| from gpt_index import SimpleDirectoryReader, GPTListIndex, GPTSimpleVectorIndex, LLMPredictor, PromptHelper | |
| from langchain.chat_models import ChatOpenAI | |
| import gradio as gr | |
| import sys | |
| import os | |
| os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY") | |
| def construct_index(directory_path): | |
| max_input_size = 4096 | |
| num_outputs = 512 | |
| max_chunk_overlap = 30 | |
| chunk_size_limit = 600 | |
| prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit) | |
| llm_predictor = LLMPredictor(llm=ChatOpenAI(temperature=0.7, model_name="gpt-3.5-turbo", max_tokens=num_outputs)) | |
| documents = SimpleDirectoryReader(directory_path).load_data() | |
| index = GPTSimpleVectorIndex(documents, llm_predictor=llm_predictor, prompt_helper=prompt_helper) | |
| index.save_to_disk('index.json') | |
| return index | |
| def chatbot(input_text): | |
| index = GPTSimpleVectorIndex.load_from_disk('index.json') | |
| response = index.query(input_text) | |
| return response.response | |
| iface = gr.Interface(fn=chatbot, | |
| inputs=gr.components.Textbox(lines=5, label="Enter your text", show_copy_button=True), | |
| outputs=gr.components.Textbox(lines=5, label="Answer", show_copy_button=True), | |
| examples=["What are the different types of 'work product' lifespan? Provide detailed answer", "Хто може бути наставником?", "Question3", "Question4", "Question5"], | |
| title="PMO Documents AI Chatbot") | |
| index = construct_index("docs") | |
| iface.launch() |