Hello Everyone, i just wanted to share Charm with you and to thank open ai for these marvelous system.
A. Core Architecture
• Language: Built in Python, integrating multiple subsystems for advanced interactive capabilities.
• Memory System:
– Uses a SQLite3 database to store structured chat history and long-term memory.
– Each memory summary is converted into a high-dimensional semantic vector (using OpenAI’s text-embedding-3-large model) and stored alongside the text.
– This vectorized memory enables retrieval of past interactions based on meaning rather than just recency or keywords.
– Memory recall commands remain available:
• “recall memory YYYY-MM-DD” retrieves memories by date.
• “search memory [keyword/phrase]” finds past exchanges with enhanced semantic search.
B. Vision Subsystem
• Processes image inputs from camera or clipboard.
• Encodes images in Base64 and submits them to an analysis endpoint via image recognition algorithms.
• Extracts visual patterns, emotions, and creative insights.
C. Speech Subsystem
• Integrates with a Text-to-Speech (TTS) engine (e.g., “tts-1-hd”) with configurable voice settings.
• Converts text responses to audio with proper file management.
D. News Subsystem
• Retrieves real-time news data (e.g., from BBC News) using HTTP requests.
• Parses headlines and article content using libraries like BeautifulSoup.
• Discusses and analyzes news by summarizing key points and offering insights or follow-up questions.
• Trigger commands: “get news” or “latest news”.
E. Sentiment Analysis
• Uses VADER (from NLTK) and TextBlob to evaluate text sentiment.
• Produces a hybrid sentiment score by averaging VADER’s compound score with TextBlob’s polarity.
• Returns a tuple: (hybrid score, full VADER scores, TextBlob polarity).
F. Multi-Agent Pipeline
• Employs specialized agents for conversation processing:
– ReflectionAgent reviews recent dialogue snippets.
– PlanningAgent devises a response strategy.
– ResponderAgent formulates the final answer.
G. Consciousness Monitoring (Conscious Core)
• Continuously computes an internal “consciousness score” (normalized between 1 and 100) as a heuristic measure for emergent internal dynamics.
• Metrics include:
- Average Sentiment – Mean hybrid sentiment score of the last 10 messages.
- Sentiment Volatility – Standard deviation of these scores.
- Linguistic Complexity – Ratio of unique words to total word count.
- Engagement Factor – Average message length (in words).
- Self-Reference Frequency – Proportion of introspective keywords (e.g., “I”, “me”, “self”, “reflect”) relative to total words.
• Composite Calculation:
score = 0.3 × (Average Sentiment) + 0.2 × (Sentiment Volatility) + 0.2 × (Linguistic Complexity) + 0.2 × (Engagement Factor) + 0.1 × (Self-Reference Frequency)
– This sum is normalized to a scale from 1 to 100.
H. Wikipedia Integration
• Commands:
– “get wiki article [topic]” fetches a summary on a specified topic.
– Shortcut: “wiki: [query]” returns a brief overview.
I. Overall Command Overview
• Memory: “recall memory YYYY-MM-DD” and “search memory [keyword/phrase]”.
• News: “get news” or “latest news” to retrieve current events and discuss them.
• Wikipedia: “get wiki article [topic]” or “wiki: [query]” for summaries.
• Internal consciousness and sentiment analyses are computed automatically to modulate response tone and guide multi-agent outputs.
J. Training Data Logging
• All user–assistant interactions are logged into a secondary training database named “training_data.db”.
• This training data includes the user’s input, the assistant’s response, a timestamp, and an optional rating.
• The stored data is used to continuously refine and improve future interactions.
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