feat: solution for 'Агент с RAG-памятью'

src/embeddings.js

@@ -0,0 +1,21 @@
+import { OllamaEmbeddings } from 'ollama-embeddings';
+
+/**
+ * Singleton instance of OllamaEmbeddings.
+ * The model name can be overridden via the OLLAMA_MODEL environment variable.
+ */
+const modelName = process.env.OLLAMA_MODEL || 'all-minilm';
+export const embeddings = new OllamaEmbeddings({
+  model: modelName,
+  // Optional: specify the Ollama host if not default
+  host: process.env.OLLAMA_HOST || 'http://localhost:11434'
+});
+
+/**
+ * Utility to embed a single string.
+ * @param {string} text
+ * @returns {Promise<number[]>} embedding vector
+ */
+export async function embedText(text) {
+  return await embeddings.embedQuery(text);
+}

src/index.js

@@ -1,3 +1,67 @@
-const Agent = require('./agent');
+import dotenv from 'dotenv';
+import readline from 'readline';
+import { search_knowledge_base } from './tools/searchKnowledgeBase.js';
+import { add_to_knowledge_base } from './tools/addToKnowledgeBase.js';
 
-module.exports = { Agent };
+dotenv.config();
+
+/**
+ * Simple command-line agent that supports two commands:
+ * 1. /search <query>  - searches the knowledge base
+ * 2. /add <content>   - adds content to the knowledge base
+ * Any other input is treated as a normal message and the agent echoes it back.
+ */
+const rl = readline.createInterface({
+  input: process.stdin,
+  output: process.stdout,
+  prompt: 'Agent> '
+});
+
+console.log('Agent with RAG memory using Ollama embeddings.');
+console.log('Commands:');
+console.log('  /search <query>   - Search knowledge base');
+console.log('  /add <content>    - Add content to knowledge base');
+console.log('  /exit             - Exit');
+rl.prompt();
+
+rl.on('line', async (line) => {
+  const trimmed = line.trim();
+  if (trimmed === '/exit') {
+    rl.close();
+    return;
+  }
+
+  if (trimmed.startsWith('/search ')) {
+    const query = trimmed.slice(8).trim();
+    if (!query) {
+      console.log('Please provide a query.');
+    } else {
+      console.log(`Searching for "${query}"...`);
+      const results = await search_knowledge_base(query);
+      if (results.length === 0) {
+        console.log('No relevant documents found.');
+      } else {
+        console.log('Top results:');
+        results.forEach((res, idx) => {
+          console.log(`${idx + 1}. [${res.id}] (${res.score.toFixed(4)})`);
+          console.log(`   ${res.content}`);
+        });
+      }
+    }
+  } else if (trimmed.startsWith('/add ')) {
+    const content = trimmed.slice(5).trim();
+    if (!content) {
+      console.log('Please provide content to add.');
+    } else {
+      const { id } = await add_to_knowledge_base(content);
+      console.log(`Content added with id ${id}.`);
+    }
+  } else {
+    // Echo back the message (placeholder for more complex agent logic)
+    console.log(`You said: ${trimmed}`);
+  }
+  rl.prompt();
+}).on('close', () => {
+  console.log('Goodbye!');
+  process.exit(0);
+});

src/tools/addToKnowledgeBase.js

@@ -0,0 +1,15 @@
+import { embeddings } from '../embeddings.js';
+import { knowledgeBase } from './searchKnowledgeBase.js';
+import { v4 as uuidv4 } from 'uuid';
+
+/**
+ * Add new content to the knowledge base.
+ * @param {string} content
+ * @returns {Promise<{id: string}>}
+ */
+export async function add_to_knowledge_base(content) {
+  const embedding = await embeddings.embedQuery(content);
+  const id = uuidv4();
+  knowledgeBase.push({ id, content, embedding });
+  return { id };
+}

src/tools/searchKnowledgeBase.js

@@ -0,0 +1,42 @@
+import { embeddings } from '../embeddings.js';
+
+/**
+ * In-memory knowledge base.
+ * Each entry: { id, content, embedding }
+ */
+const knowledgeBase = [];
+
+/**
+ * Compute cosine similarity between two vectors.
+ * @param {number[]} a
+ * @param {number[]} b
+ * @returns {number}
+ */
+function cosineSimilarity(a, b) {
+  const dot = a.reduce((sum, ai, i) => sum + ai * b[i], 0);
+  const normA = Math.sqrt(a.reduce((sum, ai) => sum + ai * ai, 0));
+  const normB = Math.sqrt(b.reduce((sum, bi) => sum + bi * bi, 0));
+  return dot / (normA * normB);
+}
+
+/**
+ * Search the knowledge base for the most relevant documents.
+ * @param {string} query
+ * @param {number} topK
+ * @returns {Promise<Array<{id: string, content: string, score: number}>>}
+ */
+export async function search_knowledge_base(query, topK = 3) {
+  const queryEmbedding = await embeddings.embedQuery(query);
+  const scored = knowledgeBase.map(entry => ({
+    id: entry.id,
+    content: entry.content,
+    score: cosineSimilarity(queryEmbedding, entry.embedding)
+  }));
+  scored.sort((a, b) => b.score - a.score);
+  return scored.slice(0, topK);
+}
+
+/**
+ * Expose the knowledge base for other modules (e.g., add tool).
+ */
+export { knowledgeBase };