A RAG agent that does math

In our third iteration of the agent we've combined the two previous agents, so we've defined both sumNumbers and a QueryEngineTool and created an array of two tools:

// define the query engine as a tool
const tools = [
  new QueryEngineTool({
    queryEngine: queryEngine,
    metadata: {
      name: "san_francisco_budget_tool",
      description: `This tool can answer detailed questions about the individual components of the budget of San Francisco in 2023-2024.`,
    },
  }),
  FunctionTool.from(sumNumbers, {
    name: "sumNumbers",
    description: "Use this function to sum two numbers",
    parameters: {
      type: "object",
      properties: {
        a: {
          type: "number",
          description: "First number to sum",
        },
        b: {
          type: "number",
          description: "Second number to sum",
        },
      },
      required: ["a", "b"],
    },
  }),
];

These tool descriptions are identical to the ones we previously defined. Now let's ask it 3 questions in a row:

let response = await agent.chat({
  message:
    "What's the budget of San Francisco for community health in 2023-24?",
});
console.log(response);

let response2 = await agent.chat({
  message:
    "What's the budget of San Francisco for public protection in 2023-24?",
});
console.log(response2);

let response3 = await agent.chat({
  message:
    "What's the combined budget of San Francisco for community health and public protection in 2023-24?",
});
console.log(response3);

We'll abbreviate the output, but here are the important things to spot:

{
  toolCall: {
    id: 'call_ZA1LPx03gO4ABre1r6XowLWq',
    name: 'san_francisco_budget_tool',
    input: { query: 'community health budget 2023-2024' }
  },
  toolResult: {
    tool: QueryEngineTool {
      queryEngine: [RetrieverQueryEngine],
      metadata: [Object]
    },
    input: { query: 'community health budget 2023-2024' },
    output: 'The proposed Fiscal Year (FY) 2023-24 budget for the Department of Public Health is $3.2 billion
  }
}

This is the first tool call, where it used the query engine to get the public health budget.

{
  toolCall: {
    id: 'call_oHu1KjEvA47ER6HYVfFIq9yp',
    name: 'san_francisco_budget_tool',
    input: { query: 'public protection budget 2023-2024' }
  },
  toolResult: {
    tool: QueryEngineTool {
      queryEngine: [RetrieverQueryEngine],
      metadata: [Object]
    },
    input: { query: 'public protection budget 2023-2024' },
    output: "The budget for Public Protection in San Francisco for Fiscal Year (FY) 2023-24 is $2,012.5 million."
  }
}

In the second tool call, it got the police budget also from the query engine.

{
  toolCall: {
    id: 'call_SzG4yGUnLbv1T7IyaLAOqg3t',
    name: 'sumNumbers',
    input: { a: 3200, b: 2012.5 }
  },
  toolResult: {
    tool: FunctionTool { _fn: [Function: sumNumbers], _metadata: [Object] },
    input: { a: 3200, b: 2012.5 },
    output: '5212.5',
    isError: false
  }
}

In the final tool call, it used the sumNumbers function to add the two budgets together. Perfect! This leads to the final answer:

{
    message: {
      content: 'The combined budget of San Francisco for community health and public protection in Fiscal Year (FY) 2023-24 is $5,212.5 million.',
      role: 'assistant',
      options: {}
    }
}

Great! Now let's improve accuracy by improving our parsing with LlamaParse.