sammo 0.1.0.2

A flexible, easy-to-use library for running and optimizing prompts for Large Language Models (LLMs).

SAMMO (📘User Guide)

A flexible, easy-to-use library for running and optimizing prompts for Large Language Models (LLMs).

How to Get Started

Go to the user guide for examples, how-tos, and API reference.

pip install sammo

Use Cases

SAMMO is designed to support

• Efficient data labeling: Supports minibatching by packing and parsing multiple datapoints into a single prompt.
• Prompt prototyping and engineering: Re-usable components and prompt structures to quickly build and test new prompts.
• Instruction optimization: Optimize instructions to do better on a given task.
• Prompt compression: Compress prompts while maintaining performance.
• Large-scale prompt execution: parallelization and rate-limiting out-of-the-box so you can run many queries in parallel and at scale without overwhelming the LLM API.

It is less useful if you want to build

• Interactive, agent-based LLM applications (→ check out AutoGen)
• Interactive, production-ready LLM applications (→ check out LangChain)

Example

This is extending the chat dialog example from Guidance by running queries in parallel.

runner = OpenAIChat(model_id="gpt-3.5-turbo", api_config=API_CONFIG)
expert_names = GenerateText(
    Template(
        "I want a response to the following question:"
        "{{input}}\n"
        "Name 3 world-class experts (past or present) who would be great at answering this? Don't answer the question yet."
    ),
    system_prompt="You are a helpful and terse assistant.",
    randomness=0,
    max_tokens=300,
)

joint_answer = GenerateText(
    "Great, now please answer the question as if these experts had collaborated in writing a joint anonymous answer.",
    history=expert_names,
    randomness=0,
    max_tokens=500,
)

questions = [
    "How can I be more productive?",
    "What will AI look like in 10 years?",
    "How do we end world hunger?",
]
print(Output(joint_answer).run(runner, questions))

Licence

This project is licensed under MIT.

Authors

SAMMO was written by Tobias Schnabel.

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

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This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [e-mail] with any additional questions or comments.