You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
117 lines
3.3 KiB
117 lines
3.3 KiB
import os
|
|
|
|
import requests
|
|
|
|
from swarms import Agent
|
|
from swarm_models import OpenAIChat
|
|
|
|
# Get the OpenAI API key from the environment variable
|
|
api_key = os.getenv("OPENAI_API_KEY")
|
|
|
|
# Create an instance of the OpenAIChat class
|
|
model = OpenAIChat(
|
|
api_key=api_key, model_name="gpt-4o-mini", temperature=0.1
|
|
)
|
|
|
|
|
|
def fetch_financial_news(
|
|
query: str = "Nvidia news", num_articles: int = 5
|
|
) -> str:
|
|
"""
|
|
Fetches financial news from the Google News API and returns a formatted string of the top news.
|
|
|
|
Args:
|
|
api_key (str): Your Google News API key.
|
|
query (str): The query term to search for news. Default is "financial".
|
|
num_articles (int): The number of top articles to fetch. Default is 5.
|
|
|
|
Returns:
|
|
str: A formatted string of the top financial news articles.
|
|
|
|
Raises:
|
|
ValueError: If the API response is invalid or there are no articles found.
|
|
requests.exceptions.RequestException: If there is an error with the request.
|
|
"""
|
|
url = "https://newsapi.org/v2/everything"
|
|
params = {
|
|
"q": query,
|
|
"apiKey": "ceabc81a7d8f45febfedadb27177f3a3",
|
|
"pageSize": num_articles,
|
|
"sortBy": "relevancy",
|
|
}
|
|
|
|
try:
|
|
response = requests.get(url, params=params)
|
|
response.raise_for_status()
|
|
data = response.json()
|
|
|
|
if "articles" not in data or len(data["articles"]) == 0:
|
|
raise ValueError(
|
|
"No articles found or invalid API response."
|
|
)
|
|
|
|
articles = data["articles"]
|
|
formatted_articles = []
|
|
|
|
for i, article in enumerate(articles, start=1):
|
|
title = article.get("title", "No Title")
|
|
description = article.get("description", "No Description")
|
|
url = article.get("url", "No URL")
|
|
formatted_articles.append(
|
|
f"{i}. {title}\nDescription: {description}\nRead more: {url}\n"
|
|
)
|
|
|
|
return "\n".join(formatted_articles)
|
|
|
|
except requests.exceptions.RequestException as e:
|
|
print(f"Request Error: {e}")
|
|
raise
|
|
except ValueError as e:
|
|
print(f"Value Error: {e}")
|
|
raise
|
|
|
|
|
|
# # Example usage:
|
|
# api_key = "ceabc81a7d8f45febfedadb27177f3a3"
|
|
# print(fetch_financial_news(api_key))
|
|
|
|
|
|
# Initialize the agent
|
|
agent = Agent(
|
|
agent_name="Financial-Analysis-Agent",
|
|
# system_prompt=FINANCIAL_AGENT_SYS_PROMPT,
|
|
llm=model,
|
|
max_loops=2,
|
|
autosave=True,
|
|
# dynamic_temperature_enabled=True,
|
|
dashboard=False,
|
|
verbose=True,
|
|
streaming_on=True,
|
|
# interactive=True, # Set to False to disable interactive mode
|
|
dynamic_temperature_enabled=True,
|
|
saved_state_path="finance_agent.json",
|
|
tools=[fetch_financial_news],
|
|
# stopping_token="Stop!",
|
|
# interactive=True,
|
|
# docs_folder="docs", # Enter your folder name
|
|
# pdf_path="docs/finance_agent.pdf",
|
|
# sop="Calculate the profit for a company.",
|
|
# sop_list=["Calculate the profit for a company."],
|
|
user_name="swarms_corp",
|
|
# # docs=
|
|
# # docs_folder="docs",
|
|
retry_attempts=3,
|
|
# context_length=1000,
|
|
# tool_schema = dict
|
|
context_length=200000,
|
|
# tool_schema=
|
|
# tools
|
|
# agent_ops_on=True,
|
|
# long_term_memory=ChromaDB(docs_folder="artifacts"),
|
|
)
|
|
|
|
|
|
# Run the agent
|
|
response = agent("What are the latest financial news on Nvidia?")
|
|
print(response)
|