Pinecone - Vercel AI SDK Starter

his chatbot leverages the power of the Pinecone search engine to provide contextually relevant responses. Below is a summary of the key steps involved in building this chatbot:

Step 1: Setting Up Your Next.js Application

• The tutorial starts by setting up a Next.js application, a JavaScript framework for building web applications.
• You create a new Next.js app using the npx create-next-app command.
• The ai package is added to the project’s dependencies.

Step 2: Create the Chatbot

• The chatbot is built in this step, both on the frontend and backend.
• A Chat component is created for the chat interface, allowing users to interact with the bot.
• The Messages component displays chat messages, and messages are sent and received using the useChat hook.
• The chatbot’s API endpoint is established in the backend, utilizing the OpenAI API to generate responses.

Step 3: Adding Context

• The importance of context in chatbot responses is emphasized.
• The knowledge base is seeded by crawling web pages and embedding their content in Pinecone.
• Matches are retrieved from embeddings to find relevant information.
• The getContext function is introduced, which retrieves context based on user messages.
• The prompt is updated to include the context block.
• A context panel is added to the chat UI, displaying context segments.
• An additional context endpoint is created to retrieve context when needed.
• End-to-end tests using Playwright are discussed.

This tutorial provides a comprehensive guide for building a chatbot with context-aware capabilities, enhancing the user experience by providing relevant and engaging responses. Developers interested in building such chatbots can follow these steps to create their own context-aware AI chatbot.