diff --git a/examples/src/chains/conversational_qa_built_in_memory.ts b/examples/src/chains/conversational_qa_built_in_memory.ts
index e28335afedd2..b174b4139b5a 100644
--- a/examples/src/chains/conversational_qa_built_in_memory.ts
+++ b/examples/src/chains/conversational_qa_built_in_memory.ts
@@ -10,151 +10,163 @@ import { PromptTemplate } from "langchain/prompts";
 import { RunnableSequence } from "langchain/schema/runnable";
 import { BaseMessage } from "langchain/schema";
 
-export const run = async () => {
-  const text = fs.readFileSync("state_of_the_union.txt", "utf8");
-
-  const textSplitter = new RecursiveCharacterTextSplitter({ chunkSize: 1000 });
-  const docs = await textSplitter.createDocuments([text]);
-
-  const vectorStore = await HNSWLib.fromDocuments(docs, new OpenAIEmbeddings());
-  const retriever = vectorStore.asRetriever();
-
-  const memory = new BufferMemory({
-    memoryKey: "chatHistory",
-    inputKey: "question", // The key for the input to the chain
-    outputKey: "text", // The key for the final conversational output of the chain
-    returnMessages: true, // If using with a chat model (e.g. gpt-3.5 or gpt-4)
-  });
-
-  const serializeDocs = (docs: Array<Document>): string =>
-    docs.map((doc) => doc.pageContent).join("\n");
-
-  const serializeChatHistory = (chatHistory: Array<BaseMessage>): string =>
-    chatHistory
-      .map((chatMessage) => {
-        if (chatMessage._getType() === "human") {
-          return `Human: ${chatMessage.content}`;
-        } else if (chatMessage._getType() === "ai") {
-          return `Assistant: ${chatMessage.content}`;
-        } else {
-          return `${chatMessage.content}`;
-        }
-      })
-      .join("\n");
-
-  /**
-   * Create two prompt templates, one for answering questions, and one for
-   * generating questions.
-   */
-  const questionPrompt = PromptTemplate.fromTemplate(
-    `Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.\n\nCONTEXT: {context}\n\nCHAT HISTORY: {chatHistory}\n\nQUESTION: {question}\n\nHelpful Answer:`
-  );
-  const questionGeneratorTemplate = PromptTemplate.fromTemplate(
-    `Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.\n\nCHAT HISTORY: {chatHistory}\n\nFOLLOWUP QUESTION: {question}\n\nStandalone question:`
-  );
-
-  // Initialize fast and slow LLMs, along with chains for each
-  const fasterModel = new ChatOpenAI({
-    modelName: "gpt-3.5-turbo",
-  });
-  const fasterChain = new LLMChain({
-    llm: fasterModel,
-    prompt: questionGeneratorTemplate,
-  });
-
-  const slowerModel = new ChatOpenAI({
-    modelName: "gpt-4",
-  });
-  const slowerChain = new LLMChain({
-    llm: slowerModel,
-    prompt: questionPrompt,
-  });
-
-  const performQuestionAnswering = async (input: {
-    question: string;
-    chatHistory: Array<BaseMessage> | null;
-    context: Array<Document>;
-  }): Promise<{ result: string; sourceDocuments: Array<Document> }> => {
-    let newQuestion = input.question;
-    // Serialize context and chat history into strings
-    const serializedDocs = serializeDocs(input.context);
-    const chatHistoryString = input.chatHistory
-      ? serializeChatHistory(input.chatHistory)
-      : null;
-
-    if (chatHistoryString) {
-      // Call the faster chain to generate a new question
-      const { text } = await fasterChain.invoke({
-        chatHistory: chatHistoryString,
-        context: serializedDocs,
-        question: input.question,
-      });
-
-      newQuestion = text;
-    }
-
-    const response = await slowerChain.invoke({
-      chatHistory: chatHistoryString ?? "",
+const text = fs.readFileSync("state_of_the_union.txt", "utf8");
+
+const textSplitter = new RecursiveCharacterTextSplitter({ chunkSize: 1000 });
+const docs = await textSplitter.createDocuments([text]);
+
+const vectorStore = await HNSWLib.fromDocuments(docs, new OpenAIEmbeddings());
+const retriever = vectorStore.asRetriever();
+
+const memory = new BufferMemory({
+  memoryKey: "chatHistory",
+  inputKey: "question", // The key for the input to the chain
+  outputKey: "text", // The key for the final conversational output of the chain
+  returnMessages: true, // If using with a chat model (e.g. gpt-3.5 or gpt-4)
+});
+
+const serializeDocs = (docs: Array<Document>): string =>
+  docs.map((doc) => doc.pageContent).join("\n");
+
+const serializeChatHistory = (chatHistory: Array<BaseMessage>): string =>
+  chatHistory
+    .map((chatMessage) => {
+      if (chatMessage._getType() === "human") {
+        return `Human: ${chatMessage.content}`;
+      } else if (chatMessage._getType() === "ai") {
+        return `Assistant: ${chatMessage.content}`;
+      } else {
+        return `${chatMessage.content}`;
+      }
+    })
+    .join("\n");
+
+/**
+ * Create two prompt templates, one for answering questions, and one for
+ * generating questions.
+ */
+const questionPrompt = PromptTemplate.fromTemplate(
+  `Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.
+----------    
+CONTEXT: {context}
+----------
+CHAT HISTORY: {chatHistory}
+----------
+QUESTION: {question}
+----------
+Helpful Answer:`
+);
+const questionGeneratorTemplate = PromptTemplate.fromTemplate(
+  `Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.
+----------
+CHAT HISTORY: {chatHistory}
+----------
+FOLLOWUP QUESTION: {question}
+----------
+Standalone question:`
+);
+
+// Initialize fast and slow LLMs, along with chains for each
+const fasterModel = new ChatOpenAI({
+  modelName: "gpt-3.5-turbo",
+});
+const fasterChain = new LLMChain({
+  llm: fasterModel,
+  prompt: questionGeneratorTemplate,
+});
+
+const slowerModel = new ChatOpenAI({
+  modelName: "gpt-4",
+});
+const slowerChain = new LLMChain({
+  llm: slowerModel,
+  prompt: questionPrompt,
+});
+
+const performQuestionAnswering = async (input: {
+  question: string;
+  chatHistory: Array<BaseMessage> | null;
+  context: Array<Document>;
+}): Promise<{ result: string; sourceDocuments: Array<Document> }> => {
+  let newQuestion = input.question;
+  // Serialize context and chat history into strings
+  const serializedDocs = serializeDocs(input.context);
+  const chatHistoryString = input.chatHistory
+    ? serializeChatHistory(input.chatHistory)
+    : null;
+
+  if (chatHistoryString) {
+    // Call the faster chain to generate a new question
+    const { text } = await fasterChain.invoke({
+      chatHistory: chatHistoryString,
       context: serializedDocs,
-      question: newQuestion,
+      question: input.question,
     });
 
-    // Save the chat history to memory
-    await memory.saveContext(
-      {
-        question: input.question,
-      },
-      {
-        text: response.text,
-      }
-    );
+    newQuestion = text;
+  }
 
-    return {
-      result: response.text,
-      sourceDocuments: input.context,
-    };
-  };
+  const response = await slowerChain.invoke({
+    chatHistory: chatHistoryString ?? "",
+    context: serializedDocs,
+    question: newQuestion,
+  });
 
-  const chain = RunnableSequence.from([
+  // Save the chat history to memory
+  await memory.saveContext(
     {
-      // Pipe the question through unchanged
-      question: (input: { question: string }) => input.question,
-      // Fetch the chat history, and return the history or null if not present
-      chatHistory: async () => {
-        const savedMemory = await memory.loadMemoryVariables({});
-        const hasHistory = savedMemory.chatHistory.length > 0;
-        return hasHistory ? savedMemory.chatHistory : null;
-      },
-      // Fetch relevant context based on the question
-      context: async (input: { question: string }) =>
-        retriever.getRelevantDocuments(input.question),
+      question: input.question,
     },
-    performQuestionAnswering,
-  ]);
+    {
+      text: response.text,
+    }
+  );
 
-  const resultOne = await chain.invoke({
-    question: "What did the president say about Justice Breyer?",
-  });
-  console.log({ resultOne });
-  /**
-   * {
-   *   resultOne: {
-   *     result: "The president thanked Justice Breyer for his service and described him as an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court.",
-   *     sourceDocuments: [...]
-   *   }
-   * }
-   */
-
-  const resultTwo = await chain.invoke({
-    question: "Was he nice?",
-  });
-  console.log({ resultTwo });
-  /**
-   * {
-   *   resultTwo: {
-   *     result: "Yes, the president's description of Justice Breyer was positive."
-   *     sourceDocuments: [...]
-   *   }
-   * }
-   */
+  return {
+    result: response.text,
+    sourceDocuments: input.context,
+  };
 };
+
+const chain = RunnableSequence.from([
+  {
+    // Pipe the question through unchanged
+    question: (input: { question: string }) => input.question,
+    // Fetch the chat history, and return the history or null if not present
+    chatHistory: async () => {
+      const savedMemory = await memory.loadMemoryVariables({});
+      const hasHistory = savedMemory.chatHistory.length > 0;
+      return hasHistory ? savedMemory.chatHistory : null;
+    },
+    // Fetch relevant context based on the question
+    context: async (input: { question: string }) =>
+      retriever.getRelevantDocuments(input.question),
+  },
+  performQuestionAnswering,
+]);
+
+const resultOne = await chain.invoke({
+  question: "What did the president say about Justice Breyer?",
+});
+console.log({ resultOne });
+/**
+ * {
+ *   resultOne: {
+ *     result: "The president thanked Justice Breyer for his service and described him as an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court.",
+ *     sourceDocuments: [...]
+ *   }
+ * }
+ */
+
+const resultTwo = await chain.invoke({
+  question: "Was he nice?",
+});
+console.log({ resultTwo });
+/**
+ * {
+ *   resultTwo: {
+ *     result: "Yes, the president's description of Justice Breyer was positive."
+ *     sourceDocuments: [...]
+ *   }
+ * }
+ */