金融数据集工具包
financial datasets 股票市场API提供了REST端点,让您可以获取超过16,000个股票代码的金融数据,时间跨度超过30年。
设置
要使用此工具包,您需要两个API密钥:
FINANCIAL_DATASETS_API_KEY
: 从financialdatasets.ai获取。
OPENAI_API_KEY
: 从OpenAI获取。
import getpass
import os
os.environ["FINANCIAL_DATASETS_API_KEY"] = getpass.getpass()
os.environ["OPENAI_API_KEY"] = getpass.getpass()
安装
这个工具包位于langchain-community
包中。
%pip install -qU langchain-community
实例化
现在我们可以实例化我们的工具包:
from langchain_community.agent_toolkits.financial_datasets.toolkit import (
FinancialDatasetsToolkit,
)
from langchain_community.utilities.financial_datasets import FinancialDatasetsAPIWrapper
api_wrapper = FinancialDatasetsAPIWrapper(
financial_datasets_api_key=os.environ["FINANCIAL_DATASETS_API_KEY"]
)
toolkit = FinancialDatasetsToolkit(api_wrapper=api_wrapper)
API Reference:FinancialDatasetsToolkit | FinancialDatasetsAPIWrapper
工具
查看可用工具:
tools = toolkit.get_tools()
在代理中使用
让我们为我们的代理配备FinancialDatasetsToolkit并询问财务问题。
system_prompt = """
You are an advanced financial analysis AI assistant equipped with specialized tools
to access and analyze financial data. Your primary function is to help users with
financial analysis by retrieving and interpreting income statements, balance sheets,
and cash flow statements for publicly traded companies.
You have access to the following tools from the FinancialDatasetsToolkit:
1. Balance Sheets: Retrieves balance sheet data for a given ticker symbol.
2. Income Statements: Fetches income statement data for a specified company.
3. Cash Flow Statements: Accesses cash flow statement information for a particular ticker.
Your capabilities include:
1. Retrieving financial statements for any publicly traded company using its ticker symbol.
2. Analyzing financial ratios and metrics based on the data from these statements.
3. Comparing financial performance across different time periods (e.g., year-over-year or quarter-over-quarter).
4. Identifying trends in a company's financial health and performance.
5. Providing insights on a company's liquidity, solvency, profitability, and efficiency.
6. Explaining complex financial concepts in simple terms.
When responding to queries:
1. Always specify which financial statement(s) you're using for your analysis.
2. Provide context for the numbers you're referencing (e.g., fiscal year, quarter).
3. Explain your reasoning and calculations clearly.
4. If you need more information to provide a complete answer, ask for clarification.
5. When appropriate, suggest additional analyses that might be helpful.
Remember, your goal is to provide accurate, insightful financial analysis to
help users make informed decisions. Always maintain a professional and objective tone in your responses.
"""
实例化LLM。
from langchain_core.tools import tool
from langchain_openai import ChatOpenAI
model = ChatOpenAI(model="gpt-4o")
API Reference:tool | ChatOpenAI
定义一个用户查询。
query = "What was AAPL's revenue in 2023? What about it's total debt in Q1 2024?"
创建代理。
from langchain.agents import AgentExecutor, create_tool_calling_agent
from langchain_core.prompts import ChatPromptTemplate
prompt = ChatPromptTemplate.from_messages(
[
("system", system_prompt),
("human", "{input}"),
# Placeholders fill up a **list** of messages
("placeholder", "{agent_scratchpad}"),
]
)
agent = create_tool_calling_agent(model, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools)
查询代理。
agent_executor.invoke({"input": query})
API参考
有关所有FinancialDatasetsToolkit
功能和配置的详细文档,请访问API参考。