Merge branch 'master' into frontier
This commit is contained in:
binary-husky
commit
c0a36e37be
@ -167,6 +167,14 @@ git clone --depth=1 https://github.com/OpenLMLab/MOSS.git request_llms/moss #
|
||||
|
||||
# 【可选步骤IV】确保config.py配置文件的AVAIL_LLM_MODELS包含了期望的模型，目前支持的全部模型如下(jittorllms系列目前仅支持docker方案)：
|
||||
AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
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||||
|
||||
# 【可选步骤V】支持本地模型INT8,INT4量化（这里所指的模型本身不是量化版本，目前deepseek-coder支持，后面测试后会加入更多模型量化选择）
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pip install bitsandbyte
|
||||
# windows用户安装bitsandbytes需要使用下面bitsandbytes-windows-webui
|
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python -m pip install bitsandbytes --prefer-binary --extra-index-url=https://jllllll.github.io/bitsandbytes-windows-webui
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pip install -U git+https://github.com/huggingface/transformers.git
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pip install -U git+https://github.com/huggingface/accelerate.git
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pip install peft
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```
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</p>
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@ -160,6 +160,14 @@ def warm_up_modules():
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enc = model_info["gpt-4"]['tokenizer']
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enc.encode("模块预热", disallowed_special=())
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def warm_up_vectordb():
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print('正在执行一些模块的预热 ...')
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from toolbox import ProxyNetworkActivate
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with ProxyNetworkActivate("Warmup_Modules"):
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import nltk
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with ProxyNetworkActivate("Warmup_Modules"): nltk.download("punkt")
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||||
if __name__ == '__main__':
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import os
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os.environ['no_proxy'] = '*' # 避免代理网络产生意外污染
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||||
@ -120,7 +120,6 @@ CHATGLM_PTUNING_CHECKPOINT = "" # 例如"/home/hmp/ChatGLM2-6B/ptuning/output/6b
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LOCAL_MODEL_DEVICE = "cpu" # 可选 "cuda"
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LOCAL_MODEL_QUANT = "FP16" # 默认 "FP16" "INT4" 启用量化INT4版本 "INT8" 启用量化INT8版本
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# 设置gradio的并行线程数（不需要修改）
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CONCURRENT_COUNT = 100
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@ -238,6 +237,10 @@ WHEN_TO_USE_PROXY = ["Download_LLM", "Download_Gradio_Theme", "Connect_Grobid",
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BLOCK_INVALID_APIKEY = False
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||||
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# 启用插件热加载
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PLUGIN_HOT_RELOAD = False
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|
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# 自定义按钮的最大数量限制
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NUM_CUSTOM_BASIC_BTN = 4
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@ -440,7 +440,7 @@ def get_crazy_functions():
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print('Load function plugin failed')
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try:
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from crazy_functions.Langchain知识库 import 知识库问答
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from crazy_functions.知识库问答 import 知识库文件注入
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function_plugins.update({
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"构建知识库（先上传文件素材,再运行此插件）": {
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"Group": "对话",
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@ -448,7 +448,7 @@ def get_crazy_functions():
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"AsButton": False,
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"AdvancedArgs": True,
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"ArgsReminder": "此处待注入的知识库名称id, 默认为default。文件进入知识库后可长期保存。可以通过再次调用本插件的方式，向知识库追加更多文档。",
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"Function": HotReload(知识库问答)
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"Function": HotReload(知识库文件注入)
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}
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||||
})
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except:
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@ -456,9 +456,9 @@ def get_crazy_functions():
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print('Load function plugin failed')
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try:
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from crazy_functions.Langchain知识库 import 读取知识库作答
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from crazy_functions.知识库问答 import 读取知识库作答
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function_plugins.update({
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"知识库问答（构建知识库后,再运行此插件）": {
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"知识库文件注入（构建知识库后,再运行此插件）": {
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||||
"Group": "对话",
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"Color": "stop",
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"AsButton": False,
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@ -590,6 +590,20 @@ def get_crazy_functions():
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print(trimmed_format_exc())
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print('Load function plugin failed')
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# try:
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# from crazy_functions.互动小游戏 import 随机小游戏
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# function_plugins.update({
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# "随机小游戏": {
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# "Group": "智能体",
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# "Color": "stop",
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# "AsButton": True,
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# "Function": HotReload(随机小游戏)
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# }
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# })
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# except:
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# print(trimmed_format_exc())
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# print('Load function plugin failed')
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# try:
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# from crazy_functions.chatglm微调工具 import 微调数据集生成
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# function_plugins.update({
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@ -1,4 +1,4 @@
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from toolbox import update_ui, get_conf, trimmed_format_exc, get_max_token
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from toolbox import update_ui, get_conf, trimmed_format_exc, get_max_token, Singleton
|
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import threading
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import os
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import logging
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@ -632,89 +632,6 @@ def get_files_from_everything(txt, type): # type='.md'
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def Singleton(cls):
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_instance = {}
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||||
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def _singleton(*args, **kargs):
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if cls not in _instance:
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_instance[cls] = cls(*args, **kargs)
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return _instance[cls]
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return _singleton
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@Singleton
|
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class knowledge_archive_interface():
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def __init__(self) -> None:
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self.threadLock = threading.Lock()
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self.current_id = ""
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self.kai_path = None
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||||
self.qa_handle = None
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self.text2vec_large_chinese = None
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||||
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def get_chinese_text2vec(self):
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if self.text2vec_large_chinese is None:
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# < -------------------预热文本向量化模组--------------- >
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from toolbox import ProxyNetworkActivate
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||||
print('Checking Text2vec ...')
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||||
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
|
||||
with ProxyNetworkActivate('Download_LLM'): # 临时地激活代理网络
|
||||
self.text2vec_large_chinese = HuggingFaceEmbeddings(model_name="GanymedeNil/text2vec-large-chinese")
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||||
|
||||
return self.text2vec_large_chinese
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|
||||
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||||
def feed_archive(self, file_manifest, id="default"):
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self.threadLock.acquire()
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# import uuid
|
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self.current_id = id
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||||
from zh_langchain import construct_vector_store
|
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self.qa_handle, self.kai_path = construct_vector_store(
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||||
vs_id=self.current_id,
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||||
files=file_manifest,
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||||
sentence_size=100,
|
||||
history=[],
|
||||
one_conent="",
|
||||
one_content_segmentation="",
|
||||
text2vec = self.get_chinese_text2vec(),
|
||||
)
|
||||
self.threadLock.release()
|
||||
|
||||
def get_current_archive_id(self):
|
||||
return self.current_id
|
||||
|
||||
def get_loaded_file(self):
|
||||
return self.qa_handle.get_loaded_file()
|
||||
|
||||
def answer_with_archive_by_id(self, txt, id):
|
||||
self.threadLock.acquire()
|
||||
if not self.current_id == id:
|
||||
self.current_id = id
|
||||
from zh_langchain import construct_vector_store
|
||||
self.qa_handle, self.kai_path = construct_vector_store(
|
||||
vs_id=self.current_id,
|
||||
files=[],
|
||||
sentence_size=100,
|
||||
history=[],
|
||||
one_conent="",
|
||||
one_content_segmentation="",
|
||||
text2vec = self.get_chinese_text2vec(),
|
||||
)
|
||||
VECTOR_SEARCH_SCORE_THRESHOLD = 0
|
||||
VECTOR_SEARCH_TOP_K = 4
|
||||
CHUNK_SIZE = 512
|
||||
resp, prompt = self.qa_handle.get_knowledge_based_conent_test(
|
||||
query = txt,
|
||||
vs_path = self.kai_path,
|
||||
score_threshold=VECTOR_SEARCH_SCORE_THRESHOLD,
|
||||
vector_search_top_k=VECTOR_SEARCH_TOP_K,
|
||||
chunk_conent=True,
|
||||
chunk_size=CHUNK_SIZE,
|
||||
text2vec = self.get_chinese_text2vec(),
|
||||
)
|
||||
self.threadLock.release()
|
||||
return resp, prompt
|
||||
|
||||
@Singleton
|
||||
class nougat_interface():
|
||||
def __init__(self):
|
||||
|
||||
35
crazy_functions/game_fns/game_utils.py
Normal file
35
crazy_functions/game_fns/game_utils.py
Normal file
@ -0,0 +1,35 @@
|
||||
|
||||
from crazy_functions.json_fns.pydantic_io import GptJsonIO, JsonStringError
|
||||
from request_llms.bridge_all import predict_no_ui_long_connection
|
||||
def get_code_block(reply):
|
||||
import re
|
||||
pattern = r"```([\s\S]*?)```" # regex pattern to match code blocks
|
||||
matches = re.findall(pattern, reply) # find all code blocks in text
|
||||
if len(matches) == 1:
|
||||
return "```" + matches[0] + "```" # code block
|
||||
raise RuntimeError("GPT is not generating proper code.")
|
||||
|
||||
def is_same_thing(a, b, llm_kwargs):
|
||||
from pydantic import BaseModel, Field
|
||||
class IsSameThing(BaseModel):
|
||||
is_same_thing: bool = Field(description="determine whether two objects are same thing.", default=False)
|
||||
|
||||
def run_gpt_fn(inputs, sys_prompt, history=[]):
|
||||
return predict_no_ui_long_connection(
|
||||
inputs=inputs, llm_kwargs=llm_kwargs,
|
||||
history=history, sys_prompt=sys_prompt, observe_window=[]
|
||||
)
|
||||
|
||||
gpt_json_io = GptJsonIO(IsSameThing)
|
||||
inputs_01 = "Identity whether the user input and the target is the same thing: \n target object: {a} \n user input object: {b} \n\n\n".format(a=a, b=b)
|
||||
inputs_01 += "\n\n\n Note that the user may describe the target object with a different language, e.g. cat and 猫 are the same thing."
|
||||
analyze_res_cot_01 = run_gpt_fn(inputs_01, "", [])
|
||||
|
||||
inputs_02 = inputs_01 + gpt_json_io.format_instructions
|
||||
analyze_res = run_gpt_fn(inputs_02, "", [inputs_01, analyze_res_cot_01])
|
||||
|
||||
try:
|
||||
res = gpt_json_io.generate_output_auto_repair(analyze_res, run_gpt_fn)
|
||||
return res.is_same_thing
|
||||
except JsonStringError as e:
|
||||
return False
|
||||
@ -1,6 +1,7 @@
|
||||
from pydantic import BaseModel, Field
|
||||
from typing import List
|
||||
from toolbox import update_ui_lastest_msg, disable_auto_promotion
|
||||
from toolbox import CatchException, update_ui, get_conf, select_api_key, get_log_folder
|
||||
from request_llms.bridge_all import predict_no_ui_long_connection
|
||||
from crazy_functions.json_fns.pydantic_io import GptJsonIO, JsonStringError
|
||||
import time
|
||||
@ -36,6 +37,57 @@ class GptAcademicState():
|
||||
state.chatbot = chatbot
|
||||
return state
|
||||
|
||||
class GatherMaterials():
|
||||
def __init__(self, materials) -> None:
|
||||
materials = ['image', 'prompt']
|
||||
|
||||
class GptAcademicGameBaseState():
|
||||
"""
|
||||
1. first init: __init__ ->
|
||||
"""
|
||||
def init_game(self, chatbot, lock_plugin):
|
||||
self.plugin_name = None
|
||||
self.callback_fn = None
|
||||
self.delete_game = False
|
||||
self.step_cnt = 0
|
||||
|
||||
def lock_plugin(self, chatbot):
|
||||
if self.callback_fn is None:
|
||||
raise ValueError("callback_fn is None")
|
||||
chatbot._cookies['lock_plugin'] = self.callback_fn
|
||||
self.dump_state(chatbot)
|
||||
|
||||
def get_plugin_name(self):
|
||||
if self.plugin_name is None:
|
||||
raise ValueError("plugin_name is None")
|
||||
return self.plugin_name
|
||||
|
||||
def dump_state(self, chatbot):
|
||||
chatbot._cookies[f'plugin_state/{self.get_plugin_name()}'] = pickle.dumps(self)
|
||||
|
||||
def set_state(self, chatbot, key, value):
|
||||
setattr(self, key, value)
|
||||
chatbot._cookies[f'plugin_state/{self.get_plugin_name()}'] = pickle.dumps(self)
|
||||
|
||||
@staticmethod
|
||||
def sync_state(chatbot, llm_kwargs, cls, plugin_name, callback_fn, lock_plugin=True):
|
||||
state = chatbot._cookies.get(f'plugin_state/{plugin_name}', None)
|
||||
if state is not None:
|
||||
state = pickle.loads(state)
|
||||
else:
|
||||
state = cls()
|
||||
state.init_game(chatbot, lock_plugin)
|
||||
state.plugin_name = plugin_name
|
||||
state.llm_kwargs = llm_kwargs
|
||||
state.chatbot = chatbot
|
||||
state.callback_fn = callback_fn
|
||||
return state
|
||||
|
||||
def continue_game(self, prompt, chatbot, history):
|
||||
# 游戏主体
|
||||
yield from self.step(prompt, chatbot, history)
|
||||
self.step_cnt += 1
|
||||
# 保存状态，收尾
|
||||
self.dump_state(chatbot)
|
||||
# 如果游戏结束，清理
|
||||
if self.delete_game:
|
||||
chatbot._cookies['lock_plugin'] = None
|
||||
chatbot._cookies[f'plugin_state/{self.get_plugin_name()}'] = None
|
||||
yield from update_ui(chatbot=chatbot, history=history)
|
||||
|
||||
0
crazy_functions/vector_fns/__init__.py
Normal file
0
crazy_functions/vector_fns/__init__.py
Normal file
70
crazy_functions/vector_fns/general_file_loader.py
Normal file
70
crazy_functions/vector_fns/general_file_loader.py
Normal file
@ -0,0 +1,70 @@
|
||||
# From project chatglm-langchain
|
||||
|
||||
|
||||
from langchain.document_loaders import UnstructuredFileLoader
|
||||
from langchain.text_splitter import CharacterTextSplitter
|
||||
import re
|
||||
from typing import List
|
||||
|
||||
class ChineseTextSplitter(CharacterTextSplitter):
|
||||
def __init__(self, pdf: bool = False, sentence_size: int = None, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
self.pdf = pdf
|
||||
self.sentence_size = sentence_size
|
||||
|
||||
def split_text1(self, text: str) -> List[str]:
|
||||
if self.pdf:
|
||||
text = re.sub(r"\n{3,}", "\n", text)
|
||||
text = re.sub('\s', ' ', text)
|
||||
text = text.replace("\n\n", "")
|
||||
sent_sep_pattern = re.compile('([﹒﹔﹖﹗．。！？]["’”」』]{0,2}|(?=["‘“「『]{1,2}|$))') # del ：；
|
||||
sent_list = []
|
||||
for ele in sent_sep_pattern.split(text):
|
||||
if sent_sep_pattern.match(ele) and sent_list:
|
||||
sent_list[-1] += ele
|
||||
elif ele:
|
||||
sent_list.append(ele)
|
||||
return sent_list
|
||||
|
||||
def split_text(self, text: str) -> List[str]: ##此处需要进一步优化逻辑
|
||||
if self.pdf:
|
||||
text = re.sub(r"\n{3,}", r"\n", text)
|
||||
text = re.sub('\s', " ", text)
|
||||
text = re.sub("\n\n", "", text)
|
||||
|
||||
text = re.sub(r'([;；.!?。！？\?])([^”’])', r"\1\n\2", text) # 单字符断句符
|
||||
text = re.sub(r'(\.{6})([^"’”」』])', r"\1\n\2", text) # 英文省略号
|
||||
text = re.sub(r'(\…{2})([^"’”」』])', r"\1\n\2", text) # 中文省略号
|
||||
text = re.sub(r'([;；!?。！？\?]["’”」』]{0,2})([^;；!?，。！？\?])', r'\1\n\2', text)
|
||||
# 如果双引号前有终止符，那么双引号才是句子的终点，把分句符\n放到双引号后，注意前面的几句都小心保留了双引号
|
||||
text = text.rstrip() # 段尾如果有多余的\n就去掉它
|
||||
# 很多规则中会考虑分号;，但是这里我把它忽略不计，破折号、英文双引号等同样忽略，需要的再做些简单调整即可。
|
||||
ls = [i for i in text.split("\n") if i]
|
||||
for ele in ls:
|
||||
if len(ele) > self.sentence_size:
|
||||
ele1 = re.sub(r'([,，.]["’”」』]{0,2})([^,，.])', r'\1\n\2', ele)
|
||||
ele1_ls = ele1.split("\n")
|
||||
for ele_ele1 in ele1_ls:
|
||||
if len(ele_ele1) > self.sentence_size:
|
||||
ele_ele2 = re.sub(r'([\n]{1,}| {2,}["’”」』]{0,2})([^\s])', r'\1\n\2', ele_ele1)
|
||||
ele2_ls = ele_ele2.split("\n")
|
||||
for ele_ele2 in ele2_ls:
|
||||
if len(ele_ele2) > self.sentence_size:
|
||||
ele_ele3 = re.sub('( ["’”」』]{0,2})([^ ])', r'\1\n\2', ele_ele2)
|
||||
ele2_id = ele2_ls.index(ele_ele2)
|
||||
ele2_ls = ele2_ls[:ele2_id] + [i for i in ele_ele3.split("\n") if i] + ele2_ls[
|
||||
ele2_id + 1:]
|
||||
ele_id = ele1_ls.index(ele_ele1)
|
||||
ele1_ls = ele1_ls[:ele_id] + [i for i in ele2_ls if i] + ele1_ls[ele_id + 1:]
|
||||
|
||||
id = ls.index(ele)
|
||||
ls = ls[:id] + [i for i in ele1_ls if i] + ls[id + 1:]
|
||||
return ls
|
||||
|
||||
def load_file(filepath, sentence_size):
|
||||
loader = UnstructuredFileLoader(filepath, mode="elements")
|
||||
textsplitter = ChineseTextSplitter(pdf=False, sentence_size=sentence_size)
|
||||
docs = loader.load_and_split(text_splitter=textsplitter)
|
||||
# write_check_file(filepath, docs)
|
||||
return docs
|
||||
|
||||
338
crazy_functions/vector_fns/vector_database.py
Normal file
338
crazy_functions/vector_fns/vector_database.py
Normal file
@ -0,0 +1,338 @@
|
||||
# From project chatglm-langchain
|
||||
|
||||
import threading
|
||||
from toolbox import Singleton
|
||||
import os
|
||||
import shutil
|
||||
import os
|
||||
import uuid
|
||||
import tqdm
|
||||
from langchain.vectorstores import FAISS
|
||||
from langchain.docstore.document import Document
|
||||
from typing import List, Tuple
|
||||
import numpy as np
|
||||
from crazy_functions.vector_fns.general_file_loader import load_file
|
||||
|
||||
embedding_model_dict = {
|
||||
"ernie-tiny": "nghuyong/ernie-3.0-nano-zh",
|
||||
"ernie-base": "nghuyong/ernie-3.0-base-zh",
|
||||
"text2vec-base": "shibing624/text2vec-base-chinese",
|
||||
"text2vec": "GanymedeNil/text2vec-large-chinese",
|
||||
}
|
||||
|
||||
# Embedding model name
|
||||
EMBEDDING_MODEL = "text2vec"
|
||||
|
||||
# Embedding running device
|
||||
EMBEDDING_DEVICE = "cpu"
|
||||
|
||||
# 基于上下文的prompt模版，请务必保留"{question}"和"{context}"
|
||||
PROMPT_TEMPLATE = """已知信息：
|
||||
{context}
|
||||
|
||||
根据上述已知信息，简洁和专业的来回答用户的问题。如果无法从中得到答案，请说 “根据已知信息无法回答该问题” 或 “没有提供足够的相关信息”，不允许在答案中添加编造成分，答案请使用中文。 问题是：{question}"""
|
||||
|
||||
# 文本分句长度
|
||||
SENTENCE_SIZE = 100
|
||||
|
||||
# 匹配后单段上下文长度
|
||||
CHUNK_SIZE = 250
|
||||
|
||||
# LLM input history length
|
||||
LLM_HISTORY_LEN = 3
|
||||
|
||||
# return top-k text chunk from vector store
|
||||
VECTOR_SEARCH_TOP_K = 5
|
||||
|
||||
# 知识检索内容相关度 Score, 数值范围约为0-1100，如果为0，则不生效，经测试设置为小于500时，匹配结果更精准
|
||||
VECTOR_SEARCH_SCORE_THRESHOLD = 0
|
||||
|
||||
NLTK_DATA_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "nltk_data")
|
||||
|
||||
FLAG_USER_NAME = uuid.uuid4().hex
|
||||
|
||||
# 是否开启跨域，默认为False，如果需要开启，请设置为True
|
||||
# is open cross domain
|
||||
OPEN_CROSS_DOMAIN = False
|
||||
|
||||
def similarity_search_with_score_by_vector(
|
||||
self, embedding: List[float], k: int = 4
|
||||
) -> List[Tuple[Document, float]]:
|
||||
|
||||
def seperate_list(ls: List[int]) -> List[List[int]]:
|
||||
lists = []
|
||||
ls1 = [ls[0]]
|
||||
for i in range(1, len(ls)):
|
||||
if ls[i - 1] + 1 == ls[i]:
|
||||
ls1.append(ls[i])
|
||||
else:
|
||||
lists.append(ls1)
|
||||
ls1 = [ls[i]]
|
||||
lists.append(ls1)
|
||||
return lists
|
||||
|
||||
scores, indices = self.index.search(np.array([embedding], dtype=np.float32), k)
|
||||
docs = []
|
||||
id_set = set()
|
||||
store_len = len(self.index_to_docstore_id)
|
||||
for j, i in enumerate(indices[0]):
|
||||
if i == -1 or 0 < self.score_threshold < scores[0][j]:
|
||||
# This happens when not enough docs are returned.
|
||||
continue
|
||||
_id = self.index_to_docstore_id[i]
|
||||
doc = self.docstore.search(_id)
|
||||
if not self.chunk_conent:
|
||||
if not isinstance(doc, Document):
|
||||
raise ValueError(f"Could not find document for id {_id}, got {doc}")
|
||||
doc.metadata["score"] = int(scores[0][j])
|
||||
docs.append(doc)
|
||||
continue
|
||||
id_set.add(i)
|
||||
docs_len = len(doc.page_content)
|
||||
for k in range(1, max(i, store_len - i)):
|
||||
break_flag = False
|
||||
for l in [i + k, i - k]:
|
||||
if 0 <= l < len(self.index_to_docstore_id):
|
||||
_id0 = self.index_to_docstore_id[l]
|
||||
doc0 = self.docstore.search(_id0)
|
||||
if docs_len + len(doc0.page_content) > self.chunk_size:
|
||||
break_flag = True
|
||||
break
|
||||
elif doc0.metadata["source"] == doc.metadata["source"]:
|
||||
docs_len += len(doc0.page_content)
|
||||
id_set.add(l)
|
||||
if break_flag:
|
||||
break
|
||||
if not self.chunk_conent:
|
||||
return docs
|
||||
if len(id_set) == 0 and self.score_threshold > 0:
|
||||
return []
|
||||
id_list = sorted(list(id_set))
|
||||
id_lists = seperate_list(id_list)
|
||||
for id_seq in id_lists:
|
||||
for id in id_seq:
|
||||
if id == id_seq[0]:
|
||||
_id = self.index_to_docstore_id[id]
|
||||
doc = self.docstore.search(_id)
|
||||
else:
|
||||
_id0 = self.index_to_docstore_id[id]
|
||||
doc0 = self.docstore.search(_id0)
|
||||
doc.page_content += " " + doc0.page_content
|
||||
if not isinstance(doc, Document):
|
||||
raise ValueError(f"Could not find document for id {_id}, got {doc}")
|
||||
doc_score = min([scores[0][id] for id in [indices[0].tolist().index(i) for i in id_seq if i in indices[0]]])
|
||||
doc.metadata["score"] = int(doc_score)
|
||||
docs.append(doc)
|
||||
return docs
|
||||
|
||||
|
||||
class LocalDocQA:
|
||||
llm: object = None
|
||||
embeddings: object = None
|
||||
top_k: int = VECTOR_SEARCH_TOP_K
|
||||
chunk_size: int = CHUNK_SIZE
|
||||
chunk_conent: bool = True
|
||||
score_threshold: int = VECTOR_SEARCH_SCORE_THRESHOLD
|
||||
|
||||
def init_cfg(self,
|
||||
top_k=VECTOR_SEARCH_TOP_K,
|
||||
):
|
||||
|
||||
self.llm = None
|
||||
self.top_k = top_k
|
||||
|
||||
def init_knowledge_vector_store(self,
|
||||
filepath,
|
||||
vs_path: str or os.PathLike = None,
|
||||
sentence_size=SENTENCE_SIZE,
|
||||
text2vec=None):
|
||||
loaded_files = []
|
||||
failed_files = []
|
||||
if isinstance(filepath, str):
|
||||
if not os.path.exists(filepath):
|
||||
print("路径不存在")
|
||||
return None
|
||||
elif os.path.isfile(filepath):
|
||||
file = os.path.split(filepath)[-1]
|
||||
try:
|
||||
docs = load_file(filepath, SENTENCE_SIZE)
|
||||
print(f"{file} 已成功加载")
|
||||
loaded_files.append(filepath)
|
||||
except Exception as e:
|
||||
print(e)
|
||||
print(f"{file} 未能成功加载")
|
||||
return None
|
||||
elif os.path.isdir(filepath):
|
||||
docs = []
|
||||
for file in tqdm(os.listdir(filepath), desc="加载文件"):
|
||||
fullfilepath = os.path.join(filepath, file)
|
||||
try:
|
||||
docs += load_file(fullfilepath, SENTENCE_SIZE)
|
||||
loaded_files.append(fullfilepath)
|
||||
except Exception as e:
|
||||
print(e)
|
||||
failed_files.append(file)
|
||||
|
||||
if len(failed_files) > 0:
|
||||
print("以下文件未能成功加载：")
|
||||
for file in failed_files:
|
||||
print(f"{file}\n")
|
||||
|
||||
else:
|
||||
docs = []
|
||||
for file in filepath:
|
||||
docs += load_file(file, SENTENCE_SIZE)
|
||||
print(f"{file} 已成功加载")
|
||||
loaded_files.append(file)
|
||||
|
||||
if len(docs) > 0:
|
||||
print("文件加载完毕，正在生成向量库")
|
||||
if vs_path and os.path.isdir(vs_path):
|
||||
try:
|
||||
self.vector_store = FAISS.load_local(vs_path, text2vec)
|
||||
self.vector_store.add_documents(docs)
|
||||
except:
|
||||
self.vector_store = FAISS.from_documents(docs, text2vec)
|
||||
else:
|
||||
self.vector_store = FAISS.from_documents(docs, text2vec) # docs 为Document列表
|
||||
|
||||
self.vector_store.save_local(vs_path)
|
||||
return vs_path, loaded_files
|
||||
else:
|
||||
raise RuntimeError("文件加载失败，请检查文件格式是否正确")
|
||||
|
||||
def get_loaded_file(self, vs_path):
|
||||
ds = self.vector_store.docstore
|
||||
return set([ds._dict[k].metadata['source'].split(vs_path)[-1] for k in ds._dict])
|
||||
|
||||
|
||||
# query 查询内容
|
||||
# vs_path 知识库路径
|
||||
# chunk_conent 是否启用上下文关联
|
||||
# score_threshold 搜索匹配score阈值
|
||||
# vector_search_top_k 搜索知识库内容条数，默认搜索5条结果
|
||||
# chunk_sizes 匹配单段内容的连接上下文长度
|
||||
def get_knowledge_based_conent_test(self, query, vs_path, chunk_conent,
|
||||
score_threshold=VECTOR_SEARCH_SCORE_THRESHOLD,
|
||||
vector_search_top_k=VECTOR_SEARCH_TOP_K, chunk_size=CHUNK_SIZE,
|
||||
text2vec=None):
|
||||
self.vector_store = FAISS.load_local(vs_path, text2vec)
|
||||
self.vector_store.chunk_conent = chunk_conent
|
||||
self.vector_store.score_threshold = score_threshold
|
||||
self.vector_store.chunk_size = chunk_size
|
||||
|
||||
embedding = self.vector_store.embedding_function.embed_query(query)
|
||||
related_docs_with_score = similarity_search_with_score_by_vector(self.vector_store, embedding, k=vector_search_top_k)
|
||||
|
||||
if not related_docs_with_score:
|
||||
response = {"query": query,
|
||||
"source_documents": []}
|
||||
return response, ""
|
||||
# prompt = f"{query}. You should answer this question using information from following documents: \n\n"
|
||||
prompt = f"{query}. 你必须利用以下文档中包含的信息回答这个问题: \n\n---\n\n"
|
||||
prompt += "\n\n".join([f"({k}): " + doc.page_content for k, doc in enumerate(related_docs_with_score)])
|
||||
prompt += "\n\n---\n\n"
|
||||
prompt = prompt.encode('utf-8', 'ignore').decode() # avoid reading non-utf8 chars
|
||||
# print(prompt)
|
||||
response = {"query": query, "source_documents": related_docs_with_score}
|
||||
return response, prompt
|
||||
|
||||
|
||||
|
||||
|
||||
def construct_vector_store(vs_id, vs_path, files, sentence_size, history, one_conent, one_content_segmentation, text2vec):
|
||||
for file in files:
|
||||
assert os.path.exists(file), "输入文件不存在：" + file
|
||||
import nltk
|
||||
if NLTK_DATA_PATH not in nltk.data.path: nltk.data.path = [NLTK_DATA_PATH] + nltk.data.path
|
||||
local_doc_qa = LocalDocQA()
|
||||
local_doc_qa.init_cfg()
|
||||
filelist = []
|
||||
if not os.path.exists(os.path.join(vs_path, vs_id)):
|
||||
os.makedirs(os.path.join(vs_path, vs_id))
|
||||
for file in files:
|
||||
file_name = file.name if not isinstance(file, str) else file
|
||||
filename = os.path.split(file_name)[-1]
|
||||
shutil.copyfile(file_name, os.path.join(vs_path, vs_id, filename))
|
||||
filelist.append(os.path.join(vs_path, vs_id, filename))
|
||||
vs_path, loaded_files = local_doc_qa.init_knowledge_vector_store(filelist, os.path.join(vs_path, vs_id), sentence_size, text2vec)
|
||||
|
||||
if len(loaded_files):
|
||||
file_status = f"已添加 {'、'.join([os.path.split(i)[-1] for i in loaded_files if i])} 内容至知识库，并已加载知识库，请开始提问"
|
||||
else:
|
||||
pass
|
||||
# file_status = "文件未成功加载，请重新上传文件"
|
||||
# print(file_status)
|
||||
return local_doc_qa, vs_path
|
||||
|
||||
@Singleton
|
||||
class knowledge_archive_interface():
|
||||
def __init__(self) -> None:
|
||||
self.threadLock = threading.Lock()
|
||||
self.current_id = ""
|
||||
self.kai_path = None
|
||||
self.qa_handle = None
|
||||
self.text2vec_large_chinese = None
|
||||
|
||||
def get_chinese_text2vec(self):
|
||||
if self.text2vec_large_chinese is None:
|
||||
# < -------------------预热文本向量化模组--------------- >
|
||||
from toolbox import ProxyNetworkActivate
|
||||
print('Checking Text2vec ...')
|
||||
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
|
||||
with ProxyNetworkActivate('Download_LLM'): # 临时地激活代理网络
|
||||
self.text2vec_large_chinese = HuggingFaceEmbeddings(model_name="GanymedeNil/text2vec-large-chinese")
|
||||
|
||||
return self.text2vec_large_chinese
|
||||
|
||||
|
||||
def feed_archive(self, file_manifest, vs_path, id="default"):
|
||||
self.threadLock.acquire()
|
||||
# import uuid
|
||||
self.current_id = id
|
||||
self.qa_handle, self.kai_path = construct_vector_store(
|
||||
vs_id=self.current_id,
|
||||
vs_path=vs_path,
|
||||
files=file_manifest,
|
||||
sentence_size=100,
|
||||
history=[],
|
||||
one_conent="",
|
||||
one_content_segmentation="",
|
||||
text2vec = self.get_chinese_text2vec(),
|
||||
)
|
||||
self.threadLock.release()
|
||||
|
||||
def get_current_archive_id(self):
|
||||
return self.current_id
|
||||
|
||||
def get_loaded_file(self, vs_path):
|
||||
return self.qa_handle.get_loaded_file(vs_path)
|
||||
|
||||
def answer_with_archive_by_id(self, txt, id, vs_path):
|
||||
self.threadLock.acquire()
|
||||
if not self.current_id == id:
|
||||
self.current_id = id
|
||||
self.qa_handle, self.kai_path = construct_vector_store(
|
||||
vs_id=self.current_id,
|
||||
vs_path=vs_path,
|
||||
files=[],
|
||||
sentence_size=100,
|
||||
history=[],
|
||||
one_conent="",
|
||||
one_content_segmentation="",
|
||||
text2vec = self.get_chinese_text2vec(),
|
||||
)
|
||||
VECTOR_SEARCH_SCORE_THRESHOLD = 0
|
||||
VECTOR_SEARCH_TOP_K = 4
|
||||
CHUNK_SIZE = 512
|
||||
resp, prompt = self.qa_handle.get_knowledge_based_conent_test(
|
||||
query = txt,
|
||||
vs_path = self.kai_path,
|
||||
score_threshold=VECTOR_SEARCH_SCORE_THRESHOLD,
|
||||
vector_search_top_k=VECTOR_SEARCH_TOP_K,
|
||||
chunk_conent=True,
|
||||
chunk_size=CHUNK_SIZE,
|
||||
text2vec = self.get_chinese_text2vec(),
|
||||
)
|
||||
self.threadLock.release()
|
||||
return resp, prompt
|
||||
@ -1,159 +1,59 @@
|
||||
from toolbox import CatchException, update_ui, get_conf, select_api_key, get_log_folder
|
||||
from crazy_functions.multi_stage.multi_stage_utils import GptAcademicState
|
||||
from toolbox import CatchException, update_ui, update_ui_lastest_msg
|
||||
from crazy_functions.multi_stage.multi_stage_utils import GptAcademicGameBaseState
|
||||
from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
from request_llms.bridge_all import predict_no_ui_long_connection
|
||||
from crazy_functions.game_fns.game_utils import get_code_block, is_same_thing
|
||||
import random
|
||||
|
||||
class 小游戏(GptAcademicState):
|
||||
def __init__(self):
|
||||
self.need_game_reset = True
|
||||
self.llm_kwargs = None
|
||||
super().__init__()
|
||||
|
||||
def lock_plugin(self, chatbot):
|
||||
chatbot._cookies['lock_plugin'] = 'crazy_functions.互动小游戏->谁是卧底'
|
||||
self.dump_state(chatbot)
|
||||
class MiniGame_ASCII_Art(GptAcademicGameBaseState):
|
||||
|
||||
def unlock_plugin(self, chatbot):
|
||||
self.reset()
|
||||
chatbot._cookies['lock_plugin'] = None
|
||||
self.dump_state(chatbot)
|
||||
|
||||
def set_state(self, chatbot, key, value):
|
||||
return super().set_state(chatbot, key, value)
|
||||
|
||||
def init_game(self, chatbot):
|
||||
chatbot.get_cookies()['lock_plugin'] = ''
|
||||
|
||||
def clean_up_game(self, chatbot):
|
||||
chatbot.get_cookies()['lock_plugin'] = None
|
||||
|
||||
def init_player(self):
|
||||
pass
|
||||
|
||||
def step(self, prompt, chatbot):
|
||||
pass
|
||||
|
||||
def continue_game(self, prompt, chatbot):
|
||||
if self.need_game_reset:
|
||||
self.need_game_reset = False
|
||||
yield from self.init_game(chatbot)
|
||||
yield from self.step(prompt, chatbot)
|
||||
self.dump_state(chatbot)
|
||||
yield update_ui(chatbot=chatbot, history=[])
|
||||
|
||||
class 小游戏_谁是卧底_玩家():
|
||||
def __init__(self, game_handle, card, llm_model, name) -> None:
|
||||
self.game_handle = game_handle
|
||||
self.card = card
|
||||
self.name = name
|
||||
self.is_out = False
|
||||
self.llm_model = llm_model
|
||||
self.is_human = llm_model == 'human'
|
||||
self.what_player_has_spoken = []
|
||||
|
||||
def speek(self, content=None):
|
||||
if content is None:
|
||||
assert not self.is_human
|
||||
speak_what = yield from
|
||||
def step(self, prompt, chatbot, history):
|
||||
if self.step_cnt == 0:
|
||||
chatbot.append(["我画你猜（动物）", "请稍等..."])
|
||||
else:
|
||||
self.what_player_has_spoken.append(content)
|
||||
if prompt.strip() == 'exit':
|
||||
self.delete_game = True
|
||||
yield from update_ui_lastest_msg(lastmsg=f"谜底是{self.obj}，游戏结束。", chatbot=chatbot, history=history, delay=0.)
|
||||
return
|
||||
chatbot.append([prompt, ""])
|
||||
yield from update_ui(chatbot=chatbot, history=history)
|
||||
|
||||
def agi_speek(self):
|
||||
inputs = f'please say something about {self.card}'
|
||||
res = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs = inputs,
|
||||
inputs_show_user=inputs,
|
||||
llm_kwargs=self.game_handle.llm_kwargs,
|
||||
chatbot=chatbot,
|
||||
history=history,
|
||||
sys_prompt=sys_prompt
|
||||
)
|
||||
pass
|
||||
if self.step_cnt == 0:
|
||||
self.lock_plugin(chatbot)
|
||||
self.cur_task = 'draw'
|
||||
|
||||
def vote(self, content=None):
|
||||
if content is None:
|
||||
assert not self.is_human
|
||||
self.vote_who = yield from
|
||||
else:
|
||||
try:
|
||||
self.vote_who = int(content)
|
||||
except:
|
||||
self.vote_who = None
|
||||
if self.cur_task == 'draw':
|
||||
avail_obj = ["狗","猫","鸟","鱼","老鼠","蛇"]
|
||||
self.obj = random.choice(avail_obj)
|
||||
inputs = "I want to play a game called Guess the ASCII art. You can draw the ASCII art and I will try to guess it. " + f"This time you draw a {self.obj}. Note that you must not indicate what you have draw in the text, and you should only produce the ASCII art wrapped by ```. "
|
||||
raw_res = predict_no_ui_long_connection(inputs=inputs, llm_kwargs=self.llm_kwargs, history=[], sys_prompt="")
|
||||
self.cur_task = 'identify user guess'
|
||||
res = get_code_block(raw_res)
|
||||
history += ['', f'the answer is {self.obj}', inputs, res]
|
||||
yield from update_ui_lastest_msg(lastmsg=res, chatbot=chatbot, history=history, delay=0.)
|
||||
|
||||
def agi_vote(self):
|
||||
pass
|
||||
|
||||
class 小游戏_谁是卧底(小游戏):
|
||||
def __init__(self):
|
||||
self.game_phase = '发言' # 投票
|
||||
super().__init__()
|
||||
|
||||
def init_game(self, chatbot):
|
||||
self.n_players = 3
|
||||
self.n_ai_players = self.n_players - 1
|
||||
card = "橙子"
|
||||
undercover_card = "橘子"
|
||||
llm_model = self.llm_kwargs['llm_model']
|
||||
self.players = [
|
||||
小游戏_谁是卧底(self, card, llm_model, str(i)) for i in range(self.n_players)
|
||||
]
|
||||
|
||||
undercover = random.randint(0, self.n_players-1)
|
||||
human = 0
|
||||
|
||||
self.players[undercover].card = undercover_card
|
||||
self.players[human].llm_model = 'human'
|
||||
super().init_game(chatbot)
|
||||
|
||||
def who_is_out(self):
|
||||
votes = {}
|
||||
for player in self.players:
|
||||
if player.is_out: continue
|
||||
if player.vote is None: continue
|
||||
if player.vote not in votes: votes[player.vote] = 0
|
||||
votes[player.vote] += 1
|
||||
max_votes = max(votes.values())
|
||||
players_with_max_votes = [player for player, vote_count in votes.items() if vote_count == max_votes]
|
||||
for player in players_with_max_votes:
|
||||
print('淘汰了', player.name)
|
||||
player.is_out = True
|
||||
return players_with_max_votes
|
||||
|
||||
def step(self, prompt, chatbot):
|
||||
|
||||
if self.game_phase == '发言':
|
||||
for player in self.players:
|
||||
if player.is_out: continue
|
||||
if player.is_human:
|
||||
player.speek(prompt)
|
||||
else:
|
||||
player.speek()
|
||||
self.game_phase = '投票'
|
||||
|
||||
elif self.game_phase == '投票':
|
||||
for player in self.players:
|
||||
if player.is_out: continue
|
||||
if player.is_human:
|
||||
player.vote(prompt)
|
||||
else:
|
||||
player.vote()
|
||||
self.who_is_out()
|
||||
if len([player for player in self.players if not player.is_out]) <= 2:
|
||||
if sum([player for player in self.players if player.is_undercover]) == 1:
|
||||
print('卧底获胜')
|
||||
else:
|
||||
print('平民获胜')
|
||||
self.need_game_reset = True
|
||||
self.game_phase = '发言'
|
||||
|
||||
else:
|
||||
raise RuntimeError
|
||||
elif self.cur_task == 'identify user guess':
|
||||
if is_same_thing(self.obj, prompt, self.llm_kwargs):
|
||||
self.delete_game = True
|
||||
yield from update_ui_lastest_msg(lastmsg="你猜对了！", chatbot=chatbot, history=history, delay=0.)
|
||||
else:
|
||||
self.cur_task = 'identify user guess'
|
||||
yield from update_ui_lastest_msg(lastmsg="猜错了，再试试，输入“exit”获取答案。", chatbot=chatbot, history=history, delay=0.)
|
||||
|
||||
|
||||
@CatchException
|
||||
def 谁是卧底(prompt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
|
||||
# 尚未完成
|
||||
history = [] # 清空历史
|
||||
state = 小游戏_谁是卧底.get_state(chatbot, 小游戏_谁是卧底)
|
||||
state.llm_kwargs = llm_kwargs
|
||||
yield from state.continue_game(prompt, chatbot)
|
||||
def 随机小游戏(prompt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
|
||||
# 清空历史
|
||||
history = []
|
||||
# 选择游戏
|
||||
cls = MiniGame_ASCII_Art
|
||||
# 如果之前已经初始化了游戏实例，则继续该实例；否则重新初始化
|
||||
state = cls.sync_state(chatbot,
|
||||
llm_kwargs,
|
||||
cls,
|
||||
plugin_name='MiniGame_ASCII_Art',
|
||||
callback_fn='crazy_functions.互动小游戏->随机小游戏',
|
||||
lock_plugin=True
|
||||
)
|
||||
yield from state.continue_game(prompt, chatbot, history)
|
||||
|
||||
@ -1,10 +1,19 @@
|
||||
from toolbox import CatchException, update_ui, ProxyNetworkActivate, update_ui_lastest_msg
|
||||
from toolbox import CatchException, update_ui, ProxyNetworkActivate, update_ui_lastest_msg, get_log_folder, get_user
|
||||
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, get_files_from_everything
|
||||
|
||||
install_msg ="""
|
||||
|
||||
1. python -m pip install torch --index-url https://download.pytorch.org/whl/cpu
|
||||
|
||||
2. python -m pip install transformers protobuf langchain sentence-transformers faiss-cpu nltk beautifulsoup4 bitsandbytes tabulate icetk --upgrade
|
||||
|
||||
3. python -m pip install unstructured[all-docs] --upgrade
|
||||
|
||||
4. python -c 'import nltk; nltk.download("punkt")'
|
||||
"""
|
||||
|
||||
@CatchException
|
||||
def 知识库问答(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
|
||||
def 知识库文件注入(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
|
||||
"""
|
||||
txt 输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
|
||||
llm_kwargs gpt模型参数, 如温度和top_p等, 一般原样传递下去就行
|
||||
@ -25,15 +34,15 @@ def 知识库问答(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_pro
|
||||
|
||||
# resolve deps
|
||||
try:
|
||||
from zh_langchain import construct_vector_store
|
||||
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
|
||||
from .crazy_utils import knowledge_archive_interface
|
||||
# from zh_langchain import construct_vector_store
|
||||
# from langchain.embeddings.huggingface import HuggingFaceEmbeddings
|
||||
from crazy_functions.vector_fns.vector_database import knowledge_archive_interface
|
||||
except Exception as e:
|
||||
chatbot.append(["依赖不足", "导入依赖失败。正在尝试自动安装，请查看终端的输出或耐心等待..."])
|
||||
chatbot.append(["依赖不足", f"{str(e)}\n\n导入依赖失败。请用以下命令安装" + install_msg])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
from .crazy_utils import try_install_deps
|
||||
try_install_deps(['zh_langchain==0.2.1', 'pypinyin'], reload_m=['pypinyin', 'zh_langchain'])
|
||||
yield from update_ui_lastest_msg("安装完成，您可以再次重试。", chatbot, history)
|
||||
# from .crazy_utils import try_install_deps
|
||||
# try_install_deps(['zh_langchain==0.2.1', 'pypinyin'], reload_m=['pypinyin', 'zh_langchain'])
|
||||
# yield from update_ui_lastest_msg("安装完成，您可以再次重试。", chatbot, history)
|
||||
return
|
||||
|
||||
# < --------------------读取文件--------------- >
|
||||
@ -62,13 +71,14 @@ def 知识库问答(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_pro
|
||||
print('Establishing knowledge archive ...')
|
||||
with ProxyNetworkActivate('Download_LLM'): # 临时地激活代理网络
|
||||
kai = knowledge_archive_interface()
|
||||
kai.feed_archive(file_manifest=file_manifest, id=kai_id)
|
||||
kai_files = kai.get_loaded_file()
|
||||
vs_path = get_log_folder(user=get_user(chatbot), plugin_name='vec_store')
|
||||
kai.feed_archive(file_manifest=file_manifest, vs_path=vs_path, id=kai_id)
|
||||
kai_files = kai.get_loaded_file(vs_path=vs_path)
|
||||
kai_files = '<br/>'.join(kai_files)
|
||||
# chatbot.append(['知识库构建成功', "正在将知识库存储至cookie中"])
|
||||
# yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
# chatbot._cookies['langchain_plugin_embedding'] = kai.get_current_archive_id()
|
||||
# chatbot._cookies['lock_plugin'] = 'crazy_functions.Langchain知识库->读取知识库作答'
|
||||
# chatbot._cookies['lock_plugin'] = 'crazy_functions.知识库文件注入->读取知识库作答'
|
||||
# chatbot.append(['完成', "“根据知识库作答”函数插件已经接管问答系统, 提问吧! 但注意, 您接下来不能再使用其他插件了，刷新页面即可以退出知识库问答模式。"])
|
||||
chatbot.append(['构建完成', f"当前知识库内的有效文件：\n\n---\n\n{kai_files}\n\n---\n\n请切换至“知识库问答”插件进行知识库访问, 或者使用此插件继续上传更多文件。"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
|
||||
@ -77,15 +87,15 @@ def 知识库问答(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_pro
|
||||
def 读取知识库作答(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port=-1):
|
||||
# resolve deps
|
||||
try:
|
||||
from zh_langchain import construct_vector_store
|
||||
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
|
||||
from .crazy_utils import knowledge_archive_interface
|
||||
# from zh_langchain import construct_vector_store
|
||||
# from langchain.embeddings.huggingface import HuggingFaceEmbeddings
|
||||
from crazy_functions.vector_fns.vector_database import knowledge_archive_interface
|
||||
except Exception as e:
|
||||
chatbot.append(["依赖不足", "导入依赖失败。正在尝试自动安装，请查看终端的输出或耐心等待..."])
|
||||
chatbot.append(["依赖不足", f"{str(e)}\n\n导入依赖失败。请用以下命令安装" + install_msg])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
from .crazy_utils import try_install_deps
|
||||
try_install_deps(['zh_langchain==0.2.1', 'pypinyin'], reload_m=['pypinyin', 'zh_langchain'])
|
||||
yield from update_ui_lastest_msg("安装完成，您可以再次重试。", chatbot, history)
|
||||
# from .crazy_utils import try_install_deps
|
||||
# try_install_deps(['zh_langchain==0.2.1', 'pypinyin'], reload_m=['pypinyin', 'zh_langchain'])
|
||||
# yield from update_ui_lastest_msg("安装完成，您可以再次重试。", chatbot, history)
|
||||
return
|
||||
|
||||
# < ------------------- --------------- >
|
||||
@ -93,7 +103,8 @@ def 读取知识库作答(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
|
||||
|
||||
if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
|
||||
kai_id = plugin_kwargs.get("advanced_arg", 'default')
|
||||
resp, prompt = kai.answer_with_archive_by_id(txt, kai_id)
|
||||
vs_path = get_log_folder(user=get_user(chatbot), plugin_name='vec_store')
|
||||
resp, prompt = kai.answer_with_archive_by_id(txt, kai_id, vs_path)
|
||||
|
||||
chatbot.append((txt, f'[知识库 {kai_id}] ' + prompt))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
|
||||
26
docs/GithubAction+NoLocal+Vectordb
Normal file
26
docs/GithubAction+NoLocal+Vectordb
Normal file
@ -0,0 +1,26 @@
|
||||
# 此Dockerfile适用于“无本地模型”的环境构建，如果需要使用chatglm等本地模型，请参考 docs/Dockerfile+ChatGLM
|
||||
# 如何构建: 先修改 `config.py`， 然后 docker build -t gpt-academic-nolocal-vs -f docs/GithubAction+NoLocal+Vectordb .
|
||||
# 如何运行: docker run --rm -it --net=host gpt-academic-nolocal-vs
|
||||
FROM python:3.11
|
||||
|
||||
# 指定路径
|
||||
WORKDIR /gpt
|
||||
|
||||
# 装载项目文件
|
||||
COPY . .
|
||||
|
||||
# 安装依赖
|
||||
RUN pip3 install -r requirements.txt
|
||||
|
||||
# 安装知识库插件的额外依赖
|
||||
RUN apt-get update && apt-get install libgl1 -y
|
||||
RUN pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cpu
|
||||
RUN pip3 install transformers protobuf langchain sentence-transformers faiss-cpu nltk beautifulsoup4 bitsandbytes tabulate icetk --upgrade
|
||||
RUN pip3 install unstructured[all-docs] --upgrade
|
||||
|
||||
# 可选步骤，用于预热模块
|
||||
RUN python3 -c 'from check_proxy import warm_up_modules; warm_up_modules()'
|
||||
RUN python3 -c 'from check_proxy import warm_up_vectordb; warm_up_vectordb()'
|
||||
|
||||
# 启动
|
||||
CMD ["python3", "-u", "main.py"]
|
||||
@ -2902,5 +2902,107 @@
|
||||
"高优先级": "High priority",
|
||||
"请配置ZHIPUAI_API_KEY": "Please configure ZHIPUAI_API_KEY",
|
||||
"单个azure模型": "Single Azure model",
|
||||
"预留参数 context 未实现": "Reserved parameter 'context' not implemented"
|
||||
"预留参数 context 未实现": "Reserved parameter 'context' not implemented",
|
||||
"在输入区输入临时API_KEY后提交": "Submit after entering temporary API_KEY in the input area",
|
||||
"鸟": "Bird",
|
||||
"图片中需要修改的位置用橡皮擦擦除为纯白色": "Erase the areas in the image that need to be modified with an eraser to pure white",
|
||||
"└── PDF文档精准解析": "└── Accurate parsing of PDF documents",
|
||||
"└── ALLOW_RESET_CONFIG 是否允许通过自然语言描述修改本页的配置": "└── ALLOW_RESET_CONFIG Whether to allow modifying the configuration of this page through natural language description",
|
||||
"等待指令": "Waiting for instructions",
|
||||
"不存在": "Does not exist",
|
||||
"选择游戏": "Select game",
|
||||
"本地大模型示意图": "Local large model diagram",
|
||||
"无视此消息即可": "You can ignore this message",
|
||||
"即RGB=255": "That is, RGB=255",
|
||||
"如需追问": "If you have further questions",
|
||||
"也可以是具体的模型路径": "It can also be a specific model path",
|
||||
"才会起作用": "Will take effect",
|
||||
"下载失败": "Download failed",
|
||||
"网页刷新后失效": "Invalid after webpage refresh",
|
||||
"crazy_functions.互动小游戏-": "crazy_functions.Interactive mini game-",
|
||||
"右对齐": "Right alignment",
|
||||
"您可以调用下拉菜单中的“LoadConversationHistoryArchive”还原当下的对话": "You can use the 'LoadConversationHistoryArchive' in the drop-down menu to restore the current conversation",
|
||||
"左对齐": "Left alignment",
|
||||
"使用默认的 FP16": "Use default FP16",
|
||||
"一小时": "One hour",
|
||||
"从而方便内存的释放": "Thus facilitating memory release",
|
||||
"如何临时更换API_KEY": "How to temporarily change API_KEY",
|
||||
"请输入 1024x1024-HD": "Please enter 1024x1024-HD",
|
||||
"使用 INT8 量化": "Use INT8 quantization",
|
||||
"3. 输入修改需求": "3. Enter modification requirements",
|
||||
"刷新界面 由于请求gpt需要一段时间": "Refreshing the interface takes some time due to the request for gpt",
|
||||
"随机小游戏": "Random mini game",
|
||||
"那么请在下面的QWEN_MODEL_SELECTION中指定具体的模型": "So please specify the specific model in QWEN_MODEL_SELECTION below",
|
||||
"表值": "Table value",
|
||||
"我画你猜": "I draw, you guess",
|
||||
"狗": "Dog",
|
||||
"2. 输入分辨率": "2. Enter resolution",
|
||||
"鱼": "Fish",
|
||||
"尚未完成": "Not yet completed",
|
||||
"表头": "Table header",
|
||||
"填localhost或者127.0.0.1": "Fill in localhost or 127.0.0.1",
|
||||
"请上传jpg格式的图片": "Please upload images in jpg format",
|
||||
"API_URL_REDIRECT填写格式是错误的": "The format of API_URL_REDIRECT is incorrect",
|
||||
"├── RWKV的支持见Wiki": "Support for RWKV is available in the Wiki",
|
||||
"如果中文Prompt效果不理想": "If the Chinese prompt is not effective",
|
||||
"/SEAFILE_LOCAL/50503047/我的资料库/学位/paperlatex/aaai/Fu_8368_with_appendix": "/SEAFILE_LOCAL/50503047/My Library/Degree/paperlatex/aaai/Fu_8368_with_appendix",
|
||||
"只有当AVAIL_LLM_MODELS包含了对应本地模型时": "Only when AVAIL_LLM_MODELS contains the corresponding local model",
|
||||
"选择本地模型变体": "Choose the local model variant",
|
||||
"如果您确信自己没填错": "If you are sure you haven't made a mistake",
|
||||
"PyPDF2这个库有严重的内存泄露问题": "PyPDF2 library has serious memory leak issues",
|
||||
"整理文件集合 输出消息": "Organize file collection and output message",
|
||||
"没有检测到任何近期上传的图像文件": "No recently uploaded image files detected",
|
||||
"游戏结束": "Game over",
|
||||
"调用结束": "Call ended",
|
||||
"猫": "Cat",
|
||||
"请及时切换模型": "Please switch models in time",
|
||||
"次中": "In the meantime",
|
||||
"如需生成高清图像": "If you need to generate high-definition images",
|
||||
"CPU 模式": "CPU mode",
|
||||
"项目目录": "Project directory",
|
||||
"动物": "Animal",
|
||||
"居中对齐": "Center alignment",
|
||||
"请注意拓展名需要小写": "Please note that the extension name needs to be lowercase",
|
||||
"重试第": "Retry",
|
||||
"实验性功能": "Experimental feature",
|
||||
"猜错了": "Wrong guess",
|
||||
"打开你的代理软件查看代理协议": "Open your proxy software to view the proxy agreement",
|
||||
"您不需要再重复强调该文件的路径了": "You don't need to emphasize the file path again",
|
||||
"请阅读": "Please read",
|
||||
"请直接输入您的问题": "Please enter your question directly",
|
||||
"API_URL_REDIRECT填错了": "API_URL_REDIRECT is filled incorrectly",
|
||||
"谜底是": "The answer is",
|
||||
"第一个模型": "The first model",
|
||||
"你猜对了！": "You guessed it right!",
|
||||
"已经接收到您上传的文件": "The file you uploaded has been received",
|
||||
"您正在调用“图像生成”插件": "You are calling the 'Image Generation' plugin",
|
||||
"刷新界面 界面更新": "Refresh the interface, interface update",
|
||||
"如果之前已经初始化了游戏实例": "If the game instance has been initialized before",
|
||||
"文件": "File",
|
||||
"老鼠": "Mouse",
|
||||
"列2": "Column 2",
|
||||
"等待图片": "Waiting for image",
|
||||
"使用 INT4 量化": "Use INT4 quantization",
|
||||
"from crazy_functions.互动小游戏 import 随机小游戏": "TranslatedText",
|
||||
"游戏主体": "TranslatedText",
|
||||
"该模型不具备上下文对话能力": "TranslatedText",
|
||||
"列3": "TranslatedText",
|
||||
"清理": "TranslatedText",
|
||||
"检查量化配置": "TranslatedText",
|
||||
"如果游戏结束": "TranslatedText",
|
||||
"蛇": "TranslatedText",
|
||||
"则继续该实例；否则重新初始化": "TranslatedText",
|
||||
"e.g. cat and 猫 are the same thing": "TranslatedText",
|
||||
"第三个模型": "TranslatedText",
|
||||
"如果你选择Qwen系列的模型": "TranslatedText",
|
||||
"列4": "TranslatedText",
|
||||
"输入“exit”获取答案": "TranslatedText",
|
||||
"把它放到子进程中运行": "TranslatedText",
|
||||
"列1": "TranslatedText",
|
||||
"使用该模型需要额外依赖": "TranslatedText",
|
||||
"再试试": "TranslatedText",
|
||||
"1. 上传图片": "TranslatedText",
|
||||
"保存状态": "TranslatedText",
|
||||
"GPT-Academic对话存档": "TranslatedText",
|
||||
"Arxiv论文精细翻译": "TranslatedText"
|
||||
}
|
||||
|
||||
@ -182,12 +182,12 @@ cached_translation = read_map_from_json(language=LANG)
|
||||
def trans(word_to_translate, language, special=False):
|
||||
if len(word_to_translate) == 0: return {}
|
||||
from crazy_functions.crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
|
||||
from toolbox import get_conf, ChatBotWithCookies
|
||||
proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION, CHATBOT_HEIGHT, LAYOUT, API_KEY = \
|
||||
get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION', 'CHATBOT_HEIGHT', 'LAYOUT', 'API_KEY')
|
||||
from toolbox import get_conf, ChatBotWithCookies, load_chat_cookies
|
||||
|
||||
cookies = load_chat_cookies()
|
||||
llm_kwargs = {
|
||||
'api_key': API_KEY,
|
||||
'llm_model': LLM_MODEL,
|
||||
'api_key': cookies['api_key'],
|
||||
'llm_model': cookies['llm_model'],
|
||||
'top_p':1.0,
|
||||
'max_length': None,
|
||||
'temperature':0.4,
|
||||
@ -245,15 +245,15 @@ def trans(word_to_translate, language, special=False):
|
||||
def trans_json(word_to_translate, language, special=False):
|
||||
if len(word_to_translate) == 0: return {}
|
||||
from crazy_functions.crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
|
||||
from toolbox import get_conf, ChatBotWithCookies
|
||||
proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION, CHATBOT_HEIGHT, LAYOUT, API_KEY = \
|
||||
get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION', 'CHATBOT_HEIGHT', 'LAYOUT', 'API_KEY')
|
||||
from toolbox import get_conf, ChatBotWithCookies, load_chat_cookies
|
||||
|
||||
cookies = load_chat_cookies()
|
||||
llm_kwargs = {
|
||||
'api_key': API_KEY,
|
||||
'llm_model': LLM_MODEL,
|
||||
'api_key': cookies['api_key'],
|
||||
'llm_model': cookies['llm_model'],
|
||||
'top_p':1.0,
|
||||
'max_length': None,
|
||||
'temperature':0.1,
|
||||
'temperature':0.4,
|
||||
}
|
||||
import random
|
||||
N_EACH_REQ = random.randint(16, 32)
|
||||
|
||||
@ -552,7 +552,7 @@ if "deepseekcoder" in AVAIL_LLM_MODELS: # deepseekcoder
|
||||
"fn_with_ui": deepseekcoder_ui,
|
||||
"fn_without_ui": deepseekcoder_noui,
|
||||
"endpoint": None,
|
||||
"max_token": 4096,
|
||||
"max_token": 2048,
|
||||
"tokenizer": tokenizer_gpt35,
|
||||
"token_cnt": get_token_num_gpt35,
|
||||
}
|
||||
|
||||
@ -6,6 +6,7 @@ from toolbox import ProxyNetworkActivate
|
||||
from toolbox import get_conf
|
||||
from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns
|
||||
from threading import Thread
|
||||
import torch
|
||||
|
||||
def download_huggingface_model(model_name, max_retry, local_dir):
|
||||
from huggingface_hub import snapshot_download
|
||||
@ -36,9 +37,46 @@ class GetCoderLMHandle(LocalLLMHandle):
|
||||
# tokenizer = download_huggingface_model(model_name, max_retry=128, local_dir=local_dir)
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
|
||||
self._streamer = TextIteratorStreamer(tokenizer)
|
||||
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
|
||||
device_map = {
|
||||
"transformer.word_embeddings": 0,
|
||||
"transformer.word_embeddings_layernorm": 0,
|
||||
"lm_head": 0,
|
||||
"transformer.h": 0,
|
||||
"transformer.ln_f": 0,
|
||||
"model.embed_tokens": 0,
|
||||
"model.layers": 0,
|
||||
"model.norm": 0,
|
||||
}
|
||||
|
||||
# 检查量化配置
|
||||
quantization_type = get_conf('LOCAL_MODEL_QUANT')
|
||||
|
||||
if get_conf('LOCAL_MODEL_DEVICE') != 'cpu':
|
||||
model = model.cuda()
|
||||
if quantization_type == "INT8":
|
||||
from transformers import BitsAndBytesConfig
|
||||
# 使用 INT8 量化
|
||||
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True, load_in_8bit=True,
|
||||
device_map=device_map)
|
||||
elif quantization_type == "INT4":
|
||||
from transformers import BitsAndBytesConfig
|
||||
# 使用 INT4 量化
|
||||
bnb_config = BitsAndBytesConfig(
|
||||
load_in_4bit=True,
|
||||
bnb_4bit_use_double_quant=True,
|
||||
bnb_4bit_quant_type="nf4",
|
||||
bnb_4bit_compute_dtype=torch.bfloat16
|
||||
)
|
||||
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True,
|
||||
quantization_config=bnb_config, device_map=device_map)
|
||||
else:
|
||||
# 使用默认的 FP16
|
||||
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True,
|
||||
torch_dtype=torch.bfloat16, device_map=device_map)
|
||||
else:
|
||||
# CPU 模式
|
||||
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True,
|
||||
torch_dtype=torch.bfloat16)
|
||||
|
||||
return model, tokenizer
|
||||
|
||||
def llm_stream_generator(self, **kwargs):
|
||||
@ -54,7 +92,10 @@ class GetCoderLMHandle(LocalLLMHandle):
|
||||
query, max_length, top_p, temperature, history = adaptor(kwargs)
|
||||
history.append({ 'role': 'user', 'content': query})
|
||||
messages = history
|
||||
inputs = self._tokenizer.apply_chat_template(messages, return_tensors="pt").to(self._model.device)
|
||||
inputs = self._tokenizer.apply_chat_template(messages, return_tensors="pt")
|
||||
if inputs.shape[1] > max_length:
|
||||
inputs = inputs[:, -max_length:]
|
||||
inputs = inputs.to(self._model.device)
|
||||
generation_kwargs = dict(
|
||||
inputs=inputs,
|
||||
max_new_tokens=max_length,
|
||||
|
||||
@ -48,11 +48,11 @@ if __name__ == "__main__":
|
||||
# for lang in ["English", "French", "Japanese", "Korean", "Russian", "Italian", "German", "Portuguese", "Arabic"]:
|
||||
# plugin_test(plugin='crazy_functions.批量Markdown翻译->Markdown翻译指定语言', main_input="README.md", advanced_arg={"advanced_arg": lang})
|
||||
|
||||
# plugin_test(plugin='crazy_functions.Langchain知识库->知识库问答', main_input="./")
|
||||
# plugin_test(plugin='crazy_functions.知识库文件注入->知识库文件注入', main_input="./")
|
||||
|
||||
# plugin_test(plugin='crazy_functions.Langchain知识库->读取知识库作答', main_input="What is the installation method？")
|
||||
# plugin_test(plugin='crazy_functions.知识库文件注入->读取知识库作答', main_input="What is the installation method？")
|
||||
|
||||
# plugin_test(plugin='crazy_functions.Langchain知识库->读取知识库作答', main_input="远程云服务器部署？")
|
||||
# plugin_test(plugin='crazy_functions.知识库文件注入->读取知识库作答', main_input="远程云服务器部署？")
|
||||
|
||||
# plugin_test(plugin='crazy_functions.Latex输出PDF结果->Latex翻译中文并重新编译PDF', main_input="2210.03629")
|
||||
|
||||
|
||||
@ -56,11 +56,11 @@ vt.get_plugin_handle = silence_stdout_fn(get_plugin_handle)
|
||||
vt.get_plugin_default_kwargs = silence_stdout_fn(get_plugin_default_kwargs)
|
||||
vt.get_chat_handle = silence_stdout_fn(get_chat_handle)
|
||||
vt.get_chat_default_kwargs = silence_stdout_fn(get_chat_default_kwargs)
|
||||
vt.chat_to_markdown_str = chat_to_markdown_str
|
||||
vt.chat_to_markdown_str = (chat_to_markdown_str)
|
||||
proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION, CHATBOT_HEIGHT, LAYOUT, API_KEY = \
|
||||
vt.get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION', 'CHATBOT_HEIGHT', 'LAYOUT', 'API_KEY')
|
||||
|
||||
def plugin_test(main_input, plugin, advanced_arg=None):
|
||||
def plugin_test(main_input, plugin, advanced_arg=None, debug=True):
|
||||
from rich.live import Live
|
||||
from rich.markdown import Markdown
|
||||
|
||||
@ -72,7 +72,10 @@ def plugin_test(main_input, plugin, advanced_arg=None):
|
||||
plugin_kwargs['main_input'] = main_input
|
||||
if advanced_arg is not None:
|
||||
plugin_kwargs['plugin_kwargs'] = advanced_arg
|
||||
my_working_plugin = silence_stdout(plugin)(**plugin_kwargs)
|
||||
if debug:
|
||||
my_working_plugin = (plugin)(**plugin_kwargs)
|
||||
else:
|
||||
my_working_plugin = silence_stdout(plugin)(**plugin_kwargs)
|
||||
|
||||
with Live(Markdown(""), auto_refresh=False, vertical_overflow="visible") as live:
|
||||
for cookies, chat, hist, msg in my_working_plugin:
|
||||
|
||||
17
tests/test_vector_plugins.py
Normal file
17
tests/test_vector_plugins.py
Normal file
@ -0,0 +1,17 @@
|
||||
"""
|
||||
对项目中的各个插件进行测试。运行方法：直接运行 python tests/test_plugins.py
|
||||
"""
|
||||
|
||||
|
||||
import os, sys
|
||||
def validate_path(): dir_name = os.path.dirname(__file__); root_dir_assume = os.path.abspath(dir_name + '/..'); os.chdir(root_dir_assume); sys.path.append(root_dir_assume)
|
||||
validate_path() # 返回项目根路径
|
||||
|
||||
if __name__ == "__main__":
|
||||
from tests.test_utils import plugin_test
|
||||
|
||||
plugin_test(plugin='crazy_functions.知识库问答->知识库文件注入', main_input="./README.md")
|
||||
|
||||
plugin_test(plugin='crazy_functions.知识库问答->读取知识库作答', main_input="What is the installation method？")
|
||||
|
||||
plugin_test(plugin='crazy_functions.知识库问答->读取知识库作答', main_input="远程云服务器部署？")
|
||||
24
toolbox.py
24
toolbox.py
@ -180,12 +180,15 @@ def HotReload(f):
|
||||
最后，使用yield from语句返回重新加载过的函数，并在被装饰的函数上执行。
|
||||
最终，装饰器函数返回内部函数。这个内部函数可以将函数的原始定义更新为最新版本，并执行函数的新版本。
|
||||
"""
|
||||
@wraps(f)
|
||||
def decorated(*args, **kwargs):
|
||||
fn_name = f.__name__
|
||||
f_hot_reload = getattr(importlib.reload(inspect.getmodule(f)), fn_name)
|
||||
yield from f_hot_reload(*args, **kwargs)
|
||||
return decorated
|
||||
if get_conf('PLUGIN_HOT_RELOAD'):
|
||||
@wraps(f)
|
||||
def decorated(*args, **kwargs):
|
||||
fn_name = f.__name__
|
||||
f_hot_reload = getattr(importlib.reload(inspect.getmodule(f)), fn_name)
|
||||
yield from f_hot_reload(*args, **kwargs)
|
||||
return decorated
|
||||
else:
|
||||
return f
|
||||
|
||||
|
||||
"""
|
||||
@ -916,7 +919,14 @@ def read_single_conf_with_lru_cache(arg):
|
||||
|
||||
@lru_cache(maxsize=128)
|
||||
def get_conf(*args):
|
||||
# 建议您复制一个config_private.py放自己的秘密, 如API和代理网址, 避免不小心传github被别人看到
|
||||
"""
|
||||
本项目的所有配置都集中在config.py中。 修改配置有三种方法，您只需要选择其中一种即可：
|
||||
- 直接修改config.py
|
||||
- 创建并修改config_private.py
|
||||
- 修改环境变量（修改docker-compose.yml等价于修改容器内部的环境变量）
|
||||
|
||||
注意：如果您使用docker-compose部署，请修改docker-compose（等价于修改容器内部的环境变量）
|
||||
"""
|
||||
res = []
|
||||
for arg in args:
|
||||
r = read_single_conf_with_lru_cache(arg)
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user