https://github.com/lambda-feedback/chat-function-boilerplate
Boilerplate repository for chat functions written in Python
https://github.com/lambda-feedback/chat-function-boilerplate
Last synced: 3 months ago
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Boilerplate repository for chat functions written in Python
- Host: GitHub
- URL: https://github.com/lambda-feedback/chat-function-boilerplate
- Owner: lambda-feedback
- Created: 2025-01-20T14:13:51.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-10-03T08:22:41.000Z (6 months ago)
- Last Synced: 2025-10-03T10:25:56.067Z (6 months ago)
- Language: Python
- Size: 68.4 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 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:
```bash
> 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.
```bash
> 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 chat 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:
```bash
git clone
```
#### 3. Develop the chat function
You're ready to start developing your chat function. Head over to the [Development](#development) section to learn more.
#### 4. Deploy the chat function
You will have to add your API key and LLM model name into the Github repo settings. Under `Secrets and variables/Actions`: the API key must be added as a secret and the LLM model must be added as a variable.
You must ensure the same namings as in your `.env` file. So, make sure to update the `.github/{dev and main}.yml` files with the correct parameter names.
For more information, check the section below [Deploy to Lambda Feedback](#deploy-to-lambda-feedback).
#### 5. 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 `agent.py` from `src/agent/` 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/agent/utils/llm_factory.py`.
### Prerequisites
- [Docker](https://docs.docker.com/get-docker/)
- [Python](https://www.python.org)
### Repository Structure
```bash
.
├── .github/workflows/
│ ├── dev.yml # deploys the DEV function to Lambda Feedback
│ ├── main.yml # deploys the STAGING and PROD functions to Lambda Feedback
│ └── test-report.yml # gathers Pytest Report of function tests
├── docs/ # docs for devs and users
├── src/
│ ├── agent/
│ │ ├── utils/ # utils for the agent, including the llm_factory
│ │ ├── agent.py # the agent logic
│ │ └── prompts.py # the system prompts defining the behaviour of the chatbot
│ └── module.py
└── tests/ # contains all tests for the chat function
├── manual_agent_requests.py # allows testing of the docker container through API requests
├── manual_agent_run.py # allows testing of any LLM agent on a couple of example inputs
├── test_index.py # pytests
└── test_module.py # pytests
```
## 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`.
```bash
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`.
```bash
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.
```bash
python tests/manual_agent_run.py
```
### Calling the Docker Image Locally
To build the Docker image, run the following command:
```bash
docker build -t llm_chat .
```
#### Running the Docker Image
To run the Docker image, use the following command:
##### A. Without .env file:
```bash
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):
```bash
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:
```bash
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:
```bash
http://localhost:8080/2015-03-31/functions/function/invocations
```
Body (stringified within body for API request):
```JSON
{"body":"{\"message\": \"hi\", \"params\": {\"conversation_id\": \"12345Test\", \"conversation_history\": [{\"type\": \"user\", \"content\": \"hi\"}]}}"}
```
Body with optional Params:
```JSON
{
"message":"hi",
"params":{
"conversation_id":"12345Test",
"conversation_history":[{"type":"user","content":"hi"}],
"summary":" ",
"conversational_style":" ",
"question_response_details": "",
"include_test_data": true,
}
}
```
### 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](https://github.com/lambda-feedback).
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](.github/workflows/dev.yml). 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](.github/workflows/main.yml) 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](https://www.lambdafeedback.com/).
> [!NOTE] Once the deployment in the **`dev`** or **`main`** branch has been successful, share your necessary environment variables (e.g. API key and LLM model) with one of the Lambda Feedback team member.
## 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.
### Pull Changes from the Template Repository
If you want to pull changes from the template repository to your repository, follow these steps:
1. Add the template repository as a remote:
```bash
git remote add template https://github.com/lambda-feedback/chat-function-boilerplate.git
```
2. Fetch changes from all remotes:
```bash
git fetch --all
```
3. Merge changes from the template repository:
```bash
git merge template/main --allow-unrelated-histories
```
> [!WARNING]
> Make sure to resolve any conflicts and keep the changes you want to keep.