langchoice 0.0.2

switch-case over free-form sentences.

PyPI: https://pypi.org/project/langchoice

🤖 About LangChoice

switch-case, but for free-form sentences. A one-liner for if-then-else over similar sentences.

The LangChoice library allows you to condition your structured programs over natural language sentence predicates or triggers. Makes it easy to define conditional flows over user inputs without implementing the sentence match operator over and over again.

📦 Installation

pip install langchoice

🧪 Getting Started

Suppose you want to detect if the user message triggered one of the following (greeting, politics) categories.

First define the text messages that define each topic category (a message group).

triggers =\
'''
greeting:
    - hello
    - hi
    - what's up?

politics:
    - what are your political beliefs?
    - thoughts on the president?
    - left wing
    - right wing
'''

1. Set up a LangStore data container.

data = yaml.safe_load(triggers)
S = LangStore(data)

2. On receiving a user message user_msg, simply match with topics!

match S.match(user_msg, threshold=1.2, debug=True): 
    case 'greeting', _ : #user_msg matches the greeting message cluster
        say_hello()
    case 'politics', _ : #user_msg matches the politics message cluster
        change_topic()
    case x :
        print(x)
        print(f'No defined triggers detected. Ask an LLM for response.')

Supports multiple matching modes:

• S.match returns the topic of nearest message.
• Use S.match_centroid to instead find the nearest topic centroid .
• (Optional) Provide thresholds for the no-topic-matches case:
  • S.match returns None if the nearest topic distance is greater than the threshold.

Debug and More!

• Use debug=True and debug_k=5 to
  • visualize distance of user_msg to topics.
  • Get match scores for each message group to debug what went wrong.

Coming Soon!

• In built assertions, which fail if the execution fails to match the expected topic / group.

• Compute the thresholds automatically for pre-specified message groups against a query evaluation set
• Fine-tune embeddings to separate message clusters better.

Implement a graph-based Conversation Flow

Roadmap

• allow switching under-the-hood sentence encoders