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Our custom-built chatbot leverages a rich dictionary, natural language parsing, dynamic memory management, and specialized external API integrations to create fluid, human-like conversations.
At the heart of the chatbot is a comprehensive dictionary containing over 142,694 words spanning nouns, verbs, adjectives, auxiliaries, and pronouns. Each entry includes detailed linguistic attributes:
Word Type: Noun, verb, adjective, etc.
Gender: Masculine / Feminine
Number: Singular / Plural
Tense: Present, Past, Future
When a user submits input, the system breaks it down into structured data:
Tokenization: Sentences are split by periods, then further divided into words by spaces.
Data Lookup: Each word is matched against the dictionary to extract its grammatical properties. Adjectives are mapped against a synonym table to expand vocabulary.
Perspective Shift: Dynamic pronouns and verbs are inverted automatically (e.g., transforming "I am" to "You are" and vice versa).
The system stores user keyword objects, syntax patterns, and context in its conversation database:
Flexible Matching: User verbs are evaluated across all tenses, and nouns are expanded using synonyms.
Logical Association: Keywords use OR conditions within word types and AND conditions across word types to identify the best contextual response.
Proportional Memory: Short inputs match short-memory responses, while detailed inputs trigger long-memory responses.
If no existing memory match is found, the engine builds a new sentence from scratch using structured grammar rules:
Words are organized dynamically based on gender, number, tense, and sentence structure patterns.
The system validates the syntax before returning a natural-sounding response to the user.
Topic Tracking & Anti-Repetition: When a subject is discussed, its keywords are cached in a temporary session to avoid repeating the same response. After a few turns, the topic counter resets.
Short-Term Memory & Questions: When the chatbot asks a question, it captures and reformulates the user’s next answer into short-term memory. If the user changes topics completely, the short-term memory array automatically flushes.
Quality Filtering: If the user’s input is negative immediately after a new memory row is created, the system deletes that row as a safety measure to prevent saving invalid or undesirable responses.
When enabled, unmatched proper nouns (such as company or brand names) are identified via the Google Natural Language API and looked up on Wikipedia.
Knowledge Retrieval: Direct queries like "What is..." or "Do you know..." prompt the bot to answer with the fetched Wikipedia summary.
Autonomous Learning (Cron Task): Every minute, a background cron job uses these stored descriptions to help the bot build independent associations and expand its memory (requires a minimum of 30 stored memory rows to make meaningful links).
The bot features an internal unit conversion matrix to solve basic arithmetic (addition, subtraction, multiplication, division).
Supported Units: Liters, milliliters, ounces, centimeters, meters, millimeters, kilometers, feet, inches, square units, and cubic units.
It automatically converts mixed units to a matching base unit before calculating or converting between distinct units.
The front-end face is powered by a custom Python application that renders geometric grid shapes (0s and 1s) to express emotions:
Includes built-in emotional facial expressions mapped to specific user keywords.
Plays animations whenever a keyword is saved to short-term memory, repeating until the current subject is flushed.
Source : learningbot
Price: $ 00.00 CAD to access learningbot extensions, forum support, chatbox support and update support
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