organize sync

langchain/demos/index-demo.js

@@ -1,6 +1,5 @@
+import IndexClient, { IndexVectorStore } from "@indexnetwork/sdk";
 import { OpenAIEmbeddings } from "@langchain/openai";
-import { IndexVectorStore } from "../dist/index.es.js";
-import IndexClient from "@indexnetwork/sdk";
 import { Wallet } from "ethers";
 
 async function main() {
@@ -15,11 +14,11 @@ async function main() {
     model: "text-embedding-ada-002",
   });
 
-  const vectorStore = await IndexVectorStore.init(embeddings, {
+  const sourceIndexId =
+    "kjzl6kcym7w8y7lvuklrt4mmon5h9u3wpkm9jd9rtdbghl9df2ujsyid8d0qxj4";
+  const vectorStore = new IndexVectorStore(embeddings, {
     client: indexClient,
-    sources: [
-      "kjzl6kcym7w8y7lvuklrt4mmon5h9u3wpkm9jd9rtdbghl9df2ujsyid8d0qxj4",
-    ],
+    sources: [sourceIndexId],
   });
 
   const retriever = vectorStore.asRetriever();

langchain/demos/index-demo2.js

@@ -0,0 +1,57 @@
+import IndexClient, { IndexVectorStore } from "@indexnetwork/sdk";
+// import IndexClient, {
+//   IndexVectorStore,
+// } from "../../sdk/js/dist/indexclient.es.js";
+import { ChatOpenAI, OpenAIEmbeddings } from "@langchain/openai";
+import { Wallet } from "ethers";
+import { ConversationalRetrievalQAChain } from "langchain/chains";
+
+async function main() {
+  try {
+    const wallet = new Wallet(process.env.PRIVATE_KEY);
+    const indexClient = new IndexClient({
+      network: "dev", // or mainnet
+      wallet, // or session
+      domain: "indexd",
+    });
+
+    // await indexClient.authenticate();
+
+    const embeddings = new OpenAIEmbeddings({
+      apiKey: process.env.OPENAI_API_KEY,
+      model: "text-embedding-ada-002",
+    });
+
+    const sourceIndexId =
+      "kjzl6kcym7w8y7lvuklrt4mmon5h9u3wpkm9jd9rtdbghl9df2ujsyid8d0qxj4";
+
+    const vectorStore = new IndexVectorStore(embeddings, {
+      client: indexClient,
+      sources: [sourceIndexId],
+    });
+
+    /* Run vector store search */
+    const question = "What is mesh.xyz?";
+    const res = await vectorStore.similaritySearch(question, 1);
+    console.log("Retieved Documents", JSON.stringify(res, null, 3));
+
+    /* Create a QA chain */
+    const model = new ChatOpenAI({
+      apiKey: process.env.OPENAI_API_KEY,
+      model: "gpt-3.5-turbo",
+    });
+
+    const chain = ConversationalRetrievalQAChain.fromLLM(
+      model,
+      vectorStore.asRetriever(),
+    );
+
+    /* Ask it a question */
+    const qa_res = await chain.invoke({ question, chat_history: [] });
+    console.log("Chat response:", JSON.stringify(qa_res, null, 3));
+  } catch (err) {
+    console.error(err);
+  }
+}
+
+main();