=json.dumps(data) |
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) |
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if response.status_code != 200: |
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raise AI21Error( |
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status_code=response.status_code, |
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message=response.text |
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) |
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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_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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completion_response = response.json() |
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try: |
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choices_list = [] |
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for idx, item in enumerate(completion_response["completions"]): |
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if len(item["data"]["text"]) > 0: |
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message_obj = Message(content=item["data"]["text"]) |
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else: |
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message_obj = Message(content=None) |
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choice_obj = Choices(finish_reason=item["finishReason"]["reason"], index=idx+1, message=message_obj) |
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choices_list.append(choice_obj) |
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model_response["choices"] = choices_list |
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except Exception as e: |
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raise AI21Error(message=traceback.format_exc(), status_code=response.status_code) |
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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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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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pass |
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