<h2>Overview</h2>
<p>The document discusses creating a ChatGPT chatbot for PDF files using the GPT-4 API, LangChain framework, Pinecone vectorstore, Typescript, Openai, and Next.js. It provides a comprehensive tutorial on setting up the environment, converting PDF files to embeddings, and running the chatbot.</p>
<h2>Features</h2>
<ul>
<li><strong>GPT-4 API Integration</strong>: Utilize the latest GPT-4 API for advanced AI capabilities.</li>
<li><strong>LangChain Framework</strong>: Simplifies the process of building scalable AI/LLM apps and chatbots.</li>
<li><strong>Pinecone Vectorstore</strong>: Store embeddings and text representations for efficient document retrieval.</li>
<li><strong>Typescript and Next.js</strong>: Use modern technologies for development.</li>
<li><strong>Interactive Chat Interface</strong>: Engage with the chatbot by typing questions related to the PDF content.</li>
<li><strong>Troubleshooting Guidelines</strong>: Comprehensive troubleshooting tips for common issues.</li>
<li><strong>Community Support</strong>: Join the Discord community for assistance and discussions.</li>
</ul>


<h2>Overview:</h2>
<p>This tutorial showcases how to create a ChatGPT chatbot for a website using LangChain, Supabase, Typescript, Openai, and Next.js. LangChain is a framework designed to ease the development of scalable AI/LLM apps, while Supabase is an open-source Postgres database capable of storing embeddings via a pg vector extension.</p>
<h2>Features:</h2>
<ul>
<li><strong>LangChain Framework</strong>: Facilitates the creation of scalable AI/LLM apps.</li>
<li><strong>Supabase Database</strong>: Enables storage of embeddings with pg vector extension.</li>
<li><strong>Next.js Integration</strong>: Utilizes Next.js to build the chatbot interface.</li>
<li><strong>OpenAI Integration</strong>: Incorporates OpenAI for text embeddings.</li>
<li><strong>Typescript Support</strong>: Codebase is written in Typescript for enhanced type safety.</li>
<li><strong>Web Scraping and Embedding</strong>: Includes scripts for extracting data from specified web pages and converting it into vectors.</li>
<li><strong>Customizable Web Loader</strong>: Allows customization of elements to be extracted from web pages.</li>
<li><strong>Interactive Chatbot</strong>: Enables interaction by running the app and typing questions for the chatbot.</li>
</ul>


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