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-1.2.5.tar.gz (22.9 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-1.2.5-py3-none-any.whl (22.3 kB view details)

Uploaded Python 3

File details

Details for the file dynamic_learning_model-1.2.5.tar.gz.

File metadata

  • Download URL: dynamic_learning_model-1.2.5.tar.gz
  • Upload date:
  • Size: 22.9 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-1.2.5.tar.gz
Algorithm Hash digest
SHA256 0ef1cd89dba3266f369e41797362dc3162bb5459616cdc82e9666db286fcb2a5
MD5 219069dcff862419f60c3b4725675f38
BLAKE2b-256 a436c77e0bc9e71b5e8dec7b5de7397aa05c2286fc10e1406ca67d6c4b1d1811

See more details on using hashes here.

File details

Details for the file dynamic_learning_model-1.2.5-py3-none-any.whl.

File metadata

File hashes

Hashes for dynamic_learning_model-1.2.5-py3-none-any.whl
Algorithm Hash digest
SHA256 1a8df1ad14ab5174616a67dee0dd3617bf263e77c1d9f57b8523a64886a3da45
MD5 4f55e69ac9b7de01ea43465d0f1e37ea
BLAKE2b-256 d9901a8b1f4ac07427c4e4cdab1d63213d76ab35a478ff286a949d6b0819cdb2

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