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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
@classmethod
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()
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