README.md 2.0 KB
'Groqing the Stock Market' with Llama 3 Function Calling
This is a simple application that leverages the yfinance API to provide insights into stocks and their prices. The application uses the Llama 3 model on Groq in conjunction with Langchain to call functions based on the user prompt.
Key Functions
get_stock_info(symbol, key): This function fetches various information about a given stock symbol. The information can be anything from the company's address to its financial ratios. The 'key' parameter specifies the type of information to fetch.
get_historical_price(symbol, start_date, end_date): This function fetches the historical stock prices for a given symbol from a specified start date to an end date. The returned data is a DataFrame with the date and closing price of the stock.
plot_price_over_time(historical_price_dfs): This function takes a list of DataFrames (each containing historical price data for a stock) and plots the prices over time using Plotly. The plot is saved to the same directory as the app.
call_functions(llm_with_tools, user_prompt): This function takes the user's question, invokes the appropriate tool (either get_stock_info or get_historical_price), and generates a response. If the user asked for historical prices, it also calls plot_price_over_time to generate a plot.
Function Calling
The function calling in this application is handled by the Groq API, abstracted with Langchain. When the user asks a question, the application invokes the appropriate tool with parameters based on the user's question. The tool's output is then used to generate a response.
Usage
You will need to store a valid Groq API Key as a secret to proceed with this example. You can generate one for free here.
You can fork and run this application on Replit or run it on the command line with python main.py.