MiniChain

A tiny library for coding with large language models. Check out the MiniChain Zoo to get a sense of how it works.

Coding

• Code (math_demo.py): Annotate Python functions that call language models.

@prompt(OpenAI(), template_file="math.pmpt.tpl")
def math_prompt(model, question):
    "Prompt to call GPT with a Jinja template"
    return model(dict(question=question))

@prompt(Python(), template="import math\n{{code}}")
def python(model, code):
    "Prompt to call Python interpreter"
    code = "\n".join(code.strip().split("\n")[1:-1])
    return model(dict(code=code))

def math_demo(question):
    "Chain them together"
    return python(math_prompt(question))

• Chains (Space): MiniChain builds a graph (think like PyTorch) of all the calls you make for debugging and error handling.

show(math_demo,
     examples=["What is the sum of the powers of 3 (3^i) that are smaller than 100?",
               "What is the sum of the 10 first positive integers?"],
     subprompts=[math_prompt, python],
     out_type="markdown").queue().launch()

• Template (math.pmpt.tpl): Prompts are separated from code.

...
Question:
A robe takes 2 bolts of blue fiber and half that much white fiber. How many bolts in total does it take?
Code:
2 + 2/2

Question:
{{question}}
Code:

• Installation

pip install minichain
export OPENAI_API_KEY="sk-***"

Examples

This library allows us to implement several popular approaches in a few lines of code.

• Retrieval-Augmented QA
• Chat with memory
• Information Extraction
• Interleaved Code (PAL) - (Gao et al 2022)
• Search Augmentation (Self-Ask) - (Press et al 2022)
• Chain-of-Thought - (Wei et al 2022)

It supports the current backends.

• OpenAI (Completions / Embeddings)
• Hugging Face 🤗
• Google Search
• Python
• Manifest-ML (AI21, Cohere, Together)
• Bash

Why Mini-Chain?

There are several very popular libraries for prompt chaining, notably: LangChain, Promptify, and GPTIndex. These library are useful, but they are extremely large and complex. MiniChain aims to implement the core prompt chaining functionality in a tiny digestable library.

Tutorial

Mini-chain is based on annotating functions as prompts.

@prompt(OpenAI())
def color_prompt(model, input):
    return model(f"Answer 'Yes' if this is a color, {input}. Answer:")

Prompt functions act like python functions, except they are lazy to access the result you need to call run().

if color_prompt("blue").run() == "Yes":
    print("It's a color")

Alternatively you can chain prompts together. Prompts are lazy, so if you want to manipulate them you need to add @transform() to your function. For example:

@transform()
def said_yes(input):
    return input == "Yes"

@prompt(OpenAI())
def adjective_prompt(model, input):
    return model(f"Give an adjective to describe {input}. Answer:")

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