GraphAcademy

Build a Neo4j-backed Chatbot with TypeScript

Getting Started

Chains

Conversation History

Retrieval Chain

Cypher Retrieval Tool

Creating an Agent

Using the Agent

Now that the agent is ready, you can hook it into the front end.

Inside modules/agent/index.ts, you will find a call() function. This function is called by the route handler when the chat form in the UI is submitted.

typescript
The call() Function
export async function call(input: string, sessionId: string): Promise<string> {
  // TODO: Replace this code with an agent
  await sleep(2000);
  return input;
}

As you can see, the function waits a second before returning the message to the user.

Create a new instance of your agent and return the results of the .invoke() method to complete this challenge.

Calling the agent

Inside the call() function, start by creating the objects that the initAgent() function expects.

The agent requires an LLM.

typescript
Create a model instance
const llm = new ChatOpenAI({
  openAIApiKey: process.env.OPENAI_API_KEY,
  // Note: only provide a baseURL when using the GraphAcademy Proxy
  configuration: {
    baseURL: process.env.OPENAI_API_BASE,
  },
});

The retrieval tool requires an embedding model.

typescript
Embeddings
const embeddings = new OpenAIEmbeddings({
  openAIApiKey: process.env.OPENAI_API_KEY,
  configuration: {
    baseURL: process.env.OPENAI_API_BASE,
  },
});

To interact with the graph, the agent should use the singleton instance created in the Initializing Neo4j lesson.

typescript
Embeddings
// Get Graph Singleton
const graph = await initGraph();

Use the initAgent() function to create a new agent instance. Use the .invoke() method to send the input argument into the agent, and pass the sessionId as a configurable.

As the function resolves to a string, you can return this value.

typescript
Embeddings
  const agent = await initAgent(llm, embeddings, graph);
  const res = await agent.invoke({ input }, { configurable: { sessionId } });

  return res;

Completed function

If you have followed the instructions correctly, your code should resemble the following:

typescript
export async function call(input: string, sessionId: string): Promise<string> {
  const llm = new ChatOpenAI({
    openAIApiKey: process.env.OPENAI_API_KEY,
    // Note: only provide a baseURL when using the GraphAcademy Proxy
    configuration: {
      baseURL: process.env.OPENAI_API_BASE,
    },
  });
  const embeddings = new OpenAIEmbeddings({
    openAIApiKey: process.env.OPENAI_API_KEY,
    configuration: {
      baseURL: process.env.OPENAI_API_BASE,
    },
  });
  // Get Graph Singleton
  const graph = await initGraph();

  const agent = await initAgent(llm, embeddings, graph);
  const res = await agent.invoke({ input }, { configurable: { sessionId } });

  return res;
}

Testing your changes

If you run the npm run dev command to start the application in development mode you should see the agent thinking and responding to questions.

npm run dev

Try asking the chatbot "who acted in the movie "Neo4j - Into the Graph"?

It works!

Once you’re happy with the response from the chatbot, hit the button below to mark the lesson as completed.

Summary

Congratulations! You should now have a working chatbot.

However, you may have noticed that the agent will respond to any question, no matter how obscene.

In the next optional challenge, you will learn how to modify the agent prompt.