import os
import gradio as gr
import nltk
import sentence_transformers
import torch
from duckduckgo_search import ddg
from duckduckgo_search.utils import SESSION
from langchain.chains import RetrievalQA
from langchain.document_loaders import UnstructuredFileLoader
from langchain.embeddings import JinaEmbeddings
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
from langchain.prompts import PromptTemplate
from langchain.prompts.prompt import PromptTemplate
from langchain.vectorstores import FAISS
from chatllm import ChatLLM
from chinese_text_splitter import ChineseTextSplitter
nltk.data.path.append('./nltk_data')
embedding_model_dict = {
"ernie-tiny": "nghuyong/ernie-3.0-nano-zh",
"ernie-base": "nghuyong/ernie-3.0-base-zh",
"text2vec-base": "GanymedeNil/text2vec-base-chinese",
"ViT-B-32": 'ViT-B-32::laion2b-s34b-b79k'
}
llm_model_dict = {
"ChatGLM-6B-int8": "THUDM/chatglm-6b-int8",
"ChatGLM-6B-int4": "THUDM/chatglm-6b-int4",
"ChatGLM-6b-int4-qe": "THUDM/chatglm-6b-int4-qe",
"Minimax": "Minimax"
}
DEVICE = "cuda" if torch.cuda.is_available(
) else "mps" if torch.backends.mps.is_available() else "cpu"
def search_web(query):
SESSION.proxies = {
"http": f"socks5h://localhost:7890",
"https": f"socks5h://localhost:7890"
}
results = ddg(query)
web_content = ''
if results:
for result in results:
web_content += result['body']
return web_content
def load_file(filepath):
if filepath.lower().endswith(".pdf"):
loader = UnstructuredFileLoader(filepath)
textsplitter = ChineseTextSplitter(pdf=True)
docs = loader.load_and_split(textsplitter)
else:
loader = UnstructuredFileLoader(filepath, mode="elements")
textsplitter = ChineseTextSplitter(pdf=False)
docs = loader.load_and_split(text_splitter=textsplitter)
return docs
def init_knowledge_vector_store(embedding_model, filepath):
if embedding_model == "ViT-B-32":
jina_auth_token = os.getenv('jina_auth_token')
embeddings = JinaEmbeddings(
jina_auth_token=jina_auth_token,
model_name=embedding_model_dict[embedding_model])
else:
embeddings = HuggingFaceEmbeddings(
model_name=embedding_model_dict[embedding_model], )
embeddings.client = sentence_transformers.SentenceTransformer(
embeddings.model_name, device=DEVICE)
docs = load_file(filepath)
vector_store = FAISS.from_documents(docs, embeddings)
return vector_store
def get_knowledge_based_answer(query,
large_language_model,
vector_store,
VECTOR_SEARCH_TOP_K,
web_content,
history_len,
temperature,
top_p,
chat_history=[]):
if web_content:
prompt_template = f"""基于以下已知信息，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。
已知网络检索内容：{web_content}""" + """
已知内容:
{context}
问题:
{question}"""
else:
prompt_template = """基于以下已知信息，请简洁并专业地回答用户的问题。
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。另外，答案请使用中文。
已知内容:
{context}
问题:
{question}"""
prompt = PromptTemplate(template=prompt_template,
input_variables=["context", "question"])
chatLLM = ChatLLM()
chatLLM.history = chat_history[-history_len:] if history_len > 0 else []
if large_language_model == "Minimax":
chatLLM.model = 'Minimax'
else:
chatLLM.load_model(
model_name_or_path=llm_model_dict[large_language_model])
chatLLM.temperature = temperature
chatLLM.top_p = top_p
knowledge_chain = RetrievalQA.from_llm(
llm=chatLLM,
retriever=vector_store.as_retriever(
search_kwargs={"k": VECTOR_SEARCH_TOP_K}),
prompt=prompt)
knowledge_chain.combine_documents_chain.document_prompt = PromptTemplate(
input_variables=["page_content"], template="{page_content}")
knowledge_chain.return_source_documents = True
result = knowledge_chain({"query": query})
return result
def clear_session():
return '', None
def predict(input,
large_language_model,
embedding_model,
file_obj,
VECTOR_SEARCH_TOP_K,
history_len,
temperature,
top_p,
use_web,
history=None):
if history == None:
history = []
print(file_obj.name)
vector_store = init_knowledge_vector_store(embedding_model, file_obj.name)
if use_web == 'True':
web_content = search_web(query=input)
else:
web_content = ''
resp = get_knowledge_based_answer(
query=input,
large_language_model=large_language_model,
vector_store=vector_store,
VECTOR_SEARCH_TOP_K=VECTOR_SEARCH_TOP_K,
web_content=web_content,
chat_history=history,
history_len=history_len,
temperature=temperature,
top_p=top_p,
)
print(resp)
history.append((input, resp['result']))
return '', history, history
if __name__ == "__main__":
block = gr.Blocks()
with block as demo:
gr.Markdown("""<h1><center>LangChain-ChatLLM-Webui</center></h1>
<center><font size=3>
本项目基于LangChain和大型语言模型系列模型, 提供基于本地知识的自动问答应用. <br>
目前项目提供基于<a href='https://github.com/THUDM/ChatGLM-6B' target="_blank">ChatGLM-6B </a>系列、Minimax的LLM和包括text2vec-base-chinese、ernie-3.0-zh系列以及由<a href='https://cloud.jina.ai/user/inference' target="_blank">Jina</a>提供的ViT-B-32::laion2b-s34b-b79k等多个Embedding模型, 支持上传 txt、docx、md等文本格式文件. <br>
后续将提供更加多样化的LLM、Embedding和参数选项供用户尝试, 欢迎关注<a href='https://github.com/thomas-yanxin/LangChain-ChatGLM-Webui' target="_blank">Github地址</a>. <br>
本项目已内置开发者自己的key，用户无需输入自己的相关key. <br>
当然，更推荐您点击右上角的<strong>Duplicate this Space</strong>，将项目Fork到自己的Space中，保护个人隐私，且避免排队！
</center></font>
""")
with gr.Row():
with gr.Column(scale=1):
model_choose = gr.Accordion("模型选择")
with model_choose:
large_language_model = gr.Dropdown(
list(llm_model_dict.keys()),
label="large language model",
value="ChatGLM-6B-int4")
embedding_model = gr.Dropdown(list(
embedding_model_dict.keys()),
label="Embedding model",
value="text2vec-base")
file = gr.File(label='请上传知识库文件, 目前支持txt、docx、md格式',
file_types=['.txt', '.md', '.docx'])
use_web = gr.Radio(["True", "False"],
label="Web Search",
value="False")
model_argument = gr.Accordion("模型参数配置")
with model_argument:
VECTOR_SEARCH_TOP_K = gr.Slider(
1,
10,
value=6,
step=1,
label="vector search top k",
interactive=True)
HISTORY_LEN = gr.Slider(0,
3,
value=0,
step=1,
label="history len",
interactive=True)
temperature = gr.Slider(0,
1,
value=0.01,
step=0.01,
label="temperature",
interactive=True)
top_p = gr.Slider(0,
1,
value=0.9,
step=0.1,
label="top_p",
interactive=True)
with gr.Column(scale=4):
chatbot = gr.Chatbot(label='ChatLLM').style(height=600)
message = gr.Textbox(label='请输入问题')
state = gr.State()
with gr.Row():
clear_history = gr.Button("🧹 清除历史对话")
send = gr.Button("🚀 发送")
send.click(predict,
inputs=[
message, large_language_model,
embedding_model, file, VECTOR_SEARCH_TOP_K,
HISTORY_LEN, temperature, top_p, use_web,
state
],
outputs=[message, chatbot, state])
clear_history.click(fn=clear_session,
inputs=[],
outputs=[chatbot, state],
queue=False)
message.submit(predict,
inputs=[
message, large_language_model,
embedding_model, file,
VECTOR_SEARCH_TOP_K, HISTORY_LEN,
temperature, top_p, use_web, state
],
outputs=[message, chatbot, state])
gr.Markdown("""提醒：<br>
1. 使用时请先上传自己的知识文件，并且文件中不含某些特殊字符，否则将返回error. <br>
2. 有任何使用问题，请通过[问题交流区](https://huggingface.co/spaces/thomas-yanxin/LangChain-ChatLLM/discussions)或[Github Issue区](https://github.com/thomas-yanxin/LangChain-ChatGLM-Webui/issues)进行反馈. <br>
""")
demo.queue().launch(server_name='0.0.0.0', share=False)