使用 LlamaIndex 和 KDB.AI 向量存储实现带有时间过滤的高级 RAG¶

注意：此示例需要 KDB.AI 端点和 API 密钥。注册免费的 KDB.AI 账户。¶

KDB.AI 是一款强大的基于知识的向量数据库和搜索引擎，通过提供高级搜索、推荐和个性化功能，可帮助您使用实时数据构建可扩展、可靠的 AI 应用。

此示例演示如何使用 KDB.AI 对特定时间点附近的金融法规进行语义搜索、摘要和分析。

要访问您的端点和 API 密钥，请在此注册 KDB.AI。

要设置您的开发环境，请遵循 KDB.AI 先决条件页面上的说明。

以下示例演示了通过 LlamaIndex 与 KDB.AI 交互的一些方式。

使用 Pip 安装依赖项¶

为了成功运行此示例，请根据您运行此 Notebook 的位置注意以下步骤

-本地运行 / 私有环境： 仓库 README.md 中的设置步骤将指导您了解先决条件以及如何使用 Jupyter 运行此示例。

-Colab / 托管环境： 在 Colab 中打开此 Notebook 并运行单元格。

In [ ]

!pip install llama-index llama-index-llms-openai llama-index-embeddings-openai llama-index-readers-file llama-index-vector-stores-kdbai
!pip install kdbai_client pandas

!pip install llama-index llama-index-llms-openai llama-index-embeddings-openai llama-index-readers-file llama-index-vector-stores-kdbai !pip install kdbai_client pandas

导入依赖项¶

In [ ]

from getpass import getpass
import re
import os
import shutil
import time
import urllib
import datetime

import pandas as pd

from llama_index.core import (
    Settings,
    SimpleDirectoryReader,
    StorageContext,
    VectorStoreIndex,
)
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.retrievers import VectorIndexRetriever
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.llms.openai import OpenAI
from llama_index.vector_stores.kdbai import KDBAIVectorStore

import kdbai_client as kdbai

OUTDIR = "pdf"
RESET = True

from getpass import getpass import re import os import shutil import time import urllib import datetime import pandas as pd from llama_index.core import ( Settings, SimpleDirectoryReader, StorageContext, VectorStoreIndex, ) from llama_index.core.node_parser import SentenceSplitter from llama_index.core.retrievers import VectorIndexRetriever from llama_index.embeddings.openai import OpenAIEmbedding from llama_index.llms.openai import OpenAI from llama_index.vector_stores.kdbai import KDBAIVectorStore import kdbai_client as kdbai OUTDIR = "pdf" RESET = True

设置 OpenAI API 密钥并选择要使用的 LLM 和 Embedding 模型：¶

In [ ]

# os.environ["OPENAI_API_KEY"] = getpass("OpenAI API key: ")
os.environ["OPENAI_API_KEY"] = (
    os.environ["OPENAI_API_KEY"]
    if "OPENAI_API_KEY" in os.environ
    else getpass("OpenAI API Key: ")
)

# os.environ["OPENAI_API_KEY"] = getpass("OpenAI API key: ") os.environ["OPENAI_API_KEY"] = ( os.environ["OPENAI_API_KEY"] if "OPENAI_API_KEY" in os.environ else getpass("OpenAI API Key: ") )

In [ ]

import os
from getpass import getpass

# Set OpenAI API
if "OPENAI_API_KEY" in os.environ:
    KDBAI_API_KEY = os.environ["OPENAI_API_KEY"]
else:
    # Prompt the user to enter the API key
    OPENAI_API_KEY = getpass("OPENAI API KEY: ")
    # Save the API key as an environment variable for the current session
    os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY

import os from getpass import getpass # Set OpenAI API if "OPENAI_API_KEY" in os.environ: KDBAI_API_KEY = os.environ["OPENAI_API_KEY"] else: # Prompt the user to enter the API key OPENAI_API_KEY = getpass("OPENAI API Key: ") # Save the API key as an environment variable for the current session os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY

In [ ]

EMBEDDING_MODEL = "text-embedding-3-small"
GENERATION_MODEL = "gpt-4o-mini"

llm = OpenAI(model=GENERATION_MODEL)
embed_model = OpenAIEmbedding(model=EMBEDDING_MODEL)

Settings.llm = llm
Settings.embed_model = embed_model

EMBEDDING_MODEL = "text-embedding-3-small" GENERATION_MODEL = "gpt-4o-mini" llm = OpenAI(model=GENERATION_MODEL) embed_model = OpenAIEmbedding(model=EMBEDDING_MODEL) Settings.llm = llm Settings.embed_model = embed_model

创建 KDB.AI 会话和表¶

In [ ]

# vector DB imports
import os
from getpass import getpass
import kdbai_client as kdbai
import time

# vector DB imports import os from getpass import getpass import kdbai_client as kdbai import time

选项 1. KDB.AI Cloud¶

要使用 KDB.AI Cloud，您需要两个会话详细信息 - URL 端点和 API 密钥。您可以在此处免费注册以获取这些信息。

您可以使用 kdbai.Session 连接到 KDB.AI Cloud 会话，并传入 KDB.AI Cloud 门户中的会话 URL 端点和 API 密钥详细信息。

如果您的系统中存在包含 KDB.AI Cloud 门户详细信息的环境变量 KDBAI_ENDPOINTS 和 KDBAI_API_KEY，这些变量将自动用于连接。如果不存在，将提示您输入 KDB.AI Cloud 门户会话 URL 端点和 API 密钥详细信息。

In [ ]

# Set up KDB.AI endpoint and API key
KDBAI_ENDPOINT = (
    os.environ["KDBAI_ENDPOINT"]
    if "KDBAI_ENDPOINT" in os.environ
    else input("KDB.AI endpoint: ")
)
KDBAI_API_KEY = (
    os.environ["KDBAI_API_KEY"]
    if "KDBAI_API_KEY" in os.environ
    else getpass("KDB.AI API key: ")
)

session = kdbai.Session(endpoint=KDBAI_ENDPOINT, api_key=KDBAI_API_KEY)

# Set up KDB.AI endpoint and API key KDBAI_ENDPOINT = ( os.environ["KDBAI_ENDPOINT"] if "KDBAI_ENDPOINT" in os.environ else input("KDB.AI endpoint: ") ) KDBAI_API_KEY = ( os.environ["KDBAI_API_KEY"] if "KDBAI_API_KEY" in os.environ else getpass("KDB.AI API key: ") ) session = kdbai.Session(endpoint=KDBAI_ENDPOINT, api_key=KDBAI_API_KEY)

选项 2. KDB.AI Server¶

要使用 KDB.AI Server，您需要下载并运行自己的容器。为此，您首先需要在此处免费注册：here。

您将收到一封包含下载实例所需的许可文件和 bearer 令牌的电子邮件。按照注册电子邮件中的说明进行操作，以启动和运行您的会话。

完成设置步骤后，您可以使用 kdbai.Session 并传入本地端点连接到您的 KDB.AI Server 会话。

In [ ]

# session = kdbai.Session()

# session = kdbai.Session()

创建您的 KDB.AI 表的模式¶

!!! 注意： embedding 列中的 'dims' 参数必须反映您选择的 embedding 模型的输出维度。

• OpenAI 'text-embedding-3-small' 的输出维度为 1536。

In [ ]

schema = [
    {"name": "document_id", "type": "bytes"},
    {"name": "text", "type": "bytes"},
    {"name": "embeddings", "type": "float32s"},
    {"name": "title", "type": "str"},
    {"name": "publication_date", "type": "datetime64[ns]"},
]


indexFlat = {
    "name": "flat_index",
    "type": "flat",
    "column": "embeddings",
    "params": {"dims": 1536, "metric": "L2"},
}

schema = [ {"name": "document_id", "type": "bytes"}, {"name": "text", "type": "bytes"}, {"name": "embeddings", "type": "float32s"}, {"name": "title", "type": "str"}, {"name": "publication_date", "type": "datetime64[ns]"}, ] indexFlat = { "name": "flat_index", "type": "flat", "column": "embeddings", "params": {"dims": 1536, "metric": "L2"}, }

In [ ]

KDBAI_TABLE_NAME = "reports"
database = session.database("default")

# First ensure the table does not already exist
for table in database.tables:
    if table.name == KDBAI_TABLE_NAME:
        table.drop()
        break

# Create the table
table = database.create_table(
    KDBAI_TABLE_NAME, schema=schema, indexes=[indexFlat]
)

KDBAI_TABLE_NAME = "reports" database = session.database("default") # First ensure the table does not already exist for table in database.tables: if table.name == KDBAI_TABLE_NAME: table.drop() break # Create the table table = database.create_table( KDBAI_TABLE_NAME, schema=schema, indexes=[indexFlat] )

财务报告 URL 和元数据¶

In [ ]

INPUT_URLS = [
    "https://www.govinfo.gov/content/pkg/PLAW-106publ102/pdf/PLAW-106publ102.pdf",
    "https://www.govinfo.gov/content/pkg/PLAW-111publ203/pdf/PLAW-111publ203.pdf",
]

METADATA = {
    "pdf/PLAW-106publ102.pdf": {
        "title": "GRAMM–LEACH–BLILEY ACT, 1999",
        "publication_date": pd.to_datetime("1999-11-12"),
    },
    "pdf/PLAW-111publ203.pdf": {
        "title": "DODD-FRANK WALL STREET REFORM AND CONSUMER PROTECTION ACT, 2010",
        "publication_date": pd.to_datetime("2010-07-21"),
    },
}

INPUT_URLS = [ "https://www.govinfo.gov/content/pkg/PLAW-106publ102/pdf/PLAW-106publ102.pdf", "https://www.govinfo.gov/content/pkg/PLAW-111publ203/pdf/PLAW-111publ203.pdf", ] METADATA = { "pdf/PLAW-106publ102.pdf": { "title": "GRAMM–LEACH–BLILEY ACT, 1999", "publication_date": pd.to_datetime("1999-11-12"), }, "pdf/PLAW-111publ203.pdf": { "title": "DODD-FRANK WALL STREET REFORM AND CONSUMER PROTECTION ACT, 2010", "publication_date": pd.to_datetime("2010-07-21"), }, }

在本地下载 PDF 文件¶

In [ ]

%%time

CHUNK_SIZE = 512 * 1024


def download_file(url):
    print("Downloading %s..." % url)
    out = os.path.join(OUTDIR, os.path.basename(url))
    try:
        response = urllib.request.urlopen(url)
    except urllib.error.URLError as e:
        logging.exception("Failed to download %s !" % url)
    else:
        with open(out, "wb") as f:
            while True:
                chunk = response.read(CHUNK_SIZE)
                if chunk:
                    f.write(chunk)
                else:
                    break
    return out


if RESET:
    if os.path.exists(OUTDIR):
        shutil.rmtree(OUTDIR)
    os.mkdir(OUTDIR)

    local_files = [download_file(x) for x in INPUT_URLS]
    local_files[:10]

%%time CHUNK_SIZE = 512 * 1024 def download_file(url): print("Downloading %s..." % url) out = os.path.join(OUTDIR, os.path.basename(url)) try: response = urllib.request.urlopen(url) except urllib.error.URLError as e: logging.exception("Failed to download %s !" % url) else: with open(out, "wb") as f: while True: chunk = response.read(CHUNK_SIZE) if chunk: f.write(chunk) else: break return out if RESET: if os.path.exists(OUTDIR): shutil.rmtree(OUTDIR) os.mkdir(OUTDIR) local_files = [download_file(x) for x in INPUT_URLS] local_files[:10]

Downloading https://www.govinfo.gov/content/pkg/PLAW-106publ102/pdf/PLAW-106publ102.pdf...

Downloading https://www.govinfo.gov/content/pkg/PLAW-111publ203/pdf/PLAW-111publ203.pdf...
CPU times: user 52.6 ms, sys: 1.2 ms, total: 53.8 ms
Wall time: 7.86 s

使用 LlamaIndex 加载本地 PDF 文件¶

In [ ]

%%time


def get_metadata(filepath):
    return METADATA[filepath]


documents = SimpleDirectoryReader(
    input_files=local_files,
    file_metadata=get_metadata,
)

docs = documents.load_data()
len(docs)

%%time def get_metadata(filepath): return METADATA[filepath] documents = SimpleDirectoryReader( input_files=local_files, file_metadata=get_metadata, ) docs = documents.load_data() len(docs)

CPU times: user 8.22 s, sys: 9.04 ms, total: 8.23 s
Wall time: 8.23 s

Out [ ]

994

使用 KDB.AI 向量存储设置 LlamaIndex RAG 管道¶

In [ ]

%%time

# llm = OpenAI(temperature=0, model=LLM)
vector_store = KDBAIVectorStore(table)

storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
    docs,
    storage_context=storage_context,
    transformations=[SentenceSplitter(chunk_size=2048, chunk_overlap=0)],
)

%%time # llm = OpenAI(temperature=0, model=LLM) vector_store = KDBAIVectorStore(table) storage_context = StorageContext.from_defaults(vector_store=vector_store) index = VectorStoreIndex.from_documents( docs, storage_context=storage_context, transformations=[SentenceSplitter(chunk_size=2048, chunk_overlap=0)], )

CPU times: user 3.67 s, sys: 31.9 ms, total: 3.7 s
Wall time: 22.3 s

In [ ]

table.query()

table.query()

Out [ ]

	document_id	text	embeddings	title	publication_date
0	b'272d7d24-c232-41b6-823e-27aa6203c100'	b'PUBLIC LAW 106\xc2\xb1102\xc3\x90NOV. 12, 19...	[0.034452137, 0.03166917, -0.011892043, 0.0184...	GRAMM–LEACH–BLILEY ACT, 1999	1999-11-12
1	b'89e3f2ee-f5a6-4e40-bb81-0632f08341f0'	b"113 STAT. 1338 PUBLIC LAW 106\xc2\xb1102\xc3...	[0.02164333, 1.0030156e-05, 0.0028665832, 0.02...	GRAMM–LEACH–BLILEY ACT, 1999	1999-11-12
2	b'56fbe82a-5458-4a4a-a5ed-026d9399151d'	b'113 STAT. 1339 PUBLIC LAW 106\xc2\xb1102\xc3...	[0.01380091, 0.026945233, 0.02838467, 0.043132...	GRAMM–LEACH–BLILEY ACT, 1999	1999-11-12
3	b'b6bf9e48-51b6-45d9-9259-b6346f93831f'	b'113 STAT. 1340 PUBLIC LAW 106\xc2\xb1102\xc3...	[0.0070182937, 0.014063503, 0.026525516, 0.040...	GRAMM–LEACH–BLILEY ACT, 1999	1999-11-12
4	b'f398b133-b4f5-4a34-94d1-9a97fdb658e5'	b"113 STAT. 1341 PUBLIC LAW 106\xc2\xb1102\xc3...	[0.025041763, 0.01968024, 0.030940715, 0.02899...	GRAMM–LEACH–BLILEY ACT, 1999	1999-11-12
...	...	...	...	...	...
989	b'8e84d1d5-d87d-4351-b7eb-5d569fdb8d9c'	b'124 STAT. 2219 PUBLIC LAW 111\xe2\x80\x93203...	[0.024505286, 0.015549232, 0.0536601, 0.028532...	DODD-FRANK WALL STREET REFORM AND CONSUMER PRO...	2010-07-21
990	b'0c47f590-050c-4374-bf8c-2a4502dc980f'	b'124 STAT. 2220 PUBLIC LAW 111\xe2\x80\x93203...	[0.014071382, -0.0044553108, 0.03662071, 0.035...	DODD-FRANK WALL STREET REFORM AND CONSUMER PRO...	2010-07-21
991	b'63a2235f-d368-43b8-a1a9-a5a11d497245'	b'124 STAT. 2221 PUBLIC LAW 111\xe2\x80\x93203...	[0.0005448305, 0.013075933, 0.044821188, 0.031...	DODD-FRANK WALL STREET REFORM AND CONSUMER PRO...	2010-07-21
992	b'bac4d75e-4867-4d89-a71e-09a6762bf3c4'	b'124 STAT. 2222 PUBLIC LAW 111\xe2\x80\x93203...	[0.032077603, 0.016817383, 0.04507993, 0.03376...	DODD-FRANK WALL STREET REFORM AND CONSUMER PRO...	2010-07-21
993	b'e262e4da-f6e1-4b9d-9232-77fc3f0c81a7'	b'124 STAT. 2223 PUBLIC LAW 111\xe2\x80\x93203...	[0.0387719, -0.025150038, 0.030345473, 0.04303...	DODD-FRANK WALL STREET REFORM AND CONSUMER PRO...	2010-07-21

994 rows × 5 columns

设置 LlamaIndex 查询引擎¶

In [ ]

%%time

# Using gpt-4o-mini, the 128k tokens context size can take 100 pages.
K = 15

query_engine = index.as_query_engine(
    similarity_top_k=K,
    vector_store_kwargs={
        "index": "flat_index",
        "filter": [["<", "publication_date", datetime.date(2008, 9, 15)]],
        "sort_columns": "publication_date",
    },
)

%%time # Using gpt-4o-mini, the 128k tokens context size can take 100 pages. K = 15 query_engine = index.as_query_engine( similarity_top_k=K, vector_store_kwargs={ "index": "flat_index", "filter": [["<", "publication_date", datetime.date(2008, 9, 15)]], "sort_columns": "publication_date", }, )

CPU times: user 512 μs, sys: 23 μs, total: 535 μs
Wall time: 550 μs

2008 年危机前¶

In [ ]

%%time

result = query_engine.query(
    """
    What was the main financial regulation in the US before the 2008 financial crisis ?
    """
)
print(result.response)

%%time result = query_engine.query( """ What was the main financial regulation in the US before the 2008 financial crisis ? """ ) print(result.response)

The main financial regulation in the US before the 2008 financial crisis was the Gramm-Leach-Bliley Act, enacted in 1999. This act facilitated the affiliation among banks, securities firms, and insurance companies, effectively repealing parts of the Glass-Steagall Act, which had previously separated these financial services. The Gramm-Leach-Bliley Act aimed to enhance competition in the financial services industry by providing a framework for the integration of various financial institutions.
CPU times: user 61.8 ms, sys: 0 ns, total: 61.8 ms
Wall time: 4.24 s

In [ ]

%%time

result = query_engine.query(
    """
    Is the Gramm-Leach-Bliley Act of 1999 enough to prevent the 2008 crisis. Search the document and explain its strenghts and weaknesses to regulate the US stock market.
    """
)
print(result.response)

%%time result = query_engine.query( """ Is the Gramm-Leach-Bliley Act of 1999 enough to prevent the 2008 crisis. Search the document and explain its strenghts and weaknesses to regulate the US stock market. """ ) print(result.response)

The Gramm-Leach-Bliley Act of 1999 aimed to enhance competition in the financial services industry by allowing affiliations among banks, securities firms, and insurance companies. Its strengths include the repeal of the Glass-Steagall Act, which had previously separated commercial banking from investment banking, thereby enabling financial institutions to diversify their services and potentially increase competition. This diversification could lead to more innovative financial products and services.

However, the Act also has notable weaknesses. By allowing greater affiliations and reducing regulatory barriers, it may have contributed to the creation of "too big to fail" institutions, which posed systemic risks to the financial system. The lack of stringent oversight and the ability for financial holding companies to engage in a wide range of activities without adequate regulation may have led to excessive risk-taking. Additionally, the Act did not sufficiently address the complexities of modern financial products, such as derivatives, which played a significant role in the 2008 financial crisis.

In summary, while the Gramm-Leach-Bliley Act aimed to foster competition and innovation in the financial sector, its regulatory framework may have inadvertently facilitated the conditions that led to the financial crisis, highlighting the need for a more robust regulatory approach to oversee the interconnectedness and risks within the financial system.
CPU times: user 45.7 ms, sys: 255 μs, total: 46 ms
Wall time: 21.9 s

2008 年危机后¶

In [ ]

%%time

# Using gpt-4o-mini, the 128k tokens context size can take 100 pages.
K = 15

query_engine = index.as_query_engine(
    similarity_top_k=K,
    vector_store_kwargs={
        "index": "flat_index",
        "filter": [[">=", "publication_date", datetime.date(2008, 9, 15)]],
        "sort_columns": "publication_date",
    },
)

%%time # Using gpt-4o-mini, the 128k tokens context size can take 100 pages. K = 15 query_engine = index.as_query_engine( similarity_top_k=K, vector_store_kwargs={ "index": "flat_index", "filter": [[">=", "publication_date", datetime.date(2008, 9, 15)]], "sort_columns": "publication_date", }, )

CPU times: user 171 μs, sys: 0 ns, total: 171 μs
Wall time: 175 μs

In [ ]

%%time

result = query_engine.query(
    """
    What happened on the 15th of September 2008 ?
    """
)
print(result.response)

%%time result = query_engine.query( """ What happened on the 15th of September 2008 ? """ ) print(result.response)

On the 15th of September 2008, Lehman Brothers, a major global financial services firm, filed for bankruptcy. This event marked one of the largest bankruptcies in U.S. history and was a significant moment in the financial crisis of 2007-2008, leading to widespread panic in financial markets and contributing to the global economic downturn.
CPU times: user 51.4 ms, sys: 0 ns, total: 51.4 ms
Wall time: 3.6 s

In [ ]

%%time

result = query_engine.query(
    """
    What was the new US financial regulation enacted after the 2008 crisis to increase the market regulation and to improve consumer sentiment ?
    """
)
print(result.response)

%%time result = query_engine.query( """ What was the new US financial regulation enacted after the 2008 crisis to increase the market regulation and to improve consumer sentiment ? """ ) print(result.response)

The new US financial regulation enacted after the 2008 crisis to increase market regulation and improve consumer sentiment is the Dodd-Frank Wall Street Reform and Consumer Protection Act, which was signed into law on July 21, 2010. This legislation aimed to promote financial stability, enhance accountability and transparency in the financial system, and protect consumers from abusive financial practices.
CPU times: user 43.7 ms, sys: 0 ns, total: 43.7 ms
Wall time: 4.55 s

深入分析¶

In [ ]

%%time

# Using gpt-4o-mini, the 128k tokens context size can take 100 pages.
K = 20

query_engine = index.as_query_engine(
    similarity_top_k=K,
    vector_store_kwargs={
        "index": "flat_index",
        "sort_columns": "publication_date",
    },
)

%%time # Using gpt-4o-mini, the 128k tokens context size can take 100 pages. K = 20 query_engine = index.as_query_engine( similarity_top_k=K, vector_store_kwargs={ "index": "flat_index", "sort_columns": "publication_date", }, )

CPU times: user 227 μs, sys: 10 μs, total: 237 μs
Wall time: 243 μs

In [ ]

%%time

result = query_engine.query(
    """
    Analyse the US financial regulations before and after the 2008 crisis and produce a report of all related arguments to explain what happened, and to ensure that does not happen again.
    Use both the provided context and your own knowledge but do mention explicitely which one you use.
    """
)
print(result.response)

%%time result = query_engine.query( """ Analyse the US financial regulations before and after the 2008 crisis and produce a report of all related arguments to explain what happened, and to ensure that does not happen again. Use both the provided context and your own knowledge but do mention explicitely which one you use. """ ) print(result.response)

The analysis of U.S. financial regulations before and after the 2008 financial crisis reveals significant changes aimed at preventing a recurrence of such a crisis. 

Before the crisis, the regulatory framework was characterized by a lack of comprehensive oversight, particularly for nonbank financial institutions. The regulatory environment allowed for excessive risk-taking, inadequate capital requirements, and insufficient transparency in financial transactions. This environment contributed to the housing bubble and the subsequent collapse of major financial institutions, leading to widespread economic turmoil.

In response to the crisis, the Dodd-Frank Wall Street Reform and Consumer Protection Act of 2010 was enacted. This legislation introduced several key reforms:

1. **Creation of the Financial Stability Oversight Council (FSOC)**: This body was established to monitor systemic risks and coordinate regulatory efforts across different financial sectors. It has the authority to recommend heightened standards and safeguards for financial activities that could pose risks to financial stability.

2. **Enhanced Regulatory Oversight**: Dodd-Frank imposed stricter regulations on bank holding companies and nonbank financial companies, particularly those with significant assets. This includes requirements for stress testing, capital planning, and the submission of resolution plans to ensure orderly wind-downs in case of failure.

3. **Consumer Protection Measures**: The establishment of the Consumer Financial Protection Bureau (CFPB) aimed to protect consumers from predatory lending practices and ensure transparency in financial products.

4. **Volcker Rule**: This provision restricts proprietary trading by banks and limits their investments in hedge funds and private equity funds, thereby reducing conflicts of interest and excessive risk-taking.

5. **Increased Transparency and Reporting Requirements**: Financial institutions are now required to disclose more information regarding their risk exposures and financial health, which enhances market discipline and investor confidence.

The arguments for these reforms center around the need for a more resilient financial system that can withstand economic shocks. The reforms aim to address the systemic risks that were prevalent before the crisis, ensuring that financial institutions maintain adequate capital buffers and engage in prudent risk management practices.

In conclusion, the regulatory landscape has shifted significantly since the 2008 crisis, with a focus on preventing excessive risk-taking, enhancing transparency, and protecting consumers. These measures are designed to create a more stable financial environment and mitigate the likelihood of future crises.
CPU times: user 180 ms, sys: 437 μs, total: 180 ms
Wall time: 10.5 s

删除 KDB.AI 表¶

使用完表后，最佳实践是将其删除。

In [ ]

table.drop()

table.drop()

参与我们的问卷调查¶

希望此示例对您有所帮助！您的反馈对我们非常重要，如果您能花一些时间填写我们的简短问卷，我们将不胜感激。您的意见有助于我们改进内容。

参与问卷调查