general-intelligence 0.5.0

Self-organizing knowledge systems for structural pattern learning

general-intelligence

general-intelligence is a framework for self-organizing, composable knowledge systems.

Rather than centering intelligence on algorithms or massive datasets, intelligence emerges from the structure, interaction, and behavior of knowledge itself. Each knowledge instance can learn, reason, react to new information, contribute to shared outputs, or even operate autonomously.

This is not a toy—it supports ML-style reasoning, prompt-response systems, and autonomous agents, all within the same model.

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Core Philosophy

• Intelligence arises from knowledge, not code.
• Knowledge is active, composable, and distributed.
• The system is flat—no central scheduler or hard-coded control.
• Knowledge interacts through context dictionaries and collaborative composition.

Each Knowledge instance decides:

• When to respond to a stimulus (on)
• When to react to new knowledge (on_add)
• When to react to knowledge removal (on_remove)
• How to contribute to shared reasoning (compose)

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Installation

pip install general-intelligence

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Quick Start

from gi import GeneralIntelligence, Knowledge
gi = GeneralIntelligence()


class EchoKnowledge(Knowledge):
    def on(self, ctx, gi):
        if hasattr(ctx, "message"):
            return f"Echo: {ctx.message}"

echo = EchoKnowledge()
gi.learn(echo)

class Context:
    def __init__(self, message):
        self.message = message

print(list(gi.on(Context("Hello"))))  # ['Echo: Hello']

All knowledge instances share a single model and respond only to contexts relevant to them.

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ML-Style Additive Knowledge

Demonstrates how GeneralIntelligence can handle tabular, additive learning:

from gi import GeneralIntelligence, Knowledge
from itertools import combinations

gi = GeneralIntelligence()

class AdditiveKnowledge(Knowledge):
    def __init__(self, n_features):
        self.n_features = n_features
        self.valid_combinations = []

    def on(self, ctx, gi):
        if hasattr(ctx, "row") and hasattr(ctx, "target"):
            row, target = ctx.row, ctx.target
            # First time: cache all single-element combinations
            if not self.valid_combinations:
                all_combs = []
                for r in range(1, self.n_features + 1):
                    all_combs.extend(combinations(range(self.n_features), r))
                self.valid_combinations = [
                    comb for comb in all_combs if sum(row[i] for i in comb) == target
                ]
            # Keep only combinations that continue to hold
            self.valid_combinations = [
                comb for comb in self.valid_combinations
                if sum(row[i] for i in comb) == target
            ]
        elif hasattr(ctx, "row"):
            row = ctx.row
            for comb in self.valid_combinations:
                return sum(row[i] for i in comb)

additive = AdditiveKnowledge(n_features=3)
gi.learn(additive)

# Training
class TrainCtx:
    def __init__(self, row, target):
        self.row = row
        self.target = target

for row, target in [([1,2,3], 3), ([0,3,1], 3)]:
    list(gi.on(TrainCtx(row, target)))

# Prediction
class PredictCtx:
    def __init__(self, row):
        self.row = row

print(next(gi.on(PredictCtx([2,1,0]))))  # Output: sum of matching combination

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Dialog / Prompt-Response Knowledge

from gi import GeneralIntelligence, Knowledge
gi = GeneralIntelligence()


class DialogKnowledge(Knowledge):
    def __init__(self):
        self.history = []

    def on(self, ctx, gi):
        if hasattr(ctx, "user"):
            self.history.append(ctx.user)
            return f"Bot: I heard '{ctx.user}'"

dialog = DialogKnowledge()
gi.learn(dialog)

class MsgCtx:
    def __init__(self, user):
        self.user = user

for response in gi.on(MsgCtx("Hello")):
    print(response)  # Bot: I heard 'Hello'

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Autonomous Knowledge Example

import threading, time
from gi import GeneralIntelligence, Knowledge
gi = GeneralIntelligence()

import threading, time


class TimerKnowledge(Knowledge):
    def __init__(self):
        self.count = 0

    def on_add(self, knowledge, gi):
        if self is knowledge:
            self.running = True
            self.thread = threading.Thread(target=self.run, args=(gi,), daemon=True)
            self.thread.start()

    def on_remove(self, knowledge, gi):
        if self is knowledge:
            self.running = False

    def run(self, gi):
        while getattr(self, "running", False):
            print("Tick:", self.count)
            self.count += 1
            time.sleep(1)

class TickCtx: pass
timer = TimerKnowledge()
gi.learn(timer)

# Let autonomous timer run a few ticks
time.sleep(5)
gi.unlearn(timer)  # stop autonomous loop

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Compositional Reasoning

Knowledge can modify shared context and collaborate:

from gi import GeneralIntelligence, Knowledge
gi = GeneralIntelligence()


class AccumulateKnowledge(Knowledge):
    def compose(self, ctx, composer, gi):
        if not hasattr(ctx, "accum"):
            ctx.accum = []
        ctx.accum.append("step")

acc = AccumulateKnowledge()
gi.learn(acc)

def final_composer(ctx):
    return getattr(ctx, "accum", [])

class DummyCtx: pass

print(gi.compose(DummyCtx(), final_composer))  # ['step']

Multiple knowledge types—ML-style, dialog, autonomous—can coexist in the same model.

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Architectural Principles

Concept	Description
Knowledge as agents	Each instance is autonomous, reactive, and composable
Structural similarity	ML-style learning can track relationships across inputs
Emergent reasoning	Intelligence arises from knowledge interactions, not centralized code
Composable context	compose allows knowledge to collaborate in flexible ways
Distributed operation	Knowledge can operate independently, including autonomous loops

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Use Cases

• Hierarchical or multimodal reasoning systems
• Interactive chatbots or agents
• Tabular ML tasks and feature discovery
• Autonomous monitoring or simulation agents
• Hybrid AI systems combining specialized knowledge modules

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Vision

GeneralIntelligence shifts AI from algorithm-driven to knowledge-driven.

Knowledge is:

• Composable
• Inspectable
• Autonomous
• Extensible across tasks and domains

A single model can host diverse knowledge types that cooperate, compete, or ignore irrelevant contexts.

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Combinatorial Knowledge (Experimental)

Located in the gi.knowledge subpackage, the CombinatorialKnowledge class is the first experimental ML-style knowledge subclass in the framework. It implements a hypothesis-driven approach to learning relationships between rows and targets:

Key Features

• Accepts arbitrary functions for combining row subsets.
• Can test relationships against targets, features, or constants.
• Supports nested hypotheses for feature-based rules.
• Tracks failures and removes hypotheses exceeding tolerance.
• Prediction returns all applicable outputs, letting the caller decide how to aggregate.

⚠️ Important: This knowledge class has not been extensively tested. It is an ideal first contribution opportunity for developers to help identify any issues, improve robustness, and extend functionality.

The class is designed as a starting point for more sophisticated ML-style knowledge modules. Future contributions could include:

• Additional combinator functions for math, logic, hybrid, and relational rules
• Multi-tasking capabilities
• Enhanced noise tolerance strategies
• Integration examples for real-world datasets
• Spotting and covering edge cases
• Writing tests

By contributing tests, fixes, and extensions, you can help build the foundation of a rich, modular knowledge ecosystem.

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Next Steps

• Specialized knowledge modules
• Community-built knowledge libraries
• Port to other languages
• Tutorials demonstrating cross-cutting knowledge interactions

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License

MIT License Copyright (c) 2025