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Gitea
2026-05-26 12:00:42 +05:30
parent edc579abec
commit df8de787c0
5 changed files with 708 additions and 73 deletions
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.env
+2
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@@ -0,0 +1,2 @@
GOOGLE_API_KEY=АlzaSyBFGЗDLQnaPХ8H413uztWT9Tzcg_yCTNTk
AZURE_OPENAI_KEY=o20WLCQfubbGTo0SnkS70lefG0tHvdZlzcUGHOPDmww0igy94Up0JQQJ99CEAC77bzfXJ3w3AAAAACOGIUYb
controller/handleOrchestration.js
+186 -40
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@@ -1,61 +1,76 @@
const axios = require('axios');
const { GoogleGenerativeAI } = require('@google/generative-ai');
const ai = new GoogleGenerativeAI({ apiKey: process.env.GEMINI_API_KEY });
require('dotenv').config();
const { OpenAI } = require('openai');
// 1. FIX: Format base endpoint to match standard Azure OpenAI layout
const azureEndpoint = "https://cpmindiayoda-resource.services.ai.azure.com";
const deploymentName = "gpt-4o-mini";
const apiVersion = "2024-08-01-preview"; // Mandatory query param for Azure endpoints
// Configure the SDK compatibility routing
const client = new OpenAI({
baseURL: `${azureEndpoint}/openai/deployments/${deploymentName}`,
apiKey: process.env.AZURE_OPENAI_KEY,
defaultHeaders: { 'api-key': process.env.AZURE_OPENAI_KEY },
// Inject the required api-version parameter into every request automatically
defaultQuery: { 'api-version': apiVersion }
});
console.log('Azure OpenAI Client Initialized with Deployment:', deploymentName);
const fetchWrenData = async (prompt, tenantId) => {
try {
const url = process.env.WREN_AI_URL || 'http://localhost:5555/api/v1/text-to-sql';
const response = await axios.post(url,
{ prompt: prompt },
{
headers: {
'X-Wren-Session-Properties': `@user_org_id=${tenantId}`,
'Content-Type': 'application/json'
}
}
);
return response.data;
return {
question: "Monthly revenue share across electronics product categories",
sql: `
SELECT product_category, SUM(revenue) AS total_revenue, order_month
FROM sales_pipeline
WHERE order_year = 2026
GROUP BY product_category, order_month
ORDER BY order_month ASC, total_revenue DESC;
`,
data: [
{ product_category: "Smartphones", total_revenue: 145000, order_month: "January" },
{ product_category: "Laptops", total_revenue: 210000, order_month: "January" },
{ product_category: "Audio Wearables", total_revenue: 65000, order_month: "January" },
{ product_category: "Smartphones", total_revenue: 160000, order_month: "February" },
{ product_category: "Laptops", total_revenue: 195000, order_month: "February" },
{ product_category: "Audio Wearables", total_revenue: 85000, order_month: "February" }
]
};
} catch (error) {
console.error('Wren AI Integration Error:', error.message);
throw new Error('Failed to fetch data payload from Wren AI endpoint');
}
};
const generateVegaSchema = async (question, dataArray) => {
try {
const model = ai.getGenerativeModel({
model: 'gemini-2.5-flash',
generationConfig: { responseMimeType: 'application/json' }
});
const systemPrompt = `You are a data visualization expert. I will provide a user's question and a JSON array of data.
Your task is to generate a strictly valid Vega-Lite JSON specification to visualize this data.
The data array will be provided to the Vega spec internally. Map the JSON keys to the correct x, y, and color axes.
Choose the best chart type (bar, line, arc) based on the question.`;
const systemPrompt = `You are a data visualization expert. I will provide a user's question and a JSON array of data. Your task is to generate a strictly valid Vega-Lite JSON specification to visualize this data. The data array will be provided to the Vega spec internally. Map the JSON keys to the correct x, y, and color axes. Choose the best chart type (bar, line, arc) based on the question.`;
const userPrompt = `User Question: "${question}"\nData JSON: ${JSON.stringify(dataArray)}`;
const result = await model.generateContent({
contents: [{ role: 'user', parts: [{ text: `${systemPrompt}\n\n${userPrompt}` }] }]
const completion = await client.chat.completions.create({
model: '',
messages: [
{ role: 'system', content: systemPrompt },
{ role: 'user', content: userPrompt }
],
response_format: { type: "json_object" }
});
return JSON.parse(result.response.text());
const rawText = completion.choices[0].message.content.trim();
return JSON.parse(rawText);
} catch (error) {
console.error('Gemini Engine Error:', error.message);
console.error('Azure OpenAI Engine Error:', error.message);
throw new Error('Failed to transform data architecture into valid Vega-Lite spec');
}
};
const handleOrchestration = async (req, res) => {
try {
const { prompt } = req.body;
const tenant_id = req.user ? req.user.client_id : null;
return res.status(200).json("ok");
if (!prompt) {
return res.status(400).json({ error: 'Prompt field is required in request body' });
@@ -64,18 +79,13 @@ const handleOrchestration = async (req, res) => {
return res.status(400).json({ error: 'Tenant context (client_id) missing from auth token' });
}
const wrenData = await fetchWrenData(prompt, tenant_id);
const vegaSchema = await generateVegaSchema(prompt, wrenData);
const vegaSchema = await generateVegaSchema(wrenData.question, wrenData.data);
if (!vegaSchema || !vegaSchema.$schema) {
return res.status(500).json({ error: 'Egress Pipeline Validation Failure: Output missing standard Vega $schema identifier' });
}
return res.status(200).json(vegaSchema);
} catch (error) {
@@ -85,3 +95,139 @@ const handleOrchestration = async (req, res) => {
};
module.exports = { handleOrchestration };
// const axios = require('axios');
// const dotenv = require('dotenv');
// dotenv.config();
// const { GoogleGenerativeAI } = require('@google/generative-ai');
// const ai = new GoogleGenerativeAI({ apiKey: process.env.GOOGLE_API_KEY });
// console.log('Gemini API Key Loaded:', process.env.GOOGLE_API_KEY);
// const fetchWrenData = async (prompt, tenantId) => {
// try {
// return {
// question: "Top sales person this month",
// sql: `
// SELECT sales_person,
// SUM(amount) AS total_sales
// FROM orders
// WHERE MONTH(created_at)=MONTH(CURRENT_DATE)
// GROUP BY sales_person
// ORDER BY total_sales DESC
// LIMIT 10;
// `,
// data: [
// {
// sales_person: "Rahul",
// total_sales: 92000
// },
// {
// sales_person: "Amit",
// total_sales: 87000
// }
// ]
// };
// } catch (error) {
// console.error('Wren AI Integration Error:', error.message);
// throw new Error('Failed to fetch data payload from Wren AI endpoint');
// }
// };
// const generateVegaSchema = async (question, dataArray) => {
// try {
// const model = ai.getGenerativeModel({
// model: 'gemini-2.5-flash',
// generationConfig: { responseMimeType: 'application/json' }
// });
// const systemPrompt = `You are a data visualization expert. I will provide a user's question and a JSON array of data.
// Your task is to generate a strictly valid Vega-Lite JSON specification to visualize this data.
// The data array will be provided to the Vega spec internally. Map the JSON keys to the correct x, y, and color axes.
// Choose the best chart type (bar, line, arc) based on the question.`;
// const userPrompt = `User Question: "${question}"\nData JSON: ${JSON.stringify(dataArray)}`;
// const result = await model.generateContent({
// contents: [{ role: 'user', parts: [{ text: `${systemPrompt}\n\n${userPrompt}` }] }]
// });
// return JSON.parse(result.response.text());
// } catch (error) {
// console.error('Gemini Engine Error:', error.message);
// throw new Error('Failed to transform data architecture into valid Vega-Lite spec');
// }
// };
// const handleOrchestration = async (req, res) => {
// try {
// const { prompt } = req.body;
// const tenant_id = req.user ? req.user.client_id : null;
// // return res.status(200).json("ok");
// if (!prompt) {
// return res.status(400).json({ error: 'Prompt field is required in request body' });
// }
// if (!tenant_id) {
// return res.status(400).json({ error: 'Tenant context (client_id) missing from auth token' });
// }
// const wrenData = await fetchWrenData(prompt, tenant_id);
// const vegaSchema = await generateVegaSchema(prompt, wrenData);
// if (!vegaSchema || !vegaSchema.$schema) {
// return res.status(500).json({ error: 'Egress Pipeline Validation Failure: Output missing standard Vega $schema identifier' });
// }
// return res.status(200).json(vegaSchema);
// } catch (error) {
// console.error('API Gateway Orchestrator Crash:', error.message);
// return res.status(500).json({ error: error.message });
// }
// };
// module.exports = { handleOrchestration };
// const fetchWrenData = async (prompt, tenantId) => {
// try {
// const url = process.env.WREN_AI_URL || 'http://localhost:5555/api/v1/text-to-sql';
// const response = await axios.post(url,
// { prompt: prompt },
// {
// headers: {
// 'X-Wren-Session-Properties': `@user_org_id=${tenantId}`,
// 'Content-Type': 'application/json'
// }
// }
// );
// return response.data;
// } catch (error) {
// console.error('Wren AI Integration Error:', error.message);
// throw new Error('Failed to fetch data payload from Wren AI endpoint');
// }
// };
index.html
+79
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@@ -0,0 +1,79 @@
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Fixed Vega-Lite Revenue Share Chart</title>
<!-- CDN Links for Libraries -->
<script src="https://jsdelivr.net"></script>
<script src="https://jsdelivr.net"></script>
<script src="https://jsdelivr.net"></script>
<style>
body {
font-family: Arial, sans-serif;
display: flex;
justify-content: center;
align-items: center;
height: 100vh;
background-color: #f4f6f9;
margin: 0;
}
#chart-container {
background: white;
padding: 30px;
border-radius: 12px;
box-shadow: 0 8px 16px rgba(0,0,0,0.1);
}
</style>
</head>
<body>
<!-- This div holds your live UI chart -->
<div id="chart-container"></div>
<script>
// सुधरा हुआ Vega-Lite JSON (सभी सिंटैक्स एरर्स फिक्स कर दिए गए हैं)
const correctedSpec = {
"$schema": "https://vega.github.io/schema/vega-lite/v5.json",
"title": "Monthly Revenue Share Across Electronics Product Categories",
"data": {
"values": [
{ "product_category": "Smartphones", "total_revenue": 145000, "order_month": "January" },
{ "product_category": "Laptops", "total_revenue": 210000, "order_month": "January" },
{ "product_category": "Audio Wearables", "total_revenue": 65000, "order_month": "January" },
{ "product_category": "Smartphones", "total_revenue": 160000, "order_month": "February" },
{ "product_category": "Laptops", "total_revenue": 195000, "order_month": "February" },
{ "product_category": "Audio Wearables", "total_revenue": 85000, "order_month": "February" }
]
},
"transform": [
{
"joinaggregate": [{ "op": "sum", "field": "total_revenue", "as": "monthly_total" }],
"groupby": ["order_month"]
},
{
"calculate": "datum.total_revenue / datum.monthly_total",
"as": "revenue_share"
}
],
"mark": "bar",
"encoding": {
"x": { "field": "product_category", "type": "nominal", "axis": { "labelAngle": 0 }, "title": "Category" },
"y": { "field": "revenue_share", "type": "quantitative", "axis": { "format": "%" }, "title": "Revenue Share" },
"color": { "field": "product_category", "type": "nominal" }
},
"facet": {
"columns": 2,
"field": "order_month",
"type": "nominal",
"title": "Timeline"
}
};
// Render the view
vegaEmbed('#chart-container', correctedSpec, { actions: false })
.then(result => console.log("UI Render Success!"))
.catch(console.error);
</script>
</body>
</html>
package-lock.json
Generated
+406 -1
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@@ -9,13 +9,173 @@
"version": "1.0.0",
"license": "ISC",
"dependencies": {
"@azure/identity": "^4.13.1",
"@clickhouse/client": "^1.18.5",
"@google/generative-ai": "^0.24.1",
"axios": "^1.16.1",
"cors": "^2.8.6",
"dotenv": "^17.4.2",
"express": "^5.2.1",
"jsonwebtoken": "^9.0.3",
"nodemon": "^3.1.14"
"nodemon": "^3.1.14",
"openai": "^6.39.0"
}
},
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"license": "MIT",
"dependencies": {
"tslib": "^2.6.2"
},
"engines": {
"node": ">=18.0.0"
}
},
"node_modules/@azure/core-auth": {
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"resolved": "https://registry.npmjs.org/@azure/core-auth/-/core-auth-1.10.1.tgz",
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"node_modules/@azure/core-client": {
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"license": "MIT",
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"@azure/core-rest-pipeline": "^1.22.0",
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"@azure/logger": "^1.0.0",
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"open": "^10.1.0",
"tslib": "^2.2.0"
},
"engines": {
"node": ">=20.0.0"
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},
"node_modules/@azure/logger": {
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"node_modules/@azure/msal-browser": {
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"resolved": "https://registry.npmjs.org/@azure/msal-browser/-/msal-browser-5.11.0.tgz",
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"license": "MIT",
"dependencies": {
"@azure/msal-common": "16.6.2"
},
"engines": {
"node": ">=0.8.0"
}
},
"node_modules/@azure/msal-common": {
"version": "16.6.2",
"resolved": "https://registry.npmjs.org/@azure/msal-common/-/msal-common-16.6.2.tgz",
"integrity": "sha512-hQjjsekAjB00cM1EmatWJlzhEoK2Qhz7Rj5gvM6tYf8iL7RM3tkxlpU9fG0+ofkulzg9AEEA6dIEnSmDr5ZqUA==",
"license": "MIT",
"engines": {
"node": ">=0.8.0"
}
},
"node_modules/@azure/msal-node": {
"version": "5.2.2",
"resolved": "https://registry.npmjs.org/@azure/msal-node/-/msal-node-5.2.2.tgz",
"integrity": "sha512-toS+2AePxqyzb0YOKttDOOiSl3jrkK9aiqIvpurpis0O34QcIS5gToqrgT39p04Dpxw3YoUU0lxJKTpSFFfA6Q==",
"license": "MIT",
"dependencies": {
"@azure/msal-common": "16.6.2",
"jsonwebtoken": "^9.0.0"
},
"engines": {
"node": ">=20"
}
},
"node_modules/@clickhouse/client": {
@@ -45,6 +205,42 @@
"node": ">=18.0.0"
}
},
"node_modules/@typespec/ts-http-runtime": {
"version": "0.3.5",
"resolved": "https://registry.npmjs.org/@typespec/ts-http-runtime/-/ts-http-runtime-0.3.5.tgz",
"integrity": "sha512-yURCknZhvywvQItHMMmFSo+fq5arCUIyz/CVk7jD89MSai7dkaX8ufjCWp3NttLojoTVbcE72ri+be/TnEbMHw==",
"license": "MIT",
"dependencies": {
"http-proxy-agent": "^7.0.0",
"https-proxy-agent": "^7.0.0",
"tslib": "^2.6.2"
},
"engines": {
"node": ">=20.0.0"
}
},
"node_modules/@typespec/ts-http-runtime/node_modules/agent-base": {
"version": "7.1.4",
"resolved": "https://registry.npmjs.org/agent-base/-/agent-base-7.1.4.tgz",
"integrity": "sha512-MnA+YT8fwfJPgBx3m60MNqakm30XOkyIoH1y6huTQvC0PwZG7ki8NacLBcrPbNoo8vEZy7Jpuk7+jMO+CUovTQ==",
"license": "MIT",
"engines": {
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}
},
"node_modules/@typespec/ts-http-runtime/node_modules/https-proxy-agent": {
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"license": "MIT",
"dependencies": {
"agent-base": "^7.1.2",
"debug": "4"
},
"engines": {
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}
},
"node_modules/accepts": {
"version": "2.0.0",
"resolved": "https://registry.npmjs.org/accepts/-/accepts-2.0.0.tgz",
@@ -176,6 +372,21 @@
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"license": "BSD-3-Clause"
},
"node_modules/bundle-name": {
"version": "4.1.0",
"resolved": "https://registry.npmjs.org/bundle-name/-/bundle-name-4.1.0.tgz",
"integrity": "sha512-tjwM5exMg6BGRI+kNmTntNsvdZS1X8BFYS6tnJ2hdH0kVxM6/eVZ2xy+FqStSWvYmtfFMDLIxurorHwDKfDz5Q==",
"license": "MIT",
"dependencies": {
"run-applescript": "^7.0.0"
},
"engines": {
"node": ">=18"
},
"funding": {
"url": "https://github.com/sponsors/sindresorhus"
}
},
"node_modules/bytes": {
"version": "3.1.2",
"resolved": "https://registry.npmjs.org/bytes/-/bytes-3.1.2.tgz",
@@ -324,6 +535,46 @@
}
}
},
"node_modules/default-browser": {
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"dependencies": {
"bundle-name": "^4.1.0",
"default-browser-id": "^5.0.0"
},
"engines": {
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}
package.json
+4 -1
View File
@@ -11,12 +11,15 @@
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