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Prompt Engineering Starter Kit

Add prompt templates / use cases

Before you begin

You have to set up your environment before you can run the starter kit.

Clone this repository

Clone the starter kit repo.

git clone https://github.com/sambanova/ai-starter-kit.git

Set up the models and config file

Set up the inference endpoint, configs and environment variables

The next step is to set up your environment variables to use one of the models available from SambaNova. If you're a current SambaNova customer, you can deploy your models with SambaStudio. If you are not a SambaNova customer, you can self-service provision API endpoints using SambaNova Cloud API.

  • If using SambaNova Cloud Please follow the instructions here for setting up your environment variables. Then in the config file set the llm api variable to "sncloud" and set the select_expert config depending on the model you want to use.

  • If using SambaStudio Please follow the instructions here for setting up endpoint and your environment variables. Then in the config file set the llm api variable to "sambastudio", set the bundle and select_expert configs if using a bundle endpoint.

Deploy the starter kit GUI

We recommend that you run the the starter kit in a virtual environment or use a container.

Option 1: Use a virtual environment

If you want to use virtualenv or conda environment

  1. Install and update pip.

    cd ai-starter-kit/prompt-engineering
python3 -m venv prompt_engineering_env
source prompt_engineering_env/bin/activate
pip install -r requirements.txt

  2. Run the following command:

    streamlit run streamlit/app.py --browser.gatherUsageStats false

You should see the following user interface:

capture of prompt_engineering_demo

Option 2: Deploy the starter kit in a Docker container

If you want to use Docker:

  1. Update the SAMBASTUDIO_KEY, SNAPI, SNSDK args in docker-compose.yaml file

  2. Run the command:

    docker-compose up --build

You will be prompted to go to the link (http://localhost:8501/) in your browser where you will be greeted with the streamlit page as above.

Use the starterkit GUI

To use the starter kit, follow these steps:

  1. Confirm the LLM to use from the text under Model display (Currently, only Llama2 and Llama3 models are available). You'll see a description of the architecture, prompting tips, and the metatag format required to optimize the model's performance.

  2. In Use Case for Sample Prompt, select a template. You have the following choices:

    • General Assistant: Provides comprehensive assistance on a wide range of topics, including answering questions, offering explanations, and giving advice. It's ideal for general knowledge, trivia, educational support, and everyday inquiries.

    • Document Search: Specializes in locating and briefing relevant information from large documents or databases. Useful for research, data analysis, and extracting key points from extensive text sources.

    • Product Selection: Assists in choosing products by comparing features, prices, and reviews. Ideal for shopping decisions, product comparisons, and understanding the pros and cons of different items.

    • Code Generation: Helps in writing, debugging, and explaining code. Useful for software development, learning programming languages, and automating simple tasks through scripting.

    • Summarization: Outputs a summary based on a given context. Essential for condensing large volumes of text

  3. In the Prompt field, review and edit the input to the model, or use directly the default prompt.

  4. Click the Send button to submit the prompt. The model will generate and display the response.

Customize the starter kit

You have several options for customizing this starter kit.

Include additional models

You can include more models with the kit. They will then show up in the Model display in the GUI according to the name of the select_expert value in the config file.

Include models using SambaNova Cloud

If you're using a SambaNova Cloud endpoint, follow these steps:

  1. In the config.json file, add the select_expert name. Then, include the model description in the models section, like the ones already there. Ensure that both names are compatible. Example:
    • select_expert value: Mistral-7B-Instruct-v0.2
    • model name under models: Mistral
  2. Populate the API key provided for SambaNova Cloud.
  3. Use create_prompt_yamls as a tool to create the prompts needed for your new model. These prompts will have a similar structure as the ones already existing in prompt_engineering/prompts folder, but will follow the metatags needed for the LLM model we want to add.

Include models using SambaStudio

If you're using a SambaStudio endpoint, follow these steps:

  1. Create a SambaStudio endpoint for inference.
  2. In the config.json file, add the select_expert name. Then, include the model description in the models section, like the ones already there. Ensure that both names are compatible. Example:
    • select_expert value: Mistral-7B-Instruct-v0.2
    • model name under models: Mistral
  3. Populate key variables on your env file.
  4. Use create_prompt_yamls as a tool to create the prompts needed for your new model. These prompts will have a similar structure as the ones already existing in prompt_engineering/prompts folder, but will follow the metatags needed for the LLM model we want to add.

Edit a prompt template

To change a template:

  1. Edit the create_prompt_yamls() method in src/llm_management.py.
  2. Execute the method to modify the prompt yaml file in the prompts folder.

Add prompt templates and use cases

To add a prompt template:

  1. Follow the instructions in Edit a template.
  2. Include the template use case in the use_cases list of config.yaml file.

Examples, third-party tools, and data sources

For further examples, we encourage you to visit any of the following resources:

Third-party tools and data sources

All the packages/tools are listed in the requirements.txt file in the project directory.