ImportError: DLL load failed while importing interpreter: The specified module could not be found. Then, save the file to the location where you created the “docs” folder (in my case, it’s the Desktop).įile “C:\Users\RHASH\OneDrive\Desktop\app.py”, line 1, inįile “C:\Users\RHASH\AppData\Local\Programs\Python\Python311\Lib\site-packages\gpt_index\_init_.py”, line 14, inįrom gpt_ import LangchainEmbeddingįile “C:\Users\RHASH\AppData\Local\Programs\Python\Python311\Lib\site-packages\gpt_index\embeddings\langchain.py”, line 6, inįrom import Embeddings as LCEmbeddingsįile “C:\Users\RHASH\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain\_init_.py”, line 6, inįrom langchain.agents import MRKLChain, ReActChain, SelfAskWithSearchChainįile “C:\Users\RHASH\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain\agents\_init_.py”, line 2, inįile “C:\Users\RHASH\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain\agents\agent.py”, line 17, inįile “C:\Users\RHASH\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain\chains\_init_.py”, line 16, inįrom _math.base import LLMMathChainįile “C:\Users\RHASH\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain\chains\llm_math\base.py”, line 6, inįile “C:\Users\RHASH\AppData\Local\Programs\Python\Python311\Lib\site-packages\numexpr\_init_.py”, line 24, inįrom numexpr.interpreter import MAX_THREADS, use_vml, _BLOCK_SIZE1_ After that, set the file name app.py and change the “Save as type” to “ All types”. Next, click on “File” in the top menu and select “ Save As…”. 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, open a code editor like Sublime Text or launch Notepad++ and paste the below code. Set Up the Software Environment to Train an AI Chatbot Install Python and Pipġ. ChatBot uses AI to instantly direct users to the right resource, allowing businesses to respond quickly without manually answering every inquiry. Plus, for the best chatbot experience, someone has to be online 24/7. So go ahead and give it a try in your own language. Set up the ChatBot trigger, and make magic happen automatically in OpenAI (GPT-3, DALL-E. 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. Google’s in-house large language model, called PaLM2, is one of the largest, most sophisticated ones on the web. The guide is meant for general users, and the instructions are explained in simple language. Of all the AI chatbots weve tested, Bard is the most well-rounded. 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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