wehub-resource-sync
208 lines
9.3 KiB
Python
208 lines
9.3 KiB
Python
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""" Fast Start #9 - Function Calls with Web Services
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This example illustrates one of the most exciting combinations in LLM-based applications, specifically
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combining function calls with external web services to drive more complex automation patterns.
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This Fast Start example is derived from the original example at: /examples/Use_Cases/web_services_slim_fx.py
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Models
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1. slim-extract-tool
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2. slim-summary-tool
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3. bling-stablelm-3b-tool
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Web Services
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1. Yfinance - stock ticker
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2. Wikipedia - company background information
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The example shows how to extract keys from one source that can then be used as a lookup in a web service to
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supplement the original source materials, and provide a secondary source, which can then also be prompted and
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used to extract, analyze and summarize key information.
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NOTE: to run this example, please install yfinance library, e.g., 'pip3 install yfinance'
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"""
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from llmware.web_services import YFinance
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from llmware.models import ModelCatalog
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from llmware.parsers import WikiParser
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from importlib import util
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if not util.find_spec("yfinance"):
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print("\nto run this example, you need to install yfinance first, e.g., pip3 install yfinance")
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# our input - financial news article
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text=("BEAVERTON, Ore.--(BUSINESS WIRE)--NIKE, Inc. (NYSE:NKE) today reported fiscal 2024 financial results for its "
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"third quarter ended February 29, 2024.) “We are making the necessary adjustments to drive NIKE’s next chapter "
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"of growth Post this Third quarter revenues were slightly up on both a reported and currency-neutral basis* "
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"at $12.4 billion NIKE Direct revenues were $5.4 billion, slightly up on a reported and currency-neutral basis "
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"NIKE Brand Digital sales decreased 3 percent on a reported basis and 4 percent on a currency-neutral basis "
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"Wholesale revenues were $6.6 billion, up 3 percent on a reported and currency-neutral basis Gross margin "
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"increased 150 basis points to 44.8 percent, including a detriment of 50 basis points due to restructuring charges "
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"Selling and administrative expense increased 7 percent to $4.2 billion, including $340 million of restructuring "
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"charges Diluted earnings per share was $0.77, including $0.21 of restructuring charges. Excluding these "
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"charges, Diluted earnings per share would have been $0.98* “We are making the necessary adjustments to "
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"drive NIKE’s next chapter of growth,” said John Donahoe, President & CEO, NIKE, Inc. “We’re encouraged by "
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"the progress we’ve seen, as we build a multiyear cycle of new innovation, sharpen our brand storytelling and "
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"work with our wholesale partners to elevate and grow the marketplace.")
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def research_example1():
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""" End-to-end example generating 30 output key:value pairs """
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# load three models in this example
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model = ModelCatalog().load_model("slim-extract-tool", temperature=0.0, sample=False)
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model2 = ModelCatalog().load_model("slim-summary-tool", sample=False,temperature=0.0,max_output=200)
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model3 = ModelCatalog().load_model("bling-stablelm-3b-tool", sample=False, temperature=0.0)
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research_summary = {}
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# extract information from the source materials
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extract_keys = ["stock ticker", "company name",
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"total revenues", "restructuring charges",
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"digital growth", "ceo comment", "quarter end date"]
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print("\nStep 1 - extract information from source text\n")
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for keys in extract_keys:
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response = model.function_call(text,params=[keys])
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dict_keys = keys.replace(" ", "_")
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print(f"update: extracting - {keys} - {response['llm_response']}")
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if dict_keys in response["llm_response"]:
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value = response["llm_response"][dict_keys][0]
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research_summary.update({dict_keys: value})
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else:
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print("could not find look up key successfully - ", response["llm_response"])
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# secondary lookups using extracted information
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print("\nStep 2 - use extracted stock ticker in web service lookup to YFinance\n")
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if "stock_ticker" in research_summary:
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ticker = research_summary["stock_ticker"]
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# a little kludge related to yfinance api
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ticker_core = ticker.split(":")[-1]
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yf = YFinance().get_stock_summary(ticker=ticker_core)
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print("yahoo finance stock info: ", yf)
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research_summary.update({"current_stock_price": yf["currentPrice"]})
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research_summary.update({"high_ltm": yf["fiftyTwoWeekHigh"]})
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research_summary.update({"low_ltm": yf["fiftyTwoWeekLow"]})
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research_summary.update({"trailing_pe": yf["trailingPE"]})
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research_summary.update({"forward_pe": yf["forwardPE"]})
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research_summary.update({"volume": yf["volume"]})
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yf2 = YFinance().get_financial_summary(ticker=ticker_core)
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print("yahoo finance fin info - ", yf2)
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research_summary.update({"market_cap": yf2["marketCap"]})
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research_summary.update({"price_to_sales": yf2["priceToSalesTrailing12Months"]})
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research_summary.update({"revenue_growth": yf2["revenueGrowth"]})
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research_summary.update({"ebitda": yf2["ebitda"]})
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research_summary.update({"gross_margin": yf2["grossMargins"]})
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research_summary.update({"currency": yf2["currency"]})
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yf3 = YFinance().get_company_summary(ticker=ticker_core)
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print("yahoo finance company info - ", yf3)
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research_summary.update({"sector": yf3["sector"]})
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research_summary.update({"website": yf3["website"]})
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research_summary.update({"industry": yf3["industry"]})
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research_summary.update({"employees": yf3["fullTimeEmployees"]})
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execs = []
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if "companyOfficers" in yf3:
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for entries in yf3["companyOfficers"]:
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if "totalPay" in entries:
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pay = entries["totalPay"]
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else:
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pay = "pay-NA"
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if "age" in entries:
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age = entries["age"]
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else:
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age = "age-NA"
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execs.append((entries["name"], entries["title"], age, pay))
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research_summary.update({"officers": execs})
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print("\nStep 3 - use extracted company name to lookup in Wikipedia web service - and add background data\n")
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if "company_name" in research_summary:
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company_name = research_summary["company_name"]
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output = WikiParser().add_wiki_topic(company_name, target_results=1)
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# get company summary
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company_overview = ""
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for i, blocks in enumerate(output["blocks"]):
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if i < 3:
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company_overview += blocks["text"]
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# call summary model to summarize
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print("-- calling summary model to summarize the first part of the Wikipedia article")
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summary = model2.function_call(company_overview, params=["company history (5)"])
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print("-- slim-summary - summary (5 points): ", summary)
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research_summary.update({"summary": summary["llm_response"]})
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# get founding date
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print("\n-- calling extract model to get key piece of information from the Wikipedia article - company founding date")
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response = model.function_call(company_overview, params=["founding date"])
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print("-- slim-extract - founding date: ", response)
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research_summary.update({"founding_date": response["llm_response"]["founding_date"][0]})
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print("\n-- calling extract model to get a short company business")
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response = model.function_call(company_overview, params=["company description"])
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print("-- slim-extract - response: ", response)
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research_summary.update({"company_description": response["llm_response"]["company_description"][0]})
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# ask other questions directly
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print("\n-- asking a question directly to the Wikipedia article about the business")
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response = model3.inference("What is an overview of company's business?", add_context=company_overview)
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print("-- bling-answer model - response: ", response)
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research_summary.update({"business_overview": response["llm_response"] })
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print("\n-- asking a question about the origin of the company's name")
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response = model3.inference("What is the origin of the company's name?", add_context=company_overview)
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print("-- bling-answer model - response: ", response)
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research_summary.update({"origin_of_name": response["llm_response"]})
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print("\n-- asking a question about the company's products")
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response = model3.inference("What are the product names", add_context=company_overview)
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print("-- bling-answer model - response: ", response)
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research_summary.update({"products": response["llm_response"]})
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print("\n\nStep 4 - Completed Research - Summary Output\n")
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print("research summary: ", research_summary)
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item_counter = 1
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for keys, values in research_summary.items():
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if isinstance(values, str):
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values = values.replace("\n", "")
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values = values.replace("\r", "")
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values = values.replace("\t", "")
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print(f"\t\t -- {item_counter} - \t - {keys.ljust(25)} - {str(values).ljust(40)}")
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item_counter += 1
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return research_summary
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if __name__ == "__main__":
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research_example1()
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