Running and Testing Chat function Locally

You can run the Python function for your chat function itself by writing a main() function, or you can call the testbench_prompts.py script that runs a similar pipeline to the module.py.

python src/agents/utils/testbench_prompts.py

You can also use the test_prompts.py script to test the chat function with example inputs from Lambda Feedback questions and synthetic conversations.

python src/agents/utils/test_prompts.py

Testing using the Docker Image

You can also build and run the docker pipeline for the chat function. The chatbot associated with the chat function is deployed onto a AWS Lambda serverless cloud function using the docker image. Hence, for final testing of your chatbot, we recommend completing those steps.

Build the Docker Image

To build the Docker image, run the following command in the root folder of the project (where the Dockerfile is located):

docker build -t llm_chat .

Running the Docker Image

To run the Docker image, use the following command:

Without .env file:

docker run -e OPENAI_API_KEY={your key} -e OPENAI_MODEL={your LLM chosen model name} -p 8080:8080 llm_chat

With container name (for interaction, e.g. copying file from inside the docker container):

docker run --env-file .env -it --name my-lambda-container -p 8080:8080 llm_chat

This will start the evaluation function and expose it on port 8080 and it will be open to be curl:

curl --location 'http://localhost:8080/2015-03-31/functions/function/invocations' \
--header 'Content-Type: application/json' \
--data '{"body":"{\"message\": \"hi\", \"params\": {\"conversation_id\": \"12345Test\", \"conversation_history\": [{\"type\": \"user\", \"content\": \"hi\"}]}}"}'

Call Docker Container From Postman

POST URL:

http://localhost:8080/2015-03-31/functions/function/invocations

Body:

{"body":"{\"message\": \"hi\", \"params\": {\"conversation_id\": \"12345Test\", \"conversation_history\": [{\"type\": \"user\", \"content\": \"hi\"}]}}"}

Body with optional Params:

{
    "message":"hi",
    "params":{
        "conversation_id":"12345Test",
        "conversation_history":[{"type":"user","content":"hi"}],
        "summary":" ",
        "conversational_style":" ",
        "question_response_details": "",
        "include_test_data": true,
        "agent_type": {agent_name}
    }
}