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Fix line/subline regressions, add in AI validation tracking and improve AI results dialog

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This commit is contained in:
Matt
2025-02-26 00:38:17 -05:00
parent 2df5428712
commit 6b101a91f6
5 changed files with 758 additions and 85 deletions
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140
inventory-server/src/routes/ai-validation.js
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@@ -98,7 +98,51 @@ router.post("/debug", async (req, res) => {
});
try {
return await generateDebugResponse(cleanedProducts, res);
const debugResponse = await generateDebugResponse(cleanedProducts, res);
// Get estimated processing time based on prompt length
if (debugResponse && debugResponse.promptLength) {
try {
// Use the pool from the app
const pool = req.app.locals.pool;
if (!pool) {
console.warn("⚠️ Local database pool not available for time estimates");
return;
}
try {
const avgTimeResults = await pool.query(
`SELECT AVG(duration_seconds) as avg_duration,
COUNT(*) as sample_count
FROM ai_validation_performance
WHERE prompt_length BETWEEN $1 * 0.8 AND $1 * 1.2`,
[debugResponse.promptLength]
);
// Add estimated time to the response
if (avgTimeResults.rows && avgTimeResults.rows[0]) {
debugResponse.estimatedProcessingTime = {
seconds: avgTimeResults.rows[0].avg_duration || null,
sampleCount: avgTimeResults.rows[0].sample_count || 0
};
console.log("📊 Retrieved processing time estimate:", debugResponse.estimatedProcessingTime);
} else {
console.log("📊 No processing time estimates available for prompt length:", debugResponse.promptLength);
}
} catch (queryError) {
console.error("⚠️ Failed to query performance metrics:", queryError);
// Check if table doesn't exist and log a more helpful message
if (queryError.code === '42P01') {
console.error("Table 'ai_validation_performance' does not exist. Make sure to run the setup-schema.sql script.");
}
}
} catch (timeEstimateError) {
console.error("Error getting time estimate:", timeEstimateError);
// Don't fail the request if time estimate fails
}
}
return res.json(debugResponse);
} catch (generateError) {
console.error("Error generating debug response:", generateError);
return res.status(500).json({
@@ -271,7 +315,7 @@ async function generateDebugResponse(productsToUse, res) {
};
console.log("Sending response with taxonomy stats:", response.taxonomyStats);
return res.json(response);
return response;
} finally {
if (promptConnection) await promptConnection.end();
if (promptTunnel.ssh) promptTunnel.ssh.end();
@@ -463,9 +507,7 @@ async function loadPrompt(connection, productsToValidate = null) {
const taxonomy = await getTaxonomyData(connection);
// Add system instructions to the prompt
const systemInstructions = `You are a specialized e-commerce product data processor for a crafting supplies website tasked with providing complete, correct, appealing, and SEO-friendly product listings. You should write professionally, but in a friendly and engaging tone.
`;
const systemInstructions = `You are a specialized e-commerce product data processor for a crafting supplies website tasked with providing complete, correct, appealing, and SEO-friendly product listings. You should write professionally, but in a friendly and engaging tone. You have meticulous attention to detail and are a master at your craft.`;
// If we have products to validate, create a filtered prompt
if (productsToValidate) {
@@ -634,6 +676,7 @@ Here is the product data to validate:`;
router.post("/validate", async (req, res) => {
try {
const { products } = req.body;
const startTime = new Date(); // Track start time for performance metrics
console.log("🔍 Received products for validation:", {
isArray: Array.isArray(products),
@@ -654,6 +697,7 @@ router.post("/validate", async (req, res) => {
let ssh = null;
let connection = null;
let promptLength = 0; // Track prompt length for performance metrics
try {
// Setup MySQL connection via SSH tunnel
@@ -672,7 +716,8 @@ router.post("/validate", async (req, res) => {
console.log("🔄 Loading prompt with filtered taxonomy...");
const prompt = await loadPrompt(connection, products);
const fullPrompt = prompt + "\n" + JSON.stringify(products);
console.log("📝 Generated prompt length:", fullPrompt.length);
promptLength = fullPrompt.length; // Store prompt length for performance metrics
console.log("📝 Generated prompt length:", promptLength);
console.log("🤖 Sending request to OpenAI...");
const completion = await openai.chat.completions.create({
@@ -698,17 +743,27 @@ router.post("/validate", async (req, res) => {
Object.keys(aiResponse)
);
// Create a detailed comparison between original and corrected data
const changeDetails = [];
// Compare original and corrected data
if (aiResponse.correctedData) {
console.log("📊 Changes summary:");
products.forEach((original, index) => {
const corrected = aiResponse.correctedData[index];
if (corrected) {
const productChanges = {
productIndex: index,
title: original.title || `Product ${index + 1}`,
changes: []
};
const changes = Object.keys(corrected).filter(
(key) =>
JSON.stringify(original[key]) !==
JSON.stringify(corrected[key])
);
if (changes.length > 0) {
console.log(`\nProduct ${index + 1} changes:`);
changes.forEach((key) => {
@@ -719,14 +774,87 @@ router.post("/validate", async (req, res) => {
console.log(
` - Corrected: ${JSON.stringify(corrected[key])}`
);
// Add to our detailed changes array
productChanges.changes.push({
field: key,
original: original[key],
corrected: corrected[key]
});
});
// Only add products that have changes
if (productChanges.changes.length > 0) {
changeDetails.push(productChanges);
}
}
}
});
}
// Record performance metrics after successful validation
const endTime = new Date();
let performanceMetrics = {
promptLength,
productCount: products.length,
processingTimeSeconds: (endTime - startTime) / 1000
};
try {
// Use the local PostgreSQL pool from the app instead of the MySQL connection
const pool = req.app.locals.pool;
if (!pool) {
console.warn("⚠️ Local database pool not available for recording metrics");
return;
}
try {
// Insert performance data into the local PostgreSQL database
await pool.query(
`INSERT INTO ai_validation_performance
(prompt_length, product_count, start_time, end_time)
VALUES ($1, $2, $3, $4)`,
[promptLength, products.length, startTime, endTime]
);
console.log("📊 Performance metrics inserted into database");
// Query for average processing time based on similar prompt lengths
try {
const avgTimeResults = await pool.query(
`SELECT AVG(duration_seconds) as avg_duration,
COUNT(*) as sample_count
FROM ai_validation_performance
WHERE prompt_length BETWEEN $1 * 0.8 AND $1 * 1.2`,
[promptLength]
);
if (avgTimeResults.rows && avgTimeResults.rows[0]) {
performanceMetrics.avgDuration = avgTimeResults.rows[0].avg_duration;
performanceMetrics.sampleCount = avgTimeResults.rows[0].sample_count;
}
console.log("📊 Performance metrics retrieved:", performanceMetrics);
} catch (queryError) {
console.error("⚠️ Failed to query performance metrics:", queryError);
}
} catch (insertError) {
console.error("⚠️ Failed to insert performance metrics:", insertError);
// Check if table doesn't exist and log a more helpful message
if (insertError.code === '42P01') {
console.error("Table 'ai_validation_performance' does not exist. Make sure to run the setup-schema.sql script.");
}
}
} catch (metricError) {
// Don't fail the request if metrics recording fails
console.error("⚠️ Failed to record performance metrics:", metricError);
}
// Include performance metrics in the response
res.json({
success: true,
changeDetails: changeDetails,
performanceMetrics,
...aiResponse,
});
} catch (parseError) {