---
title: OpenAI function calling for Sub-Question Query Engine
 | Developer Documentation
---

In this notebook, we showcase how to use OpenAI function calling to improve the robustness of our sub-question query engine.

The sub-question query engine is designed to accept swappable question generators that implement the `BaseQuestionGenerator` interface.\
To leverage the power of openai function calling API, we implemented a new `OpenAIQuestionGenerator` (powered by our `OpenAIPydanticProgram`)

## OpenAI Question Generator

Unlike the default `LLMQuestionGenerator` that supports generic LLMs via the completion API, `OpenAIQuestionGenerator` only works with the latest OpenAI models that supports the function calling API.

The benefit is that these models are fine-tuned to output JSON objects, so we can worry less about output parsing issues.

If you’re opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.

```
%pip install llama-index-question-gen-openai
```

```
!pip install llama-index
```

```
from llama_index.question_gen.openai import OpenAIQuestionGenerator
```

```
question_gen = OpenAIQuestionGenerator.from_defaults()
```

Let’s test it out!

```
from llama_index.core.tools import ToolMetadata
from llama_index.core import QueryBundle
```

```
tools = [
    ToolMetadata(
        name="march_22",
        description=(
            "Provides information about Uber quarterly financials ending March"
            " 2022"
        ),
    ),
    ToolMetadata(
        name="june_22",
        description=(
            "Provides information about Uber quarterly financials ending June"
            " 2022"
        ),
    ),
    ToolMetadata(
        name="sept_22",
        description=(
            "Provides information about Uber quarterly financials ending"
            " September 2022"
        ),
    ),
    ToolMetadata(
        name="sept_21",
        description=(
            "Provides information about Uber quarterly financials ending"
            " September 2022"
        ),
    ),
    ToolMetadata(
        name="june_21",
        description=(
            "Provides information about Uber quarterly financials ending June"
            " 2022"
        ),
    ),
    ToolMetadata(
        name="march_21",
        description=(
            "Provides information about Uber quarterly financials ending March"
            " 2022"
        ),
    ),
]
```

```
sub_questions = question_gen.generate(
    tools=tools,
    query=QueryBundle(
        "Compare the fastest growing sectors for Uber in the first two"
        " quarters of 2022"
    ),
)
```

```
sub_questions
```

```
[SubQuestion(sub_question='What were the fastest growing sectors for Uber in March 2022?', tool_name='march_22'),
 SubQuestion(sub_question='What were the fastest growing sectors for Uber in June 2022?', tool_name='june_22')]
```