Sub Question Query Engine

In this tutorial, we showcase how to use a sub question query engine to tackle the problem of answering a complex query using multiple data sources.
It first breaks down the complex query into sub questions for each relevant data source, then gather all the intermediate reponses and synthesizes a final response.

Preparation

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

python

!pip install llama-index

python

import os

os.environ["OPENAI_API_KEY"] = "sk-..."

import nest_asyncio

nest_asyncio.apply()

python

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core.tools import QueryEngineTool, ToolMetadata
from llama_index.core.query_engine import SubQuestionQueryEngine
from llama_index.core.callbacks import CallbackManager, LlamaDebugHandler
from llama_index.core import Settings

python

# Using the LlamaDebugHandler to print the trace of the sub questions
# captured by the SUB_QUESTION callback event type
llama_debug = LlamaDebugHandler(print_trace_on_end=True)
callback_manager = CallbackManager([llama_debug])

Settings.callback_manager = callback_manager

Download Data

python

!mkdir -p 'data/paul_graham/'
!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'

python

# load data
pg_essay = SimpleDirectoryReader(input_dir="./data/paul_graham/").load_data()

# build index and query engine
vector_query_engine = VectorStoreIndex.from_documents(
    pg_essay,
    use_async=True,
).as_query_engine()

Setup sub question query engine

python

# setup base query engine as tool
query_engine_tools = [
    QueryEngineTool(
        query_engine=vector_query_engine,
        metadata=ToolMetadata(
            name="pg_essay",
            description="Paul Graham essay on What I Worked On",
        ),
    ),
]

query_engine = SubQuestionQueryEngine.from_defaults(
    query_engine_tools=query_engine_tools,
    use_async=True,
)

Run queries

python

response = query_engine.query(
    "How was Paul Grahams life different before, during, and after YC?"
)

python

print(response)

python

# iterate through sub_question items captured in SUB_QUESTION event
from llama_index.core.callbacks import CBEventType, EventPayload

for i, (start_event, end_event) in enumerate(
    llama_debug.get_event_pairs(CBEventType.SUB_QUESTION)
):
    qa_pair = end_event.payload[EventPayload.SUB_QUESTION]
    print("Sub Question " + str(i) + ": " + qa_pair.sub_q.sub_question.strip())
    print("Answer: " + qa_pair.answer.strip())
    print("====================================")