Abhishek-Thakur
This video provides a step-by-step process for creating a conversational bot powered by GPT for any website. The presenter explains how to create a web crawler using Scrapy, parse the extracted data, and store output files. They also show how to create an index using Lang chain and use a conversational retrieval chain to implement GPT. Additionally, a Python-based conversational bot using radio is demonstrated with a diffuser-related example. The video offers several code snippets and encourages viewers to provide feedback and support.
In this section of the video, the presenter shares how to create a web crawler using Scrapy and how to parse the data that is extracted. The primary focus of the video is to create a conversational bot powered by GPT and the presenter uses Hugging Face's documentation as an example. The presenter demonstrates how to write a parser with Python's beautiful soup and convert HTML to text. Additionally, the presenter explains how to store output files and adds a special end-of-text token for OpenAI's GPT. Overall, the video provides insight into the process of creating a bespoke chatbot powered by GPT.
In this section, the speaker discusses how to create an index using Lang chain which involves tokenizing the text into chunks, getting embeddings from OpenAI's API, and storing them in a database. He begins by importing necessary components such as the vector stores from Chroma and OpenAI embeddings. Then, he shows how to use a direct reloader to specify the directory to find text files and a text splitter to divide the text into chunks based on character counts. The chunk overlap is also discussed to prevent errors from the OpenAI API when the prompt has too many tokens.
In this section, the speaker discusses how to create a GPT-powered conversational bot for any website. They explain that it's important to specify the embeddings you want to use and the vector database, which can be any directory, and demonstrate this process through code snippets. They also initialize the embeddings class and add a memory component for conversational buffer memory. The speaker then shows how to use the conversational retrieval chain and how to use the bot by passing a dictionary with a question key and print the result.
In this section, the speaker demonstrates how to create a conversational bot using Python and radio. The speaker starts by using an example code provided by radio to create a chatbot. The chat history is generated automatically, and the bot message is passed to the QA. The speaker then starts a server, and demonstrates how the bot can answer questions related to diffusers and its context. The speaker encourages viewers to provide feedback and support by liking, sharing, and subscribing to the video.
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