RAG con TypeScript, de cero a comparativa.

1// retriever.ts — LangChain.js + Qdrant
2import { OpenAIEmbeddings } from "@langchain/openai";
3import { QdrantVectorStore } from "@langchain/qdrant";
4
5const store = await QdrantVectorStore.fromExistingCollection(
6  new OpenAIEmbeddings({ model: "text-embedding-3-small" }),
7  { url: "http://localhost:6333", collectionName: "corpus" },
8);
9
10export const retriever = store.asRetriever({ k: 4 });
11
12export async function retrieve(query: string) {
13  const docs = await retriever.invoke(query);
14  return docs.map((d) => ({ text: d.pageContent, score: d.metadata.score }));
15}

1// retriever.ts — LlamaIndex.TS + Qdrant
2import { VectorStoreIndex, Settings } from "llamaindex";
3import { QdrantVectorStore } from "@llamaindex/qdrant";
4import { OpenAIEmbedding } from "@llamaindex/openai";
5
6Settings.embedModel = new OpenAIEmbedding({ model: "text-embedding-3-small" });
7
8const vs = new QdrantVectorStore({ url: "http://localhost:6333", collectionName: "corpus" });
9const index = await VectorStoreIndex.fromVectorStore(vs);
10const retriever = index.asRetriever({ similarityTopK: 4 });
11
12export async function retrieve(query: string) {
13  const nodes = await retriever.retrieve(query);
14  return nodes.map((n) => ({ text: n.node.getContent(), score: n.score }));
15}

1// retriever.ts — Mastra + Qdrant
2import { Mastra } from "@mastra/core";
3import { QdrantVector } from "@mastra/qdrant";
4import { openai } from "@ai-sdk/openai";
5
6const mastra = new Mastra({
7  vectors: { qdrant: new QdrantVector({ url: "http://localhost:6333" }) },
8});
9
10export async function retrieve(query: string, k = 4) {
11  const { embedding } = await mastra.embed({ model: openai.embedding("text-embedding-3-small"), value: query });
12  return await mastra.vectors.qdrant.query({ indexName: "corpus", queryVector: embedding, topK: k });
13}