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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 | |
| import pathlib | |
| import shutil | |
| from tqdm import tqdm | |
| import colorama | |
| from duckduckgo_search import DDGS | |
| from itertools import islice | |
| import asyncio | |
| import aiohttp | |
| from enum import Enum | |
| from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler | |
| from langchain.callbacks.manager import BaseCallbackManager | |
| from typing import Any, Dict, List, Optional, Union | |
| from langchain.callbacks.base import BaseCallbackHandler | |
| from langchain.input import print_text | |
| from langchain.schema import AgentAction, AgentFinish, LLMResult | |
| from threading import Thread, Condition | |
| from collections import deque | |
| from langchain.chat_models.base import BaseChatModel | |
| from langchain.schema import HumanMessage, AIMessage, SystemMessage, BaseMessage | |
| from ..presets import * | |
| from ..index_func import * | |
| from ..utils import * | |
| from .. import shared | |
| from ..config import retrieve_proxy | |
| 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'<!-- S O PREFIX --><p class="agent-prefix">{action_name}: {action_input}\n</p><!-- E O PREFIX -->' | |
| 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 | |
| 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 "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 "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 | |
| else: | |
| model_type = ModelType.LLaMA | |
| return model_type | |
| class BaseLLMModel: | |
| def __init__( | |
| self, | |
| model_name, | |
| system_prompt=INITIAL_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 = [] | |
| if model_name in MODEL_METADATA: | |
| self.model_name = MODEL_METADATA[model_name]["model_name"] | |
| else: | |
| 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.single_turn = False | |
| self.history_file_path = get_first_history_name(user) | |
| 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() | |
| if display_append: | |
| display_append = '\n\n<hr class="append-display no-in-raw" />' + 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: | |
| index = construct_index(self.api_key, file_src=files) | |
| status = i18n("索引构建完成") | |
| 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.chains.summarize import load_summarize_chain | |
| from langchain.prompts import PromptTemplate | |
| from langchain.chat_models import ChatOpenAI | |
| from langchain.callbacks import StdOutCallbackHandler | |
| 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 DDGS() 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"<a href=\"{result['href']}\" target=\"_blank\">{idx+1}. {result['title']}</a>" | |
| ) | |
| reference_results = add_source_numbers(reference_results) | |
| # display_append = "<ol>\n\n" + "".join(display_append) + "</ol>" | |
| display_append = '<div class = "source-a">' + \ | |
| "".join(display_append) + '</div>' | |
| 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_identifier}" + "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_identifier}" + "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.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 '<div class="user-message">' in inputs: | |
| inputs = inputs.split('<div class="user-message">')[1] | |
| inputs = inputs.split("</div>")[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 | |
| 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): | |
| 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 | |
| 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_identifier) | |
| history_name = self.history_file_path[:-5] | |
| choices = [history_name] + get_history_names(self.user_identifier) | |
| system_prompt = self.system_prompt if remain_system_prompt else "" | |
| return [], self.token_message([0]), gr.Radio.update(choices=choices, value=history_name), system_prompt | |
| 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.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, user_name): | |
| if filename == "": | |
| return gr.update() | |
| if not filename.endswith(".json"): | |
| filename += ".json" | |
| self.delete_chat_history(self.history_file_path, user_name) | |
| # 命名重复检测 | |
| repeat_file_index = 2 | |
| full_path = os.path.join(HISTORY_DIR, user_name, filename) | |
| while os.path.exists(full_path): | |
| full_path = os.path.join(HISTORY_DIR, 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.system_prompt, self.history, chatbot, user_name) | |
| return init_history_list(user_name) | |
| def auto_name_chat_history(self, name_chat_method, user_question, chatbot, user_name, 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, user_name) | |
| else: | |
| return gr.update() | |
| def auto_save(self, chatbot): | |
| save_file(self.history_file_path, self.system_prompt, | |
| self.history, chatbot, self.user_identifier) | |
| def export_markdown(self, filename, chatbot, user_name): | |
| if filename == "": | |
| return | |
| if not filename.endswith(".md"): | |
| filename += ".md" | |
| save_file(filename, self.system_prompt, self.history, chatbot, user_name) | |
| def load_chat_history(self, new_history_file_path=None, username=None): | |
| logging.debug(f"{self.user_identifier} 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_identifier) | |
| new_history_file_path = new_history_file_path.name | |
| shutil.copyfile(new_history_file_path, os.path.join( | |
| HISTORY_DIR, self.user_identifier, 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_identifier, 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: | |
| 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: | |
| pass | |
| if len(json_s["chatbot"]) < len(json_s["history"])//2: | |
| logging.info("Trimming corrupted history...") | |
| json_s["history"] = json_s["history"][-len(json_s["chatbot"]):] | |
| logging.info(f"Trimmed history: {json_s['history']}") | |
| logging.debug(f"{self.user_identifier} 加载对话历史完毕") | |
| self.history = json_s["history"] | |
| return os.path.basename(self.history_file_path), json_s["system"], json_s["chatbot"] | |
| 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}") | |
| return self.history_file_path, "", [] | |
| def delete_chat_history(self, filename, user_name): | |
| 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, 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(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(user_name), [] | |
| def auto_load(self): | |
| filepath = get_history_filepath(self.user_identifier) | |
| if not filepath: | |
| self.history_file_path = new_auto_history_filename( | |
| self.user_identifier) | |
| else: | |
| self.history_file_path = filepath | |
| filename, system_prompt, chatbot = self.load_chat_history() | |
| filename = filename[:-5] | |
| return filename, 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() | |
| 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 | |