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("""

🚀Finance ChatBot🚀

""") 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)