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import time |
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import uuid |
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from selenium import webdriver |
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from selenium.webdriver.chrome.options import Options |
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from selenium.webdriver.common.by import By |
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from selenium.webdriver.support import expected_conditions as EC |
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from selenium.webdriver.support.ui import WebDriverWait |
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import click |
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import requests |
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from requests import get |
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from uuid import uuid4 |
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from re import findall |
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from requests.exceptions import RequestException |
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from curl_cffi.requests import get, RequestsError |
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import g4f |
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from random import randint |
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from PIL import Image |
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import io |
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import re |
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import json |
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import yaml |
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from ..AIutel import Optimizers |
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from ..AIutel import Conversation |
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from ..AIutel import AwesomePrompts, sanitize_stream |
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from ..AIbase import Provider, AsyncProvider |
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from Helpingai_T2 import Perplexity |
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from webscout import exceptions |
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from typing import Any, AsyncGenerator, Dict |
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import logging |
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import httpx |
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class Cohere(Provider): |
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def __init__( |
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self, |
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api_key: str, |
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is_conversation: bool = True, |
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max_tokens: int = 600, |
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model: str = "command-r-plus", |
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temperature: float = 0.7, |
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system_prompt: str = "You are helpful AI", |
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timeout: int = 30, |
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intro: str = None, |
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filepath: str = None, |
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update_file: bool = True, |
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proxies: dict = {}, |
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history_offset: int = 10250, |
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act: str = None, |
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top_k: int = -1, |
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top_p: float = 0.999, |
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): |
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"""Initializes Cohere |
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Args: |
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api_key (str): Cohere API key. |
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is_conversation (bool, optional): Flag for chatting conversationally. Defaults to True. |
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max_tokens (int, optional): Maximum number of tokens to be generated upon completion. Defaults to 600. |
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model (str, optional): Model to use for generating text. Defaults to "command-r-plus". |
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temperature (float, optional): Diversity of the generated text. Higher values produce more diverse outputs. |
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Defaults to 0.7. |
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system_prompt (str, optional): A system_prompt or context to set the style or tone of the generated text. |
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Defaults to "You are helpful AI". |
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timeout (int, optional): Http request timeout. Defaults to 30. |
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intro (str, optional): Conversation introductory prompt. Defaults to None. |
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filepath (str, optional): Path to file containing conversation history. Defaults to None. |
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update_file (bool, optional): Add new prompts and responses to the file. Defaults to True. |
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proxies (dict, optional): Http request proxies. Defaults to {}. |
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history_offset (int, optional): Limit conversation history to this number of last texts. Defaults to 10250. |
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act (str|int, optional): Awesome prompt key or index. (Used as intro). Defaults to None. |
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""" |
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self.session = requests.Session() |
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self.is_conversation = is_conversation |
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self.max_tokens_to_sample = max_tokens |
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self.api_key = api_key |
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self.model = model |
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self.temperature = temperature |
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self.system_prompt = system_prompt |
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self.chat_endpoint = "https://production.api.os.cohere.ai/coral/v1/chat" |
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self.stream_chunk_size = 64 |
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self.timeout = timeout |
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self.last_response = {} |
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self.headers = { |
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"Content-Type": "application/json", |
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"Authorization": f"Bearer {self.api_key}", |
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} |
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self.__available_optimizers = ( |
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method |
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for method in dir(Optimizers) |
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if callable(getattr(Optimizers, method)) and not method.startswith("__") |
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) |
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self.session.headers.update(self.headers) |
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Conversation.intro = ( |
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AwesomePrompts().get_act( |
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act, raise_not_found=True, default=None, case_insensitive=True |
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) |
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if act |
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else intro or Conversation.intro |
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) |
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self.conversation = Conversation( |
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is_conversation, self.max_tokens_to_sample, filepath, update_file |
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) |
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self.conversation.history_offset = history_offset |
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self.session.proxies = proxies |
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|
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def ask( |
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self, |
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prompt: str, |
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stream: bool = False, |
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raw: bool = False, |
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optimizer: str = None, |
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conversationally: bool = False, |
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) -> dict: |
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"""Chat with AI |
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Args: |
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prompt (str): Prompt to be send. |
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stream (bool, optional): Flag for streaming response. Defaults to False. |
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raw (bool, optional): Stream back raw response as received. Defaults to False. |
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optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None. |
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conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False. |
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Returns: |
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dict : {} |
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```json |
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{ |
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"text" : "How may I assist you today?" |
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} |
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``` |
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""" |
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conversation_prompt = self.conversation.gen_complete_prompt(prompt) |
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if optimizer: |
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if optimizer in self.__available_optimizers: |
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conversation_prompt = getattr(Optimizers, optimizer)( |
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conversation_prompt if conversationally else prompt |
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) |
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else: |
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raise Exception( |
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f"Optimizer is not one of {self.__available_optimizers}" |
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) |
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self.session.headers.update(self.headers) |
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payload = { |
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"message": conversation_prompt, |
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"model": self.model, |
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"temperature": self.temperature, |
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"preamble": self.system_prompt, |
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} |
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def for_stream(): |
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response = self.session.post( |
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self.chat_endpoint, json=payload, stream=True, timeout=self.timeout |
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) |
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if not response.ok: |
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raise Exception( |
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f"Failed to generate response - ({response.status_code}, {response.reason}) - {response.text}" |
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) |
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for value in response.iter_lines( |
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decode_unicode=True, |
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chunk_size=self.stream_chunk_size, |
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): |
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try: |
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resp = json.loads(value.strip().split("\n")[-1]) |
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self.last_response.update(resp) |
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yield value if raw else resp |
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except json.decoder.JSONDecodeError: |
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pass |
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self.conversation.update_chat_history( |
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prompt, self.get_message(self.last_response) |
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) |
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def for_non_stream(): |
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for _ in for_stream(): |
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pass |
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return self.last_response |
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return for_stream() if stream else for_non_stream() |
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def chat( |
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self, |
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prompt: str, |
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stream: bool = False, |
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optimizer: str = None, |
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conversationally: bool = False, |
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) -> str: |
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"""Generate response `str` |
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Args: |
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prompt (str): Prompt to be send. |
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stream (bool, optional): Flag for streaming response. Defaults to False. |
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optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None. |
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conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False. |
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Returns: |
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str: Response generated |
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""" |
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def for_stream(): |
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for response in self.ask( |
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prompt, True, optimizer=optimizer, conversationally=conversationally |
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): |
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yield self.get_message(response) |
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def for_non_stream(): |
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return self.get_message( |
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self.ask( |
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prompt, |
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False, |
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optimizer=optimizer, |
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conversationally=conversationally, |
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) |
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) |
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return for_stream() if stream else for_non_stream() |
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def get_message(self, response: dict) -> str: |
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"""Retrieves message only from response |
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Args: |
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response (dict): Response generated by `self.ask` |
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Returns: |
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str: Message extracted |
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""" |
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assert isinstance(response, dict), "Response should be of dict data-type only" |
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return response["result"]["chatStreamEndEvent"]["response"]["text"] |
