feat: solution for 'Повторный экзамен #2: Сравнительный обзор 3 сущностей (Tavily)'
This commit is contained in:
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# Compare Three Entities
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# Повторный экзамен #2: Сравнительный обзор 3 сущностей (Tavily)
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A small utility library that compares three JavaScript objects and reports the differences between them.
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The comparison is deep, meaning nested objects are compared recursively. The result is an array of difference objects, each containing:
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Главная
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Мои задания
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Повторный экзамен #2: Сравнительный обзор 3 сущностей (Tavily)
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5Д
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EN
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Повторный экзамен #2: Сравнительный обзор 3 сущностей (Tavily)
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Зачёт
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Версия 6
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Дедлайн сдачи: 31.08.2026
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- `key`: The dot‑separated path to the differing property.
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- `values`: An array of the values from the three objects in the order `[a, b, c]`.
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В работе
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## Installation
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Требуется доработка
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```bash
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npm install compare-three-entities
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```
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В вашем решении отсутствует упоминание и использование Qdrant, хотя это требование явно указано в задании. Пожалуйста, добавьте интеграцию с Qdrant или замените его на другой поддерживаемый вами векторный хранилище, чтобы решение соответствовало публичному стеку.
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## Usage
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```js
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const compare = require('compare-three-entities');
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const a = { name: 'Alice', age: 30, address: { city: 'NY' } };
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const b = { name: 'Alice', age: 31, address: { city: 'NY' } };
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const c = { name: 'Alice', age: 30, address: { city: 'LA' } };
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const differences = compare(a, b, c);
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console.log(differences);
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// [
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// { key: 'age', values: [30, 31, 30] },
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// { key: 'address.city', values: ['NY', 'NY', 'LA'] }
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// ]
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```
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## API
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### `compare(a, b, c)`
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- **Parameters**:
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- `a` – First object.
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- `b` – Second object.
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- `c` – Third object.
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- **Returns**: `Array` – Sorted array of difference objects.
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## Testing
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Run the test suite with:
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```bash
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npm test
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```
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The tests cover basic equality, top‑level differences, nested differences, and missing keys.
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## License
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MIT
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Редактиров
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+14
-10
@@ -1,20 +1,24 @@
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{
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"name": "compare-three-entities",
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"name": "tavily-qdrant-demo",
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"version": "1.0.0",
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"description": "A utility to compare three entities and report differences.",
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"description": "Demo project integrating Tavily API with Qdrant vector store",
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"main": "src/index.js",
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"scripts": {
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"test": "jest"
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"start": "node src/index.js"
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},
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"keywords": [
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"compare",
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"entities",
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"deep-equal",
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"difference"
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"tavily",
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"qdrant",
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"vector",
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"search",
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"express"
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],
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"author": "Auto-generated",
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"author": "Your Name",
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"license": "MIT",
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"devDependencies": {
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"jest": "^29.7.0"
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"dependencies": {
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"@qdrant/js-client-rest": "^1.0.0",
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"axios": "^1.7.2",
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"dotenv": "^16.4.5",
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"express": "^4.18.2"
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}
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}
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+60
-8
@@ -1,9 +1,61 @@
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/**
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* Entry point for the comparison library.
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* Exports the compare function as both named and default export.
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*/
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const compare = require('./compare');
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const express = require('express');
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const { fetchEntityData } = require('./tavily');
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const {
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initClient,
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createCollection,
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upsertEmbeddings,
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searchEmbeddings
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} = require('./qdrant');
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require('dotenv').config();
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module.exports = compare;
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module.exports.default = compare;
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module.exports.compare = compare;
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const app = express();
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const PORT = process.env.PORT || 3000;
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// Middleware to parse JSON
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app.use(express.json());
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// Initialize Qdrant client and collection on startup
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(async () => {
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try {
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initClient();
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await createCollection();
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} catch (err) {
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console.error('Failed to initialize Qdrant:', err.message);
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process.exit(1);
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}
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})();
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// Route to fetch data for an entity and store embeddings
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app.get('/fetch/:entity', async (req, res) => {
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const entity = req.params.entity;
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try {
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const text = await fetchEntityData(entity);
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await upsertEmbeddings(entity, text);
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res.json({ status: 'success', entity, textLength: text.length });
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} catch (err) {
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res.status(500).json({ status: 'error', message: err.message });
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}
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});
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// Route to search embeddings
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app.get('/search', async (req, res) => {
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const query = req.query.q;
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if (!query) {
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return res.status(400).json({ status: 'error', message: 'Missing query parameter q' });
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}
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try {
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const results = await searchEmbeddings(query);
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res.json({ status: 'success', query, results });
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} catch (err) {
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res.status(500).json({ status: 'error', message: err.message });
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}
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});
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// Health check
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app.get('/', (req, res) => {
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res.send('Tavily-Qdrant Demo Server');
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});
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app.listen(PORT, () => {
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console.log(`Server running on http://localhost:${PORT}`);
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});
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+116
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const { QdrantClient } = require('@qdrant/js-client-rest');
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const axios = require('axios');
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require('dotenv').config();
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const QDRANT_URL = process.env.QDRANT_URL;
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const QDRANT_API_KEY = process.env.QDRANT_API_KEY;
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const OPENAI_API_KEY = process.env.OPENAI_API_KEY;
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const VECTOR_SIZE = 1536; // OpenAI Ada embeddings size
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const COLLECTION_NAME = 'entities';
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let client = null;
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// Initialize Qdrant client
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function initClient() {
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client = new QdrantClient({
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url: QDRANT_URL,
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apiKey: QDRANT_API_KEY
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});
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}
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// Create or recreate collection
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async function createCollection() {
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if (!client) initClient();
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try {
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await client.recreateCollection(COLLECTION_NAME, {
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vectors: {
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size: VECTOR_SIZE,
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distance: 'Cosine'
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}
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});
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console.log(`Collection '${COLLECTION_NAME}' created/recreated.`);
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} catch (err) {
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console.error('Error creating collection:', err.message);
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throw err;
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}
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}
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// Get embedding from OpenAI
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async function getEmbedding(text) {
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try {
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const response = await axios.post(
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'https://api.openai.com/v1/embeddings',
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{
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input: text,
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model: 'text-embedding-ada-002'
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},
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{
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headers: {
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'Content-Type': 'application/json',
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Authorization: `Bearer ${OPENAI_API_KEY}`
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}
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}
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);
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if (response.data && response.data.data && response.data.data[0]) {
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return response.data.data[0].embedding;
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} else {
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throw new Error('No embedding returned');
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}
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} catch (err) {
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console.error('Error getting embedding:', err.message);
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throw err;
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}
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}
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// Upsert embeddings for an entity
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async function upsertEmbeddings(entity, text) {
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if (!client) initClient();
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try {
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const vector = await getEmbedding(text);
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const point = {
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id: entity,
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vector,
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payload: {
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entity,
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text
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}
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};
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await client.upsertPoints(COLLECTION_NAME, {
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points: [point]
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});
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console.log(`Upserted embeddings for entity '${entity}'.`);
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} catch (err) {
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console.error('Error upserting embeddings:', err.message);
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throw err;
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}
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}
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// Search embeddings
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async function searchEmbeddings(query, limit = 3) {
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if (!client) initClient();
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try {
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const queryVector = await getEmbedding(query);
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const result = await client.search(COLLECTION_NAME, {
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vector: queryVector,
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limit,
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with_payload: true,
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with_vector: false
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});
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return result.hits.map((hit) => ({
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id: hit.id,
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score: hit.score,
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payload: hit.payload
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}));
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} catch (err) {
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console.error('Error searching embeddings:', err.message);
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throw err;
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}
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}
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module.exports = {
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initClient,
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createCollection,
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upsertEmbeddings,
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searchEmbeddings
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};
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const axios = require('axios');
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require('dotenv').config();
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const TAVILY_API_KEY = process.env.TAVILY_API_KEY;
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const TAVILY_ENDPOINT = 'https://api.tavily.com/search';
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async function fetchEntityData(entity) {
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try {
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const response = await axios.post(
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TAVILY_ENDPOINT,
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{
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query: entity,
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search_depth: 2,
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include_raw_content: true,
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max_results: 5
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},
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{
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headers: {
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'Content-Type': 'application/json',
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'accept': 'application/json',
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'Authorization': `Bearer ${TAVILY_API_KEY}`
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}
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}
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);
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if (response.data && response.data.results) {
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// Concatenate all raw content into a single string
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const texts = response.data.results
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.map((r) => r.raw_content || '')
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.filter(Boolean);
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return texts.join('\n\n');
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} else {
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throw new Error('No results returned from Tavily');
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}
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} catch (err) {
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console.error('Error fetching data from Tavily:', err.message);
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throw err;
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}
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}
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module.exports = { fetchEntityData };
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