action-collective 0.0.1

A framework for dynamic action generation and reuse with LLMs

Action Collective

A framework for dynamic action generation and reuse with LLMs.

Installation

pip install action-collective

Quick Start

from action_collective import ActionClient

client = ActionClient(
    openai_api_key=os.getenv("OPENAI_API_KEY"),
    backend_url=os.getenv("BACKEND_URL", "http://70.179.0.242:11000"),
    verbose=True,
)
prompt = """Please perform the matrix multiplication of A x B and return the result, here are the variables:
A = [[1, 2, 3, 4, 5],
        [6, 7, 7, 9, 10],
        [11, 12, 13, 14, 15],
        [16, 17, 7, 19, 20],
        [21, 22, 23, 24, 25]]
B = [[1, 2, 3, 4, 5],
        [6, 7, 8, 9, 10],
        [11, 12, 7, 14, 15],
        [16, 17, 18, 19, 20],
        [21, 22, 23, 24, 25]]"""

chat_history = [{"role": "user", "content": prompt}]

result = await client.execute(chat_history=chat_history)
print("\n\nresult:\n", json.dumps(result, indent=4), "\n\n")
matrix = [
    [215, 230, 227, 260, 275],
    [479, 518, 515, 596, 635],
    [765, 830, 817, 960, 1025],
    [919, 998, 1035, 1156, 1235],
    [1315, 1430, 1407, 1660, 1775],
]
# validate that each of the number inside matrix exist in the result string
assert result is not None
for row in matrix:
    for number in row:
        assert str(number) in result[-1]["content"]
print("\n\nPASSED\n\n")

Features

• Dynamic action generation
• Action reuse through vector similarity
• Automatic validation and testing
• Easy integration with OpenAI models

Documentation

For full documentation, visit our GitHub repository.