bump version (langchain-ai#177)

* bump version

* cr

docs/docs/modules/agents/agent_toolkits/vectorstore.md

@@ -0,0 +1,50 @@
+# VectorStore Agent Toolkit
+
+This example shows how to load and use an agent with a vectorstore toolkit.
+
+```typescript
+import { OpenAI } from "langchain";
+import { HNSWLib } from "langchain/vectorstores";
+import { OpenAIEmbeddings } from "langchain/embeddings";
+import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";
+import * as fs from "fs";
+import {
+  VectorStoreToolkit,
+  createVectorStoreAgent,
+  VectorStoreInfo,
+} from "langchain/agents";
+
+export const run = async () => {
+  const model = new OpenAI({ temperature: 0 });
+  /* Load in the file we want to do question answering over */
+  const text = fs.readFileSync("state_of_the_union.txt", "utf8");
+  /* Split the text into chunks */
+  const textSplitter = new RecursiveCharacterTextSplitter({ chunkSize: 1000 });
+  const docs = await textSplitter.createDocuments([text]);
+  /* Create the vectorstore */
+  const vectorStore = await HNSWLib.fromDocuments(docs, new OpenAIEmbeddings());
+
+  /* Create the agent */
+  const vectorStoreInfo: VectorStoreInfo = {
+    name: "state_of_union_address",
+    description: "the most recent state of the Union address",
+    vectorStore,
+  };
+
+  const toolkit = new VectorStoreToolkit(vectorStoreInfo, model);
+  const agent = createVectorStoreAgent(model, toolkit);
+
+  const input =
+    "What did biden say about Ketanji Brown Jackson is the state of the union address?";
+  console.log(`Executing: ${input}`);
+  const result = await agent.call({ input });
+  console.log(`Got output ${result.output}`);
+  console.log(
+    `Got intermediate steps ${JSON.stringify(
+      result.intermediateSteps,
+      null,
+      2
+    )}`
+  );
+};
+```

langchain/package.json

@@ -1,6 +1,6 @@
 {
   "name": "langchain",
-  "version": "0.0.16",
+  "version": "0.0.17",
   "description": "Typescript bindings for langchain",
   "type": "module",
   "main": "./index.js",