""" This example illustrates a more complex recipe to generate a structured financial research dictionary with
30 keys and values produced, using a combination of models and web services:
Models
1. slim-extract-tool
2. slim-summary-tool
3. bling-stablelm-3b-tool
Web Services
1. Yfinance - stock ticker
2. Wikipedia - company background information
The example shows how to extract keys from one source that can then be used as a lookup in a web service to
supplement the original source materials, and provide a secondary source, which can then also be prompted and
used to extract, analyze and summarize key information.
NOTE: to run this example, please install yfinance library, e.g., 'pip3 install yfinance'
"""
from llmware.web_services import YFinance
from llmware.models import ModelCatalog
from llmware.parsers import WikiParser
from importlib import util
if not util.find_spec("yfinance"):
print("\nto run this example, you need to install yfinance first, e.g., pip3 install yfinance")
# our input - financial news article
text=("BEAVERTON, Ore.--(BUSINESS WIRE)--NIKE, Inc. (NYSE:NKE) today reported fiscal 2024 financial results for its "
"third quarter ended February 29, 2024.) “We are making the necessary adjustments to drive NIKE’s next chapter "
"of growth Post this Third quarter revenues were slightly up on both a reported and currency-neutral basis* "
"at $12.4 billion NIKE Direct revenues were $5.4 billion, slightly up on a reported and currency-neutral basis "
"NIKE Brand Digital sales decreased 3 percent on a reported basis and 4 percent on a currency-neutral basis "
"Wholesale revenues were $6.6 billion, up 3 percent on a reported and currency-neutral basis Gross margin "
"increased 150 basis points to 44.8 percent, including a detriment of 50 basis points due to restructuring charges "
"Selling and administrative expense increased 7 percent to $4.2 billion, including $340 million of restructuring "
"charges Diluted earnings per share was $0.77, including $0.21 of restructuring charges. Excluding these "
"charges, Diluted earnings per share would have been $0.98* “We are making the necessary adjustments to "
"drive NIKE’s next chapter of growth,” said John Donahoe, President & CEO, NIKE, Inc. “We’re encouraged by "
"the progress we’ve seen, as we build a multiyear cycle of new innovation, sharpen our brand storytelling and "
"work with our wholesale partners to elevate and grow the marketplace.")
def research_example1():
""" End-to-end example generating 30 output key:value pairs """
# load three models in this example
model = ModelCatalog().load_model("slim-extract-tool", temperature=0.0, sample=False)
model2 = ModelCatalog().load_model("slim-summary-tool", sample=False,temperature=0.0,max_output=200)
model3 = ModelCatalog().load_model("bling-stablelm-3b-tool", sample=False, temperature=0.0)
research_summary = {}
# extract information from the source materials
extract_keys = ["stock ticker", "company name",
"total revenues", "restructuring charges",
"digital growth", "ceo comment", "quarter end date"]
print("\nStep 1 - extract information from source text\n")
for keys in extract_keys:
response = model.function_call(text,params=[keys])
dict_keys = keys.replace(" ", "_")
print(f"update: extracting - {keys} - {response['llm_response']}")
if dict_keys in response["llm_response"]:
value = response["llm_response"][dict_keys][0]
research_summary.update({dict_keys: value})
else:
print("could not find look up key successfully - ", response["llm_response"])
# secondary lookups using extracted information
print("\nStep 2 - use extracted stock ticker in web service lookup to YFinance\n")
if "stock_ticker" in research_summary:
ticker = research_summary["stock_ticker"]
# a little kludge related to yfinance api
ticker_core = ticker.split(":")[-1]
yf = YFinance().get_stock_summary(ticker=ticker_core)
print("yahoo finance stock info: ", yf)
research_summary.update({"current_stock_price": yf["currentPrice"]})
research_summary.update({"high_ltm": yf["fiftyTwoWeekHigh"]})
research_summary.update({"low_ltm": yf["fiftyTwoWeekLow"]})
research_summary.update({"trailing_pe": yf["trailingPE"]})
research_summary.update({"forward_pe": yf["forwardPE"]})
research_summary.update({"volume": yf["volume"]})
yf2 = YFinance().get_financial_summary(ticker=ticker_core)
print("yahoo finance fin info - ", yf2)
research_summary.update({"market_cap": yf2["marketCap"]})
research_summary.update({"price_to_sales": yf2["priceToSalesTrailing12Months"]})
research_summary.update({"revenue_growth": yf2["revenueGrowth"]})
research_summary.update({"ebitda": yf2["ebitda"]})
research_summary.update({"gross_margin": yf2["grossMargins"]})
research_summary.update({"currency": yf2["currency"]})
yf3 = YFinance().get_company_summary(ticker=ticker_core)
print("yahoo finance company info - ", yf3)
research_summary.update({"sector": yf3["sector"]})
research_summary.update({"website": yf3["website"]})
research_summary.update({"industry": yf3["industry"]})
research_summary.update({"employees": yf3["fullTimeEmployees"]})
execs = []
if "companyOfficers" in yf3:
for entries in yf3["companyOfficers"]:
if "totalPay" in entries:
pay = entries["totalPay"]
else:
pay = "pay-NA"
if "age" in entries:
age = entries["age"]
else:
age = "age-NA"
execs.append((entries["name"], entries["title"], age, pay))
research_summary.update({"officers": execs})
print("\nStep 3 - use extracted company name to lookup in Wikipedia web service - and add background data\n")
if "company_name" in research_summary:
company_name = research_summary["company_name"]
output = WikiParser().add_wiki_topic(company_name, target_results=1)
# get company summary
company_overview = ""
for i, blocks in enumerate(output["blocks"]):
if i < 3:
company_overview += blocks["text"]
# call summary model to summarize
print("-- calling summary model to summarize the first part of the Wikipedia article")
summary = model2.function_call(company_overview, params=["company history (5)"])
print("-- slim-summary - summary (5 points): ", summary)
research_summary.update({"summary": summary["llm_response"]})
# get founding date
print("\n-- calling extract model to get key piece of information from the Wikipedia article - company founding date")
response = model.function_call(company_overview, params=["founding date"])
print("-- slim-extract - founding date: ", response)
research_summary.update({"founding_date": response["llm_response"]["founding_date"][0]})
print("\n-- calling extract model to get a short company business")
response = model.function_call(company_overview, params=["company description"])
print("-- slim-extract - response: ", response)
research_summary.update({"company_description": response["llm_response"]["company_description"][0]})
# ask other questions directly
print("\n-- asking a question directly to the Wikipedia article about the business")
response = model3.inference("What is an overview of company's business?", add_context=company_overview)
print("-- bling-answer model - response: ", response)
research_summary.update({"business_overview": response["llm_response"] })
print("\n-- asking a question about the origin of the company's name")
response = model3.inference("What is the origin of the company's name?", add_context=company_overview)
print("-- bling-answer model - response: ", response)
research_summary.update({"origin_of_name": response["llm_response"]})
print("\n-- asking a question about the company's products")
response = model3.inference("What are the product names", add_context=company_overview)
print("-- bling-answer model - response: ", response)
research_summary.update({"products": response["llm_response"]})
print("\n\nStep 4 - Completed Research - Summary Output\n")
print("research summary: ", research_summary)
item_counter = 1
for keys, values in research_summary.items():
if isinstance(values, str):
values = values.replace("\n", "")
values = values.replace("\r", "")
values = values.replace("\t", "")
print(f"\t\t -- {item_counter} - \t - {keys.ljust(25)} - {str(values).ljust(40)}")
item_counter += 1
return research_summary
if __name__ == "__main__":
research_example1()