如何将问题和GPT API响应变量添加到Gradio chatbot()中？

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英文:

How to add question and GPT Api response variables to Gradio chatbot()?

问题

我有一个程序，它从先前创建和工作的JSON文件中提取信息，然后使用ChatGPT API来查询此文档。我需要在机器人正常滚动时输入初始用户输入，然后是对他的输入的响应，并且我需要使用gr.Chatbot选项（gr.Interface不是一个选项）。不幸的是，gr.Chatbot() 文档不够详细，以至于我无法解决它。我已经创建了question_answer_NEW函数，还尝试使用Gradio文档中提供的示例中的bot函数，但没有结果。谢谢。以下是代码部分：

  1. # 你的代码

如果你需要进一步的帮助，请告诉我。

英文:

I have a program that extracts the information form a previously-created and working json file, and then uses chatGPT API to query this document. I need to input the initial user input and then the response to his input all within the bot scrolling up normally, and I need to use the gr.Chatbot option (gr.Interface is not an option). Unfortunately the grChatbot() docs are not detailed enough so I can't solve it. I have created the function question_answer_NEW and also tried with the "bot" function provided as example in Gradio docs, but no results. Thank you. Here's the code:

  1. import gradio as gr
  2. import random
  3. import time
  4. from llama_index import SimpleDirectoryReader, GPTListIndex, readers, GPTSimpleVectorIndex, LLMPredictor, PromptHelper
  5. from langchain.chat_models import ChatOpenAI
  6. from langchain import OpenAI
  7. import sys
  8. import os
  9. from IPython.display import Markdown, display
  10. import gradio, openai
  11. os.environ["OPENAI_API_KEY"] = 'sk-...' #present in code
  12. messages = [{"role": "system", "content": "You are a helpful customer service assistant."}]
  14. global query
  15. global question
  16. global response
  18. def question_answer_NEW(question):
  19.     index = GPTSimpleVectorIndex.load_from_disk('index.json')
  20.     for i in range(3): #while True: 
  21.         query = question
  22.         response = index.query(query, response_mode="compact")        
  23.         messages.append({"User asks": question, "System response": response.response.strip()})
  24.         #print(messages[-1])''
  25.         print(query, response.response.strip())
  26.         return query, response.response.strip()
  29. with gr.Blocks() as demo:
  30.     chatbot = gr.Chatbot()
  31.     msg = gr.Textbox() #placeholder = 'test'
  32.     clear = gr.Button("Clear")
  33.     
  34.     global user_message
  36.     def user(user_message, history):
  37.         print('I am user message', user_message)
  38.         print(history + [[user_message, None]])
  39.         return "", history + [[user_message, None]]
  40.     
  43.     def bot(history):
  44.         index = GPTSimpleVectorIndex.load_from_disk('index.json')
  45.         bot_message = index.query(user_message, response_mode="compact")
  46.         print('I am bot message', bot_message)
  47.         history[-1][1] = bot_message
  48.         time.sleep(1)
  49.         return history
  50.         return bot_message
  52.     msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(bot, chatbot, chatbot)
  53.     clear.click(lambda: None, None, chatbot, queue=False)
  55. if __name__ == "__main__":
  56.     demo.launch()

答案1

得分: 1

I made it work. This is the solution:

  1. from IPython.display import Markdown, display
  2. from llama_index import SimpleDirectoryReader, GPTListIndex, readers, GPTSimpleVectorIndex, LLMPredictor, PromptHelper
  3. from langchain.chat_models import ChatOpenAI
  4. from langchain import OpenAI
  5. import gradio as gr
  6. import random, time
  7. import sys
  8. import os
  10. os.environ["OPENAI_API_KEY"] = '' #insert your key there
  12. def construct_index(directory_path):
  13.     global index
  14.     # set maximum input size
  15.     max_input_size = 4096
  16.     # set number of output tokens
  17.     num_outputs = 500
  18.     # set maximum chunk overlap
  19.     max_chunk_overlap = 20
  20.     # set chunk size limit
  21.     chunk_size_limit = 600 
  23.     llm_predictor = LLMPredictor(llm = ChatOpenAI(temperature=0.1, model_name='gpt-3.5-turbo', max_tokens=num_outputs)) #original temp was .5
  24.     prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)
  26.     documents = SimpleDirectoryReader(directory_path).load_data()
  27.     global index
  28.     index = GPTSimpleVectorIndex(
  29.         documents, llm_predictor=llm_predictor, prompt_helper=prompt_helper)
  30.     index.save_to_disk('index.json')
  31.     return index
  33. construct_index("context_data/data/done")
  35. # Define chat function
  36. def chat(chat_history, user_input):
  37.     index = GPTSimpleVectorIndex.load_from_disk('index.json')
  38.     bot_response = index.query(user_input)
  39.     response = ""
  40.     for letter in ''.join(bot_response.response):
  41.         response += letter + ""
  42.         yield chat_history + [(user_input, response.strip())]
  44. # Build interface
  45. with gr.Blocks() as demo:
  46.     with gr.Tab('Chat with this helpful AI assistant'):
  47.         chatbot = gr.Chatbot()
  48.         message = gr.Textbox(label = 'Write your message here and press "enter"')
  49.         message.submit(chat, [chatbot, message], chatbot).then(lambda: None, None, message, queue=False)
  51. demo.queue().launch(debug = True, share = True)

Please note that the code part has not been translated.

英文:

I made it work. This is the solution:

  1. from IPython.display import Markdown, display
  2. from llama_index import SimpleDirectoryReader, GPTListIndex, readers, GPTSimpleVectorIndex, LLMPredictor, PromptHelper
  3. from langchain.chat_models import ChatOpenAI
  4. from langchain import OpenAI
  5. import gradio as gr
  6. import random, time
  7. import sys
  8. import os
  10. os.environ["OPENAI_API_KEY"] = '' #insert your key there
  12. def construct_index(directory_path):
  13.     global index
  14.     # set maximum input size
  15.     max_input_size = 4096
  16.     # set number of output tokens
  17.     num_outputs = 500
  18.     # set maximum chunk overlap
  19.     max_chunk_overlap = 20
  20.     # set chunk size limit
  21.     chunk_size_limit = 600 
  23.     llm_predictor = LLMPredictor(llm = ChatOpenAI(temperature=0.1, model_name='gpt-3.5-turbo', max_tokens=num_outputs)) #original temp was .5
  24.     prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)
  25.  
  26.     documents = SimpleDirectoryReader(directory_path).load_data()
  27.     global index
  28.     index = GPTSimpleVectorIndex(
  29.         documents, llm_predictor=llm_predictor, prompt_helper=prompt_helper)
  30.     index.save_to_disk('index.json')
  31.     return index
  33. construct_index("context_data/data/done")
  35. #Define chat function
  36. def chat(chat_history, user_input):
  37.       index = GPTSimpleVectorIndex.load_from_disk('index.json')
  38.       bot_response = index.query(user_input)
  39.       response = ""
  40.       for letter in ''.join(bot_response.response): #[bot_response[i:i+1] for i in range(0, len(bot_response), 1)]:
  41.           response += letter + ""
  42.           yield chat_history + [(user_input, response.strip())]
  43.      
  44. #Build interface
  45. with gr.Blocks() as demo:
  46.     with gr.Tab('Chat with this helpful AI assistant'):
  47.           chatbot = gr.Chatbot()
  48.           message = gr.Textbox (label = 'Write your message here and press "enter"')
  49.           message.submit(chat, [chatbot, message], chatbot).then(lambda: None, None, message, queue=False)
  51. demo.queue().launch(debug = True, share = True)

huangapple
  • 本文由 发表于 2023年3月31日 18:55:21
  • 转载请务必保留本文链接：https://go.coder-hub.com/75897729.html
  • gradio
  • langchain
  • openai-api
  • python
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