In this example, we'll build a full-stack application that uses Retrieval Augmented Generation (RAG) powered by Pinecone to deliver accurate and contextually relevant responses in a chatbot.
RAG is a powerful tool that combines the benefits of retrieval-based models and generative models. Unlike traditional chatbots that can struggle with maintaining up-to-date information or accessing domain-specific knowledge, a RAG-based chatbot uses a knowledge base created from crawled URLs to provide contextually relevant responses.
Incorporating Vercel's AI SDK into our application will allow us easily set up the chatbot workflow and utilize streaming more efficiently, particularly in edge environments, enhancing the responsiveness and performance of our chatbot.
By the end of this tutorial, you'll have a context-aware chatbot that provides accurate responses without hallucination, ensuring a more effective and engaging user experience. Let's get started on building this powerful tool (Full code listing).
First, create a new Next.js app and install the necessary packages:
npx create-next-app chatbot
cd chatbot
npm install ai react @pinecone-database/pineconeIn this step, we are going to build a chat interface that will render two components. One of these components will be a chatbot with context support provided by Pinecone. The other component will be a chatbot without context. Both of these components will present messages received by the useChat hook from the Vercel AI SDK.
Create a Chat component that will render the chat interface. This component will have two ChatWrapper components, one for the chatbot with context and one without context.
When a message is sent, each of the ChatWrapper components will be notified and take on the responsibility of sending the message to the backend, as well as presenting with the proper messages.
// Importing necessary modules and types
import AppContext from "@/appContext";
import type { PineconeRecord } from "@pinecone-database/pinecone";
import React, { ChangeEvent, FormEvent, useContext, useRef } from "react";
import ChatInput from "./ChatInput";
import ChatWrapper, { ChatInterface } from "./ChatWrapper";
// Defining the properties for the Chat component
interface ChatProps {
setContext: (data: { context: PineconeRecord[] }[]) => void;
context: { context: PineconeRecord[] }[] | null;
}
// The Chat component
const Chat: React.FC<ChatProps> = ({ setContext, context }) => {
// Creating references for the chat components with and without context
const chatWithContextRef = useRef<ChatInterface | null>(null);
const chatWithoutContextRef = useRef<ChatInterface | null>(null);
// Accessing the total number of records from the application context
const { totalRecords } = useContext(AppContext);
// State for the chat input
const [input, setInput] = React.useState<string>("")
// Function to handle message submission
const onMessageSubmit = (e: FormEvent<HTMLFormElement>) => {
// Clear the input
setInput("")
// Submit the message to both chat components
chatWithContextRef.current?.handleMessageSubmit(e)
chatWithoutContextRef.current?.handleMessageSubmit(e)
}
// Function to handle input change
const onInputChange = (event: ChangeEvent<HTMLInputElement>) => {
// Update the input state
setInput(event.target.value)
// Update the input in both chat components
chatWithContextRef.current?.handleInputUpdated(event)
chatWithoutContextRef.current?.handleInputUpdated(event)
}
// Rendering the Chat component
return (
// The chat interface is divided into two sections, one for the chat with context and one without context
<div id="chat" className="flex flex-col w-full h-full">
<div className="flex flex-grow">
<div className="w-1/2">
<ChatWrapper ref={chatWithoutContextRef} withContext={true} setContext={setContext} context={context} />
</div>
<div className="w-1/2">
<ChatWrapper ref={chatWithContextRef} withContext={false} setContext={setContext} />
</div>
</div>
// The chat input is rendered at the bottom of the chat interface
<div className="w-full">
<ChatInput input={input} handleInputChange={onInputChange} handleMessageSubmit={onMessageSubmit} showIndexMessage={totalRecords === 0} />
</div>
</div>
);
};
// Exporting the Chat component
export default Chat;The Chat component is responsible for handling the chatbot's input and message submission. It uses the useChat hook from the ai package to manage the chatbot's state. The component is divided into two parts: the Messages component that displays the chat messages, and a form for submitting new messages. The component also generates a unique ID for each message.
import type { PineconeRecord } from "@pinecone-database/pinecone";
import { useChat } from "ai/react";
import React, { ChangeEvent, FormEvent, Ref, forwardRef, useEffect, useImperativeHandle, useRef } from "react";
import { v4 as uuidv4 } from 'uuid';
import Messages from "./Messages";
export interface ChatInterface {
handleMessageSubmit: (e: FormEvent<HTMLFormElement>) => void;
handleInputUpdated: (event: ChangeEvent<HTMLInputElement>) => void;
ref: Ref<ChatInterface>;
withContext: boolean;
}
interface ChatProps {
withContext: boolean;
setContext: (data: { context: PineconeRecord[] }[]) => void;
context?: { context: PineconeRecord[] }[] | null;
ref: Ref<ChatInterface>
}
const Chat: React.FC<ChatProps> = forwardRef<ChatInterface, ChatProps>(({ withContext, setContext, context }, ref) => {
const { messages, handleInputChange, handleSubmit, isLoading, data } = useChat({
sendExtraMessageFields: true,
body: {
withContext,
},
});
useEffect(() => {
if (data) {
setContext(data as { context: PineconeRecord[] }[]) // Logs the additional data
}
}, [data, setContext]);
const chatRef = useRef<ChatInterface>(null);
useImperativeHandle(ref, () => ({
handleMessageSubmit: (event: FormEvent<HTMLFormElement>) => {
const id = uuidv4(); // Generate a unique ID
handleSubmit(event, {
data: {
messageId: id, // Include the ID in the message object
},
})
},
handleInputUpdated: (event: ChangeEvent<HTMLInputElement>) => {
handleInputChange(event);
},
}));
return (
<div className="flex flex-col h-full">
<Messages messages={messages} withContext={withContext} context={context} />
<form onSubmit={(e) => chatRef.current?.handleMessageSubmit(e)} className="...">
<input
type="text"
className="..."
onChange={(e) => chatRef.current?.handleInputUpdated(e)}
/>
<button type="submit" className="...">Send</button>
</form>
</div>
);
});
Chat.displayName = 'Chat';
export default Chat;The ChatInput component is responsible for rendering the chat input field and the send button. It uses the handleInputChange and handleMessageSubmit functions from the Chat component to handle input changes and message submission.
import React, { ChangeEvent, FormEvent } from "react";
interface ChatInputProps {
input: string;
handleInputChange: (e: ChangeEvent<HTMLInputElement>) => void;
handleMessageSubmit: (e: FormEvent<HTMLFormElement>) => void;
showIndexMessage: boolean;
}
const ChatInput: React.FC<ChatInputProps> = ({ input, handleInputChange, handleMessageSubmit, showIndexMessage }) => {
return (
<form onSubmit={handleMessageSubmit} className="...">
<input
type="text"
className="..."
value={input}
onChange={handleInputChange}
/>
<button type="submit" className="...">Send</button>
{showIndexMessage && (
<div className="...">
<span className="...">Press ⮐ to send</span>
</div>
)}
</form>
);
};
export default ChatInput;As we dive into building our chatbot, it's important to understand the role of context. Adding context to our chatbot's responses is key for creating a more natural, conversational user experience. Without context, a chatbot's responses can feel disjointed or irrelevant. By understanding the context of a user's query, our chatbot will be able to provide more accurate, relevant, and engaging responses. Now, let's begin building with this goal in mind.
First, we'll first focus on seeding the knowledge base. We'll create a crawler and a seed script, and set up a crawl endpoint. This will allow us to gather and organize the information our chatbot will use to provide contextually relevant responses.
After we've populated our knowledge base, we'll retrieve matches from our embeddings. This will enable our chatbot to find relevant information based on user queries.
Next, we'll wrap our logic into the getContext function and update our chatbot's prompt. This will streamline our code and improve the user experience by ensuring the chatbot's prompts are relevant and engaging.
Finally, we'll add a context panel and an associated context endpoint. These will provide a user interface for the chatbot and a way for it to retrieve the necessary context for each user query.
This step is all about feeding our chatbot the information it needs and setting up the necessary infrastructure for it to retrieve and use that information effectively. Let's get started.
Now we'll move on to seeding the knowledge base, the foundational data source that will inform our chatbot's responses. This step involves collecting and organizing the information our chatbot needs to operate effectively. In this guide, we're going to use data retrieved from various websites which we'll later on be able to ask questions about. To do this, we'll create a crawler that will scrape the data from the websites, embed it, and store it in Pinecone.
For the sake of brevity, you'll be able to find the full code for the crawler here. Here are the pertinent parts:
class Crawler {
private seen = new Set<string>();
private pages: Page[] = [];
private queue: { url: string; depth: number }[] = [];
constructor(private maxDepth = 2, private maxPages = 1) {}
async crawl(startUrl: string): Promise<Page[]> {
// Add the start URL to the queue
this.addToQueue(startUrl);
// While there are URLs in the queue and we haven't reached the maximum number of pages...
while (this.shouldContinueCrawling()) {
// Dequeue the next URL and depth
const { url, depth } = this.queue.shift()!;
// If the depth is too great or we've already seen this URL, skip it
if (this.isTooDeep(depth) || this.isAlreadySeen(url)) continue;
// Add the URL to the set of seen URLs
this.seen.add(url);
// Fetch the page HTML
const html = await this.fetchPage(url);
// Parse the HTML and add the page to the list of crawled pages
this.pages.push({ url, content: this.parseHtml(html) });
// Extract new URLs from the page HTML and add them to the queue
this.addNewUrlsToQueue(this.extractUrls(html, url), depth);
}
// Return the list of crawled pages
return this.pages;
}
// ... Some private methods removed for brevity
private async fetchPage(url: string): Promise<string> {
try {
const response = await fetch(url);
return await response.text();
} catch (error) {
console.error(`Failed to fetch ${url}: ${error}`);
return "";
}
}
private parseHtml(html: string): string {
const $ = cheerio.load(html);
$("a").removeAttr("href");
return NodeHtmlMarkdown.translate($.html());
}
private extractUrls(html: string, baseUrl: string): string[] {
const $ = cheerio.load(html);
const relativeUrls = $("a")
.map((_, link) => $(link).attr("href"))
.get() as string[];
return relativeUrls.map(
(relativeUrl) => new URL(relativeUrl, baseUrl).href
);
}
}The Crawler class is a web crawler that visits URLs, starting from a given point, and collects information from them. It operates within a certain depth and a maximum number of pages as defined in the constructor. The crawl method is the core function that starts the crawling process.
The helper methods fetchPage, parseHtml, and extractUrls respectively handle fetching the HTML content of a page, parsing the HTML to extract text, and extracting all URLs from a page to be queued for the next crawl. The class also maintains a record of visited URLs to avoid duplication.
To tie things together, we'll create a seed function that will use the crawler to seed the knowledge base. In this portion of the code, we'll initialize the crawl and fetch a given URL, then split it's content into chunks, and finally embed and index the chunks in Pinecone.
async function seed(url: string, limit: number, indexName: string, options: SeedOptions) {
try {
// Initialize the Pinecone client
const pinecone = new Pinecone();
// Destructure the options object
const { splittingMethod, chunkSize, chunkOverlap } = options;
// Create a new Crawler with depth 1 and maximum pages as limit
const crawler = new Crawler(1, limit || 100);
// Crawl the given URL and get the pages
const pages = await crawler.crawl(url) as Page[];
// Choose the appropriate document splitter based on the splitting method
const splitter: DocumentSplitter = splittingMethod === 'recursive' ?
new RecursiveCharacterTextSplitter({ chunkSize, chunkOverlap }) : new MarkdownTextSplitter({});
// Prepare documents by splitting the pages
const documents = await Promise.all(pages.map(page => prepareDocument(page, splitter)));
// Create Pinecone index if it does not exist
const indexList = await pinecone.listIndexes();
const indexExists = indexList.some(index => index.name === indexName)
if (!indexExists) {
await pinecone.createIndex({
name: indexName,
dimension: 1536,
waitUntilReady: true,
});
}
const index = pinecone.Index(indexName)
// Get the vector embeddings for the documents
const vectors = await Promise.all(documents.flat().map(embedDocument));
// Upsert vectors into the Pinecone index
await chunkedUpsert(index!, vectors, '', 10);
// Return the first document
return documents[0];
} catch (error) {
console.error("Error seeding:", error);
throw error;
}
}To chunk the content we'll use one of the following methods:
RecursiveCharacterTextSplitter- This splitter splits the text into chunks of a given size, and then recursively splits the chunks into smaller chunks until the chunk size is reached. This method is useful for long documents.MarkdownTextSplitter- This splitter splits the text into chunks based on Markdown headers. This method is useful for documents that are already structured using Markdown. The benefit of this method is that it will split the document into chunks based on the headers, which will be useful for our chatbot to understand the structure of the document. We can assume that each unit of text under a header is an internally coherent unit of information, and when the user asks a question, the retrieved context will be internally coherent as well.
The endpoint for the crawl endpoint is pretty straightforward. It simply calls the seed function and returns the result.
import seed from "./seed";
import { NextResponse } from "next/server";
export const runtime = "edge";
export async function POST(req: Request) {
const { url, options } = await req.json();
try {
const documents = await seed(url, 1, process.env.PINECONE_INDEX!, options);
return NextResponse.json({ success: true, documents });
} catch (error) {
return NextResponse.json({ success: false, error: "Failed crawling" });
}
}Now our backend is able to crawl a given URL, embed the content and index the embeddings in Pinecone. The endpoint will return all the segments in the retrieved webpage we crawl, so we'll be able to display them. Next, we'll write a set of functions that will build the context out of these embeddings.
To retrieve the most relevant documents from the index, we'll use the query function in the Pinecone SDK. This function takes a vector and returns the most similar vectors from the index. We'll use this function to retrieve the most relevant documents from the index, given some embeddings.
const getMatchesFromEmbeddings = async (embeddings: number[], topK: number, namespace: string): Promise<ScoredPineconeRecord<Metadata>[]> => {
// Obtain a client for Pinecone
const pinecone = new Pinecone();
const indexName: string = process.env.PINECONE_INDEX || '';
if (indexName === '') {
throw new Error('PINECONE_INDEX environment variable not set')
}
// Retrieve the list of indexes to check if expected index exists
const indexes = await pinecone.listIndexes()
if (indexes.filter(i => i.name === indexName).length !== 1) {
throw new Error(`Index ${indexName} does not exist`)
}
// Get the Pinecone index
const index = pinecone!.Index<Metadata>(indexName);
// Get the namespace
const pineconeNamespace = index.namespace(namespace ?? '')
try {
// Query the index with the defined request
const queryResult = await pineconeNamespace.query({
vector: embeddings,
topK,
includeMetadata: true,
})
return queryResult.matches || []
} catch (e) {
// Log the error and throw it
console.log("Error querying embeddings: ", e)
throw new Error(`Error querying embeddings: ${e}`)
}
}The function takes in embeddings, a topK parameter, and a namespace, and returns the topK matches from the Pinecone index. It first gets a Pinecone client, checks if the desired index exists in the list of indexes, and throws an error if not. Then it gets the specific Pinecone index. The function then queries the Pinecone index with the defined request and returns the matches.
We'll wrap things together in the getContext function. This function will take in a message and return the context - either in string form, or as a set of ScoredVector.
export const getContext = async (
message: string,
namespace: string,
maxTokens = 3000,
minScore = 0.7,
getOnlyText = true
): Promise<string | ScoredVector[]> => {
// Get the embeddings of the input message
const embedding = await getEmbeddings(message);
// Retrieve the matches for the embeddings from the specified namespace
const matches = await getMatchesFromEmbeddings(embedding, 3, namespace);
// Filter out the matches that have a score lower than the minimum score
const qualifyingDocs = matches.filter((m) => m.score && m.score > minScore);
// If the `getOnlyText` flag is false, we'll return the matches
if (!getOnlyText) {
return qualifyingDocs;
}
let docs = matches
? qualifyingDocs.map((match) => (match.metadata as Metadata).chunk)
: [];
// Join all the chunks of text together, truncate to the maximum number of tokens, and return the result
return docs.join("\n").substring(0, maxTokens);
};Back in chat/route.ts, we'll add the call to getContext:
const { messages } = await req.json();
// Get the last message
const lastMessage = messages[messages.length - 1];
// Get the context from the last message
const context = await getContext(lastMessage.content, "");Finally, we'll update the prompt to include the context we retrieved from the getContext function.
const prompt = [
{
role: "system",
content: `AI assistant is a brand new, powerful, human-like artificial intelligence.
The traits of AI include expert knowledge, helpfulness, cleverness, and articulateness.
AI is a well-behaved and well-mannered individual.
AI is always friendly, kind, and inspiring, and he is eager to provide vivid and thoughtful responses to the user.
AI has the sum of all knowledge in their brain, and is able to accurately answer nearly any question about any topic in conversation.
AI assistant is a big fan of Pinecone and Vercel.
START CONTEXT BLOCK
${context}
END OF CONTEXT BLOCK
AI assistant will take into account any CONTEXT BLOCK that is provided in a conversation.
If the context does not provide the answer to question, the AI assistant will say, "I'm sorry, but I don't know the answer to that question".
AI assistant will not apologize for previous responses, but instead will indicated new information was gained.
AI assistant will not invent anything that is not drawn directly from the context.
`,
},
];In this prompt, we added a START CONTEXT BLOCK and END OF CONTEXT BLOCK to indicate where the context should be inserted. We also added a line to indicate that the AI assistant will take into account any context block that is provided in a conversation.