from __future__ import annotations import asyncio import json import logging import os import pathlib import shutil import sys import traceback from collections import deque from enum import Enum from itertools import islice from threading import Condition, Thread from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union import aiohttp import colorama import commentjson as cjson import requests import urllib3 from duckduckgo_search import DDGS from huggingface_hub import hf_hub_download from langchain.callbacks.base import BaseCallbackHandler, BaseCallbackManager from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler from langchain.chat_models.base import BaseChatModel from langchain.input import print_text from langchain.schema import (AgentAction, AgentFinish, AIMessage, BaseMessage, HumanMessage, LLMResult, SystemMessage) from tqdm import tqdm from .. import shared from ..config import retrieve_proxy from ..index_func import * from ..presets import * from ..utils import * class CallbackToIterator: def __init__(self): self.queue = deque() self.cond = Condition() self.finished = False def callback(self, result): with self.cond: self.queue.append(result) self.cond.notify() # Wake up the generator. def __iter__(self): return self def __next__(self): with self.cond: # Wait for a value to be added to the queue. while not self.queue and not self.finished: self.cond.wait() if not self.queue: raise StopIteration() return self.queue.popleft() def finish(self): with self.cond: self.finished = True self.cond.notify() # Wake up the generator if it's waiting. def get_action_description(text): match = re.search("```(.*?)```", text, re.S) json_text = match.group(1) # 把json转化为python字典 json_dict = json.loads(json_text) # 提取'action'和'action_input'的值 action_name = json_dict["action"] action_input = json_dict["action_input"] if action_name != "Final Answer": return f'

{action_name}: {action_input}\n

' else: return "" class ChuanhuCallbackHandler(BaseCallbackHandler): def __init__(self, callback) -> None: """Initialize callback handler.""" self.callback = callback def on_agent_action( self, action: AgentAction, color: Optional[str] = None, **kwargs: Any ) -> Any: self.callback(get_action_description(action.log)) def on_tool_end( self, output: str, color: Optional[str] = None, observation_prefix: Optional[str] = None, llm_prefix: Optional[str] = None, **kwargs: Any, ) -> None: """If not the final action, print out observation.""" # if observation_prefix is not None: # self.callback(f"\n\n{observation_prefix}") # self.callback(output) # if llm_prefix is not None: # self.callback(f"\n\n{llm_prefix}") if observation_prefix is not None: logging.info(observation_prefix) self.callback(output) if llm_prefix is not None: logging.info(llm_prefix) def on_agent_finish( self, finish: AgentFinish, color: Optional[str] = None, **kwargs: Any ) -> None: # self.callback(f"{finish.log}\n\n") logging.info(finish.log) def on_llm_new_token(self, token: str, **kwargs: Any) -> None: """Run on new LLM token. Only available when streaming is enabled.""" self.callback(token) def on_chat_model_start( self, serialized: Dict[str, Any], messages: List[List[BaseMessage]], **kwargs: Any, ) -> Any: """Run when a chat model starts running.""" pass class ModelType(Enum): Unknown = -1 OpenAI = 0 ChatGLM = 1 LLaMA = 2 XMChat = 3 StableLM = 4 MOSS = 5 YuanAI = 6 Minimax = 7 ChuanhuAgent = 8 GooglePaLM = 9 LangchainChat = 10 Midjourney = 11 Spark = 12 OpenAIInstruct = 13 Claude = 14 Qwen = 15 OpenAIVision = 16 ERNIE = 17 DALLE3 = 18 GoogleGemini = 19 GoogleGemma = 20 Ollama = 21 @classmethod def get_type(cls, model_name: str): model_type = None model_name_lower = model_name.lower() if "gpt" in model_name_lower: if "instruct" in model_name_lower: model_type = ModelType.OpenAIInstruct elif "vision" in model_name_lower: model_type = ModelType.OpenAIVision else: model_type = ModelType.OpenAI elif "chatglm" in model_name_lower: model_type = ModelType.ChatGLM elif "ollama" in model_name_lower: model_type = ModelType.Ollama elif "llama" in model_name_lower or "alpaca" in model_name_lower: model_type = ModelType.LLaMA elif "xmchat" in model_name_lower: model_type = ModelType.XMChat elif "stablelm" in model_name_lower: model_type = ModelType.StableLM elif "moss" in model_name_lower: model_type = ModelType.MOSS elif "yuanai" in model_name_lower: model_type = ModelType.YuanAI elif "minimax" in model_name_lower: model_type = ModelType.Minimax elif "川虎助理" in model_name_lower: model_type = ModelType.ChuanhuAgent elif "palm" in model_name_lower: model_type = ModelType.GooglePaLM elif "gemini" in model_name_lower: model_type = ModelType.GoogleGemini elif "midjourney" in model_name_lower: model_type = ModelType.Midjourney elif "azure" in model_name_lower or "api" in model_name_lower: model_type = ModelType.LangchainChat elif "星火大模型" in model_name_lower: model_type = ModelType.Spark elif "claude" in model_name_lower: model_type = ModelType.Claude elif "qwen" in model_name_lower: model_type = ModelType.Qwen elif "ernie" in model_name_lower: model_type = ModelType.ERNIE elif "dall" in model_name_lower: model_type = ModelType.DALLE3 elif "gemma" in model_name_lower: model_type = ModelType.GoogleGemma else: model_type = ModelType.LLaMA return model_type def download(repo_id, filename, retry=10): if os.path.exists("./models/downloaded_models.json"): with open("./models/downloaded_models.json", "r") as f: downloaded_models = json.load(f) if repo_id in downloaded_models: return downloaded_models[repo_id]["path"] else: downloaded_models = {} while retry > 0: try: model_path = hf_hub_download( repo_id=repo_id, filename=filename, cache_dir="models", resume_download=True, ) downloaded_models[repo_id] = {"path": model_path} with open("./models/downloaded_models.json", "w") as f: json.dump(downloaded_models, f) break except: print("Error downloading model, retrying...") retry -= 1 if retry == 0: raise Exception("Error downloading model, please try again later.") return model_path class BaseLLMModel: def __init__( self, model_name, system_prompt=INITIAL_SYSTEM_PROMPT, temperature=1.0, top_p=1.0, n_choices=1, stop="", max_generation_token=None, presence_penalty=0, frequency_penalty=0, logit_bias=None, user="", single_turn=False, ) -> None: self.history = [] self.all_token_counts = [] try: self.model_name = MODEL_METADATA[model_name]["model_name"] except: self.model_name = model_name self.model_type = ModelType.get_type(model_name) try: self.token_upper_limit = MODEL_METADATA[model_name]["token_limit"] except KeyError: self.token_upper_limit = DEFAULT_TOKEN_LIMIT self.interrupted = False self.system_prompt = system_prompt self.api_key = None self.need_api_key = False self.history_file_path = get_first_history_name(user) self.user_name = user self.chatbot = [] self.default_single_turn = single_turn self.default_temperature = temperature self.default_top_p = top_p self.default_n_choices = n_choices self.default_stop_sequence = stop self.default_max_generation_token = max_generation_token self.default_presence_penalty = presence_penalty self.default_frequency_penalty = frequency_penalty self.default_logit_bias = logit_bias self.default_user_identifier = user self.single_turn = single_turn self.temperature = temperature self.top_p = top_p self.n_choices = n_choices self.stop_sequence = stop self.max_generation_token = max_generation_token self.presence_penalty = presence_penalty self.frequency_penalty = frequency_penalty self.logit_bias = logit_bias self.user_identifier = user self.metadata = {} def get_answer_stream_iter(self): """Implement stream prediction. Conversations are stored in self.history, with the most recent question in OpenAI format. Should return a generator that yields the next word (str) in the answer. """ logging.warning( "Stream prediction is not implemented. Using at once prediction instead." ) response, _ = self.get_answer_at_once() yield response def get_answer_at_once(self): """predict at once, need to be implemented conversations are stored in self.history, with the most recent question, in OpenAI format Should return: the answer (str) total token count (int) """ logging.warning("at once predict not implemented, using stream predict instead") response_iter = self.get_answer_stream_iter() count = 0 for response in response_iter: count += 1 return response, sum(self.all_token_counts) + count def billing_info(self): """get billing infomation, inplement if needed""" # logging.warning("billing info not implemented, using default") return BILLING_NOT_APPLICABLE_MSG def count_token(self, user_input): """get token count from input, implement if needed""" # logging.warning("token count not implemented, using default") return len(user_input) def stream_next_chatbot(self, inputs, chatbot, fake_input=None, display_append=""): def get_return_value(): return chatbot, status_text status_text = i18n("开始实时传输回答……") if fake_input: chatbot.append((fake_input, "")) else: chatbot.append((inputs, "")) user_token_count = self.count_token(inputs) self.all_token_counts.append(user_token_count) logging.debug(f"输入token计数: {user_token_count}") stream_iter = self.get_answer_stream_iter() if display_append: display_append = ( '\n\n
' + display_append ) partial_text = "" token_increment = 1 for partial_text in stream_iter: if type(partial_text) == tuple: partial_text, token_increment = partial_text chatbot[-1] = (chatbot[-1][0], partial_text + display_append) self.all_token_counts[-1] += token_increment status_text = self.token_message() yield get_return_value() if self.interrupted: self.recover() break self.history.append(construct_assistant(partial_text)) def next_chatbot_at_once(self, inputs, chatbot, fake_input=None, display_append=""): if fake_input: chatbot.append((fake_input, "")) else: chatbot.append((inputs, "")) if fake_input is not None: user_token_count = self.count_token(fake_input) else: user_token_count = self.count_token(inputs) self.all_token_counts.append(user_token_count) ai_reply, total_token_count = self.get_answer_at_once() self.history.append(construct_assistant(ai_reply)) if fake_input is not None: self.history[-2] = construct_user(fake_input) chatbot[-1] = (chatbot[-1][0], ai_reply + display_append) if fake_input is not None: self.all_token_counts[-1] += count_token(construct_assistant(ai_reply)) else: self.all_token_counts[-1] = total_token_count - sum(self.all_token_counts) status_text = self.token_message() return chatbot, status_text def handle_file_upload(self, files, chatbot, language): """if the model accepts multi modal input, implement this function""" status = gr.Markdown.update() if files: try: construct_index(self.api_key, file_src=files) status = i18n("索引构建完成") except Exception as e: import traceback traceback.print_exc() status = i18n("索引构建失败!") + str(e) return gr.Files.update(), chatbot, status def summarize_index(self, files, chatbot, language): status = gr.Markdown.update() if files: index = construct_index(self.api_key, file_src=files) status = i18n("总结完成") logging.info(i18n("生成内容总结中……")) os.environ["OPENAI_API_KEY"] = self.api_key from langchain.callbacks import StdOutCallbackHandler from langchain.chains.summarize import load_summarize_chain from langchain.chat_models import ChatOpenAI from langchain.prompts import PromptTemplate prompt_template = ( "Write a concise summary of the following:\n\n{text}\n\nCONCISE SUMMARY IN " + language + ":" ) PROMPT = PromptTemplate(template=prompt_template, input_variables=["text"]) llm = ChatOpenAI() chain = load_summarize_chain( llm, chain_type="map_reduce", return_intermediate_steps=True, map_prompt=PROMPT, combine_prompt=PROMPT, ) summary = chain( {"input_documents": list(index.docstore.__dict__["_dict"].values())}, return_only_outputs=True, )["output_text"] print(i18n("总结") + f": {summary}") chatbot.append([i18n("上传了") + str(len(files)) + "个文件", summary]) return chatbot, status def prepare_inputs( self, real_inputs, use_websearch, files, reply_language, chatbot, load_from_cache_if_possible=True, ): display_append = [] limited_context = False if type(real_inputs) == list: fake_inputs = real_inputs[0]["text"] else: fake_inputs = real_inputs if files: from langchain.embeddings.huggingface import HuggingFaceEmbeddings from langchain.vectorstores.base import VectorStoreRetriever limited_context = True msg = "加载索引中……" logging.info(msg) index = construct_index( self.api_key, file_src=files, load_from_cache_if_possible=load_from_cache_if_possible, ) assert index is not None, "获取索引失败" msg = "索引获取成功,生成回答中……" logging.info(msg) with retrieve_proxy(): retriever = VectorStoreRetriever( vectorstore=index, search_type="similarity", search_kwargs={"k": 6} ) # retriever = VectorStoreRetriever(vectorstore=index, search_type="similarity_score_threshold", search_kwargs={ # "k": 6, "score_threshold": 0.2}) try: relevant_documents = retriever.get_relevant_documents(fake_inputs) except AssertionError: return self.prepare_inputs( fake_inputs, use_websearch, files, reply_language, chatbot, load_from_cache_if_possible=False, ) reference_results = [ [d.page_content.strip("�"), os.path.basename(d.metadata["source"])] for d in relevant_documents ] reference_results = add_source_numbers(reference_results) display_append = add_details(reference_results) display_append = "\n\n" + "".join(display_append) if type(real_inputs) == list: real_inputs[0]["text"] = ( replace_today(PROMPT_TEMPLATE) .replace("{query_str}", fake_inputs) .replace("{context_str}", "\n\n".join(reference_results)) .replace("{reply_language}", reply_language) ) else: real_inputs = ( replace_today(PROMPT_TEMPLATE) .replace("{query_str}", real_inputs) .replace("{context_str}", "\n\n".join(reference_results)) .replace("{reply_language}", reply_language) ) elif use_websearch: search_results = [] with retrieve_proxy() as proxy: if proxy[0] or proxy[1]: proxies = {} if proxy[0]: proxies["http"] = proxy[0] if proxy[1]: proxies["https"] = proxy[1] else: proxies = None with DDGS(proxies=proxies) as ddgs: ddgs_gen = ddgs.text(fake_inputs, backend="lite") for r in islice(ddgs_gen, 10): search_results.append(r) reference_results = [] for idx, result in enumerate(search_results): logging.debug(f"搜索结果{idx + 1}:{result}") domain_name = urllib3.util.parse_url(result["href"]).host reference_results.append([result["body"], result["href"]]) display_append.append( # f"{idx+1}. [{domain_name}]({result['href']})\n" f"{idx+1}. {result['title']}" ) reference_results = add_source_numbers(reference_results) # display_append = "
    \n\n" + "".join(display_append) + "
" display_append = ( '
' + "".join(display_append) + "
" ) if type(real_inputs) == list: real_inputs[0]["text"] = ( replace_today(WEBSEARCH_PTOMPT_TEMPLATE) .replace("{query}", fake_inputs) .replace("{web_results}", "\n\n".join(reference_results)) .replace("{reply_language}", reply_language) ) else: real_inputs = ( replace_today(WEBSEARCH_PTOMPT_TEMPLATE) .replace("{query}", fake_inputs) .replace("{web_results}", "\n\n".join(reference_results)) .replace("{reply_language}", reply_language) ) else: display_append = "" return limited_context, fake_inputs, display_append, real_inputs, chatbot def predict( self, inputs, chatbot, stream=False, use_websearch=False, files=None, reply_language="中文", should_check_token_count=True, ): # repetition_penalty, top_k status_text = "开始生成回答……" if type(inputs) == list: logging.info( "用户" + f"{self.user_name}" + "的输入为:" + colorama.Fore.BLUE + "(" + str(len(inputs) - 1) + " images) " + f"{inputs[0]['text']}" + colorama.Style.RESET_ALL ) else: logging.info( "用户" + f"{self.user_name}" + "的输入为:" + colorama.Fore.BLUE + f"{inputs}" + colorama.Style.RESET_ALL ) if should_check_token_count: if type(inputs) == list: yield chatbot + [(inputs[0]["text"], "")], status_text else: yield chatbot + [(inputs, "")], status_text if reply_language == "跟随问题语言(不稳定)": reply_language = "the same language as the question, such as English, 中文, 日本語, Español, Français, or Deutsch." ( limited_context, fake_inputs, display_append, inputs, chatbot, ) = self.prepare_inputs( real_inputs=inputs, use_websearch=use_websearch, files=files, reply_language=reply_language, chatbot=chatbot, ) yield chatbot + [(fake_inputs, "")], status_text if ( self.need_api_key and self.api_key is None and not shared.state.multi_api_key ): status_text = STANDARD_ERROR_MSG + NO_APIKEY_MSG logging.info(status_text) chatbot.append((fake_inputs, "")) if len(self.history) == 0: self.history.append(construct_user(fake_inputs)) self.history.append("") self.all_token_counts.append(0) else: self.history[-2] = construct_user(fake_inputs) yield chatbot + [(fake_inputs, "")], status_text return elif len(fake_inputs.strip()) == 0: status_text = STANDARD_ERROR_MSG + NO_INPUT_MSG logging.info(status_text) yield chatbot + [(fake_inputs, "")], status_text return if self.single_turn: self.history = [] self.all_token_counts = [] if type(inputs) == list: self.history.append(inputs) else: self.history.append(construct_user(inputs)) try: if stream: logging.debug("使用流式传输") iter = self.stream_next_chatbot( inputs, chatbot, fake_input=fake_inputs, display_append=display_append, ) for chatbot, status_text in iter: yield chatbot, status_text else: logging.debug("不使用流式传输") chatbot, status_text = self.next_chatbot_at_once( inputs, chatbot, fake_input=fake_inputs, display_append=display_append, ) yield chatbot, status_text except Exception as e: traceback.print_exc() status_text = STANDARD_ERROR_MSG + beautify_err_msg(str(e)) yield chatbot, status_text if len(self.history) > 1 and self.history[-1]["content"] != fake_inputs: logging.info( "回答为:" + colorama.Fore.BLUE + f"{self.history[-1]['content']}" + colorama.Style.RESET_ALL ) if limited_context: # self.history = self.history[-4:] # self.all_token_counts = self.all_token_counts[-2:] self.history = [] self.all_token_counts = [] max_token = self.token_upper_limit - TOKEN_OFFSET if sum(self.all_token_counts) > max_token and should_check_token_count: count = 0 while ( sum(self.all_token_counts) > self.token_upper_limit * REDUCE_TOKEN_FACTOR and sum(self.all_token_counts) > 0 ): count += 1 del self.all_token_counts[0] del self.history[:2] logging.info(status_text) status_text = f"为了防止token超限,模型忘记了早期的 {count} 轮对话" yield chatbot, status_text self.chatbot = chatbot self.auto_save(chatbot) def retry( self, chatbot, stream=False, use_websearch=False, files=None, reply_language="中文", ): logging.debug("重试中……") if len(self.history) > 1: inputs = self.history[-2]["content"] del self.history[-2:] if len(self.all_token_counts) > 0: self.all_token_counts.pop() elif len(chatbot) > 0: inputs = chatbot[-1][0] if '
' in inputs: inputs = inputs.split('
')[1] inputs = inputs.split("
")[0] elif len(self.history) == 1: inputs = self.history[-1]["content"] del self.history[-1] else: yield chatbot, f"{STANDARD_ERROR_MSG}上下文是空的" return iter = self.predict( inputs, chatbot, stream=stream, use_websearch=use_websearch, files=files, reply_language=reply_language, ) for x in iter: yield x logging.debug("重试完毕") # def reduce_token_size(self, chatbot): # logging.info("开始减少token数量……") # chatbot, status_text = self.next_chatbot_at_once( # summarize_prompt, # chatbot # ) # max_token_count = self.token_upper_limit * REDUCE_TOKEN_FACTOR # num_chat = find_n(self.all_token_counts, max_token_count) # logging.info(f"previous_token_count: {self.all_token_counts}, keeping {num_chat} chats") # chatbot = chatbot[:-1] # self.history = self.history[-2*num_chat:] if num_chat > 0 else [] # self.all_token_counts = self.all_token_counts[-num_chat:] if num_chat > 0 else [] # msg = f"保留了最近{num_chat}轮对话" # logging.info(msg) # logging.info("减少token数量完毕") # return chatbot, msg + "," + self.token_message(self.all_token_counts if len(self.all_token_counts) > 0 else [0]) def interrupt(self): self.interrupted = True def recover(self): self.interrupted = False def set_token_upper_limit(self, new_upper_limit): self.token_upper_limit = new_upper_limit self.auto_save() def set_temperature(self, new_temperature): self.temperature = new_temperature self.auto_save() def set_top_p(self, new_top_p): self.top_p = new_top_p self.auto_save() def set_n_choices(self, new_n_choices): self.n_choices = new_n_choices self.auto_save() def set_stop_sequence(self, new_stop_sequence: str): new_stop_sequence = new_stop_sequence.split(",") self.stop_sequence = new_stop_sequence self.auto_save() def set_max_tokens(self, new_max_tokens): self.max_generation_token = new_max_tokens self.auto_save() def set_presence_penalty(self, new_presence_penalty): self.presence_penalty = new_presence_penalty self.auto_save() def set_frequency_penalty(self, new_frequency_penalty): self.frequency_penalty = new_frequency_penalty self.auto_save() def set_logit_bias(self, logit_bias): self.logit_bias = logit_bias self.auto_save() def encoded_logit_bias(self): if self.logit_bias is None: return {} logit_bias = self.logit_bias.split() bias_map = {} encoding = tiktoken.get_encoding("cl100k_base") for line in logit_bias: word, bias_amount = line.split(":") if word: for token in encoding.encode(word): bias_map[token] = float(bias_amount) return bias_map def set_user_identifier(self, new_user_identifier): self.user_identifier = new_user_identifier self.auto_save() def set_system_prompt(self, new_system_prompt): self.system_prompt = new_system_prompt self.auto_save() def set_key(self, new_access_key): if "*" not in new_access_key: self.api_key = new_access_key.strip() msg = i18n("API密钥更改为了") + hide_middle_chars(self.api_key) logging.info(msg) return self.api_key, msg else: return gr.update(), gr.update() def set_single_turn(self, new_single_turn): self.single_turn = new_single_turn self.auto_save() def reset(self, remain_system_prompt=False): self.history = [] self.all_token_counts = [] self.interrupted = False self.history_file_path = new_auto_history_filename(self.user_name) history_name = self.history_file_path[:-5] choices = get_history_names(self.user_name) if history_name not in choices: choices.insert(0, history_name) system_prompt = self.system_prompt if remain_system_prompt else "" self.single_turn = self.default_single_turn self.temperature = self.default_temperature self.top_p = self.default_top_p self.n_choices = self.default_n_choices self.stop_sequence = self.default_stop_sequence self.max_generation_token = self.default_max_generation_token self.presence_penalty = self.default_presence_penalty self.frequency_penalty = self.default_frequency_penalty self.logit_bias = self.default_logit_bias self.user_identifier = self.default_user_identifier return ( [], self.token_message([0]), gr.Radio.update(choices=choices, value=history_name), system_prompt, self.single_turn, self.temperature, self.top_p, self.n_choices, self.stop_sequence, self.token_upper_limit, self.max_generation_token, self.presence_penalty, self.frequency_penalty, self.logit_bias, self.user_identifier, ) def delete_first_conversation(self): if self.history: del self.history[:2] del self.all_token_counts[0] return self.token_message() def delete_last_conversation(self, chatbot): if len(chatbot) > 0 and STANDARD_ERROR_MSG in chatbot[-1][1]: msg = "由于包含报错信息,只删除chatbot记录" chatbot = chatbot[:-1] return chatbot, self.history if len(self.history) > 0: self.history = self.history[:-2] if len(chatbot) > 0: msg = "删除了一组chatbot对话" chatbot = chatbot[:-1] if len(self.all_token_counts) > 0: msg = "删除了一组对话的token计数记录" self.all_token_counts.pop() msg = "删除了一组对话" self.chatbot = chatbot self.auto_save(chatbot) return chatbot, msg def token_message(self, token_lst=None): if token_lst is None: token_lst = self.all_token_counts token_sum = 0 for i in range(len(token_lst)): token_sum += sum(token_lst[: i + 1]) return ( i18n("Token 计数: ") + f"{sum(token_lst)}" + i18n(",本次对话累计消耗了 ") + f"{token_sum} tokens" ) def rename_chat_history(self, filename, chatbot): if filename == "": return gr.update() if not filename.endswith(".json"): filename += ".json" self.delete_chat_history(self.history_file_path) # 命名重复检测 repeat_file_index = 2 full_path = os.path.join(HISTORY_DIR, self.user_name, filename) while os.path.exists(full_path): full_path = os.path.join( HISTORY_DIR, self.user_name, f"{repeat_file_index}_{filename}" ) repeat_file_index += 1 filename = os.path.basename(full_path) self.history_file_path = filename save_file(filename, self, chatbot) return init_history_list(self.user_name) def auto_name_chat_history( self, name_chat_method, user_question, chatbot, single_turn_checkbox ): if len(self.history) == 2 and not single_turn_checkbox: user_question = self.history[0]["content"] if type(user_question) == list: user_question = user_question[0]["text"] filename = replace_special_symbols(user_question)[:16] + ".json" return self.rename_chat_history(filename, chatbot) else: return gr.update() def auto_save(self, chatbot=None): if chatbot is not None: save_file(self.history_file_path, self, chatbot) def export_markdown(self, filename, chatbot): if filename == "": return if not filename.endswith(".md"): filename += ".md" save_file(filename, self, chatbot) def load_chat_history(self, new_history_file_path=None): logging.debug(f"{self.user_name} 加载对话历史中……") if new_history_file_path is not None: if type(new_history_file_path) != str: # copy file from new_history_file_path.name to os.path.join(HISTORY_DIR, self.user_name) new_history_file_path = new_history_file_path.name shutil.copyfile( new_history_file_path, os.path.join( HISTORY_DIR, self.user_name, os.path.basename(new_history_file_path), ), ) self.history_file_path = os.path.basename(new_history_file_path) else: self.history_file_path = new_history_file_path try: if self.history_file_path == os.path.basename(self.history_file_path): history_file_path = os.path.join( HISTORY_DIR, self.user_name, self.history_file_path ) else: history_file_path = self.history_file_path if not self.history_file_path.endswith(".json"): history_file_path += ".json" with open(history_file_path, "r", encoding="utf-8") as f: saved_json = json.load(f) try: if type(saved_json["history"][0]) == str: logging.info("历史记录格式为旧版,正在转换……") new_history = [] for index, item in enumerate(saved_json["history"]): if index % 2 == 0: new_history.append(construct_user(item)) else: new_history.append(construct_assistant(item)) saved_json["history"] = new_history logging.info(new_history) except: pass if len(saved_json["chatbot"]) < len(saved_json["history"]) // 2: logging.info("Trimming corrupted history...") saved_json["history"] = saved_json["history"][ -len(saved_json["chatbot"]) : ] logging.info(f"Trimmed history: {saved_json['history']}") logging.debug(f"{self.user_name} 加载对话历史完毕") self.history = saved_json["history"] self.single_turn = saved_json.get("single_turn", self.single_turn) self.temperature = saved_json.get("temperature", self.temperature) self.top_p = saved_json.get("top_p", self.top_p) self.n_choices = saved_json.get("n_choices", self.n_choices) self.stop_sequence = list(saved_json.get("stop_sequence", self.stop_sequence)) self.token_upper_limit = saved_json.get( "token_upper_limit", self.token_upper_limit ) self.max_generation_token = saved_json.get( "max_generation_token", self.max_generation_token ) self.presence_penalty = saved_json.get( "presence_penalty", self.presence_penalty ) self.frequency_penalty = saved_json.get( "frequency_penalty", self.frequency_penalty ) self.logit_bias = saved_json.get("logit_bias", self.logit_bias) self.user_identifier = saved_json.get("user_identifier", self.user_name) self.metadata = saved_json.get("metadata", self.metadata) self.chatbot = saved_json["chatbot"] return ( os.path.basename(self.history_file_path)[:-5], saved_json["system"], saved_json["chatbot"], self.single_turn, self.temperature, self.top_p, self.n_choices, ",".join(self.stop_sequence), self.token_upper_limit, self.max_generation_token, self.presence_penalty, self.frequency_penalty, self.logit_bias, self.user_identifier, ) except: # 没有对话历史或者对话历史解析失败 logging.info(f"没有找到对话历史记录 {self.history_file_path}") self.reset() return ( os.path.basename(self.history_file_path), "", [], self.single_turn, self.temperature, self.top_p, self.n_choices, ",".join(self.stop_sequence), self.token_upper_limit, self.max_generation_token, self.presence_penalty, self.frequency_penalty, self.logit_bias, self.user_identifier, ) def delete_chat_history(self, filename): if filename == "CANCELED": return gr.update(), gr.update(), gr.update() if filename == "": return i18n("你没有选择任何对话历史"), gr.update(), gr.update() if not filename.endswith(".json"): filename += ".json" if filename == os.path.basename(filename): history_file_path = os.path.join(HISTORY_DIR, self.user_name, filename) else: history_file_path = filename md_history_file_path = history_file_path[:-5] + ".md" try: os.remove(history_file_path) os.remove(md_history_file_path) return i18n("删除对话历史成功"), get_history_list(self.user_name), [] except: logging.info(f"删除对话历史失败 {history_file_path}") return ( i18n("对话历史") + filename + i18n("已经被删除啦"), get_history_list(self.user_name), [], ) def auto_load(self): self.history_file_path = new_auto_history_filename(self.user_name) return self.load_chat_history() def like(self): """like the last response, implement if needed""" return gr.update() def dislike(self): """dislike the last response, implement if needed""" return gr.update() def deinitialize(self): """deinitialize the model, implement if needed""" pass def clear_cuda_cache(self): import gc import torch gc.collect() torch.cuda.empty_cache() class Base_Chat_Langchain_Client(BaseLLMModel): def __init__(self, model_name, user_name=""): super().__init__(model_name, user=user_name) self.need_api_key = False self.model = self.setup_model() def setup_model(self): # inplement this to setup the model then return it pass def _get_langchain_style_history(self): history = [SystemMessage(content=self.system_prompt)] for i in self.history: if i["role"] == "user": history.append(HumanMessage(content=i["content"])) elif i["role"] == "assistant": history.append(AIMessage(content=i["content"])) return history def get_answer_at_once(self): assert isinstance( self.model, BaseChatModel ), "model is not instance of LangChain BaseChatModel" history = self._get_langchain_style_history() response = self.model.generate(history) return response.content, sum(response.content) def get_answer_stream_iter(self): it = CallbackToIterator() assert isinstance( self.model, BaseChatModel ), "model is not instance of LangChain BaseChatModel" history = self._get_langchain_style_history() def thread_func(): self.model( messages=history, callbacks=[ChuanhuCallbackHandler(it.callback)] ) it.finish() t = Thread(target=thread_func) t.start() partial_text = "" for value in it: partial_text += value yield partial_text