---
title: Sub Question Query Engine powered by NVIDIA NIMs
 | LlamaIndex OSS Documentation
---

A Sub Question Query Engine takes a single, complex question and breaks it into multiple sub-questions, each of which can be answered by a different tool. We’ll use NVIDIA NIMs to power our sub-question generation and answer retrieval.

### NVIDIA NIMs

NIM supports models across domains like chat, embedding, and re-ranking models from the community as well as NVIDIA. These models are optimized by NVIDIA to deliver the best performance on NVIDIA accelerated infrastructure and deployed as a NIM, an easy-to-use, prebuilt containers that deploy anywhere using a single command on NVIDIA accelerated infrastructure.

NVIDIA hosted deployments of NIMs are available to test on the [NVIDIA API catalog](https://build.nvidia.com/). After testing, NIMs can be exported from NVIDIA’s API catalog using the NVIDIA AI Enterprise license and run on-premises or in the cloud, giving enterprises ownership and full control of their IP and AI application.

NIMs are packaged as container images on a per model basis and are distributed as NGC container images through the NVIDIA NGC Catalog. At their core, NIMs provide easy, consistent, and familiar APIs for running inference on an AI model.

## Setup

Import our dependencies and set up our NVIDIA API key from the API catalog, <https://build.nvidia.com> for the two models we’ll use hosted on the catalog (embedding and re-ranking models).

**To get started:**

1. Create a free account with [NVIDIA](https://build.nvidia.com/), which hosts NVIDIA AI Foundation models.

2. Click on your model of choice.

3. Under Input select the Python tab, and click `Get API Key`. Then click `Generate Key`.

4. Copy and save the generated key as NVIDIA\_API\_KEY. From there, you should have access to the endpoints.

**Install our dependencies:**

- LlamaIndex core for most things
- NVIDIA NIM LLM and embeddings for LLM actions
- `llama-index-readers-file` to power the PDF reader in `SimpleDirectoryReader`

```
!pip install llama-index-core llama-index-llms-nvidia llama-index-embeddings-nvidia llama-index-readers-file llama-index-utils-workflow
```

Bring in our dependencies as imports:

```
import os, json
from llama_index.core import (
    SimpleDirectoryReader,
    VectorStoreIndex,
    StorageContext,
    load_index_from_storage,
    Settings,
)
from llama_index.core.tools import QueryEngineTool, ToolMetadata
from llama_index.core.workflow import (
    step,
    Context,
    Workflow,
    Event,
    StartEvent,
    StopEvent,
)
from llama_index.core.agent.workflow import ReActAgent
from llama_index.llms.nvidia import NVIDIA
from llama_index.embeddings.nvidia import NVIDIAEmbedding
from llama_index.utils.workflow import draw_all_possible_flows
```

# Define the Sub Question Query Engine as a Workflow

- Our StartEvent goes to `query()`, which takes care of several things:

  - Accepts and stores the original query
  - Stores the LLM to handle the queries
  - Stores the list of tools to enable sub-questions
  - Passes the original question to the LLM, asking it to split up the question into sub-questions
  - Fires off a `QueryEvent` for every sub-question generated

- QueryEvents go to `sub_question()`, which instantiates a new ReAct agent with the full list of tools available and lets it select which one to use.

  - This is slightly better than the actual SQQE built-in to LlamaIndex, which cannot use multiple tools
  - Each QueryEvent generates an `AnswerEvent`

- AnswerEvents go to `combine_answers()`.

  - This uses `self.collect_events()` to wait for every QueryEvent to return an answer.
  - All the answers are then combined into a final prompt for the LLM to consolidate them into a single response
  - A StopEvent is generated to return the final result

```
class QueryEvent(Event):
    question: str




class AnswerEvent(Event):
    question: str
    answer: str




class SubQuestionQueryEngine(Workflow):
    @step
    async def query(self, ctx: Context, ev: StartEvent) -> QueryEvent:
        if hasattr(ev, "query"):
            await ctx.store.set("original_query", ev.query)
            print(f"Query is {await ctx.store.get('original_query')}")


        if hasattr(ev, "llm"):
            await ctx.store.set("llm", ev.llm)


        if hasattr(ev, "tools"):
            await ctx.store.set("tools", ev.tools)


        response = (await ctx.store.get("llm")).complete(
            f"""
            Given a user question, and a list of tools, output a list of
            relevant sub-questions, such that the answers to all the
            sub-questions put together will answer the question. Respond
            in pure JSON without any markdown, like this:
            {{
                "sub_questions": [
                    "What is the population of San Francisco?",
                    "What is the budget of San Francisco?",
                    "What is the GDP of San Francisco?"
                ]
            }}
            Here is the user question: {await ctx.store.get('original_query')}


            And here is the list of tools: {await ctx.store.get('tools')}
            """
        )


        print(f"Sub-questions are {response}")


        response_obj = json.loads(str(response))
        sub_questions = response_obj["sub_questions"]


        await ctx.store.set("sub_question_count", len(sub_questions))


        for question in sub_questions:
            self.send_event(QueryEvent(question=question))


        return None


    @step
    async def sub_question(self, ctx: Context, ev: QueryEvent) -> AnswerEvent:
        print(f"Sub-question is {ev.question}")


        agent = ReActAgent(
            tools=await ctx.store.get("tools"),
            llm=await ctx.store.get("llm"),
        )
        response = await agent.run(ev.question)


        return AnswerEvent(question=ev.question, answer=str(response))


    @step
    async def combine_answers(
        self, ctx: Context, ev: AnswerEvent
    ) -> StopEvent | None:
        ready = ctx.collect_events(
            ev, [AnswerEvent] * await ctx.store.get("sub_question_count")
        )
        if ready is None:
            return None


        answers = "\n\n".join(
            [
                f"Question: {event.question}: \n Answer: {event.answer}"
                for event in ready
            ]
        )


        prompt = f"""
            You are given an overall question that has been split into sub-questions,
            each of which has been answered. Combine the answers to all the sub-questions
            into a single answer to the original question.


            Original question: {await ctx.store.get('original_query')}


            Sub-questions and answers:
            {answers}
        """


        print(f"Final prompt is {prompt}")


        response = (await ctx.store.get("llm")).complete(prompt)


        print("Final response is", response)


        return StopEvent(result=str(response))
```

```
draw_all_possible_flows(
    SubQuestionQueryEngine, filename="sub_question_query_engine.html"
)
```

Visualizing this flow looks pretty linear, since it doesn’t capture that `query()` can generate multiple parallel `QueryEvents` which get collected into `combine_answers`.

![Screenshot 2024-08-07 at 3.31.55 PM.png](data:image/png;base64,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)

# Download data to demo

```
!mkdir -p "./data/sf_budgets/"
!wget "https://www.dropbox.com/scl/fi/xt3squt47djba0j7emmjb/2016-CSF_Budget_Book_2016_FINAL_WEB_with-cover-page.pdf?rlkey=xs064cjs8cb4wma6t5pw2u2bl&dl=0" -O "./data/sf_budgets/2016 - CSF_Budget_Book_2016_FINAL_WEB_with-cover-page.pdf"
!wget "https://www.dropbox.com/scl/fi/jvw59g5nscu1m7f96tjre/2017-Proposed-Budget-FY2017-18-FY2018-19_1.pdf?rlkey=v988oigs2whtcy87ti9wti6od&dl=0" -O "./data/sf_budgets/2017 - 2017-Proposed-Budget-FY2017-18-FY2018-19_1.pdf"
!wget "https://www.dropbox.com/scl/fi/izknlwmbs7ia0lbn7zzyx/2018-o0181-18.pdf?rlkey=p5nv2ehtp7272ege3m9diqhei&dl=0" -O "./data/sf_budgets/2018 - 2018-o0181-18.pdf"
!wget "https://www.dropbox.com/scl/fi/1rstqm9rh5u5fr0tcjnxj/2019-Proposed-Budget-FY2019-20-FY2020-21.pdf?rlkey=3s2ivfx7z9bev1r840dlpbcgg&dl=0" -O "./data/sf_budgets/2019 - 2019-Proposed-Budget-FY2019-20-FY2020-21.pdf"
!wget "https://www.dropbox.com/scl/fi/7teuwxrjdyvgw0n8jjvk0/2021-AAO-FY20-21-FY21-22-09-11-2020-FINAL.pdf?rlkey=6br3wzxwj5fv1f1l8e69nbmhk&dl=0" -O "./data/sf_budgets/2021 - 2021-AAO-FY20-21-FY21-22-09-11-2020-FINAL.pdf"
!wget "https://www.dropbox.com/scl/fi/zhgqch4n6xbv9skgcknij/2022-AAO-FY2021-22-FY2022-23-FINAL-20210730.pdf?rlkey=h78t65dfaz3mqbpbhl1u9e309&dl=0" -O "./data/sf_budgets/2022 - 2022-AAO-FY2021-22-FY2022-23-FINAL-20210730.pdf"
!wget "https://www.dropbox.com/scl/fi/vip161t63s56vd94neqlt/2023-CSF_Proposed_Budget_Book_June_2023_Master_Web.pdf?rlkey=hemoce3w1jsuf6s2bz87g549i&dl=0" -O "./data/sf_budgets/2023 - 2023-CSF_Proposed_Budget_Book_June_2023_Master_Web.pdf"
```

# Load data and run the workflow

Just like using the built-in Sub-Question Query Engine, we create our query tools and instantiate an LLM and pass them in.

Each tool is its own query engine based on a single (very lengthy) San Francisco budget document, each of which is 300+ pages. To save time on repeated runs, we persist our generated indexes to disk.

```
import getpass


if os.environ.get("NVIDIA_API_KEY", "").startswith("nvapi-"):
    print("Valid NVIDIA_API_KEY already in environment. Delete to reset")
else:
    nvapi_key = getpass.getpass("NVAPI Key (starts with nvapi-): ")
    assert nvapi_key.startswith(
        "nvapi-"
    ), f"{nvapi_key[:5]}... is not a valid key"
    os.environ["NVIDIA_API_KEY"] = nvapi_key


folder = "./data/sf_budgets/"
files = os.listdir(folder)


Settings.embed_model = NVIDIAEmbedding(
    model="nvidia/nv-embedqa-e5-v5", truncate="END"
)
Settings.llm = NVIDIA()


query_engine_tools = []
for file in files:
    year = file.split(" - ")[0]
    index_persist_path = f"./storage/budget-{year}/"


    if os.path.exists(index_persist_path):
        storage_context = StorageContext.from_defaults(
            persist_dir=index_persist_path
        )
        index = load_index_from_storage(storage_context)
    else:
        documents = SimpleDirectoryReader(
            input_files=[folder + file]
        ).load_data()
        index = VectorStoreIndex.from_documents(documents)
        index.storage_context.persist(index_persist_path)


    engine = index.as_query_engine()
    query_engine_tools.append(
        QueryEngineTool(
            query_engine=engine,
            metadata=ToolMetadata(
                name=f"budget_{year}",
                description=f"You can ask this tool natural-language questions about San Francisco's budget in {year}",
            ),
        )
    )
```

```
engine = SubQuestionQueryEngine(timeout=120, verbose=True)
result = await engine.run(
    llm=Settings.llm,
    tools=query_engine_tools,
    query="How has the total amount of San Francisco's budget changed from 2016 to 2023?",
)


print(result)
```

Our debug output is lengthy! You can see the sub-questions being generated and then `sub_question()` being repeatedly invoked, each time generating a brief log of ReAct agent thoughts and actions to answer each smaller question.

You can see `combine_answers` running multiple times; these were triggered by each `AnswerEvent` but before all 8 `AnswerEvents` were collected. On its final run it generates a full prompt, combines the answers and returns the result.