feat: solution for 'Повторный экзамен #2: Сравнительный обзор 3 сущностей (Tavily)'

README.md

@@ -1,54 +1,19 @@
-# Compare Three Entities
+# Повторный экзамен #2: Сравнительный обзор 3 сущностей (Tavily)
 
-A small utility library that compares three JavaScript objects and reports the differences between them.  
+Главная
-The comparison is deep, meaning nested objects are compared recursively. The result is an array of difference objects, each containing:
+Мои задания
+Повторный экзамен #2: Сравнительный обзор 3 сущностей (Tavily)
+5Д
+EN
+Повторный экзамен #2: Сравнительный обзор 3 сущностей (Tavily)
+Зачёт
+Версия 6
+Дедлайн сдачи: 31.08.2026
 
-- `key`: The dot‑separated path to the differing property.
+В работе
-- `values`: An array of the values from the three objects in the order `[a, b, c]`.
 
-## Installation
+Требуется доработка
 
-```bash
+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.
-npm install compare-three-entities
-```
 
-## Usage
+Редактиров
-
-```js
-const compare = require('compare-three-entities');
-
-const a = { name: 'Alice', age: 30, address: { city: 'NY' } };
-const b = { name: 'Alice', age: 31, address: { city: 'NY' } };
-const c = { name: 'Alice', age: 30, address: { city: 'LA' } };
-
-const differences = compare(a, b, c);
-console.log(differences);
-// [
-//   { key: 'age', values: [30, 31, 30] },
-//   { key: 'address.city', values: ['NY', 'NY', 'LA'] }
-// ]
-```
-
-## API
-
-### `compare(a, b, c)`
-
-- **Parameters**:
-  - `a` – First object.
-  - `b` – Second object.
-  - `c` – Third object.
-- **Returns**: `Array` – Sorted array of difference objects.
-
-## Testing
-
-Run the test suite with:
-
-```bash
-npm test
-```
-
-The tests cover basic equality, top‑level differences, nested differences, and missing keys.
-
-## License
-
-MIT

package.json

@@ -1,20 +1,24 @@
 {
-  "name": "compare-three-entities",
+  "name": "tavily-qdrant-demo",
   "version": "1.0.0",
-  "description": "A utility to compare three entities and report differences.",
+  "description": "Demo project integrating Tavily API with Qdrant vector store",
   "main": "src/index.js",
   "scripts": {
-    "test": "jest"
+    "start": "node src/index.js"
   },
   "keywords": [
-    "compare",
+    "tavily",
-    "entities",
+    "qdrant",
-    "deep-equal",
+    "vector",
-    "difference"
+    "search",
+    "express"
   ],
-  "author": "Auto-generated",
+  "author": "Your Name",
   "license": "MIT",
-  "devDependencies": {
+  "dependencies": {
-    "jest": "^29.7.0"
+    "@qdrant/js-client-rest": "^1.0.0",
+    "axios": "^1.7.2",
+    "dotenv": "^16.4.5",
+    "express": "^4.18.2"
   }
 }

src/index.js

@@ -1,9 +1,61 @@
-/**
+const express = require('express');
- * Entry point for the comparison library.
+const { fetchEntityData } = require('./tavily');
- * Exports the compare function as both named and default export.
+const {
- */
+  initClient,
-const compare = require('./compare');
+  createCollection,
+  upsertEmbeddings,
+  searchEmbeddings
+} = require('./qdrant');
+require('dotenv').config();
 
-module.exports = compare;
+const app = express();
-module.exports.default = compare;
+const PORT = process.env.PORT || 3000;
-module.exports.compare = compare;
+
+// Middleware to parse JSON
+app.use(express.json());
+
+// Initialize Qdrant client and collection on startup
+(async () => {
+  try {
+    initClient();
+    await createCollection();
+  } catch (err) {
+    console.error('Failed to initialize Qdrant:', err.message);
+    process.exit(1);
+  }
+})();
+
+// Route to fetch data for an entity and store embeddings
+app.get('/fetch/:entity', async (req, res) => {
+  const entity = req.params.entity;
+  try {
+    const text = await fetchEntityData(entity);
+    await upsertEmbeddings(entity, text);
+    res.json({ status: 'success', entity, textLength: text.length });
+  } catch (err) {
+    res.status(500).json({ status: 'error', message: err.message });
+  }
+});
+
+// Route to search embeddings
+app.get('/search', async (req, res) => {
+  const query = req.query.q;
+  if (!query) {
+    return res.status(400).json({ status: 'error', message: 'Missing query parameter q' });
+  }
+  try {
+    const results = await searchEmbeddings(query);
+    res.json({ status: 'success', query, results });
+  } catch (err) {
+    res.status(500).json({ status: 'error', message: err.message });
+  }
+});
+
+// Health check
+app.get('/', (req, res) => {
+  res.send('Tavily-Qdrant Demo Server');
+});
+
+app.listen(PORT, () => {
+  console.log(`Server running on http://localhost:${PORT}`);
+});

src/qdrant.js

@@ -0,0 +1,116 @@
+const { QdrantClient } = require('@qdrant/js-client-rest');
+const axios = require('axios');
+require('dotenv').config();
+
+const QDRANT_URL = process.env.QDRANT_URL;
+const QDRANT_API_KEY = process.env.QDRANT_API_KEY;
+const OPENAI_API_KEY = process.env.OPENAI_API_KEY;
+const VECTOR_SIZE = 1536; // OpenAI Ada embeddings size
+
+const COLLECTION_NAME = 'entities';
+
+let client = null;
+
+// Initialize Qdrant client
+function initClient() {
+  client = new QdrantClient({
+    url: QDRANT_URL,
+    apiKey: QDRANT_API_KEY
+  });
+}
+
+// Create or recreate collection
+async function createCollection() {
+  if (!client) initClient();
+  try {
+    await client.recreateCollection(COLLECTION_NAME, {
+      vectors: {
+        size: VECTOR_SIZE,
+        distance: 'Cosine'
+      }
+    });
+    console.log(`Collection '${COLLECTION_NAME}' created/recreated.`);
+  } catch (err) {
+    console.error('Error creating collection:', err.message);
+    throw err;
+  }
+}
+
+// Get embedding from OpenAI
+async function getEmbedding(text) {
+  try {
+    const response = await axios.post(
+      'https://api.openai.com/v1/embeddings',
+      {
+        input: text,
+        model: 'text-embedding-ada-002'
+      },
+      {
+        headers: {
+          'Content-Type': 'application/json',
+          Authorization: `Bearer ${OPENAI_API_KEY}`
+        }
+      }
+    );
+    if (response.data && response.data.data && response.data.data[0]) {
+      return response.data.data[0].embedding;
+    } else {
+      throw new Error('No embedding returned');
+    }
+  } catch (err) {
+    console.error('Error getting embedding:', err.message);
+    throw err;
+  }
+}
+
+// Upsert embeddings for an entity
+async function upsertEmbeddings(entity, text) {
+  if (!client) initClient();
+  try {
+    const vector = await getEmbedding(text);
+    const point = {
+      id: entity,
+      vector,
+      payload: {
+        entity,
+        text
+      }
+    };
+    await client.upsertPoints(COLLECTION_NAME, {
+      points: [point]
+    });
+    console.log(`Upserted embeddings for entity '${entity}'.`);
+  } catch (err) {
+    console.error('Error upserting embeddings:', err.message);
+    throw err;
+  }
+}
+
+// Search embeddings
+async function searchEmbeddings(query, limit = 3) {
+  if (!client) initClient();
+  try {
+    const queryVector = await getEmbedding(query);
+    const result = await client.search(COLLECTION_NAME, {
+      vector: queryVector,
+      limit,
+      with_payload: true,
+      with_vector: false
+    });
+    return result.hits.map((hit) => ({
+      id: hit.id,
+      score: hit.score,
+      payload: hit.payload
+    }));
+  } catch (err) {
+    console.error('Error searching embeddings:', err.message);
+    throw err;
+  }
+}
+
+module.exports = {
+  initClient,
+  createCollection,
+  upsertEmbeddings,
+  searchEmbeddings
+};

src/tavily.js

@@ -0,0 +1,41 @@
+const axios = require('axios');
+require('dotenv').config();
+
+const TAVILY_API_KEY = process.env.TAVILY_API_KEY;
+const TAVILY_ENDPOINT = 'https://api.tavily.com/search';
+
+async function fetchEntityData(entity) {
+  try {
+    const response = await axios.post(
+      TAVILY_ENDPOINT,
+      {
+        query: entity,
+        search_depth: 2,
+        include_raw_content: true,
+        max_results: 5
+      },
+      {
+        headers: {
+          'Content-Type': 'application/json',
+          'accept': 'application/json',
+          'Authorization': `Bearer ${TAVILY_API_KEY}`
+        }
+      }
+    );
+
+    if (response.data && response.data.results) {
+      // Concatenate all raw content into a single string
+      const texts = response.data.results
+        .map((r) => r.raw_content || '')
+        .filter(Boolean);
+      return texts.join('\n\n');
+    } else {
+      throw new Error('No results returned from Tavily');
+    }
+  } catch (err) {
+    console.error('Error fetching data from Tavily:', err.message);
+    throw err;
+  }
+}
+
+module.exports = { fetchEntityData };