Skip to main content

A Dynamic Learning Model for processing NLP queries using hybrid AI and reasoning.

Project description

Python SQLite

Dynamic Learning Model

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 to improve over time as it interacts with users.

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 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 own domain-specific knowledge.

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

  • This model uses SpaCy, SQLite, & NLTK for many of its functions

NOTICE: This is a public package, to install it, run: pip install dynamic-learning-model

image

image

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

dynamic_learning_model-1.1.1.tar.gz (22.8 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

dynamic_learning_model-1.1.1-py3-none-any.whl (22.1 kB view details)

Uploaded Python 3

File details

Details for the file dynamic_learning_model-1.1.1.tar.gz.

File metadata

  • Download URL: dynamic_learning_model-1.1.1.tar.gz
  • Upload date:
  • Size: 22.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.0

File hashes

Hashes for dynamic_learning_model-1.1.1.tar.gz
Algorithm Hash digest
SHA256 2c8dd24f2612fa87d65cd58ce21411bddbc04c9e797d6cf0c2e591184a334fd4
MD5 41a397d1271805e2c19e23a8352af0d1
BLAKE2b-256 6fe53443b16f608e41564d4a0ad96abf7023926851aec070765ee7e2dfaa2a1b

See more details on using hashes here.

File details

Details for the file dynamic_learning_model-1.1.1-py3-none-any.whl.

File metadata

File hashes

Hashes for dynamic_learning_model-1.1.1-py3-none-any.whl
Algorithm Hash digest
SHA256 759238d9e599d8d3a2bb296e690996f621386946b7675e134c130f8b1fe83cfc
MD5 633ebaac23373934291bc496de965f50
BLAKE2b-256 8c9ce3f195d0f620cdc863c3dad07777c938e242ca06dd4abb9d2843cb383eff

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page