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YourFunctionName

Brief description of what this chat function does, from the developer perspective

Inputs

Specific input parameters which can be supplied when the calling this chat function.

Outputs

Output schema/values for this function

Examples

List of example inputs and outputs for this function, each under a different sub-heading

Testing the Chat Function

To test your function, you can run the unit tests, call the code directly through a python script, or build the respective chat function docker container locally and call it through an API request. Below you can find details on those processes.

Run Unit Tests

You can run the unit tests using pytest.

pytest

Run the Chat Script

You can run the Python function itself. Make sure to have a main function in either src/module.py or index.py.

python src/module.py

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

python tests/manual_agent_run.py

Calling the Docker Image Locally

To build the Docker image, run the following command:

docker build -t llm_chat .

Running the Docker Image

To run the Docker image, use the following command:

A. Without .env file:
docker run -e OPENAI_API_KEY={your key} -e OPENAI_MODEL={your LLM chosen model name} -p 8080:8080 llm_chat
B. 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 chat 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\",

Call Docker Container

A. Call Docker with Python Requests

In the tests/ folder you can find the manual_agent_requests.py script that calls the POST URL of the running docker container. It reads any kind of input files with the expected schema. You can use this to test your curl calls of the chatbot.

B. Call Docker Container through API request

POST URL:

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

Body (stringified within body for API request):

{"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,
    }
}