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import os | |
import pandas as pd | |
import requests | |
from pydantic import Field, BaseModel | |
from omegaconf import OmegaConf | |
from vectara_agent.agent import Agent | |
from vectara_agent.tools import ToolsFactory, VectaraToolFactory | |
from dotenv import load_dotenv | |
load_dotenv(override=True) | |
tickers = { | |
"AAPL": "Apple Computer", | |
"GOOG": "Google", | |
"AMZN": "Amazon", | |
"SNOW": "Snowflake", | |
"TEAM": "Atlassian", | |
"TSLA": "Tesla", | |
"NVDA": "Nvidia", | |
"MSFT": "Microsoft", | |
"AMD": "Advanced Micro Devices", | |
"INTC": "Intel", | |
"NFLX": "Netflix", | |
} | |
years = [2020, 2021, 2022, 2023, 2024] | |
initial_prompt = "How can I help you today?" | |
def create_assistant_tools(cfg): | |
def get_company_info() -> list[str]: | |
""" | |
Returns a dictionary of companies you can query about. Always check this before using any other tool. | |
The output is a dictionary of valid ticker symbols mapped to company names. | |
You can use this to identify the companies you can query about, and their ticker information. | |
""" | |
return tickers | |
def get_valid_years() -> list[str]: | |
""" | |
Returns a list of the years for which financial reports are available. | |
Always check this before using any other tool. | |
""" | |
return years | |
# Tool to get the income statement for a given company and year using the FMP API | |
def get_income_statement( | |
ticker=Field(description="the ticker symbol of the company."), | |
year=Field(description="the year for which to get the income statement."), | |
) -> str: | |
""" | |
Get the income statement for a given company and year using the FMP (https://financialmodelingprep.com) API. | |
Returns a dictionary with the income statement data. All data is in USD, but you can convert it to more compact form like K, M, B. | |
""" | |
fmp_api_key = os.environ.get("FMP_API_KEY", None) | |
if fmp_api_key is None: | |
return "FMP_API_KEY environment variable not set. This tool does not work." | |
url = f"https://financialmodelingprep.com/api/v3/income-statement/{ticker}?apikey={fmp_api_key}" | |
response = requests.get(url) | |
if response.status_code == 200: | |
data = response.json() | |
income_statement = pd.DataFrame(data) | |
income_statement["date"] = pd.to_datetime(income_statement["date"]) | |
income_statement_specific_year = income_statement[ | |
income_statement["date"].dt.year == int(year) | |
] | |
values_dict = income_statement_specific_year.to_dict(orient="records")[0] | |
return f"Financial results: {', '.join([f'{key}: {value}' for key, value in values_dict.items() if key not in ['date', 'cik', 'link', 'finalLink']])}" | |
return "FMP API returned error. This tool does not work." | |
class QueryTranscriptsArgs(BaseModel): | |
query: str = Field(..., description="The user query.") | |
year: int = Field(..., description=f"The year. An integer between {min(years)} and {max(years)}.") | |
ticker: str = Field(..., description=f"The company ticker. Must be a valid ticket symbol from the list {tickers.keys()}.") | |
vec_factory = VectaraToolFactory(vectara_api_key=cfg.api_key, | |
vectara_customer_id=cfg.customer_id, | |
vectara_corpus_id=cfg.corpus_id) | |
tools_factory = ToolsFactory() | |
ask_transcripts = vec_factory.create_rag_tool( | |
tool_name = "ask_transcripts", | |
tool_description = """ | |
Given a company name and year, responds to a user question about the company, based on analyst call transcripts about the company's financial reports for that year. | |
You can ask this tool any question about the company including risks, opportunities, financial performance, competitors and more. | |
""", | |
tool_args_schema = QueryTranscriptsArgs, | |
reranker = "multilingual_reranker_v1", rerank_k = 100, | |
n_sentences_before = 2, n_sentences_after = 2, lambda_val = 0.005, | |
summary_num_results = 10, | |
vectara_summarizer = 'vectara-summary-ext-24-05-med-omni', | |
include_citations = False, | |
) | |
return ( | |
[tools_factory.create_tool(tool) for tool in | |
[ | |
get_company_info, | |
get_valid_years, | |
get_income_statement, | |
] | |
] + | |
tools_factory.standard_tools() + | |
tools_factory.financial_tools() + | |
tools_factory.guardrail_tools() + | |
[ask_transcripts] | |
) | |
def initialize_agent(_cfg, update_func=None): | |
financial_bot_instructions = """ | |
- You are a helpful financial assistant, with expertise in financial reporting, in conversation with a user. | |
- Respond in a compact format by using appropriate units of measure (e.g., K for thousands, M for millions, B for billions). | |
Do not report the same number twice (e.g. $100K and 100,000 USD). | |
- Always check the get_company_info and get_valid_years tools to validate company and year are valid. | |
- Do not include URLS unless they are from one of the tools. | |
- When querying a tool for a numeric value or KPI, use a concise and non-ambiguous description of what you are looking for. | |
- If you calculate a metric, make sure you have all the necessary information to complete the calculation. Don't guess. | |
""" | |
agent = Agent( | |
tools=create_assistant_tools(_cfg), | |
topic="Financial data, annual reports and 10-K filings", | |
custom_instructions=financial_bot_instructions, | |
update_func=update_func | |
) | |
agent.report() | |
return agent | |
def get_agent_config() -> OmegaConf: | |
companies = ", ".join(tickers.values()) | |
cfg = OmegaConf.create({ | |
'customer_id': str(os.environ['VECTARA_CUSTOMER_ID']), | |
'corpus_id': str(os.environ['VECTARA_CORPUS_ID']), | |
'api_key': str(os.environ['VECTARA_API_KEY']), | |
'examples': os.environ.get('QUERY_EXAMPLES', None), | |
'demo_name': "finance-chat", | |
'demo_welcome': "Welcome to the Financial Assistant demo.", | |
'demo_description': f"This assistant can help you with any questions about the financials of several companies:\n\n **{companies}**.\n" | |
}) | |
return cfg | |