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通过自然语言查询访问金融市场数据。

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概述

FMP（Financial Modeling Prep）LangChain 集成提供了一种通过自然语言查询无缝访问金融市场数据的方式。该集成提供两个主要组件：
  • FMPDataToolkit：根据自然语言查询创建工具集合
  • FMPDataTool：自动选择并调用相应端点的单一统一工具
该集成利用 LangChain 的语义搜索能力，将用户查询与最相关的 FMP API 端点进行匹配，使金融数据访问更加直观高效。

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配置

!pip install -U langchain-fmp-data

import os

# 替换为您的实际 API 密钥
os.environ["FMP_API_KEY"] = "your-fmp-api-key"  # pragma: allowlist secret
os.environ["OPENAI_API_KEY"] = "your-openai-api-key"  # pragma: allowlist secret
配置 LangSmith 以获得最佳可观测性（可选但推荐）： 
os.environ["LANGSMITH_TRACING"] = "true"
# os.environ["LANGSMITH_API_KEY"] = getpass.getpass()

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实例化

FMP LangChain 集成有两种主要实例化方式：
  1. 使用 FMPDataToolkit
from langchain_fmp_data import FMPDataToolkit

query = "Get stock market prices and technical indicators"
# 基础实例化
toolkit = FMPDataToolkit(query=query)

# 指定特定查询焦点的实例化
market_toolkit = FMPDataToolkit(
    query=query,
    num_results=5,
)

# 自定义配置实例化
custom_toolkit = FMPDataToolkit(
    query="Financial analysis",
    num_results=3,
    similarity_threshold=0.4,
    cache_dir="/custom/cache/path",
)
  1. 使用 FMPDataTool
from langchain_fmp_data import FMPDataTool
from langchain_fmp_data.tools import ResponseFormat

# 基础实例化
tool = FMPDataTool()

# 使用自定义设置的高级实例化
advanced_tool = FMPDataTool(
    max_iterations=50,
    temperature=0.2,
)

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调用

工具可通过多种方式调用：

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直接调用

# 使用 FMPDataTool
tool_direct = FMPDataTool()

# 基础查询
# fmt: off
result = tool.invoke({"query": "What's Apple's current stock price?"})
# fmt: on

# 指定格式的高级查询
# fmt: off
detailed_result = tool_direct.invoke(
    {
        "query": "Compare Tesla and Ford's profit margins",
        "response_format": ResponseFormat.BOTH,
    }
)
# fmt: on

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与 LangChain Agent 结合使用

from langchain.agents import AgentExecutor, create_openai_functions_agent
from langchain_openai import ChatOpenAI

# 配置
llm = ChatOpenAI(temperature=0)
toolkit = FMPDataToolkit(
    query="Stock analysis",
    num_results=3,
)
tools = toolkit.get_tools()

# 创建 Agent
prompt = "You are a helpful assistant. Answer the user's questions based on the provided context."
agent = create_openai_functions_agent(llm, tools, prompt)
agent_executor = AgentExecutor(
    agent=agent,
    tools=tools,
)

# 运行查询
# fmt: off
response = agent_executor.invoke({"input": "What's the PE ratio of Microsoft?"})
# fmt: on

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高级用法

您可以自定义工具的行为： 
# 使用自定义设置初始化
advanced_tool = FMPDataTool(
    max_iterations=50,  # 为复杂查询增加最大迭代次数
    temperature=0.2,  # 调整温度以控制响应的专注程度
)

# 复杂多部分分析示例
query = """
Analyze Apple's financial health by:
1. Examining current ratios and debt levels
2. Comparing profit margins to industry average
3. Looking at cash flow trends
4. Assessing growth metrics
"""
# fmt: off
response = advanced_tool.invoke(
    {
        "query": query,
        "response_format": ResponseFormat.BOTH}
)
# fmt: on
print("Detailed Financial Analysis:")
print(response)

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链式调用

您可以像其他工具一样将其与所需模型进行链式调用。 
from langchain_core.output_parsers import StrOutputParser
from langchain_openai import ChatOpenAI

# 配置
llm = ChatOpenAI(temperature=0)
toolkit = FMPDataToolkit(query="Stock analysis", num_results=3)
tools = toolkit.get_tools()

llm_with_tools = llm.bind(functions=tools)
output_parser = StrOutputParser()
# 创建链
runner = llm_with_tools | output_parser

# 运行链
# fmt: off
response = runner.invoke(
    {
        "input": "What's the PE ratio of Microsoft?"
    }
)
# fmt: on

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API 参考

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FMPDataToolkit

用于创建 FMP API 工具集合的主类： 
from typing import Any

from langchain.tools import BaseTool


class FMPDataToolkit:
    """Creates a collection of FMP data tools based on queries."""

    def __init__(
        self,
        query: str | None = None,
        num_results: int = 3,
        similarity_threshold: float = 0.3,
        cache_dir: str | None = None,
    ): ...

    def get_tools(self) -> list[BaseTool]:
        """Returns a list of relevant FMP API tools based on the query."""
        ...

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FMPDataTool

自动选择合适 FMP 端点的统一工具： 
# fmt: off
class FMPDataTool:
    """Single unified tool for accessing FMP data through natural language."""

    def __init__(
            self,
            max_iterations: int = 3,
            temperature: float = 0.0,
    ): ...

    def invoke(
            self,
            input: dict[str, Any],
    ) -> str | dict[str, Any]:
        """Execute a natural language query against FMP API."""
        ...

# fmt: on

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ResponseFormat

用于控制响应格式的枚举类： 
from enum import Enum


class ResponseFormat(str, Enum):
    RAW = "raw"  # 原始 API 响应
    ANALYSIS = "text"  # 自然语言分析
    BOTH = "both"  # 原始数据和分析均返回
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