feat: solution for 'Повторный экзамен: FAQ-бот — ChromaDB + один MCP-tool'

src/vectorstore.js

@@ -0,0 +1,40 @@
+import { ChromaClient } from "chromadb";
+import { OpenAIEmbeddings } from "langchain/embeddings/openai";
+import { OpenAI } from "langchain/llms/openai";
+
+const client = new ChromaClient({
+  path: process.env.CHROMA_DB_PATH || "./chromadb",
+});
+
+const embeddings = new OpenAIEmbeddings({
+  openAIApiKey: process.env.OPENAI_API_KEY,
+});
+
+export async function addDocument(collectionName, text, metadata = {}) {
+  const collection = await client.getOrCreateCollection({
+    name: collectionName,
+  });
+  const embedding = await embeddings.embedQuery(text);
+  await collection.add({
+    documents: [text],
+    embeddings: [embedding],
+    metadatas: [metadata],
+  });
+}
+
+export async function getSimilarDocuments(collectionName, query, k = 5) {
+  const collection = await client.getOrCreateCollection({
+    name: collectionName,
+  });
+  const embedding = await embeddings.embedQuery(query);
+  const results = await collection.query({
+    queryEmbeddings: [embedding],
+    nResults: k,
+  });
+  return results.ids[0].map((id, idx) => ({
+    id,
+    score: results.scores[0][idx],
+    document: results.documents[0][idx],
+    metadata: results.metadatas[0][idx],
+  }));
+}