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| from __future__ import annotations | |
| from typing import TYPE_CHECKING, List | |
| import logging | |
| import json | |
| import commentjson as cjson | |
| import os | |
| import sys | |
| import requests | |
| import urllib3 | |
| import traceback | |
| from tqdm import tqdm | |
| import colorama | |
| from duckduckgo_search import ddg | |
| import asyncio | |
| import aiohttp | |
| from enum import Enum | |
| from .presets import * | |
| from .llama_func import * | |
| from .utils import * | |
| from . import shared | |
| from .config import retrieve_proxy | |
| class ModelType(Enum): | |
| Unknown = -1 | |
| OpenAI = 0 | |
| ChatGLM = 1 | |
| LLaMA = 2 | |
| XMChat = 3 | |
| def get_type(cls, model_name: str): | |
| model_type = None | |
| model_name_lower = model_name.lower() | |
| if "gpt" in model_name_lower: | |
| model_type = ModelType.OpenAI | |
| elif "chatglm" in model_name_lower: | |
| model_type = ModelType.ChatGLM | |
| 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 | |
| else: | |
| model_type = ModelType.Unknown | |
| return model_type | |
| class BaseLLMModel: | |
| def __init__( | |
| self, | |
| model_name, | |
| system_prompt="", | |
| temperature=1.0, | |
| top_p=1.0, | |
| n_choices=1, | |
| stop=None, | |
| max_generation_token=None, | |
| presence_penalty=0, | |
| frequency_penalty=0, | |
| logit_bias=None, | |
| user="", | |
| ) -> None: | |
| self.history = [] | |
| self.all_token_counts = [] | |
| self.model_name = model_name | |
| self.model_type = ModelType.get_type(model_name) | |
| try: | |
| self.token_upper_limit = MODEL_TOKEN_LIMIT[model_name] | |
| 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.single_turn = False | |
| self.temperature = temperature | |
| self.top_p = top_p | |
| self.n_choices = n_choices | |
| self.stop_sequence = stop | |
| self.max_generation_token = None | |
| self.presence_penalty = presence_penalty | |
| self.frequency_penalty = frequency_penalty | |
| self.logit_bias = logit_bias | |
| self.user_identifier = user | |
| def get_answer_stream_iter(self): | |
| """stream predict, need to be implemented | |
| conversations are stored in self.history, with the most recent question, in OpenAI format | |
| should return a generator, each time give the next word (str) in the answer | |
| """ | |
| logging.warning("stream predict not implemented, using at once predict 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() | |
| for partial_text in stream_iter: | |
| chatbot[-1] = (chatbot[-1][0], partial_text + display_append) | |
| self.all_token_counts[-1] += 1 | |
| 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): | |
| """if the model accepts multi modal input, implement this function""" | |
| status = gr.Markdown.update() | |
| if files: | |
| construct_index(self.api_key, file_src=files) | |
| status = "索引构建完成" | |
| return gr.Files.update(), chatbot, status | |
| def prepare_inputs(self, real_inputs, use_websearch, files, reply_language, chatbot): | |
| fake_inputs = None | |
| display_append = [] | |
| limited_context = False | |
| fake_inputs = real_inputs | |
| if files: | |
| from llama_index.indices.vector_store.base_query import GPTVectorStoreIndexQuery | |
| from llama_index.indices.query.schema import QueryBundle | |
| from langchain.embeddings.huggingface import HuggingFaceEmbeddings | |
| from langchain.chat_models import ChatOpenAI | |
| from llama_index import ( | |
| GPTSimpleVectorIndex, | |
| ServiceContext, | |
| LangchainEmbedding, | |
| OpenAIEmbedding, | |
| ) | |
| limited_context = True | |
| msg = "加载索引中……" | |
| logging.info(msg) | |
| # yield chatbot + [(inputs, "")], msg | |
| index = construct_index(self.api_key, file_src=files) | |
| 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) | |
| if local_embedding or self.model_type != ModelType.OpenAI: | |
| embed_model = LangchainEmbedding(HuggingFaceEmbeddings(model_name = "sentence-transformers/distiluse-base-multilingual-cased-v2")) | |
| else: | |
| embed_model = OpenAIEmbedding() | |
| # yield chatbot + [(inputs, "")], msg | |
| with retrieve_proxy(): | |
| prompt_helper = PromptHelper( | |
| max_input_size=4096, | |
| num_output=5, | |
| max_chunk_overlap=20, | |
| chunk_size_limit=600, | |
| ) | |
| from llama_index import ServiceContext | |
| service_context = ServiceContext.from_defaults( | |
| prompt_helper=prompt_helper, embed_model=embed_model | |
| ) | |
| query_object = GPTVectorStoreIndexQuery( | |
| index.index_struct, | |
| service_context=service_context, | |
| similarity_top_k=5, | |
| vector_store=index._vector_store, | |
| docstore=index._docstore, | |
| ) | |
| query_bundle = QueryBundle(real_inputs) | |
| nodes = query_object.retrieve(query_bundle) | |
| reference_results = [n.node.text for n in nodes] | |
| reference_results = add_source_numbers(reference_results, use_source=False) | |
| display_append = add_details(reference_results) | |
| display_append = "\n\n" + "".join(display_append) | |
| 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: | |
| limited_context = True | |
| search_results = ddg(real_inputs, max_results=5) | |
| 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"<li><a href=\"{result['href']}\" target=\"_blank\">{domain_name}</a></li>\n" | |
| ) | |
| reference_results = add_source_numbers(reference_results) | |
| display_append = "<ol>\n\n" + "".join(display_append) + "</ol>" | |
| real_inputs = ( | |
| replace_today(WEBSEARCH_PTOMPT_TEMPLATE) | |
| .replace("{query}", real_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……" | |
| logging.info( | |
| "输入为：" + colorama.Fore.BLUE + f"{inputs}" + colorama.Style.RESET_ALL | |
| ) | |
| if should_check_token_count: | |
| 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((inputs, "")) | |
| if len(self.history) == 0: | |
| self.history.append(construct_user(inputs)) | |
| self.history.append("") | |
| self.all_token_counts.append(0) | |
| else: | |
| self.history[-2] = construct_user(inputs) | |
| yield chatbot + [(inputs, "")], status_text | |
| return | |
| elif len(inputs.strip()) == 0: | |
| status_text = STANDARD_ERROR_MSG + NO_INPUT_MSG | |
| logging.info(status_text) | |
| yield chatbot + [(inputs, "")], status_text | |
| return | |
| if self.single_turn: | |
| self.history = [] | |
| self.all_token_counts = [] | |
| 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 + str(e) | |
| yield chatbot, status_text | |
| if len(self.history) > 1 and self.history[-1]["content"] != 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 | |
| def retry( | |
| self, | |
| chatbot, | |
| stream=False, | |
| use_websearch=False, | |
| files=None, | |
| reply_language="中文", | |
| ): | |
| logging.debug("重试中……") | |
| if len(self.history) > 0: | |
| inputs = self.history[-2]["content"] | |
| del self.history[-2:] | |
| self.all_token_counts.pop() | |
| elif len(chatbot) > 0: | |
| inputs = chatbot[-1][0] | |
| 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 | |
| print(f"token上限设置为{new_upper_limit}") | |
| def set_temperature(self, new_temperature): | |
| self.temperature = new_temperature | |
| def set_top_p(self, new_top_p): | |
| self.top_p = new_top_p | |
| def set_n_choices(self, new_n_choices): | |
| self.n_choices = new_n_choices | |
| def set_stop_sequence(self, new_stop_sequence: str): | |
| new_stop_sequence = new_stop_sequence.split(",") | |
| self.stop_sequence = new_stop_sequence | |
| def set_max_tokens(self, new_max_tokens): | |
| self.max_generation_token = new_max_tokens | |
| def set_presence_penalty(self, new_presence_penalty): | |
| self.presence_penalty = new_presence_penalty | |
| def set_frequency_penalty(self, new_frequency_penalty): | |
| self.frequency_penalty = new_frequency_penalty | |
| def set_logit_bias(self, logit_bias): | |
| logit_bias = 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) | |
| self.logit_bias = bias_map | |
| def set_user_identifier(self, new_user_identifier): | |
| self.user_identifier = new_user_identifier | |
| def set_system_prompt(self, new_system_prompt): | |
| self.system_prompt = new_system_prompt | |
| def set_key(self, 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 | |
| def set_single_turn(self, new_single_turn): | |
| self.single_turn = new_single_turn | |
| def reset(self): | |
| self.history = [] | |
| self.all_token_counts = [] | |
| self.interrupted = False | |
| return [], self.token_message([0]) | |
| 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.pop() | |
| return chatbot, self.history | |
| if len(self.history) > 0: | |
| self.history.pop() | |
| self.history.pop() | |
| if len(chatbot) > 0: | |
| msg = "删除了一组chatbot对话" | |
| chatbot.pop() | |
| 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" | |
| 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 save_chat_history(self, filename, chatbot, user_name): | |
| if filename == "": | |
| return | |
| if not filename.endswith(".json"): | |
| filename += ".json" | |
| return save_file(filename, self.system_prompt, self.history, chatbot, user_name) | |
| def export_markdown(self, filename, chatbot, user_name): | |
| if filename == "": | |
| return | |
| if not filename.endswith(".md"): | |
| filename += ".md" | |
| return save_file(filename, self.system_prompt, self.history, chatbot, user_name) | |
| def load_chat_history(self, filename, chatbot, user_name): | |
| logging.debug(f"{user_name} 加载对话历史中……") | |
| if type(filename) != str: | |
| filename = filename.name | |
| try: | |
| with open(os.path.join(HISTORY_DIR, user_name, filename), "r") as f: | |
| json_s = json.load(f) | |
| try: | |
| if type(json_s["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(json_s["history"]): | |
| if index % 2 == 0: | |
| new_history.append(construct_user(item)) | |
| else: | |
| new_history.append(construct_assistant(item)) | |
| json_s["history"] = new_history | |
| logging.info(new_history) | |
| except: | |
| # 没有对话历史 | |
| pass | |
| logging.debug(f"{user_name} 加载对话历史完毕") | |
| self.history = json_s["history"] | |
| return filename, json_s["system"], json_s["chatbot"] | |
| except FileNotFoundError: | |
| logging.warning(f"{user_name} 没有找到对话历史文件，不执行任何操作") | |
| return filename, self.system_prompt, chatbot | |
| 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() | |