我们有一个多步查询引擎,能够将复杂的查询分解为顺序子问题。本指南将指导您如何设置它!
如果您在colab上打开这个笔记本,您可能需要安装LlamaIndex 🦙。
%pip install llama-index-llms-openai
!pip install llama-index
!mkdir -p 'data/paul_graham/'
!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.llms.openai import OpenAI
from IPython.display import Markdown, display
# LLM (gpt-3.5)
gpt35 = OpenAI(temperature=0, model="gpt-3.5-turbo")
# LLM (gpt-4)
gpt4 = OpenAI(temperature=0, model="gpt-4")
# 加载文档
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
index = VectorStoreIndex.from_documents(documents)
from llama_index.core.indices.query.query_transform.base import StepDecomposeQueryTransform
# gpt-4
step_decompose_transform = StepDecomposeQueryTransform(llm=gpt4, verbose=True)
# gpt-3
step_decompose_transform_gpt3 = StepDecomposeQueryTransform(
llm=gpt35, verbose=True
)
index_summary = "Used to answer questions about the author"
# 将日志级别设置为DEBUG,以获得更详细的输出
from llama_index.core.query_engine import MultiStepQueryEngine
query_engine = index.as_query_engine(llm=gpt4)
query_engine = MultiStepQueryEngine(
query_engine=query_engine,
query_transform=step_decompose_transform,
index_summary=index_summary,
)
response_gpt4 = query_engine.query(
"作者启动的加速器计划的第一批参与者是谁?",
)
> Current query: Who was in the first batch of the accelerator program the author started? > New query: Who is the author of the accelerator program? > Current query: Who was in the first batch of the accelerator program the author started? > New query: Who was in the first batch of the accelerator program started by Paul Graham? > Current query: Who was in the first batch of the accelerator program the author started? > New query: None
display(Markdown(f"<b>{response_gpt4}</b>"))
In the first batch of the accelerator program started by the author, the participants included the founders of Reddit, Justin Kan and Emmett Shear who later founded Twitch, Aaron Swartz who had helped write the RSS spec and later became a martyr for open access, and Sam Altman who later became the second president of YC.
sub_qa = response_gpt4.metadata["sub_qa"]
tuples = [(t[0], t[1].response) for t in sub_qa]
print(tuples)
[('Who is the author of the accelerator program?', 'The author of the accelerator program is Paul Graham.'), ('Who was in the first batch of the accelerator program started by Paul Graham?', 'The first batch of the accelerator program started by Paul Graham included the founders of Reddit, Justin Kan and Emmett Shear who later founded Twitch, Aaron Swartz who had helped write the RSS spec and later became a martyr for open access, and Sam Altman who later became the second president of YC.')]
response_gpt4 = query_engine.query(
"In which city did the author found his first company, Viaweb?",
)
> Current query: In which city did the author found his first company, Viaweb? > New query: Who is the author who founded Viaweb? > Current query: In which city did the author found his first company, Viaweb? > New query: In which city did Paul Graham found his first company, Viaweb? > Current query: In which city did the author found his first company, Viaweb? > New query: None
print(response_gpt4)
The author founded his first company, Viaweb, in Cambridge.
query_engine = index.as_query_engine(llm=gpt35)
query_engine = MultiStepQueryEngine(
query_engine=query_engine,
query_transform=step_decompose_transform_gpt3,
index_summary=index_summary,
)
response_gpt3 = query_engine.query(
"In which city did the author found his first company, Viaweb?",
)
> Current query: In which city did the author found his first company, Viaweb? > New query: None
print(response_gpt3)
Empty Response