from hugchat import hugchat import time from typing import Any, List, Mapping, Optional from langchain.callbacks.manager import CallbackManagerForLLMRun from langchain.llms.base import LLM # THIS IS A CUSTOM LLM WRAPPER Based on hugchat library # Reference : # - Langchain custom LLM wrapper : https://python.langchain.com/docs/modules/model_io/models/llms/how_to/custom_llm # - HugChat library : https://github.com/Soulter/hugging-chat-api # - I am Alessandro Ciciarelli the owner of IntelligenzaArtificialeItalia.net , my dream is to democratize AI and make it accessible to everyone. class HuggingChat(LLM): """HuggingChat LLM wrapper.""" chatbot : Optional[hugchat.ChatBot] = None email: Optional[str] = None psw: Optional[str] = None cookie_path : Optional[str] = None conversation : Optional[str] = None model: Optional[int] = 0 # 0 = OpenAssistant/oasst-sft-6-llama-30b-xor , 1 = meta-llama/Llama-2-70b-chat-hf temperature: Optional[float] = 0.9 top_p: Optional[float] = 0.95 repetition_penalty: Optional[float] = 1.2 top_k: Optional[int] = 50 truncate: Optional[int] = 1024 watermark: Optional[bool] = False max_new_tokens: Optional[int] = 1024 stop: Optional[list] = [""] return_full_text: Optional[bool] = False stream_resp: Optional[bool] = True use_cache: Optional[bool] = False is_retry: Optional[bool] = False retry_count: Optional[int] = 5 avg_response_time: float = 0.0 log : Optional[bool] = False @property def _llm_type(self) -> str: return "đŸ¤—CUSTOM LLM WRAPPER Based on hugging-chat-api library" def create_chatbot(self) -> None: if not any([self.email, self.psw, self.cookie_path]): raise ValueError("email, psw, or cookie_path is required.") try: if self.email and self.psw: # Create a ChatBot using email and psw from hugchat.login import Login start_time = time.time() sign = Login(self.email, self.psw) cookies = sign.login() end_time = time.time() if self.log : print(f"\n[LOG] Login successfull in {round(end_time - start_time)} seconds") else: # Create a ChatBot using cookie_path cookies = self.cookie_path and hugchat.ChatBot(cookie_path=self.cookie_path) self.chatbot = cookies.get_dict() and hugchat.ChatBot(cookies=cookies.get_dict()) if self.log : print(f"[LOG] LLM WRAPPER created successfully") except Exception as e: raise ValueError("LogIn failed. Please check your credentials or cookie_path. " + str(e)) # Setup ChatBot info self.chatbot.switch_llm(self.model) if self.log : print(f"[LOG] LLM WRAPPER switched to model { 'OpenAssistant/oasst-sft-6-llama-30b-xor' if self.model == 0 else 'meta-llama/Llama-2-70b-chat-hf'}") self.conversation = self.conversation or self.chatbot.new_conversation() self.chatbot.change_conversation(self.conversation) if self.log : print(f"[LOG] LLM WRAPPER changed conversation to {self.conversation}\n") def _call( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> str: if stop: raise ValueError("stop kwargs are not permitted.") self.create_chatbot() if not self.chatbot else None try: if self.log : print(f"[LOG] LLM WRAPPER called with prompt: {prompt}") start_time = time.time() resp = self.chatbot.chat( prompt, temperature=self.temperature, top_p=self.top_p, repetition_penalty=self.repetition_penalty, top_k=self.top_k, truncate=self.truncate, watermark=self.watermark, max_new_tokens=self.max_new_tokens, stop=self.stop, return_full_text=self.return_full_text, stream=self.stream_resp, use_cache=self.use_cache, is_retry=self.is_retry, retry_count=self.retry_count, ) end_time = time.time() self.avg_response_time = (self.avg_response_time + (end_time - start_time)) / 2 if self.avg_response_time else end_time - start_time if self.log : print(f"[LOG] LLM WRAPPER response time: {round(end_time - start_time)} seconds") if self.log : print(f"[LOG] LLM WRAPPER avg response time: {round(self.avg_response_time)} seconds") if self.log : print(f"[LOG] LLM WRAPPER response: {resp}\n\n") return str(resp) except Exception as e: raise ValueError("ChatBot failed, please check your parameters. " + str(e)) @property def _identifying_params(self) -> Mapping[str, Any]: """Get the identifying parameters.""" parms = { "model": "HuggingChat", "temperature": self.temperature, "top_p": self.top_p, "repetition_penalty": self.repetition_penalty, "top_k": self.top_k, "truncate": self.truncate, "watermark": self.watermark, "max_new_tokens": self.max_new_tokens, "stop": self.stop, "return_full_text": self.return_full_text, "stream": self.stream_resp, "use_cache": self.use_cache, "is_retry": self.is_retry, "retry_count": self.retry_count, "avg_response_time": self.avg_response_time, } return parms @property def _get_avg_response_time(self) -> float: """Get the average response time.""" return self.avg_response_time