用于子问题查询引擎的 Guidance¶

在本 Notebook 中，我们展示如何使用 guidance 来提高我们的子问题查询引擎的鲁棒性。

子问题查询引擎被设计为接受实现 BaseQuestionGenerator 接口的可替换问题生成器。
为了利用 guidance 的强大功能，我们实现了一个新的 GuidanceQuestionGenerator（由我们的 GuidancePydanticProgram 支持）

Guidance 问题生成器¶

与默认的 LLMQuestionGenerator 不同，guidance 保证我们将获得所需的结构化输出，并消除输出解析错误。

如果你在 colab 上打开此 Notebook，你可能需要安装 LlamaIndex 🦙。

In [ ]

%pip install llama-index-question-gen-guidance

%pip install llama-index-question-gen-guidance

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!pip install llama-index

!pip install llama-index

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from llama_index.question_gen.guidance import GuidanceQuestionGenerator
from guidance.llms import OpenAI as GuidanceOpenAI

from llama_index.question_gen.guidance import GuidanceQuestionGenerator from guidance.llms import OpenAI as GuidanceOpenAI

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question_gen = GuidanceQuestionGenerator.from_defaults(
    guidance_llm=GuidanceOpenAI("text-davinci-003"), verbose=False
)

question_gen = GuidanceQuestionGenerator.from_defaults( guidance_llm=GuidanceOpenAI("text-davinci-003"), verbose=False )

让我们来试试吧！

In [ ]

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

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

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tools = [
    ToolMetadata(
        name="lyft_10k",
        description="Provides information about Lyft financials for year 2021",
    ),
    ToolMetadata(
        name="uber_10k",
        description="Provides information about Uber financials for year 2021",
    ),
]

tools = [ ToolMetadata( name="lyft_10k", description="Provides information about Lyft financials for year 2021", ), ToolMetadata( name="uber_10k", description="Provides information about Uber financials for year 2021", ), ]

In [ ]

sub_questions = question_gen.generate(
    tools=tools,
    query=QueryBundle("Compare and contrast Uber and Lyft financial in 2021"),
)

sub_questions = question_gen.generate( tools=tools, query=QueryBundle("Compare and contrast Uber and Lyft financial in 2021"), )

In [ ]

sub_questions

sub_questions

Out[ ]

[SubQuestion(sub_question='What is the revenue of Uber', tool_name='uber_10k'),
 SubQuestion(sub_question='What is the EBITDA of Uber', tool_name='uber_10k'),
 SubQuestion(sub_question='What is the net income of Uber', tool_name='uber_10k'),
 SubQuestion(sub_question='What is the revenue of Lyft', tool_name='lyft_10k'),
 SubQuestion(sub_question='What is the EBITDA of Lyft', tool_name='lyft_10k'),
 SubQuestion(sub_question='What is the net income of Lyft', tool_name='lyft_10k')]

将 Guidance 问题生成器与子问题查询引擎一起使用¶

准备数据和基础查询引擎¶

In [ ]

from llama_index.core import SimpleDirectoryReader, VectorStoreIndex
from llama_index.core.response.pprint_utils import pprint_response

from llama_index.core.tools import QueryEngineTool, ToolMetadata
from llama_index.core.query_engine import SubQuestionQueryEngine

from llama_index.core import SimpleDirectoryReader, VectorStoreIndex from llama_index.core.response.pprint_utils import pprint_response from llama_index.core.tools import QueryEngineTool, ToolMetadata from llama_index.core.query_engine import SubQuestionQueryEngine

下载数据

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!mkdir -p 'data/10k/'
!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/10k/uber_2021.pdf' -O 'data/10k/uber_2021.pdf'
!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/10k/lyft_2021.pdf' -O 'data/10k/lyft_2021.pdf'

!mkdir -p 'data/10k/' !wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/10k/uber_2021.pdf' -O 'data/10k/uber_2021.pdf' !wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/10k/lyft_2021.pdf' -O 'data/10k/lyft_2021.pdf'

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lyft_docs = SimpleDirectoryReader(
    input_files=["./data/10k/lyft_2021.pdf"]
).load_data()
uber_docs = SimpleDirectoryReader(
    input_files=["./data/10k/uber_2021.pdf"]
).load_data()

lyft_docs = SimpleDirectoryReader( input_files=["./data/10k/lyft_2021.pdf"] ).load_data() uber_docs = SimpleDirectoryReader( input_files=["./data/10k/uber_2021.pdf"] ).load_data()

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lyft_index = VectorStoreIndex.from_documents(lyft_docs)
uber_index = VectorStoreIndex.from_documents(uber_docs)

lyft_index = VectorStoreIndex.from_documents(lyft_docs) uber_index = VectorStoreIndex.from_documents(uber_docs)

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lyft_engine = lyft_index.as_query_engine(similarity_top_k=3)
uber_engine = uber_index.as_query_engine(similarity_top_k=3)

lyft_engine = lyft_index.as_query_engine(similarity_top_k=3) uber_engine = uber_index.as_query_engine(similarity_top_k=3)

构建子问题查询引擎并运行一些查询！¶

In [ ]

query_engine_tools = [
    QueryEngineTool(
        query_engine=lyft_engine,
        metadata=ToolMetadata(
            name="lyft_10k",
            description=(
                "Provides information about Lyft financials for year 2021"
            ),
        ),
    ),
    QueryEngineTool(
        query_engine=uber_engine,
        metadata=ToolMetadata(
            name="uber_10k",
            description=(
                "Provides information about Uber financials for year 2021"
            ),
        ),
    ),
]

s_engine = SubQuestionQueryEngine.from_defaults(
    question_gen=question_gen,  # use guidance based question_gen defined above
    query_engine_tools=query_engine_tools,
)

query_engine_tools = [ QueryEngineTool( query_engine=lyft_engine, metadata=ToolMetadata( name="lyft_10k", description=( "Provides information about Lyft financials for year 2021" ), ), ), QueryEngineTool( query_engine=uber_engine, metadata=ToolMetadata( name="uber_10k", description=( "Provides information about Uber financials for year 2021" ), ), ), ] s_engine = SubQuestionQueryEngine.from_defaults( question_gen=question_gen, # 使用上面定义的基于 guidance 的 question_gen query_engine_tools=query_engine_tools, )

In [ ]

response = s_engine.query(
    "Compare and contrast the customer segments and geographies that grew the"
    " fastest"
)

response = s_engine.query( "Compare and contrast the customer segments and geographies that grew the" " fastest" )

Generated 4 sub questions.
[uber_10k] Q: What customer segments grew the fastest for Uber
[uber_10k] A: in 2021?

The customer segments that grew the fastest for Uber in 2021 were its Mobility Drivers, Couriers, Riders, and Eaters. These segments experienced growth due to the continued stay-at-home order demand related to COVID-19, as well as Uber's membership programs, such as Uber One, Uber Pass, Eats Pass, and Rides Pass. Additionally, Uber's marketplace-centric advertising helped to connect merchants and brands with its platform network, further driving growth.
[uber_10k] Q: What geographies grew the fastest for Uber
[uber_10k] A: 
Based on the context information, it appears that Uber experienced the most growth in large metropolitan areas, such as Chicago, Miami, New York City, Sao Paulo, and London. Additionally, Uber experienced growth in suburban and rural areas, as well as in countries such as Argentina, Germany, Italy, Japan, South Korea, and Spain.
[lyft_10k] Q: What customer segments grew the fastest for Lyft
[lyft_10k] A: 
The customer segments that grew the fastest for Lyft were ridesharing, light vehicles, and public transit. Ridesharing grew as Lyft was able to predict demand and proactively incentivize drivers to be available for rides in the right place at the right time. Light vehicles grew as users were looking for options that were more active, usually lower-priced, and often more efficient for short trips during heavy traffic. Public transit grew as Lyft integrated third-party public transit data into the Lyft App to offer users a robust view of transportation options around them.
[lyft_10k] Q: What geographies grew the fastest for Lyft
[lyft_10k] A: 
It is not possible to answer this question with the given context information.

In [ ]

print(response)

print(response)

The customer segments that grew the fastest for Uber in 2021 were its Mobility Drivers, Couriers, Riders, and Eaters. These segments experienced growth due to the continued stay-at-home order demand related to COVID-19, as well as Uber's membership programs, such as Uber One, Uber Pass, Eats Pass, and Rides Pass. Additionally, Uber's marketplace-centric advertising helped to connect merchants and brands with its platform network, further driving growth. Uber experienced the most growth in large metropolitan areas, such as Chicago, Miami, New York City, Sao Paulo, and London. Additionally, Uber experienced growth in suburban and rural areas, as well as in countries such as Argentina, Germany, Italy, Japan, South Korea, and Spain.

The customer segments that grew the fastest for Lyft were ridesharing, light vehicles, and public transit. Ridesharing grew as Lyft was able to predict demand and proactively incentivize drivers to be available for rides in the right place at the right time. Light vehicles grew as users were looking for options that were more active, usually lower-priced, and often more efficient for short trips during heavy traffic. Public transit grew as Lyft integrated third-party public transit data into the Lyft App to offer users a robust view of transportation options around them. It is not possible to answer the question of which geographies grew the fastest for Lyft with the given context information.

In summary, Uber and Lyft both experienced growth in customer segments related to their respective services, such as Mobility Drivers, Couriers, Riders, and Eaters for Uber, and ridesharing, light vehicles, and public transit for Lyft. Uber experienced the most growth in large metropolitan areas, as well as in suburban and rural areas, and in countries such as Argentina, Germany, Italy, Japan, South Korea, and Spain. It is not possible to answer the question of which geographies grew the fastest for Lyft with the given context information.