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feat(backend): implement BullMQ background worker queue for AI feedback (#317) #347

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feat(backend): implement BullMQ background worker queue for AI feedback (#317) #347

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32 changes: 32 additions & 0 deletions backend/config/queue.js
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Original file line number Diff line number Diff line change
@@ -0,0 +1,32 @@
const { Queue } = require("bullmq");
const Redis = require("ioredis");

// Initialize Redis connection
const redisOptions = {
maxRetriesPerRequest: null,
};

const redisConnection = new Redis(process.env.REDIS_URL || "redis://localhost:6379", redisOptions);

redisConnection.on("error", (err) => {
console.error("Redis connection error:", err);
});

// Initialize the queue
const aiQueue = new Queue("ai-jobs", {
connection: redisConnection,
defaultJobOptions: {
attempts: 3,
backoff: {
type: "exponential",
delay: 1000,
},
removeOnComplete: true,
removeOnFail: false,
},
});

module.exports = {
redisConnection,
aiQueue,
};
201 changes: 33 additions & 168 deletions backend/controllers/aiController.js
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Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,7 @@ const {
const Session = require("../models/Session");
const Question = require("../models/Question");

const ai = new GoogleGenerativeAI(process.env.GEMINI_API_KEY);
const { aiQueue } = require("../config/queue");

/**
* Generate interview questions and answers using the Gemini AI service.
Expand Down Expand Up @@ -67,73 +67,22 @@ const generateInterviewQuestions = async (req, res) => {
seenQuestions,
});

const candidateModels = [
process.env.GEMINI_MODEL,
"models/gemini-2.5-flash",
"models/gemini-flash-latest",
"models/gemini-2.0-flash",
].filter(Boolean);

let lastErr = null;
let result = null;
let usedModel = null;

for (const m of candidateModels) {
try {
console.log(`Trying model: ${m}`);
const model = ai.getGenerativeModel({ model: m });
result = await model.generateContent([prompt]);
usedModel = m;
console.log(`Successfully used model: ${m}`);
break;
} catch (e) {
console.error(`Model ${m} failed:`, e.message);
lastErr = e;
continue;
}
}

if (!result) throw lastErr || new Error("All Gemini models failed");

const rawText = await result.response.text();
let cleanedText = rawText
.replace(/^(\s*```json\s*|\s*```\s*)+/i, "")
.replace(/(\s*```\s*)+$/i, "")
.trim();

try {
const data = JSON.parse(cleanedText);

// Validate Gemini response structure
const questionsSchema = Joi.array().items(
Joi.object({
question: Joi.string().required(),
answer: Joi.string().required(),
})
);
const { error: validationError } = questionsSchema.validate(
Array.isArray(data) ? data : data.question
);
if (validationError) {
return res.status(500).json({ message: "Invalid AI response format", details: validationError.message });
}
const job = await aiQueue.add("generate-questions", {
role,
experience,
topicsToFocus,
numberOfQuestions,
seenQuestions,
});

if (Array.isArray(data)) {
res.status(200).json({ model: usedModel, question: data });
} else {
res.status(200).json({ model: usedModel, ...data });
}
} catch (err) {
console.error("Gemini returned invalid JSON:", cleanedText);
res.status(500).json({
message: "Gemini returned invalid JSON",
raw: rawText,
});
}
res.status(202).json({
message: "Generate questions job accepted",
jobId: job.id,
});
} catch (error) {
console.error("Gemini API Error:", error);
console.error("Gemini Queue API Error:", error);
res.status(500).json({
message: "Failed to generate questions",
message: "Failed to enqueue generate-questions job",
error: error.message,
});
}
Expand Down Expand Up @@ -165,65 +114,18 @@ const generateConceptExplanation = async (req, res) => {
return res.status(400).json({ message: "Missing question" });
}

const prompt = conceptExplainPrompt(question);

const candidateModels = [
process.env.GEMINI_MODEL,
"models/gemini-2.5-flash",
"models/gemini-flash-latest",
"models/gemini-2.0-flash",
].filter(Boolean);
let lastErr = null;
let result = null;
let usedModel = null;
for (const m of candidateModels) {
try {
console.log(`Trying model: ${m}`);
const model = ai.getGenerativeModel({ model: m });
result = await model.generateContent([prompt]);
usedModel = m;
console.log(`Successfully used model: ${m}`);
break;
} catch (e) {
console.error(`Model ${m} failed:`, e.message);
lastErr = e;
continue;
}
}
if (!result) throw lastErr || new Error("All Gemini models failed");

const rawText = await result.response.text();
// Clean: remove all leading/trailing code block markers (```json, ```), even if repeated, and trim
let cleanedText = rawText
.replace(/^\s*```json\s*/i, "")
.replace(/^\s*```\s*/i, "")
.replace(/(\s*```\s*)+$/i, "")
.trim();

try {
const data = JSON.parse(cleanedText);

// Validate Gemini response structure
const explanationSchema = Joi.object({
title: Joi.string().required(),
explanation: Joi.string().required(),
});
const { error: validationError } = explanationSchema.validate(data);
if (validationError) {
return res.status(500).json({ message: "Invalid AI response format", details: validationError.message });
}
const job = await aiQueue.add("generate-explanation", {
question,
});

res.status(200).json({ model: usedModel, ...data });
} catch (err) {
res.status(500).json({
message: "Gemini returned invalid JSON",
raw: rawText,
});
}
res.status(202).json({
message: "Generate explanation job accepted",
jobId: job.id,
});
} catch (error) {
console.error("Gemini API Error:", error);
console.error("Gemini Queue API Error:", error);
res.status(500).json({
message: "Failed to generate explanation",
message: "Failed to enqueue generate-explanation job",
error: error.message,
});
}
Expand All @@ -237,56 +139,19 @@ const generateInterviewTips = async (req, res) => {
return res.status(400).json({ message: "Missing required fields" });
}

const prompt = interviewTipsPrompt({ role, experience });

const candidateModels = [
process.env.GEMINI_MODEL,
"models/gemini-2.5-flash",
"models/gemini-flash-latest",
"models/gemini-2.0-flash",
].filter(Boolean);

let lastErr = null;
let result = null;
let usedModel = null;

for (const m of candidateModels) {
try {
console.log(`Trying model: ${m}`);
const model = ai.getGenerativeModel({ model: m });
result = await model.generateContent([prompt]);
usedModel = m;
console.log(`Successfully used model: ${m}`);
break;
} catch (e) {
console.error(`Model ${m} failed:`, e.message);
lastErr = e;
continue;
}
}

if (!result) throw lastErr || new Error("All Gemini models failed");

const rawText = await result.response.text();
let cleanedText = rawText
.replace(/^(\s*```json\s*|\s*```\s*)+/i, "")
.replace(/(\s*```\s*)+$/i, "")
.trim();
const job = await aiQueue.add("generate-tips", {
role,
experience,
});

try {
const data = JSON.parse(cleanedText);
res.status(200).json({ model: usedModel, ...data });
} catch (err) {
console.error("Gemini returned invalid JSON:", cleanedText);
res.status(500).json({
message: "Gemini returned invalid JSON",
raw: rawText,
});
}
res.status(202).json({
message: "Generate tips job accepted",
jobId: job.id,
});
} catch (error) {
console.error("Gemini API Error:", error);
console.error("Gemini Queue API Error:", error);
res.status(500).json({
message: "Failed to generate interview tips",
message: "Failed to enqueue generate-tips job",
error: error.message,
});
}
Expand Down
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