Prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)ĭocuments = SimpleDirectoryReader(directory_path).load_data()ĭocuments, llm_predictor=llm_predictor, prompt_helper=prompt_helper Llm_predictor = LLMPredictor(llm=OpenAI(temperature=0.5, model_name="text-davinci-003", max_tokens=num_outputs)) The code below describes the functions required to build the index and query it: This is known as embedding, and it must be repeated whenever fresh data is received. We need to install two dependencies called gpt index and langchain, which can be done with the following lines:įrom llama_index import SimpleDirectoryReader, GPTListIndex, readers, GPTSimpleVectorIndex, LLMPredictor, PromptHelperįrom IPython.display import Markdown, display Let’s begin by installing the necessary Python packages You simply need to provide the data you want the chatbot to use, and GPT-Index will take care of the rest. With GPT-Index, you don’t need to be an expert in NLP or machine learning.
GPT-Index is a powerful tool that allows you to create a chatbot based on the data feed by you. However, building a chatbot from scratch can be a daunting task, requiring significant expertise in natural language processing (NLP) and machine learning. Chatbots have become increasingly popular in recent years, with businesses of all sizes leveraging them to improve customer engagement, reduce response times, and enhance user experience.
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