dynamic-learning-model 4.0.1

A Dynamic-Learning Model (DLM) chatbot with memory and compute reasoning modes.

Dynamic Learning Model

ABOUT:

The Dynamic Learning Model (DLM) is a hybrid AI system designed to learn, adapt, and intelligently respond to user queries. It combines natural language understanding with structured reasoning, continually improving as it is trained.

Key capabilities include:

• FAQ Handling: Learns and responds to frequently asked questions based on the knowledge it has been trained on.

• Chain-of-Thought (CoT) Reasoning: Performs clear, step-by-step logic to solve non-ambiguous arithmetic, geometric, and unit conversion problems.

• Custom Knowledge Integration: DLM is fully extensible. You can initialize it with an empty SQL database and train it with your domain-specific knowledge.

Whether you're building a student support bot, a domain-specific assistant, or a computation system, DLM offers a flexible foundation to power your intelligent applications

REQUIRED PARAMETERS:

• The constructor requires passing in two parameters:
  • Bot Mode:
    • 'learn' = Enables training using the memory model. The bot can be updated with new information
    • 'apply' = The bot automatically switches between its "compute" and "memory" model depending on the query asked
  • Empty SQL Database for training the bot with queries and for the memory model
• The ask() method also requires passing in two parameters:
  • Query: "What is the definition of FAFSA" (as an example)
  • Display Thought: "True" to allow the bot's Chain of Thought to be displayed, or else "False"

GET STARTED:

• To install, run:

pip install dynamic-learning-model

• Python 3.12.0 is required to use this bot in your program.

('learn' mode [training queries])

from dlm import DLM

training_bot = DLM("learn", "college_knowledge.db")

training_bot.ask("What is FAFSA in college?", True)

('apply' mode [deployment/production use after training])

from dlm import DLM

commercial_bot = DLM("apply", "college_knowledge.db")

commercial_bot.ask("What is the difference between FAFSA and CADAA in California?", False)

# or

commercial_bot.ask("Tell me the result for the following: 5 * 5 * 5 + 5 / 5", True)

HIGH-LEVEL PIPELINE VISUAL: