https://github.com/lazauk/aoai-gptfunctioncalling-stock
Use of Function Calling with Azure OpenAI GPT models to retrieve the lowest and highest stock prices for specific symbol and date.
https://github.com/lazauk/aoai-gptfunctioncalling-stock
ai azure function-calling gpt openai openai-function-call
Last synced: 18 days ago
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Use of Function Calling with Azure OpenAI GPT models to retrieve the lowest and highest stock prices for specific symbol and date.
- Host: GitHub
- URL: https://github.com/lazauk/aoai-gptfunctioncalling-stock
- Owner: LazaUK
- License: mit
- Created: 2023-07-20T21:38:09.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2023-12-19T17:10:45.000Z (over 1 year ago)
- Last Synced: 2025-04-07T18:06:57.472Z (about 1 month ago)
- Topics: ai, azure, function-calling, gpt, openai, openai-function-call
- Language: Jupyter Notebook
- Homepage:
- Size: 52.7 KB
- Stars: 3
- Watchers: 1
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Azure OpenAI - Function Calling to retrieve Stock Price
As of 20th of July 2023, Azure OpenAI now supports the use of Function Calling with GPT-3.5 and GPT-4 models. This repo contains Jupyter notebook to test functional calling first-hand by retrieving stock price from [Alpha Vantage API](https://www.alphavantage.co/).
### 0. Pre-requisites:
1. Install the latest version of openai Python package and set **openai.api_version** code variable to July 2023 version or above;
``` Python
openai.api_version = "2023-07-01-preview"
```
3. Retrieve GPT model deployment name, API endpoint and API key from Azure OpenAI settings and assign them to "**OPENAI_API_DEPLOY**", "**OPENAI_API_BASE**" and "**OPENAI_API_KEY**" environment variables, respectively;
4. Generator your Alpha Vantage API key on the vendor's Web site and assign it to "**ALPHAVANTAGE_API_KEY**" environment variable.
### 1. Function to get stock price from Alpha Vantage API:
We'll use **get_stock_price** custom function to interact with stock API and retrieve the lowest and highest prices on specific date for the stock symbol of interest. JSON structure returned by API endpoint is parsed to extract only specific time series values.
``` Python
def get_stock_price(symbol, date):
print(f"Getting stock price for {symbol} on {date}")
try:
stock_url = f"https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&symbol={symbol}&apikey={av_api_key}"
stock_data = requests.get(stock_url)
stock_low = stock_data.json()["Time Series (Daily)"][date]["3. low"]
stock_high = stock_data.json()["Time Series (Daily)"][date]["2. high"]
return stock_low, stock_high
except:
return "NA", "NA"
```
### 2. JSON structure for GPT function definition:
Azure OpenAI GPT models v0613 were trained to understand function structure that contain "**name**" and "**parameters**" fields. In my example, I indicate that the model can extract and match 2 mandatory properties: stock symbol for requested company and date in _YYYY-MM-DD_ format.
``` JSON
functions = [
{
"name": "get_stock_price",
"description": "Retrieve lowest and highest stock price for a given stock symbol and date",
"parameters": {
"type": "object",
"properties": {
"symbol": {
"type": "string",
"description": "Stock symbol, for example MSFT for Microsoft"
},
"date": {
"type": "string",
"description": "Date in YYYY-MM-DD format"
}
},
"required": ["symbol", "date"],
}
}
]
```
### 3. Helper function to check provided arguments:
I copied function "**check_args**" from [Azure Samples code](https://github.com/Azure-Samples/openai/tree/main/Basic_Samples/Functions) "as is". It helps to verify whether GPT model provided all requirements arguments / parameters, and also if it tried to submit ones which our function doesn't expect.
### 4. Helper function to interact with Azure OpenAI GPT model:
This helper function follows 3-step logic:
1. First, it submits user's prompts, informs GPT model about available functions and sets calling mode to "**auto**", so that the model automatically decides whether it wants to call a function based on the prompt details and matching function's capabilities
``` Python
response = openai.ChatCompletion.create(
deployment_id=aoai_deployment,
messages=messages,
functions=functions,
function_call="auto",
)
```
2. Next, it checks what functions the model wanted to call, if any.
``` Python
response_message = response["choices"][0]["message"]
# Step 2: Check if GPT model wanted to call a function
if response_message.get("function_call"):
print("Recommended function call:")
print(response_message.get("function_call"))
print()
```
3. Finally, if there is a matching function and it passes previous help function's test on validity of its arguments, I call the target function with parameter values extracted from the user's prompt.
``` Python
function_response1, function_response2 = function_to_call(function_args["symbol"], function_args["date"])
function_response = f"Lowest stock price: {function_response1}, Highest stock price: {function_response2}"
```
### 5. Practical testing:
The last cell in the notebook helps to test everything end to end. On requested stock price for Microsoft shares from 20th of July, 2023 it correctly retrieves the lowest and highest prices of the day.
``` JSON
Recommended function call:
{
"name": "get_stock_price",
"arguments": "{\n \"symbol\": \"MSFT\",\n \"date\": \"2023-07-20\"\n}"
}
Getting stock price for MSFT on 2023-07-20
Output of function call:
Lowest stock price: 345.3700, Highest stock price: 357.9700
Messages in second request:
{'role': 'user', 'content': 'What was the stock price of Microsoft shares on 20th of July 2023?'}
{'role': 'assistant', 'name': 'get_stock_price', 'content': '{\n "symbol": "MSFT",\n "date": "2023-07-20"\n}'}
{'role': 'function', 'name': 'get_stock_price', 'content': 'Lowest stock price: 345.3700, Highest stock price: 357.9700'}
{
"role": "assistant",
"content": "On the 20th of July 2023, the stock price of Microsoft (MSFT) had a low of $345.37 and a high of $357.97."
}
```