From afc051686850569b2f50aa2493332b69d1dc4d00 Mon Sep 17 00:00:00 2001
From: Marcus Schiesser <mail@marcusschiesser.de>
Date: Mon, 18 Dec 2023 14:52:37 +0700
Subject: [PATCH] add RAG to mistral example
---
examples/mistral.ts | 41 ++++++++++++++++++++++++++++++++++++++++-
1 file changed, 40 insertions(+), 1 deletion(-)
diff --git a/examples/mistral.ts b/examples/mistral.ts
index 4687add02..259119504 100644
--- a/examples/mistral.ts
+++ b/examples/mistral.ts
@@ -1,4 +1,35 @@
-import { MistralAI, MistralAIEmbedding } from "llamaindex";
+import * as fs from "fs/promises";
+import {
+ BaseEmbedding,
+ Document,
+ LLM,
+ MistralAI,
+ MistralAIEmbedding,
+ VectorStoreIndex,
+ serviceContextFromDefaults,
+} from "llamaindex";
+
+async function rag(llm: LLM, embedModel: BaseEmbedding, query: string) {
+ // Load essay from abramov.txt in Node
+ const path = "node_modules/llamaindex/examples/abramov.txt";
+
+ const essay = await fs.readFile(path, "utf-8");
+
+ // Create Document object with essay
+ const document = new Document({ text: essay, id_: path });
+
+ // Split text and create embeddings. Store them in a VectorStoreIndex
+ const serviceContext = serviceContextFromDefaults({ llm, embedModel });
+
+ const index = await VectorStoreIndex.fromDocuments([document], {
+ serviceContext,
+ });
+
+ // Query the index
+ const queryEngine = index.asQueryEngine();
+ const response = await queryEngine.query(query);
+ return response.response;
+}
(async () => {
// embeddings
@@ -26,4 +57,12 @@ import { MistralAI, MistralAIEmbedding } from "llamaindex";
for await (const chunk of stream) {
process.stdout.write(chunk);
}
+
+ // rag
+ const ragResponse = await rag(
+ llm,
+ embedding,
+ "What did the author do in college?",
+ );
+ console.log(ragResponse);
})();
--
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