![]() ![]() Num_output = 1000 # number of output tokens Parser.add_argument(‘-o’, ‘-output’, default=’./gpt_store’, help=”Set output directory path”) Parser.add_argument(‘-i’, ‘-input’, default=’docs’, help=’Set input directory path’) Parser.add_argument(‘-t’, ‘-train’, action=’store_true’, help=”Train the model”) Parser = argparse.ArgumentParser(description=”Launch chatbot”) Installing the latest versions of the libraries, I had to make the following modifications to the code to get it to work.įrom llama_index import SimpleDirectoryReader, LLMPredictor, GPTVectorStoreIndex, PromptHelper, ServiceContext, load_index_from_storage, StorageContext This is what the code looks like in the code editor. Inputs=gr.components.Textbox(lines=7, label="Enter your text"),Ģ. Response = index.query(input_text, response_mode="compact") Index = GPTSimpleVectorIndex.load_from_disk('index.json') Index = GPTSimpleVectorIndex(documents, llm_predictor=llm_predictor, prompt_helper=prompt_helper) Llm_predictor = LLMPredictor(llm=ChatOpenAI(temperature=0.7, model_name="gpt-3.5-turbo", max_tokens=num_outputs))ĭocuments = SimpleDirectoryReader(directory_path).load_data() Prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit) Once again, I have taken great help from armrrs on Google Colab and tweaked the code to make it compatible with PDF files and create a Gradio interface on top.įrom gpt_index import SimpleDirectoryReader, GPTListIndex, GPTSimpleVectorIndex, LLMPredictor, PromptHelperįrom langchain.chat_models import ChatOpenAI Now, launch Notepad++ (or your choice of code editor) and paste the below code into a new file. Open this link and download the setup file for your platform.ġ. First off, you need to install Python (Pip) on your computer. Again, do not fret over the installation process, it’s pretty straightforward. Along the process, you will learn what each library does. After that, we will install Python libraries, which include OpenAI, GPT Index, Gradio, and PyPDF2. To give you a brief idea, we will install Python and Pip. In this article, we will set up everything from scratch so new users can also understand the setup process. Like our previous article, you should know that Python and Pip must be installed along with several libraries. Set Up the Software Environment to Train an AI Chatbot So go ahead and give it a try in your own language. Finally, the data set should be in English to get the best results, but according to OpenAI, it will also work with popular international languages like French, Spanish, German, etc. However, if you want to train a large set of data running into thousands of pages, it’s strongly recommended to use a powerful computer.Ĥ. ![]() I used a Chromebook to train the AI model using a book with 100 pages (~100MB). However, you can use any low-end computer for testing purposes, and it will work without any issues. Since we are going to train an AI Chatbot based on our own data, it’s recommended to use a capable computer with a good CPU and GPU. If you followed our previous ChatGPT bot article, it would be even easier to understand the process.ģ. So even if you have a cursory knowledge of computers and don’t know how to code, you can easily train and create a Q&A AI chatbot in a few minutes. The guide is meant for general users, and the instructions are explained in simple language. In this article, I’m using Windows 11, but the steps are nearly identical for other platforms.Ģ. You can train the AI chatbot on any platform, whether Windows, macOS, Linux, or ChromeOS. ![]() ![]() Notable Points Before You Train AI with Your Own Dataġ.
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