Lambda Feedback Chat Function Boilerplate

This repository contains the code needed to develop a modular chatbot to be used on Lambda-Feedback platform [written in Python].

Quickstart

This chapter helps you to quickly set up a new Python chat module function using this repository.

Note

To develop this function further, you will require the following environment variables in your .env file:

> If you use OpenAI:
OPENAI_API_KEY
OPENAI_MODEL

> If you use GoogleAI:
GOOGLE_AI_API_KEY
GOOGLE_AI_MODEL

Note

If you decide to use another endpoint such as Azure or Ollama or any other, please update the github workflow files to use the right secrets and variables for testing.

> If you use Azure-OpenAI:
AZURE_OPENAI_API_KEY
AZURE_OPENAI_ENDPOINT
AZURE_OPENAI_API_VERSION
AZURE_OPENAI_CHAT_DEPLOYMENT_NAME
AZURE_OPENAI_EMBEDDING_3072_DEPLOYMENT
AZURE_OPENAI_EMBEDDING_1536_DEPLOYMENT
AZURE_OPENAI_EMBEDDING_3072_MODEL
AZURE_OPENAI_EMBEDDING_1536_MODEL

> For monitoring of the LLM calls (follow instructions on how to set up on langsmith online):
LANGCHAIN_TRACING_V2
LANGCHAIN_ENDPOINT
LANGCHAIN_API_KEY
LANGCHAIN_PROJECT

1. Create a new repository

In GitHub, choose Use this template > Create a new repository in the repository toolbar.

Choose the owner, and pick a name for the new repository.

[!IMPORTANT] If you want to deploy the evaluation function to Lambda Feedback, make sure to choose the Lambda Feedback organization as the owner.

Set the visibility to Public or Private.

[!IMPORTANT] If you want to use GitHub deployment protection rules, make sure to set the visibility to Public.

Click on Create repository.

2. Clone the new repository

Clone the new repository to your local machine using the following command:

git clone <repository-url>

3. Develop the chat function

You're ready to start developing your chat function. Head over to the Development section to learn more.

4. Update the README

In the README.md file, change the title and description so it fits the purpose of your chat function.

Also, don't forget to update or delete the Quickstart chapter from the README.md file after you've completed these steps.

Development

You can create your own invocation to your own agents hosted anywhere. Copy or update the base_agent from src/agents/ and edit it to match your LLM agent requirements. Import the new invocation in the module.py file.

You agent can be based on an LLM hosted anywhere, you have available currently OpenAI, AzureOpenAI, and Ollama models but you can introduce your own API call in the src/agents/llm_factory.py.

Prerequisites

• Docker
• Python

Repository Structure

.github/workflows/
    dev.yml                           # deploys the DEV function to Lambda Feedback
    main.yml                          # deploys the STAGING function to Lambda Feedback
    test-report.yml                   # gathers Pytest Report of function tests

src/module.py       # chat_module function implementation
src/module_test.py  # chat_module function tests
src/agents/         # find all agents developed for the chat functionality
src/agents/utils/test_prompts.py      # allows testing of any LLM agent on a couple of example inputs containing Lambda Feedback Questions and synthetic student conversations

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 testbench_agents.py script to test the agents with example inputs from Lambda Feedback questions and synthetic conversations.

python src/agents/utils/testbench_agents.py

Building the Docker Image

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:

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 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\", \"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}
    }
}

Call Docker with Python Requests

In the src/agents/utils folder you can find the requests_test.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.

Deploy to Lambda Feedback

Deploying the chat function to Lambda Feedback is simple and straightforward, as long as the repository is within the Lambda Feedback organization.

During development, we recommend using the dev branch. This branch will deploy a version of the function onto AWS using the GitHub Actions Dev workflow. After deploying, please, contact one of the Lambda Feedback admins to allow the function to be accessible onto dev.lambdafeedback.com.

[!WARNING] The dev environment of the platform is always under use, so the platform might have beta/in-testing features that might cause unexpected issues.

After you are pleased with the performance of your Chatbot and have configured the repository, a GitHub Actions workflow will automatically build and deploy the chat function to Lambda Feedback as soon as changes are pushed to the main branch of the repository. This deployment will upload the function onto staging.lambdafeedback.com, and will also initiate an approval stage for prod environment. Once you reach this stage, please contact an admin from Lambda Feedback to review the code and approve it such that the code can be accessible onto the main Lambda Feedback platform.

Troubleshooting

Containerized Function Fails to Start

If your chat function is working fine when run locally, but not when containerized, there is much more to consider. Here are some common issues and solution approaches:

Run-time dependencies

Make sure that all run-time dependencies are installed in the Docker image.

• Python packages: Make sure to add the dependency to the requirements.txt or pyproject.toml file, and run pip install -r requirements.txt or poetry install in the Dockerfile.
• System packages: If you need to install system packages, add the installation command to the Dockerfile.
• ML models: If your chat function depends on ML models, make sure to include them in the Docker image.
• Data files: If your chat function depends on data files, make sure to include them in the Docker image.