| 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"] = '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' | |
| def construct_index(directory_path): | |
| max_input_size = 4096 | |
| num_outputs = 512 | |
| max_chunk_overlap = 20 | |
| 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): | |
| predetermined_text = "I want you to take the statement at the start of this query and answer it using information contained in documents in the 'docs' directory. I want you to answer as a highly experienced insurance industry expert." | |
| input_text = input_text + predetermined_text | |
| index = GPTSimpleVectorIndex.load_from_disk('index.json') | |
| response = index.query(input_text, response_mode="compact") | |
| return response.response | |
| iface = gr.Interface(fn=chatbot, | |
| inputs=gr.components.Textbox(lines=7, label="What would you like to know about insurance?"), | |
| outputs="text", | |
| title="AI Loss Adjuster") | |
| index = construct_index("docs") | |
| iface.launch() | |