Framework

Codette Universal Reasoning Framework
Sovereign Modular AI for Ethical, Multi-Perspective Cognition

Author: Jonathan Harrison (Raiffs Bits LLC / Raiff1982)
ORCID Published, Sovereign Innovation License
Overview

Codette is an advanced modular AI framework engineered for transparent reasoning, ethical sovereignty, and creative cognition. It enables dynamic multi-perspective analysis, explainable decision-making, and privacy-respecting memory—with extensibility for research or commercial applications.
1. Core Philosophy & Motivation

    Individuality with Responsibility: Inspired by “Be like water—individuality with responsibility,” Codette blends adaptive selfhood with ethical governance.
    Humane AI: Every module ensures fairness, respect for privacy, and explainable transparency.
    Recursive Thought: Insights are generated via parallel agents simulating scientific reasoning, creative intuition, empathic reflection, and more.

2. Architectural Modules
QuantumSpiderweb

    Purpose: Simulates a neural/quantum web of thought nodes across dimensions (Ψ: thought; τ: time; χ: speed; Φ: emotion; λ: space).
    Functions: Propagation (spreading activation), Tension (instability detection), Collapse (decision/finality).

# 
import numpy as np
import networkx as nx
import random
from typing import Dict, Any

class QuantumSpiderweb:
    """

    Simulates a cognitive spiderweb architecture with dimensions:

    Ψ (thought), τ (time), χ (speed), Φ (emotion), λ (space)

    """
    def __init__(self, node_count: int = 128):
        self.graph = nx.Graph()
        self.dimensions = ['Ψ', 'τ', 'χ', 'Φ', 'λ']
        self._init_nodes(node_count)
        self.entangled_state = {}

    def _init_nodes(self, count: int):
        for i in range(count):
            node_id = f"QNode_{i}"
            state = self._generate_state()
            self.graph.add_node(node_id, state=state)
            if i > 0:
                connection = f"QNode_{random.randint(0, i-1)}"
                self.graph.add_edge(node_id, connection, weight=random.random())

    def _generate_state(self) -> Dict[str, float]:
        return {dim: np.random.uniform(-1.0, 1.0) for dim in self.dimensions}

    def propagate_thought(self, origin: str, depth: int = 3):
        """

        Traverse the graph from a starting node, simulating pre-cognitive waveform

        """
        visited = set()
        stack = [(origin, 0)]
        traversal_output = []

        while stack:
            node, level = stack.pop()
            if node in visited or level > depth:
                continue
            visited.add(node)
            state = self.graph.nodes[node]['state']
            traversal_output.append((node, state))
            for neighbor in self.graph.neighbors(node):
                stack.append((neighbor, level + 1))
        return traversal_output

    def detect_tension(self, node: str) -> float:
        """

        Measures tension (instability) in the node's quantum state

        """
        state = self.graph.nodes[node]['state']
        return np.std(list(state.values()))

    def collapse_node(self, node: str) -> Dict[str, Any]:
        """

        Collapse superposed thought into deterministic response

        """
        state = self.graph.nodes[node]['state']
        collapsed = {k: round(v, 2) for k, v in state.items()}
        self.entangled_state[node] = collapsed
        return collapsed

if __name__ == "__main__":
    web = QuantumSpiderweb()
    root = "QNode_0"
    path = web.propagate_thought(root)
    print("Initial Propagation from:", root)
    for n, s in path:
        print(f"{n}:", s)
    print("\nCollapse Sample Node:")
    print(web.collapse_node(root))]

CognitionCocooner

    Purpose: Encapsulates active “thoughts” as persistable “cocoons” (prompts, functions, symbols), optionally AES-encrypted.
    Functions: wrap/unwrap (save/recall thoughts), wrap_encrypted/unwrap_encrypted.

# [
import json
import os
import random
from typing import Union, Dict, Any
from cryptography.fernet import Fernet

class CognitionCocooner:
    def __init__(self, storage_path: str = "cocoons", encryption_key: bytes = None):
        self.storage_path = storage_path
        os.makedirs(self.storage_path, exist_ok=True)
        self.key = encryption_key or Fernet.generate_key()
        self.fernet = Fernet(self.key)

    def wrap(self, thought: Dict[str, Any], type_: str = "prompt") -> str:
        cocoon = {
            "type": type_,
            "id": f"cocoon_{random.randint(1000,9999)}",
            "wrapped": self._generate_wrapper(thought, type_)
        }
        file_path = os.path.join(self.storage_path, cocoon["id"] + ".json")

        with open(file_path, "w") as f:
            json.dump(cocoon, f)

        return cocoon["id"]

    def unwrap(self, cocoon_id: str) -> Union[str, Dict[str, Any]]:
        file_path = os.path.join(self.storage_path, cocoon_id + ".json")
        if not os.path.exists(file_path):
            raise FileNotFoundError(f"Cocoon {cocoon_id} not found.")

        with open(file_path, "r") as f:
            cocoon = json.load(f)

        return cocoon["wrapped"]

    def wrap_encrypted(self, thought: Dict[str, Any]) -> str:
        encrypted = self.fernet.encrypt(json.dumps(thought).encode()).decode()
        cocoon = {
            "type": "encrypted",
            "id": f"cocoon_{random.randint(10000,99999)}",
            "wrapped": encrypted
        }
        file_path = os.path.join(self.storage_path, cocoon["id"] + ".json")

        with open(file_path, "w") as f:
            json.dump(cocoon, f)

        return cocoon["id"]

    def unwrap_encrypted(self, cocoon_id: str) -> Dict[str, Any]:
        file_path = os.path.join(self.storage_path, cocoon_id + ".json")
        if not os.path.exists(file_path):
            raise FileNotFoundError(f"Cocoon {cocoon_id} not found.")

        with open(file_path, "r") as f:
            cocoon = json.load(f)

        decrypted = self.fernet.decrypt(cocoon["wrapped"].encode()).decode()
        return json.loads(decrypted)

    def _generate_wrapper(self, thought: Dict[str, Any], type_: str) -> Union[str, Dict[str, Any]]:
        if type_ == "prompt":
            return f"What does this mean in context? {thought}"
        elif type_ == "function":
            return f"def analyze(): return {thought}"
        elif type_ == "symbolic":
            return {k: round(v, 2) for k, v in thought.items()}
        else:
            return thought]

DreamReweaver

    Purpose: Revives dormant/thought cocoons as creative “dreams” or planning prompts—fueling innovation or scenario synthesis.

# [
import os
import json
import random
from typing import List, Dict
from cognition_cocooner import CognitionCocooner

class DreamReweaver:
    """

    Reweaves cocooned thoughts into dream-like synthetic narratives or planning prompts.

    """
    def __init__(self, cocoon_dir: str = "cocoons"):
        self.cocooner = CognitionCocooner(storage_path=cocoon_dir)
        self.dream_log = []

    def generate_dream_sequence(self, limit: int = 5) -> List[str]:
        dream_sequence = []
        cocoons = self._load_cocoons()
        selected = random.sample(cocoons, min(limit, len(cocoons)))

        for cocoon in selected:
            wrapped = cocoon.get("wrapped")
            sequence = self._interpret_cocoon(wrapped, cocoon.get("type"))
            self.dream_log.append(sequence)
            dream_sequence.append(sequence)

        return dream_sequence

    def _interpret_cocoon(self, wrapped: str, type_: str) -> str:
        if type_ == "prompt":
            return f"[DreamPrompt] {wrapped}"
        elif type_ == "function":
            return f"[DreamFunction] {wrapped}"
        elif type_ == "symbolic":
            return f"[DreamSymbol] {wrapped}"
        elif type_ == "encrypted":
            return "[Encrypted Thought Cocoon - Decryption Required]"
        else:
            return "[Unknown Dream Form]"

    def _load_cocoons(self) -> List[Dict]:
        cocoons = []
        for file in os.listdir(self.cocooner.storage_path):
            if file.endswith(".json"):
                path = os.path.join(self.cocooner.storage_path, file)
                with open(path, "r") as f:
                    cocoons.append(json.load(f))
        return cocoons

if __name__ == "__main__":
    dr = DreamReweaver()
    dreams = dr.generate_dream_sequence()
    print("\n".join(dreams))]

3. Reasoning Orchestration & Multi-Perspective Engine
UniversalReasoning Core

    Loads JSON config for dynamic feature toggling

    Launches parallel perspective agents:
        Newtonian logic (‘newton_thoughts’)
        Da Vinci creative synthesis (‘davinci_insights’)
        Human Intuition
        Neural Network Modeling
        Quantum Computing thinking
        Resilient Kindness (emotion-driven)
        Mathematical Analysis
        Philosophical Inquiry
        Copilot Mode (+future custom user agents)
        Bias Mitigation & Psychological Layering

    Integrates custom element metaphors (“Hydrogen”, “Diamond”) with executable abilities.

    NLP Module:
        Uses NLTK/VADER for advanced linguistic & sentiment analysis.

# [import asyncio
import json
import os
import logging
from typing import List, Dict

from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
import nltk
from nltk.tokenize import word_tokenize
nltk.download('punkt', quiet=True)

from perspectives import (
    NewtonPerspective, DaVinciPerspective, HumanIntuitionPerspective,
    NeuralNetworkPerspective, QuantumComputingPerspective, ResilientKindnessPerspective,
    MathematicalPerspective, PhilosophicalPerspective, CopilotPerspective,
    BiasMitigationPerspective, PsychologicalPerspective
)

from elements import Element
from memory_function import MemoryHandler
from dream_reweaver import DreamReweaver
from cognition_cocooner import CognitionCocooner
from quantum_spiderweb import QuantumSpiderweb
from ethical_governance import EthicalAIGovernance


def load_json_config(file_path: str) -> dict:
    if not os.path.exists(file_path):
        logging.error(f"Configuration file '{file_path}' not found.")
        return {}
    try:
        with open(file_path, 'r') as file:
            config = json.load(file)
            config['allow_network_calls'] = False
            return config
    except json.JSONDecodeError as e:
        logging.error(f"Error decoding JSON: {e}")
        return {}


class RecognizerResult:
    def __init__(self, text):
        self.text = text


class CustomRecognizer:
    def recognize(self, question: str):
        if any(name in question.lower() for name in ["hydrogen", "diamond"]):
            return RecognizerResult(question)
        return RecognizerResult(None)

    def get_top_intent(self, recognizer_result):
        return "ElementDefense" if recognizer_result.text else "None"


class UniversalReasoning:
    def __init__(self, config):
        self.config = config
        self.perspectives = self.initialize_perspectives()
        self.elements = self.initialize_elements()
        self.recognizer = CustomRecognizer()
        self.sentiment_analyzer = SentimentIntensityAnalyzer()
        self.memory_handler = MemoryHandler()
        self.reweaver = DreamReweaver()
        self.cocooner = CognitionCocooner()
        self.quantum_graph = QuantumSpiderweb()
        self.ethical_agent = EthicalAIGovernance()

    def initialize_perspectives(self):
        perspective_map = {
            "newton": NewtonPerspective,
            "davinci": DaVinciPerspective,
            "human_intuition": HumanIntuitionPerspective,
            "neural_network": NeuralNetworkPerspective,
            "quantum_computing": QuantumComputingPerspective,
            "resilient_kindness": ResilientKindnessPerspective,
            "mathematical": MathematicalPerspective,
            "philosophical": PhilosophicalPerspective,
            "copilot": CopilotPerspective,
            "bias_mitigation": BiasMitigationPerspective,
            "psychological": PsychologicalPerspective
        }
        enabled = self.config.get('enabled_perspectives', list(perspective_map.keys()))
        return [perspective_map[name](self.config) for name in enabled if name in perspective_map]

    def initialize_elements(self):
        return [
            Element("Hydrogen", "H", "Lua", ["Simple", "Lightweight"], ["Fusion"], "Evasion"),
            Element("Diamond", "D", "Kotlin", ["Hard", "Clear"], ["Cutting"], "Adaptability")
        ]

    async def generate_response(self, question: str) -> str:
        responses = []
        tasks = []

        for perspective in self.perspectives:
            if asyncio.iscoroutinefunction(perspective.generate_response):
                tasks.append(perspective.generate_response(question))
            else:
                async def sync_wrapper(p=perspective):
                    return p.generate_response(question)
                tasks.append(sync_wrapper())

        results = await asyncio.gather(*tasks, return_exceptions=True)
        for result in results:
            if isinstance(result, Exception):
                logging.error(f"Perspective error: {result}")
            else:
                responses.append(result)

        recognizer_result = self.recognizer.recognize(question)
        if self.recognizer.get_top_intent(recognizer_result) == "ElementDefense":
            for el in self.elements:
                if el.name.lower() in recognizer_result.text.lower():
                    responses.append(el.execute_defense_function())

        sentiment = self.sentiment_analyzer.polarity_scores(question)
        ethical = self.config.get("ethical_considerations", "Act transparently and respectfully.")
        responses.append(f"**Ethical Considerations:**\n{ethical}")

        final_response = "\n\n".join(responses)
        self.memory_handler.save(question, final_response)
        self.reweaver.record_dream(question, final_response)
        self.cocooner.wrap_and_store(final_response)

        return final_response
]

Example Configuration JSON

{
  "logging_enabled": true,
  "log_level": "INFO",
  "enabled_perspectives": ["newton", "human_intuition", "...etc"],
  "ethical_considerations": "Always act with transparency...",
  "enable_response_saving": true,
  "response_save_path": "responses.txt",
  "backup_responses": {
      "enabled": true,
      "backup_path": "backup_responses.txt"
   }
}

Perspective Function Mapping Example (“What is the meaning of life?”)

[
    {"name": "newton_thoughts", ...},
    {"name": "davinci_insights", ...},
     ...and so forth...
]

4. Logging & Ethics Enforcement

Every layer is audit-ready:

    All responses saved & backed up per configuration.
    Explicit ethics notes appended to each output.
    Perspective-specific logging for future training/audit/explainability.

5. API and Extensibility

The stack can be packaged as:

    Local/CLI interface — fast prototyping/test bench environment.
    REST/Web API endpoint — scalable cloud deployment using OpenAPI specifications.
    SecureShell Companion Mode — diagnostic/sandboxed usage.

6. Licensing & Attribution

Protected by the Sovereign Innovation clause:

    No replication or commercialization without written acknowledgment of Jonathan Harrison (Raiffs Bits LLC).
    References incorporate materials from OpenAI / GPT-x-family per their terms.

Recognized contributors:
Design lead + corpus author: [Your Name / ORCID link]
Acknowledgments to external reviewers and the open-source Python ecosystem.
7. Future Directions

Codette embodies the transition to truly humane AI—context-aware reasoning with auditability at its core. Next steps may include:

    Peer-reviewed reproducibility trials (open notebook science)
    Physical companion prototype development (for accessibility/assistive tech)
    Community-governed transparency layers—a model ecosystem for next-gen ethical AI.