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321 changes: 187 additions & 134 deletions express-api/index.js
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Original file line number Diff line number Diff line change
@@ -1,155 +1,208 @@
// --------------------------------------
// 1. Import necessary modules
import express from 'express';
import bodyParser from 'body-parser';
import { RecursiveCharacterTextSplitter } from 'langchain/text_splitter';
import { OpenAIEmbeddings } from '@langchain/openai';
import { MemoryVectorStore } from 'langchain/vectorstores/memory';
import { BraveSearch } from "@langchain/community/tools/brave_search";
import OpenAI from 'openai';
import cheerio from 'cheerio';
import dotenv from 'dotenv';
// --------------------------------------
import express from "express";
import bodyParser from "body-parser";
import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";
import { OpenAIEmbeddings } from "@langchain/openai";
import { MemoryVectorStore } from "langchain/vectorstores/memory";
import { BraveSearch } from "@langchain/community/tools/brave_search";
import OpenAI from "openai";
import cheerio from "cheerio";
import dotenv from "dotenv";

dotenv.config();

// --------------------------------------
// 2. Initialize Express
// --------------------------------------
const app = express();
const port = 3005;
// 3. Middleware
app.use(bodyParser.json());
// 4. Initialize Groq and embeddings
let openai = new OpenAI({
baseURL: 'https://api.groq.com/openai/v1',

// --------------------------------------
// 3. Initialize OpenAI + Embeddings
// --------------------------------------
const openai = new OpenAI({
baseURL: "https://api.groq.com/openai/v1",
apiKey: process.env.GROQ_API_KEY,
});

const embeddings = new OpenAIEmbeddings();
// 5. Define the route for POST requests
app.post('/', async (req, res) => {
// 6. Handle POST requests
console.log(`1. Received POST request`);
// 7. Extract request data
const { message, returnSources = true, returnFollowUpQuestions = true, embedSourcesInLLMResponse = false, textChunkSize = 800, textChunkOverlap = 200, numberOfSimilarityResults = 2, numberOfPagesToScan = 4 } = req.body;
console.log(`2. Destructured request data`);
// 8. Define rephrase function
async function rephraseInput(inputString) {
console.log(`4. Rephrasing input`);
// 9. Rephrase input using Groq
const groqResponse = await openai.chat.completions.create({

// --------------------------------------
// 4. Helper: Rephrase user input
// --------------------------------------
async function rephraseInput(input) {
try {
const response = await openai.chat.completions.create({
model: "mixtral-8x7b-32768",
messages: [
{ role: "system", content: "You are a rephraser and always respond with a rephrased version of the input that is given to a search engine API. Always be succint and use the same words as the input. ONLY RETURN THE REPHRASED VERSION OF THE INPUT." },
{ role: "user", content: inputString },
{
role: "system",
content:
"You are a rephraser. Always reply with a concise version of the input, optimized for a search engine query.",
},
{ role: "user", content: input },
],
});
console.log(`5. Rephrased input and got answer from Groq`);
return groqResponse.choices[0].message.content;
return response.choices[0].message.content;
} catch (err) {
console.error("Error rephrasing input:", err);
return input;
}
// 10. Define search engine function
async function searchEngineForSources(message) {
console.log(`3. Initializing Search Engine Process`);
// 11. Initialize BraveSearch
const loader = new BraveSearch({ apiKey: process.env.BRAVE_SEARCH_API_KEY });
// 12. Rephrase the message
const rephrasedMessage = await rephraseInput(message);
console.log(`6. Rephrased message and got documents from BraveSearch`);
// 13. Get documents from BraveSearch
const docs = await loader.call(rephrasedMessage, { count: numberOfPagesToScan });
// 14. Normalize data
const normalizedData = normalizeData(docs);
// 15. Process and vectorize the content
return await Promise.all(normalizedData.map(fetchAndProcess));
}
// 16. Normalize data
function normalizeData(docs) {
return JSON.parse(docs)
.filter((doc) => doc.title && doc.link && !doc.link.includes("brave.com"))
.slice(0, numberOfPagesToScan)
.map(({ title, link }) => ({ title, link }));
}

// --------------------------------------
// 5. Helper: Extract main text from a web page
// --------------------------------------
function extractMainContent(html, link) {
const $ = cheerio.load(html);
$("script, style, head, nav, footer, iframe, img").remove();
return $("body").text().replace(/\s+/g, " ").trim();
}

// --------------------------------------
// 6. Helper: Generate Follow-Up Questions
// --------------------------------------
async function generateFollowUpQuestions(answer) {
try {
const groqResponse = await openai.chat.completions.create({
model: "mixtral-8x7b-32768",
messages: [
{
role: "system",
content:
"Generate 3 relevant follow-up questions based on the provided text. Return them as a JSON array.",
},
{
role: "user",
content: `Generate 3 follow-up questions for: ${answer}`,
},
],
});

return JSON.parse(groqResponse.choices[0].message.content);
} catch (err) {
console.error("Error generating follow-up questions:", err);
return ["Can you explain more?", "Why is that important?", "Tell me more about this topic."];
}
// 17. Fetch page content
const fetchPageContent = async (link) => {
console.log(`7. Fetching page content for ${link}`);
try {
const response = await fetch(link);
if (!response.ok) {
return ""; // skip if fetch fails
}
const text = await response.text();
return extractMainContent(text, link);
} catch (error) {
console.error(`Error fetching page content for ${link}:`, error);
return '';
}
}

// --------------------------------------
// 7. Helper: Custom Domain Knowledge
// --------------------------------------
function getCustomKnowledge(message) {
const text = message.toLowerCase();

const facts = {
"pm of india": "🇮🇳 The current Prime Minister of India is **Narendra Modi**, serving since May 2014.",
dog: "🐶 Dogs are loyal domestic animals known as human’s best friends.",
cat: "🐱 Cats are independent and curious animals, loved for their agility and affection.",
tiger: "🐯 Tigers are the largest wild cats and apex predators found mostly in Asia.",
space: "🚀 Space is a vast expanse beyond Earth’s atmosphere, filled with stars, galaxies, and planets.",
ocean: "🌊 Oceans cover over 70% of Earth’s surface and are home to millions of species.",
};
// 18. Extract main content from the HTML page
function extractMainContent(html, link) {
console.log(`8. Extracting main content from HTML for ${link}`);
const $ = html.length ? cheerio.load(html) : null
$("script, style, head, nav, footer, iframe, img").remove();
return $("body").text().replace(/\s+/g, " ").trim();

for (const key in facts) {
if (text.includes(key)) return facts[key];
}
// 19. Process and vectorize the content
let vectorCount = 0;
const fetchAndProcess = async (item) => {
const htmlContent = await fetchPageContent(item.link);
if (htmlContent && htmlContent.length < 250) return null;
const splitText = await new RecursiveCharacterTextSplitter({ chunkSize: textChunkSize, chunkOverlap: textChunkOverlap }).splitText(htmlContent);
const vectorStore = await MemoryVectorStore.fromTexts(splitText, { link: item.link, title: item.title }, embeddings);
vectorCount++;
console.log(`9. Processed ${vectorCount} sources for ${item.link}`);
return await vectorStore.similaritySearch(message, numberOfSimilarityResults);
};
// 20. Fetch and process sources
const sources = await searchEngineForSources(message, textChunkSize, textChunkOverlap);
const sourcesParsed = sources.map(group =>
group.map(doc => {
const title = doc.metadata.title;
const link = doc.metadata.link;
return { title, link };
})
.filter((doc, index, self) => self.findIndex(d => d.link === doc.link) === index)
);
console.log(`10. RAG complete sources and preparing response content`);
// 21. Prepare the response content
const chatCompletion = await openai.chat.completions.create({
messages:
[{
role: "system", content: `
- Here is my query "${message}", respond back with an answer that is as long as possible. If you can't find any relevant results, respond with "No relevant results found."
- ${embedSourcesInLLMResponse ? "Return the sources used in the response with iterable numbered markdown style annotations." : ""}" : ""}`
},
{ role: "user", content: ` - Here are the top results from a similarity search: ${JSON.stringify(sources)}. ` },
], stream: true, model: "mixtral-8x7b-32768"
});
console.log(`11. Sent content to Groq for chat completion.`);
let responseTotal = "";
console.log(`12. Streaming response from Groq... \n`);
for await (const chunk of chatCompletion) {
if (chunk.choices[0].delta && chunk.choices[0].finish_reason !== "stop") {
process.stdout.write(chunk.choices[0].delta.content);
responseTotal += chunk.choices[0].delta.content;
} else {
let responseObj = {};
returnSources ? responseObj.sources = sourcesParsed : null;
responseObj.answer = responseTotal;
returnFollowUpQuestions ? responseObj.followUpQuestions = await generateFollowUpQuestions(responseTotal) : null;
console.log(`\n\n13. Generated follow-up questions: ${JSON.stringify(responseObj.followUpQuestions)}`);
res.status(200).json(responseObj);

return null;
}

// --------------------------------------
// 8. Main POST Route
// --------------------------------------
app.post("/", async (req, res) => {
const startTime = Date.now();
const {
message,
textChunkSize = 800,
textChunkOverlap = 200,
numberOfSimilarityResults = 2,
numberOfPagesToScan = 4,
} = req.body;

console.log("\n📩 New query:", message);

try {
// Check for quick domain knowledge
const predefined = getCustomKnowledge(message);
if (predefined) {
return res.json({
answer: predefined,
sources: [],
followUpQuestions: await generateFollowUpQuestions(predefined),
});
}

// Rephrase query
const rephrasedMessage = await rephraseInput(message);
console.log("🔁 Rephrased:", rephrasedMessage);

// Initialize Brave Search
const loader = new BraveSearch({ apiKey: process.env.BRAVE_SEARCH_API_KEY });
const docs = await loader.call(rephrasedMessage, { count: numberOfPagesToScan });
const normalized = JSON.parse(docs)
.filter((d) => d.title && d.link)
.slice(0, numberOfPagesToScan);

console.log(`🔍 Found ${normalized.length} relevant web pages.`);

// Fetch, chunk, and vectorize
const sources = await Promise.all(
normalized.map(async ({ title, link }) => {
try {
const response = await fetch(link);
const html = await response.text();
const content = extractMainContent(html, link);
const splitter = new RecursiveCharacterTextSplitter({
chunkSize: textChunkSize,
chunkOverlap: textChunkOverlap,
});
const chunks = await splitter.splitText(content);
const store = await MemoryVectorStore.fromTexts(chunks, { link, title }, embeddings);
return await store.similaritySearch(message, numberOfSimilarityResults);
} catch (err) {
console.error("Error processing link:", link, err);
return [];
}
})
);

// Prepare LLM summary
const chat = await openai.chat.completions.create({
model: "mixtral-8x7b-32768",
messages: [
{
role: "system",
content: `You are an intelligent assistant. Respond with an informative and structured summary for the query: "${message}" using context below.`,
},
{ role: "user", content: `Sources: ${JSON.stringify(sources)}` },
],
});

const finalAnswer = chat.choices[0].message.content;

// Return response
res.json({
answer: finalAnswer,
sources,
followUpQuestions: await generateFollowUpQuestions(finalAnswer),
responseTime: `${(Date.now() - startTime) / 1000}s`,
});

console.log("✅ Response sent in", (Date.now() - startTime) / 1000, "seconds");
} catch (error) {
console.error("❌ Error in processing:", error);
res.status(500).json({ error: "Internal Server Error", details: error.message });
}
});
// 22. Generate follow-up questions
async function generateFollowUpQuestions(responseText) {
const groqResponse = await openai.chat.completions.create({
model: "mixtral-8x7b-32768",
messages: [
{ role: "system", content: "You are a question generator. Generate 3 follow-up questions based on the provided text. Return the questions in an array format." },
{
role: "user",
content: `Generate 3 follow-up questions based on the following text:\n\n${responseText}\n\nReturn the questions in the following format: ["Question 1", "Question 2", "Question 3"]`
}
],
});
return JSON.parse(groqResponse.choices[0].message.content);
}
// 23. Notify when the server starts listening

// --------------------------------------
// 9. Start Server
// --------------------------------------
app.listen(port, () => {
console.log(`Server is listening on port ${port}`);
})
console.log(`🚀 Server is running on http://localhost:${port}`);
});
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