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OpenAI function calling for Sub-Question Query Engine

docs/examples/output_parsing/openai_sub_question.ipynb

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OpenAI function calling for Sub-Question Query Engine

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 🦙.

python
%pip install llama-index-question-gen-openai
python
!pip install llama-index
python
from llama_index.question_gen.openai import OpenAIQuestionGenerator
python
question_gen = OpenAIQuestionGenerator.from_defaults()

Let's test it out!

python
from llama_index.core.tools import ToolMetadata
from llama_index.core import QueryBundle
python
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"
        ),
    ),
]
python
sub_questions = question_gen.generate(
    tools=tools,
    query=QueryBundle(
        "Compare the fastest growing sectors for Uber in the first two"
        " quarters of 2022"
    ),
)
python
sub_questions