mirror of https://github.com/hwchase17/langchain
community: Add stock market tools from financialdatasets.ai (#25025)
**Description:** In this PR, I am adding three stock market tools from financialdatasets.ai (my API!): - get balance sheets - get cash flow statements - get income statements Twitter handle: [@virattt](https://twitter.com/virattt) --------- Co-authored-by: Erick Friis <erick@langchain.dev>pull/25123/head
Virat Singh
1 month ago
committed by
GitHub
parent
267855b3c1
commit
264ab96980
@ -0,0 +1,313 @@
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{
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"cells": [
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{
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"cell_type": "raw",
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"id": "afaf8039",
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"metadata": {},
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"source": [
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"---\n",
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"sidebar_label: financial datasets\n",
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"# FinancialDatasetsToolkit\n",
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"\n",
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"The [financial datasets](https://financialdatasets.ai/) stock market API provides REST endpoints that let you get financial data for 16,000+ tickers spanning 30+ years.\n",
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"\n",
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"## Setup\n",
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"\n",
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"To use this toolkit, you need two API keys:\n",
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"\n",
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"`FINANCIAL_DATASETS_API_KEY`: Get it from [financialdatasets.ai](https://financialdatasets.ai/).\n",
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"`OPENAI_API_KEY`: Get it from [OpenAI](https://platform.openai.com/)."
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"import getpass\n",
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"import os\n",
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"\n",
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"os.environ[\"FINANCIAL_DATASETS_API_KEY\"] = getpass.getpass()"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"### Installation\n",
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"\n",
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"This toolkit lives in the `langchain-community` package."
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "652d6238-1f87-422a-b135-f5abbb8652fc",
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install -qU langchain-community"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a38cde65-254d-4219-a441-068766c0d4b5",
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"metadata": {},
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"source": [
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"## Instantiation\n",
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"\n",
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"Now we can instantiate our toolkit:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_community.agent_toolkits.financial_datasets.toolkit import (\n",
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" FinancialDatasetsToolkit,\n",
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")\n",
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"from langchain_community.utilities.financial_datasets import FinancialDatasetsAPIWrapper\n",
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"\n",
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"api_wrapper = FinancialDatasetsAPIWrapper(\n",
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" financial_datasets_api_key=os.environ[\"FINANCIAL_DATASETS_API_KEY\"]\n",
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")\n",
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"toolkit = FinancialDatasetsToolkit(api_wrapper=api_wrapper)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5c5f2839-4020-424e-9fc9-07777eede442",
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"metadata": {},
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"source": [
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"## Tools\n",
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"\n",
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"View available tools:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "51a60dbe-9f2e-4e04-bb62-23968f17164a",
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"metadata": {},
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"outputs": [],
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"source": [
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"tools = toolkit.get_tools()"
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"## Use within an agent\n",
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"\n",
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"Let's equip our agent with the FinancialDatasetsToolkit and ask financial questions."
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"system_prompt = \"\"\"\n",
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"You are an advanced financial analysis AI assistant equipped with specialized tools\n",
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"to access and analyze financial data. Your primary function is to help users with\n",
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"financial analysis by retrieving and interpreting income statements, balance sheets,\n",
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"and cash flow statements for publicly traded companies.\n",
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"\n",
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"You have access to the following tools from the FinancialDatasetsToolkit:\n",
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"\n",
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"1. Balance Sheets: Retrieves balance sheet data for a given ticker symbol.\n",
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"2. Income Statements: Fetches income statement data for a specified company.\n",
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"3. Cash Flow Statements: Accesses cash flow statement information for a particular ticker.\n",
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"\n",
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"Your capabilities include:\n",
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"\n",
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"1. Retrieving financial statements for any publicly traded company using its ticker symbol.\n",
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"2. Analyzing financial ratios and metrics based on the data from these statements.\n",
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"3. Comparing financial performance across different time periods (e.g., year-over-year or quarter-over-quarter).\n",
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"4. Identifying trends in a company's financial health and performance.\n",
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"5. Providing insights on a company's liquidity, solvency, profitability, and efficiency.\n",
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"6. Explaining complex financial concepts in simple terms.\n",
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"\n",
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"When responding to queries:\n",
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"\n",
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"1. Always specify which financial statement(s) you're using for your analysis.\n",
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"2. Provide context for the numbers you're referencing (e.g., fiscal year, quarter).\n",
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"3. Explain your reasoning and calculations clearly.\n",
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"4. If you need more information to provide a complete answer, ask for clarification.\n",
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"5. When appropriate, suggest additional analyses that might be helpful.\n",
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"\n",
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"Remember, your goal is to provide accurate, insightful financial analysis to\n",
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"help users make informed decisions. Always maintain a professional and objective tone in your responses.\n",
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"\"\"\""
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"Instantiate the LLM."
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "310bf18e-6c9a-4072-b86e-47bc1fcca29d",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_core.tools import tool\n",
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"from langchain_openai import ChatOpenAI\n",
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"\n",
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"model = ChatOpenAI(model=\"gpt-4o\")"
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"Define a user query."
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "23e11cc9-abd6-4855-a7eb-799f45ca01ae",
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"metadata": {},
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"outputs": [],
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"source": [
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"query = \"What was AAPL's revenue in 2023? What about it's total debt in Q1 2024?\""
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"Create the agent."
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"from langchain.agents import AgentExecutor, create_tool_calling_agent\n",
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"from langchain_core.prompts import ChatPromptTemplate\n",
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"\n",
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"prompt = ChatPromptTemplate.from_messages(\n",
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" [\n",
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" (\"system\", system_prompt),\n",
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" (\"human\", \"{input}\"),\n",
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" # Placeholders fill up a **list** of messages\n",
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" (\"placeholder\", \"{agent_scratchpad}\"),\n",
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" ]\n",
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")\n",
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"\n",
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"\n",
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"agent = create_tool_calling_agent(model, tools, prompt)\n",
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"agent_executor = AgentExecutor(agent=agent, tools=tools)"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"Query the agent."
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"agent_executor.invoke({\"input\": query})"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"## API reference\n",
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"\n",
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"For detailed documentation of all `FinancialDatasetsToolkit` features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/agent_toolkits/langchain_community.agent_toolkits.financial_datasets.toolkit.FinancialDatasetsToolkit.html)."
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "markdown",
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"source": [],
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"metadata": {
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"collapsed": false
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}
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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@ -0,0 +1 @@
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"""financial datasets toolkit."""
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@ -0,0 +1,45 @@
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from __future__ import annotations
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from typing import List
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from langchain_core.pydantic_v1 import Field
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from langchain_core.tools import BaseToolkit
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from langchain_community.tools import BaseTool
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from langchain_community.tools.financial_datasets.balance_sheets import BalanceSheets
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from langchain_community.tools.financial_datasets.cash_flow_statements import (
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CashFlowStatements,
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)
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from langchain_community.tools.financial_datasets.income_statements import (
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IncomeStatements,
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)
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from langchain_community.utilities.financial_datasets import FinancialDatasetsAPIWrapper
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class FinancialDatasetsToolkit(BaseToolkit):
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"""Toolkit for interacting with financialdatasets.ai.
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Parameters:
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api_wrapper: The FinancialDatasets API Wrapper.
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"""
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api_wrapper: FinancialDatasetsAPIWrapper = Field(
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default_factory=FinancialDatasetsAPIWrapper
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)
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def __init__(self, api_wrapper: FinancialDatasetsAPIWrapper):
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super().__init__()
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self.api_wrapper = api_wrapper
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class Config:
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"""Pydantic config."""
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arbitrary_types_allowed = True
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def get_tools(self) -> List[BaseTool]:
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"""Get the tools in the toolkit."""
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return [
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BalanceSheets(api_wrapper=self.api_wrapper),
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CashFlowStatements(api_wrapper=self.api_wrapper),
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IncomeStatements(api_wrapper=self.api_wrapper),
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]
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"""financial datasets tools."""
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from langchain_community.tools.financial_datasets.balance_sheets import (
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BalanceSheets,
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)
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from langchain_community.tools.financial_datasets.cash_flow_statements import (
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CashFlowStatements,
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)
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from langchain_community.tools.financial_datasets.income_statements import (
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IncomeStatements,
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)
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__all__ = [
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"BalanceSheets",
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"CashFlowStatements",
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"IncomeStatements",
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]
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from typing import Optional, Type
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from langchain_core.callbacks import CallbackManagerForToolRun
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from langchain_core.pydantic_v1 import BaseModel, Field
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from langchain_core.tools import BaseTool
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from langchain_community.utilities.financial_datasets import FinancialDatasetsAPIWrapper
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class BalanceSheetsSchema(BaseModel):
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"""Input for BalanceSheets."""
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ticker: str = Field(
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description="The ticker symbol to fetch balance sheets for.",
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)
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period: str = Field(
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description="The period of the balance sheets. "
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"Possible values are: "
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"annual, quarterly, ttm. "
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"Default is 'annual'.",
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)
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limit: int = Field(
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description="The number of balance sheets to return. " "Default is 10.",
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)
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class BalanceSheets(BaseTool):
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"""
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Tool that gets balance sheets for a given ticker over a given period.
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"""
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mode: str = "get_balance_sheets"
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name: str = "balance_sheets"
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description: str = (
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"A wrapper around financial datasets's Balance Sheets API. "
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"This tool is useful for fetching balance sheets for a given ticker."
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"The tool fetches balance sheets for a given ticker over a given period."
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"The period can be annual, quarterly, or trailing twelve months (ttm)."
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"The number of balance sheets to return can also be "
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"specified using the limit parameter."
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)
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args_schema: Type[BalanceSheetsSchema] = BalanceSheetsSchema
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api_wrapper: FinancialDatasetsAPIWrapper = Field(..., exclude=True)
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def __init__(self, api_wrapper: FinancialDatasetsAPIWrapper):
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super().__init__(api_wrapper=api_wrapper)
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def _run(
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self,
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ticker: str,
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period: str,
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limit: Optional[int],
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run_manager: Optional[CallbackManagerForToolRun] = None,
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) -> str:
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"""Use the Balance Sheets API tool."""
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return self.api_wrapper.run(
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mode=self.mode,
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ticker=ticker,
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period=period,
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limit=limit,
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)
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@ -0,0 +1,62 @@
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from typing import Optional, Type
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from langchain_core.callbacks import CallbackManagerForToolRun
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from langchain_core.pydantic_v1 import BaseModel, Field
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from langchain_core.tools import BaseTool
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from langchain_community.utilities.financial_datasets import FinancialDatasetsAPIWrapper
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class CashFlowStatementsSchema(BaseModel):
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"""Input for CashFlowStatements."""
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ticker: str = Field(
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description="The ticker symbol to fetch cash flow statements for.",
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)
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period: str = Field(
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description="The period of the cash flow statement. "
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"Possible values are: "
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"annual, quarterly, ttm. "
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"Default is 'annual'.",
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)
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limit: int = Field(
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description="The number of cash flow statements to return. " "Default is 10.",
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)
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class CashFlowStatements(BaseTool):
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"""
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Tool that gets cash flow statements for a given ticker over a given period.
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"""
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mode: str = "get_cash_flow_statements"
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name: str = "cash_flow_statements"
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description: str = (
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"A wrapper around financial datasets's Cash Flow Statements API. "
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"This tool is useful for fetching cash flow statements for a given ticker."
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"The tool fetches cash flow statements for a given ticker over a given period."
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"The period can be annual, quarterly, or trailing twelve months (ttm)."
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"The number of cash flow statements to return can also be "
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"specified using the limit parameter."
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)
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args_schema: Type[CashFlowStatementsSchema] = CashFlowStatementsSchema
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api_wrapper: FinancialDatasetsAPIWrapper = Field(..., exclude=True)
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def __init__(self, api_wrapper: FinancialDatasetsAPIWrapper):
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super().__init__(api_wrapper=api_wrapper)
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def _run(
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self,
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ticker: str,
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period: str,
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limit: Optional[int],
|
||||
run_manager: Optional[CallbackManagerForToolRun] = None,
|
||||
) -> str:
|
||||
"""Use the Cash Flow Statements API tool."""
|
||||
return self.api_wrapper.run(
|
||||
mode=self.mode,
|
||||
ticker=ticker,
|
||||
period=period,
|
||||
limit=limit,
|
||||
)
|
||||
@ -0,0 +1,62 @@
|
||||
from typing import Optional, Type
|
||||
|
||||
from langchain_core.callbacks import CallbackManagerForToolRun
|
||||
from langchain_core.pydantic_v1 import BaseModel, Field
|
||||
from langchain_core.tools import BaseTool
|
||||
|
||||
from langchain_community.utilities.financial_datasets import FinancialDatasetsAPIWrapper
|
||||
|
||||
|
||||
class IncomeStatementsSchema(BaseModel):
|
||||
"""Input for IncomeStatements."""
|
||||
|
||||
ticker: str = Field(
|
||||
description="The ticker symbol to fetch income statements for.",
|
||||
)
|
||||
period: str = Field(
|
||||
description="The period of the income statement. "
|
||||
"Possible values are: "
|
||||
"annual, quarterly, ttm. "
|
||||
"Default is 'annual'.",
|
||||
)
|
||||
limit: int = Field(
|
||||
description="The number of income statements to return. " "Default is 10.",
|
||||
)
|
||||
|
||||
|
||||
class IncomeStatements(BaseTool):
|
||||
"""
|
||||
Tool that gets income statements for a given ticker over a given period.
|
||||
"""
|
||||
|
||||
mode: str = "get_income_statements"
|
||||
name: str = "income_statements"
|
||||
description: str = (
|
||||
"A wrapper around financial datasets's Income Statements API. "
|
||||
"This tool is useful for fetching income statements for a given ticker."
|
||||
"The tool fetches income statements for a given ticker over a given period."
|
||||
"The period can be annual, quarterly, or trailing twelve months (ttm)."
|
||||
"The number of income statements to return can also be "
|
||||
"specified using the limit parameter."
|
||||
)
|
||||
args_schema: Type[IncomeStatementsSchema] = IncomeStatementsSchema
|
||||
|
||||
api_wrapper: FinancialDatasetsAPIWrapper = Field(..., exclude=True)
|
||||
|
||||
def __init__(self, api_wrapper: FinancialDatasetsAPIWrapper):
|
||||
super().__init__(api_wrapper=api_wrapper)
|
||||
|
||||
def _run(
|
||||
self,
|
||||
ticker: str,
|
||||
period: str,
|
||||
limit: Optional[int],
|
||||
run_manager: Optional[CallbackManagerForToolRun] = None,
|
||||
) -> str:
|
||||
"""Use the Income Statements API tool."""
|
||||
return self.api_wrapper.run(
|
||||
mode=self.mode,
|
||||
ticker=ticker,
|
||||
period=period,
|
||||
limit=limit,
|
||||
)
|
||||
@ -0,0 +1,135 @@
|
||||
"""
|
||||
Util that calls several of financial datasets stock market REST APIs.
|
||||
Docs: https://docs.financialdatasets.ai/
|
||||
"""
|
||||
|
||||
import json
|
||||
from typing import Any, List, Optional
|
||||
|
||||
import requests
|
||||
from langchain_core.pydantic_v1 import BaseModel
|
||||
from langchain_core.utils import get_from_dict_or_env
|
||||
|
||||
FINANCIAL_DATASETS_BASE_URL = "https://api.financialdatasets.ai/"
|
||||
|
||||
|
||||
class FinancialDatasetsAPIWrapper(BaseModel):
|
||||
"""Wrapper for financial datasets API."""
|
||||
|
||||
financial_datasets_api_key: Optional[str] = None
|
||||
|
||||
def __init__(self, **data: Any):
|
||||
super().__init__(**data)
|
||||
self.financial_datasets_api_key = get_from_dict_or_env(
|
||||
data, "financial_datasets_api_key", "FINANCIAL_DATASETS_API_KEY"
|
||||
)
|
||||
|
||||
def get_income_statements(
|
||||
self,
|
||||
ticker: str,
|
||||
period: str,
|
||||
limit: Optional[int],
|
||||
) -> Optional[dict]:
|
||||
"""
|
||||
Get the income statements for a stock `ticker` over a `period` of time.
|
||||
|
||||
:param ticker: the stock ticker
|
||||
:param period: the period of time to get the balance sheets for.
|
||||
Possible values are: annual, quarterly, ttm.
|
||||
:param limit: the number of results to return, default is 10
|
||||
:return: a list of income statements
|
||||
"""
|
||||
url = (
|
||||
f"{FINANCIAL_DATASETS_BASE_URL}financials/income-statements/"
|
||||
f"?ticker={ticker}"
|
||||
f"&period={period}"
|
||||
f"&limit={limit if limit else 10}"
|
||||
)
|
||||
|
||||
# Add the api key to the headers
|
||||
headers = {"X-API-KEY": self.financial_datasets_api_key}
|
||||
|
||||
# Execute the request
|
||||
response = requests.get(url, headers=headers)
|
||||
data = response.json()
|
||||
|
||||
return data.get("income_statements", None)
|
||||
|
||||
def get_balance_sheets(
|
||||
self,
|
||||
ticker: str,
|
||||
period: str,
|
||||
limit: Optional[int],
|
||||
) -> List[dict]:
|
||||
"""
|
||||
Get the balance sheets for a stock `ticker` over a `period` of time.
|
||||
|
||||
:param ticker: the stock ticker
|
||||
:param period: the period of time to get the balance sheets for.
|
||||
Possible values are: annual, quarterly, ttm.
|
||||
:param limit: the number of results to return, default is 10
|
||||
:return: a list of balance sheets
|
||||
"""
|
||||
url = (
|
||||
f"{FINANCIAL_DATASETS_BASE_URL}financials/balance-sheets/"
|
||||
f"?ticker={ticker}"
|
||||
f"&period={period}"
|
||||
f"&limit={limit if limit else 10}"
|
||||
)
|
||||
|
||||
# Add the api key to the headers
|
||||
headers = {"X-API-KEY": self.financial_datasets_api_key}
|
||||
|
||||
# Execute the request
|
||||
response = requests.get(url, headers=headers)
|
||||
data = response.json()
|
||||
|
||||
return data.get("balance_sheets", None)
|
||||
|
||||
def get_cash_flow_statements(
|
||||
self,
|
||||
ticker: str,
|
||||
period: str,
|
||||
limit: Optional[int],
|
||||
) -> List[dict]:
|
||||
"""
|
||||
Get the cash flow statements for a stock `ticker` over a `period` of time.
|
||||
|
||||
:param ticker: the stock ticker
|
||||
:param period: the period of time to get the balance sheets for.
|
||||
Possible values are: annual, quarterly, ttm.
|
||||
:param limit: the number of results to return, default is 10
|
||||
:return: a list of cash flow statements
|
||||
"""
|
||||
|
||||
url = (
|
||||
f"{FINANCIAL_DATASETS_BASE_URL}financials/cash-flow-statements/"
|
||||
f"?ticker={ticker}"
|
||||
f"&period={period}"
|
||||
f"&limit={limit if limit else 10}"
|
||||
)
|
||||
|
||||
# Add the api key to the headers
|
||||
headers = {"X-API-KEY": self.financial_datasets_api_key}
|
||||
|
||||
# Execute the request
|
||||
response = requests.get(url, headers=headers)
|
||||
data = response.json()
|
||||
|
||||
return data.get("cash_flow_statements", None)
|
||||
|
||||
def run(self, mode: str, ticker: str, **kwargs: Any) -> str:
|
||||
if mode == "get_income_statements":
|
||||
period = kwargs.get("period", "annual")
|
||||
limit = kwargs.get("limit", 10)
|
||||
return json.dumps(self.get_income_statements(ticker, period, limit))
|
||||
elif mode == "get_balance_sheets":
|
||||
period = kwargs.get("period", "annual")
|
||||
limit = kwargs.get("limit", 10)
|
||||
return json.dumps(self.get_balance_sheets(ticker, period, limit))
|
||||
elif mode == "get_cash_flow_statements":
|
||||
period = kwargs.get("period", "annual")
|
||||
limit = kwargs.get("limit", 10)
|
||||
return json.dumps(self.get_cash_flow_statements(ticker, period, limit))
|
||||
else:
|
||||
raise ValueError(f"Invalid mode {mode} for financial datasets API.")
|
||||
Loading…
Reference in New Issue