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import gradio as gr | |
import os | |
import json | |
import requests | |
from langchain import FAISS | |
from langchain.embeddings import CohereEmbeddings, OpenAIEmbeddings | |
from langchain import VectorDBQA | |
from langchain.chat_models import ChatOpenAI | |
from prompts import MyTemplate | |
from langchain.prompts.chat import ( | |
ChatPromptTemplate, | |
SystemMessagePromptTemplate, | |
HumanMessagePromptTemplate, | |
) | |
# Streaming endpoint | |
API_URL = "https://api.openai.com/v1/chat/completions" | |
cohere_key = '5IRbILAbjTI0VcqTsktBfKsr13Lych9iBAFbLpkj' | |
faiss_store = './indexer/{}' | |
docsearch = None | |
def gen_conversation(conversations): | |
messages = [] | |
for data in conversations: | |
temp1 = {} | |
temp1["role"] = "user" | |
temp1["content"] = data[0] | |
temp2 = {} | |
temp2["role"] = "assistant" | |
temp2["content"] = data[1] | |
messages.append(temp1) | |
messages.append(temp2) | |
return messages | |
def predict(inputs, top_p, temperature, openai_api_key, enable_index, max_tokens, topic, | |
chat_counter, chatbot=[], history=[]): | |
global docsearch | |
topic = topic[0] | |
headers = { | |
"Content-Type": "application/json", | |
"Authorization": f"Bearer {openai_api_key}" | |
} | |
print(f"chat_counter - {chat_counter}") | |
print(f'Histroy - {history}') # History: Original Input and Output in flatten list | |
print(f'chatbot - {chatbot}') # Chat Bot: 上一轮回复的[[user, AI]] | |
history.append(inputs) | |
# Debugging | |
if enable_index: | |
# Faiss 检索最近的embedding | |
store = faiss_store.format(topic) | |
if docsearch is None: | |
print('Loading FAISS') | |
docsearch = FAISS.load_local(store, OpenAIEmbeddings(openai_api_key=openai_api_key)) | |
else: | |
print('Faiss already loaded') | |
# 构建模板 | |
llm = ChatOpenAI(openai_api_key=openai_api_key, max_tokens=max_tokens) | |
messages_combine = [ | |
SystemMessagePromptTemplate.from_template(MyTemplate['chat_combine_template']), | |
HumanMessagePromptTemplate.from_template("{question}") | |
] | |
p_chat_combine = ChatPromptTemplate.from_messages(messages_combine) | |
messages_reduce = [ | |
SystemMessagePromptTemplate.from_template(MyTemplate['chat_reduce_template']), | |
HumanMessagePromptTemplate.from_template("{question}") | |
] | |
p_chat_reduce = ChatPromptTemplate.from_messages(messages_reduce) | |
chain = VectorDBQA.from_chain_type(llm=llm, chain_type="map_reduce", vectorstore=docsearch, | |
k=4, | |
chain_type_kwargs={"question_prompt": p_chat_reduce, | |
"combine_prompt": p_chat_combine} | |
) | |
result = chain({"query": inputs}) | |
print(result) | |
result = result['result'] | |
# 生成返回值 | |
history.append(result) | |
chat = [(history[i], history[i + 1]) for i in range(0, len(history) - 1, 2)] | |
chat_counter += 1 | |
yield chat, history, chat_counter | |
else: | |
if chat_counter == 0: | |
messages = [{"role": "user", "content": f"{inputs}"}] | |
else: | |
# 如果有历史对话,把对话拼接进入上下文 | |
messages = gen_conversation(chatbot) | |
messages.append({'role': 'user', 'content': inputs}) | |
# messages | |
payload = { | |
"model": "gpt-3.5-turbo", | |
"messages": messages, # [{"role": "user", "content": f"{inputs}"}], | |
"temperature": temperature, # 1.0, | |
"top_p": top_p, # 1.0, | |
"n": 1, | |
"stream": True, | |
"presence_penalty": 0, | |
"frequency_penalty": 0, | |
} | |
print(f"payload is - {payload}") | |
chat_counter += 1 | |
# 请求OpenAI | |
response = requests.post(API_URL, headers=headers, json=payload, stream=True) | |
token_counter = 0 | |
partial_words = "" | |
# 逐字返回 | |
counter = 0 | |
for chunk in response.iter_lines(): | |
if counter == 0: | |
counter += 1 | |
continue | |
counter += 1 | |
# check whether each line is non-empty | |
if chunk: | |
# decode each line as response data is in bytes | |
delta = json.loads(chunk.decode()[6:])['choices'][0]["delta"] | |
if len(delta) == 0: | |
break | |
partial_words += delta["content"] | |
# Keep Updating history | |
if token_counter == 0: | |
history.append(" " + partial_words) | |
else: | |
history[-1] = partial_words | |
chat = [(history[i], history[i + 1]) for i in | |
range(0, len(history) - 1, 2)] # convert to tuples of list | |
token_counter += 1 | |
yield chat, history, chat_counter | |
def reset_textbox(): | |
return gr.update(value='') | |
with gr.Blocks(css="""#col_container {width: 1000px; margin-left: auto; margin-right: auto;} | |
#chatbot {height: 520px; overflow: auto;}""") as demo: | |
gr.HTML("""<h1 align="center">🚀Finance ChatBot🚀</h1>""") | |
with gr.Column(elem_id="col_container"): | |
openai_api_key = gr.Textbox(type='password', label="输入OPEN API Key") | |
# inputs, top_p, temperature, top_k, repetition_penalty | |
with gr.Accordion("Parameters", open=True): | |
with gr.Row(): | |
top_p = gr.Slider(minimum=-0, maximum=1.0, value=0.9, step=0.05, interactive=True, | |
label="Top-p (nucleus sampling)", ) | |
temperature = gr.Slider(minimum=-0, maximum=5.0, value=0.8, step=0.1, interactive=True, | |
label="Temperature", ) | |
max_tokens = gr.Slider(minimum=100, maximum=1000, value=200, step=100, interactive=True, | |
label="Max Tokens", ) | |
chat_counter = gr.Number(value=0, precision=0, label='对话轮数') | |
with gr.Row(): | |
enable_index = gr.Checkbox(label='是', info='开启文档问答模式/聊天模式') | |
enable_search = gr.Checkbox(label='是', info='是否使用搜索') | |
topic = gr.CheckboxGroup(["两会", "数字经济", "硅谷银行"], label='使用文档索引') | |
chatbot = gr.Chatbot(elem_id='chatbot') | |
inputs = gr.Textbox(placeholder="您有什么问题可以问我", label="输入数字经济,两会,硅谷银行相关的提问") | |
state = gr.State([]) | |
with gr.Row(): | |
clear = gr.Button("Clear Conversation") | |
run = gr.Button("Run") | |
inputs.submit(predict, | |
[inputs, top_p, temperature, openai_api_key, enable_index, max_tokens, topic, chat_counter, chatbot, | |
state], | |
[chatbot, state, chat_counter], ) | |
run.click(predict, | |
[inputs, top_p, temperature, openai_api_key, enable_index, max_tokens, topic, chat_counter, chatbot, | |
state], | |
[chatbot, state, chat_counter], ) | |
# 每次对话结束都重置对话 | |
clear.click(reset_textbox, [], [inputs], queue=False) | |
inputs.submit(reset_textbox, [], [inputs]) | |
demo.queue().launch(debug=True) | |