This version of the application use the LangChain framework, which provides many features and benefits that we'll use in later iterations of the application.
- Python 3.9+
- Splunk Distribution of the OpenTelemetry Collector
- An OpenAI account (set via the
OPENAI_API_KEYenvironment variable) that has access to utilize the API
Execute the following commands to run the application:
# clone the repo if you haven't already
git clone https://github.com/dmitchsplunk/langchain-with-splunk.git
# navigate to the directory repo
cd langchain-with-splunk/v2
# create a virtual environment
python3 -m venv openai-env
# activate the virtual environment
source openai-env/bin/activate
# install the required packages
pip3 install -r ./requirements.txt
# define the service name and environment
export OTEL_SERVICE_NAME=my-llm-app
export OTEL_RESOURCE_ATTRIBUTES='deployment.environment=test'
# run the application
splunk-py-trace flask run -p 8080
We can use the existing question.json file with the following content:
{
"question":"Hello, World!"
}
Then open a second terminal window to exercise the application using the following curl command.
curl -d "@question.json" -H "Content-Type: application/json" -X POST http://localhost:8080/askquestion
It will respond with something like:
Hello! How can I assist you today?
You should see a trace in Splunk Observability Cloud such as the following:
