💬🤖 如何构建一个聊天机器人¶
LlamaIndex充当您的数据和语言学习模型(LLMs)之间的桥梁,提供了一个工具包,使您能够为各种任务(如问答和摘要)建立围绕您的数据的查询接口。
在本教程中,我们将带您逐步构建一个使用Data Agent构建的上下文增强型聊天机器人。这个代理由LLMs提供支持,能够智能地在您的数据上执行任务。最终结果是一个聊天机器人代理,配备了LlamaIndex提供的强大的数据接口工具,用于回答关于您的数据的查询。
注意:本教程是在之前创建一个关于SEC 10-K申报的查询接口的基础上进行的 - 在这里查看。
上下文¶
在本指南中,我们将构建一个“10-K聊天机器人”,使用来自Dropbox的原始UBER 10-K HTML申报。用户可以与聊天机器人交互,询问与10-K申报相关的问题。
准备工作¶
%pip install llama-index-readers-file
%pip install llama-index-embeddings-openai
%pip install llama-index-agent-openai
%pip install llama-index-llms-openai
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
import nest_asyncio
nest_asyncio.apply()
# 设置文本换行
from IPython.display import HTML,display
def set_css():
display(
HTML(
"""
<style>
pre {
white-space: pre-wrap;
}
</style>
"""
)
)
get_ipython().events.register("pre_run_cell", set_css)
导入数据¶
首先让我们下载2019年至2022年的原始10-k文件。
# 注意:代码示例假定您正在Jupyter笔记本中操作。
# 下载文件
!mkdir data
!wget "https://www.dropbox.com/s/948jr9cfs7fgj99/UBER.zip?dl=1" -O data/UBER.zip
!unzip data/UBER.zip -d data
为了将HTML文件解析为格式化文本,我们使用Unstructured库。多亏了LlamaHub,我们可以直接集成Unstructured,从而将任何文本转换为LlamaIndex可以接受的文档格式。
首先,我们安装必要的软件包:
然后我们可以使用UnstructuredReader
来将HTML文件解析为Document
对象的列表。
from llama_index.readers.file import UnstructuredReader
from pathlib import Path
years = [2022, 2021, 2020, 2019]
loader = UnstructuredReader()
doc_set = {}
all_docs = []
for year in years:
year_docs = loader.load_data(
file=Path(f"./data/UBER/UBER_{year}.html"), split_documents=False
)
# 将年份元数据插入到每个年份中
for d in year_docs:
d.metadata = {"year": year}
doc_set[year] = year_docs
all_docs.extend(year_docs)
# 初始化简单的向量索引
# 注意:如果索引已经加载,请不要运行此单元格!
from llama_index.core import VectorStoreIndex, StorageContext
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.llms.openai import OpenAI
from llama_index.core import Settings
Settings.chunk_size = 512
Settings.chunk_overlap = 64
Settings.llm = OpenAI(model="gpt-3.5-turbo")
Settings.embed_model = OpenAIEmbedding(model="text-embedding-3-small")
index_set = {}
for year in years:
storage_context = StorageContext.from_defaults()
cur_index = VectorStoreIndex.from_documents(
doc_set[year],
storage_context=storage_context,
)
index_set[year] = cur_index
storage_context.persist(persist_dir=f"./storage/{year}")
要从磁盘加载索引,请执行以下操作:
# 从磁盘加载索引
from llama_index.core import load_index_from_storage
index_set = {}
for year in years:
storage_context = StorageContext.from_defaults(
persist_dir=f"./storage/{year}"
)
cur_index = load_index_from_storage(
storage_context,
)
index_set[year] = cur_index
from llama_index.core.tools import QueryEngineTool, ToolMetadata
individual_query_engine_tools = [
QueryEngineTool(
query_engine=index_set[year].as_query_engine(),
metadata=ToolMetadata(
name=f"vector_index_{year}",
description=(
"useful for when you want to answer queries about the"
f" {year} SEC 10-K for Uber"
),
),
)
for year in years
]
现在我们可以创建子问题查询引擎,这将允许我们在10-K申报中综合回答。我们传入上面定义的individual_query_engine_tools
。
from llama_index.core.query_engine import SubQuestionQueryEngine
query_engine = SubQuestionQueryEngine.from_defaults(
query_engine_tools=individual_query_engine_tools,
)
设置聊天机器人代理¶
我们使用LlamaIndex数据代理来设置外部聊天机器人代理,该代理可以访问一组工具。具体来说,我们将使用一个OpenAIAgent,利用OpenAI API的函数调用。我们希望为每个索引(对应于给定年份)使用我们之前定义的单独工具,以及上面定义的子问题查询引擎的工具。
首先,我们为子问题查询引擎定义一个QueryEngineTool
:
query_engine_tool = QueryEngineTool(
query_engine=query_engine,
metadata=ToolMetadata(
name="sub_question_query_engine",
description=(
"useful for when you want to answer queries that require analyzing"
" multiple SEC 10-K documents for Uber"
),
),
)
然后,我们将上面定义的工具组合成一个代理程序的工具列表:
tools = individual_query_engine_tools + [query_engine_tool]
最后,我们调用 OpenAIAgent.from_tools
来创建代理,传入我们上面定义的工具列表。
from llama_index.agent.openai import OpenAIAgent
agent = OpenAIAgent.from_tools(tools, verbose=True)
response = agent.chat("hi, i am bob")
print(str(response))
Added user message to memory: hi, i am bob Hello Bob! How can I assist you today?
如果我们用一个关于特定年份的10-k的查询来测试它,代理将使用相关的向量索引工具。
response = agent.chat(
"What were some of the biggest risk factors in 2020 for Uber?"
)
print(str(response))
Added user message to memory: What were some of the biggest risk factors in 2020 for Uber? === Calling Function === Calling function: vector_index_2020 with args: { "input": "biggest risk factors" } Got output: The biggest risk factors mentioned in the context are: 1. The adverse impact of the COVID-19 pandemic and actions taken to mitigate it on the business. 2. The potential reclassification of drivers as employees, workers, or quasi-employees instead of independent contractors. 3. Intense competition in the mobility, delivery, and logistics industries. 4. The need to lower fares or service fees and offer driver incentives and consumer discounts to remain competitive. 5. Significant losses incurred and the uncertainty of achieving profitability. 6. Difficulty in attracting and maintaining a critical mass of platform users. 7. Operational, compliance, and cultural challenges. 8. Negative media coverage and reputation issues. 9. Inability to optimize organizational structure or manage growth effectively. 10. Safety incidents that harm the ability to attract and retain platform users. 11. Risks associated with substantial investments in new offerings and technologies. 12. Potential fines or enforcement measures due to challenges faced. 13. Uncertainty and potential long-term financial impact of the COVID-19 pandemic, including changes in user behavior and demand for mobility services. 14. Potential adverse impact from business partners and third-party vendors affected by the pandemic. 15. Volatility in financial markets and its effect on stock price and access to capital markets. These are the biggest risk factors mentioned in the given context. ======================== The biggest risk factors for Uber in 2020 were: 1. The adverse impact of the COVID-19 pandemic and actions taken to mitigate it on the business. 2. The potential reclassification of drivers as employees, workers, or quasi-employees instead of independent contractors. 3. Intense competition in the mobility, delivery, and logistics industries. 4. The need to lower fares or service fees and offer driver incentives and consumer discounts to remain competitive. 5. Significant losses incurred and the uncertainty of achieving profitability. 6. Difficulty in attracting and maintaining a critical mass of platform users. 7. Operational, compliance, and cultural challenges. 8. Negative media coverage and reputation issues. 9. Inability to optimize organizational structure or manage growth effectively. 10. Safety incidents that harm the ability to attract and retain platform users. 11. Risks associated with substantial investments in new offerings and technologies. 12. Potential fines or enforcement measures due to challenges faced. 13. Uncertainty and potential long-term financial impact of the COVID-19 pandemic, including changes in user behavior and demand for mobility services. 14. Potential adverse impact from business partners and third-party vendors affected by the pandemic. 15. Volatility in financial markets and its effect on stock price and access to capital markets. These risk factors highlight the challenges and uncertainties faced by Uber in 2020.
最后,如果我们用一个查询来比较/对比不同年份的风险因素,代理将使用子问题查询引擎工具。
cross_query_str = (
"Compare/contrast the risk factors described in the Uber 10-K across"
" years. Give answer in bullet points."
)
response = agent.chat(cross_query_str)
print(str(response))
Added user message to memory: Compare/contrast the risk factors described in the Uber 10-K across years. Give answer in bullet points. === Calling Function === Calling function: sub_question_query_engine with args: { "input": "Compare/contrast the risk factors described in the Uber 10-K across years" } Generated 4 sub questions. [vector_index_2022] Q: What are the risk factors described in the 2022 SEC 10-K for Uber? [vector_index_2021] Q: What are the risk factors described in the 2021 SEC 10-K for Uber? [vector_index_2020] Q: What are the risk factors described in the 2020 SEC 10-K for Uber? [vector_index_2019] Q: What are the risk factors described in the 2019 SEC 10-K for Uber? [vector_index_2022] A: The risk factors described in the 2022 SEC 10-K for Uber are not provided in the given context information. [vector_index_2021] A: The risk factors described in the 2021 SEC 10-K for Uber are not provided in the given context information. [vector_index_2019] A: The risk factors described in the 2019 SEC 10-K for Uber include potential infringement of intellectual property, the need to protect proprietary information, dependence on rapid technological advances, seasonality in revenue generation, fluctuations in usage of the platform, seasonal increases in revenue for certain quarters, and the potential impact of employee actions. [vector_index_2020] A: The risk factors described in the 2020 SEC 10-K for Uber include the potential adverse effects on their business, financial condition, and results of operations. These risks could cause a decline in the trading price of their common stock and harm their business prospects. Additionally, there may be risks and uncertainties not currently known to Uber or that they do not believe are material. For a more detailed discussion of these risk factors, please refer to the "Risk Factors" section in Uber's Annual Report on Form 10-K. Got output: The risk factors described in the Uber 10-K vary across different years. In the 2020 SEC 10-K, the risk factors include potential adverse effects on their business, financial condition, and results of operations. However, the 2019 SEC 10-K includes additional risk factors such as potential infringement of intellectual property, the need to protect proprietary information, dependence on rapid technological advances, seasonality in revenue generation, fluctuations in usage of the platform, seasonal increases in revenue for certain quarters, and the potential impact of employee actions. It is important to note that the specific risk factors may change from year to year based on the evolving business environment and circumstances. ======================== === Calling Function === Calling function: vector_index_2022 with args: { "input": "risk factors" } Got output: Some of the risk factors mentioned in the context include the potential failure to meet regulatory requirements related to climate change, the impact of contagious diseases and pandemics on the business, the occurrence of catastrophic events, the uncertainty surrounding future pandemics or disease outbreaks, and the competitive nature of the mobility, delivery, and logistics industries. Additionally, the classification of drivers as employees instead of independent contractors, the need to lower fares or service fees to remain competitive, and the company's history of significant losses and anticipated increase in operating expenses are also mentioned as risk factors. ======================== === Calling Function === Calling function: vector_index_2021 with args: { "input": "risk factors" } Got output: The COVID-19 pandemic and the impact of actions to mitigate the pandemic have adversely affected and may continue to adversely affect parts of our business. Our business would be adversely affected if Drivers were classified as employees, workers or quasi-employees instead of independent contractors. The mobility, delivery, and logistics industries are highly competitive, with well-established and low-cost alternatives that have been available for decades, low barriers to entry, low switching costs, and well-capitalized competitors in nearly every major geographic region. To remain competitive in certain markets, we have in the past lowered, and may continue to lower, fares or service fees, and we have in the past offered, and may continue to offer, significant Driver incentives and consumer discounts and promotions. We have incurred significant losses since inception, including in the United States and other major markets. We expect our operating expenses to increase significantly in the foreseeable future, and we may not achieve or maintain profitability. If we are unable to attract or maintain a critical mass of Drivers, consumers, merchants, shippers, and carriers, whether as a result of competition or other factors, our platform will become less appealing to platform users. Maintaining and enhancing our brand and reputation is critical to our business prospects. We have previously received significant media coverage and negative publicity regarding our brand and reputation, and while we have taken significant steps to rehabilitate our brand and reputation, failure to maintain and enhance our brand and reputation will cause our business to suffer. Our historical workplace culture and forward-leaning approach created operational, compliance, and cultural challenges and our efforts to address these challenges may not be successful. If we are unable to optimize our organizational structure or effectively manage our growth, our financial performance and future prospects will be adversely affected. Platform users may engage in, or be subject to, criminal, violent, inappropriate, or dangerous activity that results in major safety incidents, which may harm our ability to attract and retain Drivers, consumers, merchants, shippers, and carriers. We are making substantial investments in new offerings and technologies, and may increase such investments in the future. These new ventures are inherently risky, and we may never realize any expected benefits from them. ======================== === Calling Function === Calling function: vector_index_2020 with args: { "input": "risk factors" } Got output: The risk factors mentioned in the context include the adverse impact of the COVID-19 pandemic, potential reclassification of drivers as employees, intense competition in the mobility, delivery, and logistics industries, the need to lower fares and offer incentives to remain competitive, significant losses and increased operating expenses, the importance of attracting and maintaining platform users, operational and cultural challenges, negative media coverage affecting brand reputation, difficulties in managing growth and organizational structure, safety incidents, risks associated with new ventures and investments, legal uncertainties, challenges in international operations, currency fluctuations, tax consequences, financial reporting burdens, political and economic instability, public health concerns, and limited influence over minority-owned affiliates. These risk factors could have an adverse effect on the business, financial condition, operating results, and prospects of the company. ======================== === Calling Function === Calling function: vector_index_2019 with args: { "input": "risk factors" } Got output: The personal mobility, meal delivery, and logistics industries are highly competitive, with well-established and low-cost alternatives that have been available for decades, low barriers to entry, low switching costs, and well-capitalized competitors in nearly every major geographic region. If we are unable to compete effectively in these industries, our business and financial prospects would be adversely impacted. ======================== Here is a comparison of the risk factors described in the Uber 10-K across years: 2022: - Potential failure to meet regulatory requirements related to climate change - Impact of contagious diseases and pandemics on the business - Occurrence of catastrophic events - Uncertainty surrounding future pandemics or disease outbreaks - Competitive nature of the mobility, delivery, and logistics industries - Classification of drivers as employees instead of independent contractors - Need to lower fares or service fees to remain competitive - History of significant losses and anticipated increase in operating expenses 2021: - Adverse impact of the COVID-19 pandemic and actions to mitigate it - Potential reclassification of drivers as employees instead of independent contractors - Intense competition in the mobility, delivery, and logistics industries - Need to lower fares or service fees and offer driver incentives and consumer discounts - Significant losses incurred and uncertainty of achieving profitability - Difficulty in attracting and maintaining a critical mass of platform users - Operational, compliance, and cultural challenges - Negative media coverage and reputation issues - Inability to optimize organizational structure or manage growth effectively - Safety incidents that harm the ability to attract and retain platform users - Risks associated with substantial investments in new offerings and technologies 2020: - Adverse impact of the COVID-19 pandemic and actions taken to mitigate it - Potential reclassification of drivers as employees, workers, or quasi-employees instead of independent contractors - Intense competition in the mobility, delivery, and logistics industries - Need to lower fares or service fees and offer driver incentives and consumer discounts - Significant losses incurred and uncertainty of achieving profitability - Difficulty in attracting and maintaining a critical mass of platform users - Operational, compliance, and cultural challenges - Negative media coverage and reputation issues - Inability to optimize organizational structure or manage growth effectively - Safety incidents that harm the ability to attract and retain platform users - Risks associated with substantial investments in new offerings and technologies - Potential fines or enforcement measures due to challenges faced - Uncertainty and potential long-term financial impact of the COVID-19 pandemic - Potential adverse impact from business partners and third-party vendors affected by the pandemic - Volatility in financial markets and its effect on stock price and access to capital markets 2019: - Highly competitive personal mobility, meal delivery, and logistics industries - Potential inability to compete effectively in these industries These bullet points highlight the similarities and differences in the risk factors described in the Uber 10-K across years.
设置聊天机器人循环¶
现在我们已经设置好了聊天机器人,只需要再进行几个步骤,就可以设置一个基本的交互循环,与我们增强的SEC聊天机器人进行交谈了!
agent = OpenAIAgent.from_tools(tools) # 默认情况下为verbose=False
while True:
text_input = input("用户:")
if text_input == "exit":
break
response = agent.chat(text_input)
print(f"机器人:{response}")
# 用户:Uber在2022年有哪些法律诉讼?
Agent: In 2022, Uber is facing several legal proceedings. Here are some of them: 1. California: The state Attorney General and city attorneys filed a complaint against Uber and Lyft, alleging that drivers are misclassified as independent contractors. A preliminary injunction was issued but stayed pending appeal. The Court of Appeal affirmed the lower court's ruling, and Uber filed a petition for review with the California Supreme Court. However, the Supreme Court declined the petition for review. The lawsuit is ongoing, focusing on claims by the California Attorney General for periods prior to the enactment of Proposition 22. 2. Massachusetts: The Attorney General of Massachusetts filed a complaint against Uber, alleging that drivers are employees entitled to wage and labor law protections. Uber's motion to dismiss the complaint was denied, and a summary judgment motion is pending. 3. New York: Uber is facing allegations of misclassification and employment violations by the state Attorney General. The resolution of this matter is uncertain. 4. Switzerland: Several administrative bodies in Switzerland have issued rulings classifying Uber drivers as employees for social security or labor purposes. Uber is challenging these rulings before the Social Security and Administrative Tribunals. These are some of the legal proceedings against Uber in 2022. The outcomes and potential losses in these cases are uncertain.