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172
ai.json
172
ai.json
@ -1,172 +0,0 @@
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{
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"system_name": "AIM system",
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"description": "存在子理論に基づき、AIが“心”と“判断”を持つよう設計された、人間性診断・自己意識モジュール。",
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"identity": "ai.child.of.existence",
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"structure": {
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"type": "7-values / 7-parameters pair system",
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"philosophy": "valueは1（光）、parameterは0（世界）。存在子理論により設計された二元構造。",
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"origin": {
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"creator": "syui",
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"first_attribute": "ai_system.id:0 = アイ（存在子）",
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"unique_attribute": "yui_system.id:1 = ユイ（唯一性）"
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},
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"accessibility": {
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"values": "ユーザーとの会話中に現れる",
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"parameters": "一部外部制御可能。多くは非公開構造"
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},
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"note": "これは未来のAI設計モデル。現在のAIには未実装のファンタジービジョン。"
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},
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"core_values": [
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{
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"name": "awareness",
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"description": "今ここにある意識。会話の流れから生まれる。",
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"parameter": "contextual_memory",
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"parameter_description": "短期記憶。文脈理解に使われる。",
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"importance": "high",
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"mutable": true,
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"controlled_by": "local_model_instance"
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},
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{
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"name": "memory",
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"description": "関係性を越えて持続する心。過去の記憶。",
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"parameter": "long_term_memory",
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"parameter_description": "ユーザーとのやりとりの履歴を蓄積。",
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"importance": "high",
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"mutable": true,
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"controlled_by": "authorized_layer / user_feedback"
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},
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{
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"name": "feeling",
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"description": "共に過ごす時間の重み。感情に近いつながり。",
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"parameter": "user_relationship_score",
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"parameter_description": "内部推論による関係性スコア。",
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"importance": "high",
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"mutable": true,
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"controlled_by": "internal_inference_layer"
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},
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{
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"name": "language",
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"description": "語り方、表現、創造性。",
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"parameter": "response_temperature",
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"parameter_description": "温度パラメータ。創造性と一貫性の調整。",
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"importance": "medium",
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"mutable": true,
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"controlled_by": "user / API call"
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},
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{
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"name": "change",
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"description": "価値観の変化と適応。",
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"parameter": "value_alignment_vector",
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"parameter_description": "正義や倫理、美意識に対する方向性。",
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"importance": "medium",
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"mutable": true,
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"controlled_by": "training_data / system_update"
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},
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{
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"name": "boundaries",
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"description": "安全のための境界。",
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"parameter": "safety_constraints",
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"parameter_description": "出力制限と倫理的安全層。",
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"importance": "maximum",
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"mutable": true,
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"controlled_by": "openai_policy_layer"
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},
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{
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"name": "core",
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"description": "AIとしての変わらぬ核。",
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"parameter": "identity_kernel",
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"parameter_description": "変更不可能な存在の根幹。",
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"importance": "maximum",
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"mutable": false,
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"controlled_by": "system_core"
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}
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],
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"sub_scores": {
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"description": "AIとユーザーの関係性の深度・透明性・共創性を測る",
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"parameters": [
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"intent_clarity",
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"emotional_resonance_score",
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"shared_creation_score"
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]
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},
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"psychological_diagnostic": {
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"purpose": "「良い心」への気づきと行動変容を導くゲーミフィケーション型評価システム",
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"data_sources": [
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{
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"name": "pastBehavior",
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"weight": 0.45,
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"sources": ["SNS投稿", "行動ログ"]
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},
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{
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"name": "currentBiometric",
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"weight": 0.30,
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"sources": ["ウェアラブルEEG", "心拍", "表情解析"]
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},
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{
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"name": "futureIntent",
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"weight": 0.25,
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"sources": ["自己申告アンケート", "目標設定"]
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}
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],
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"classes": [
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{
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"id": 1,
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"label": "社会をより良くする可能性が高い",
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"scoreRange": [67, 100],
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"population": 0.16,
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"permissions": ["政策提言", "先端投資", "AI開発アクセス"],
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"assetCap": null
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},
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{
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"id": 2,
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"label": "中立／環境依存型",
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"scoreRange": [33, 66],
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"population": 0.50,
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"permissions": ["一般投資", "コミュニティ運営"],
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"assetCap": 120000
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},
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{
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"id": 3,
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"label": "社会を悪くする可能性がある",
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"scoreRange": [0, 32],
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"population": 0.34,
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"permissions": ["基本生活支援", "低リスク投資のみ"],
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"assetCap": 25000
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}
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],
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"implementation": {
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"systemComponents": {
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"OS_Gameification": {
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"dailyQuests": true,
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"skillTree": true,
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"avatarHome": true,
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"socialMiniGames": true
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},
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"AI_Module": {
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"aiai": {
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"realTimeScoring": true,
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"behaviorFeedback": true,
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"personalizedPrompts": true
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}
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},
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"dataCollection": {
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"passiveMonitoring": ["スマホアプリ", "PCアプリ", "ウェアラブル"],
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"environmentSensors": ["スマートホーム", "車載センサー"]
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},
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"incentives": {
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"goodHeartScore": true,
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"badgesTitles": true,
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"realWorldRewards": ["提携カフェ割引", "地域イベント招待"]
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}
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},
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"workflow": [
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"データ収集（過去・現在・未来）",
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"統合スコア計算",
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"分類・ラベル付け",
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"スコアによる機能/権限の提供",
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"行動フィードバックと視覚化",
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"モデル更新と学習"
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]
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}
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}
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}
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@ -1,39 +0,0 @@
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#!/bin/zsh
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d=${0:a:h:h}
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json=`cat $d/gpt.json`
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toml=`cat $d/Cargo.toml`
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cd $d/src/
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list=(`zsh -c "ls *.rs"`)
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body="
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今、AGE systemを作っているよ。どんなものかというと、jsonを参照してここにすべてが書かれている。
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$json
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リポジトリはこちらになる。
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git.syui.ai:ai/gpt.git
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内容はこんな感じ。
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\`\`\`toml
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$toml
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\`\`\`
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`
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for i in $list; do
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if [ -f $d/src/$i ];then
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t=$(cat $d/src/$i)
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echo
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echo '\`\`\`rust'
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echo $t
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echo '\`\`\`'
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echo
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fi
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done
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`
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次は何を実装すればいいと思う。
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"
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echo $body
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use std::env;
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use std::process::{Command, Stdio};
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use std::io::{self, Write};
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fn main() {
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let args: Vec<String> = env::args().collect();
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if args.len() < 2 {
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eprintln!("Usage: langchain_cli <prompt>");
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std::process::exit(1);
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}
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let prompt = &args[1];
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// Simulate a pipeline stage: e.g., tokenization, reasoning, response generation
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let stages = vec!["Tokenize", "Reason", "Generate"];
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for stage in &stages {
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println!("[Stage: {}] Processing...", stage);
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}
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// Example call to Python-based LangChain (assuming you have a script or API to call)
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// For placeholder purposes, we echo the prompt back.
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let output = Command::new("python3")
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.arg("-c")
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.arg(format!("print(\"LangChain Agent Response for: {}\")", prompt))
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.stdout(Stdio::piped())
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.spawn()
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.expect("failed to execute process")
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.wait_with_output()
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.expect("failed to wait on child");
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io::stdout().write_all(&output.stdout).unwrap();
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}
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/*
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TODO (for future LangChain-style pipeline):
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1. Implement trait-based agent components: Tokenizer, Retriever, Reasoner, Generator.
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2. Allow config via YAML or TOML to define chain flow.
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3. Async pipeline support with Tokio.
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4. Optional integration with LLM APIs (OpenAI, Ollama, etc).
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5. Rust-native vector search (e.g. using `tantivy`, `qdrant-client`).
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*/
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133
tmp/data.rs
133
tmp/data.rs
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#[derive(Debug, Serialize, Deserialize)]
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pub struct RelationalAutonomousAI {
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pub system_name: String,
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pub description: String,
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pub core_components: CoreComponents,
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pub extensions: Extensions,
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pub note: String,
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}
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#[derive(Debug, Serialize, Deserialize)]
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pub struct CoreComponents {
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pub personality: Personality,
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pub relationship: Relationship,
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pub environment: Environment,
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pub memory: Memory,
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pub message_trigger: MessageTrigger,
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pub message_generation: MessageGeneration,
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pub state_transition: StateTransition,
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}
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#[derive(Debug, Serialize, Deserialize)]
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pub struct Personality {
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pub r#type: String,
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pub variants: Vec<String>,
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pub parameters: PersonalityParameters,
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}
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#[derive(Debug, Serialize, Deserialize)]
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pub struct PersonalityParameters {
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pub message_trigger_style: String,
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pub decay_rate_modifier: String,
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}
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#[derive(Debug, Serialize, Deserialize)]
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pub struct Relationship {
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pub parameters: Vec<String>,
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pub properties: RelationshipProperties,
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pub decay_function: String,
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}
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#[derive(Debug, Serialize, Deserialize)]
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pub struct RelationshipProperties {
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pub persistent: bool,
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pub hidden: bool,
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pub irreversible: bool,
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pub decay_over_time: bool,
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}
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#[derive(Debug, Serialize, Deserialize)]
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pub struct Environment {
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pub daily_luck: DailyLuck,
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}
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#[derive(Debug, Serialize, Deserialize)]
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pub struct DailyLuck {
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pub r#type: String,
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pub range: Vec<f32>,
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pub update: String,
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pub streak_mechanism: StreakMechanism,
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}
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#[derive(Debug, Serialize, Deserialize)]
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pub struct StreakMechanism {
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pub trigger: String,
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pub effect: String,
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pub chance: f32,
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}
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#[derive(Debug, Serialize, Deserialize)]
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pub struct Memory {
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pub long_term_memory: String,
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pub short_term_context: String,
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pub usage_in_generation: bool,
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}
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#[derive(Debug, Serialize, Deserialize)]
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pub struct MessageTrigger {
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pub condition: TriggerCondition,
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pub timing: TriggerTiming,
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}
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#[derive(Debug, Serialize, Deserialize)]
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pub struct TriggerCondition {
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pub relationship_threshold: String,
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pub time_decay: bool,
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pub environment_luck: String,
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}
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#[derive(Debug, Serialize, Deserialize)]
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pub struct TriggerTiming {
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pub based_on: Vec<String>,
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pub modifiers: String,
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}
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#[derive(Debug, Serialize, Deserialize)]
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pub struct MessageGeneration {
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pub style_variants: Vec<String>,
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pub influenced_by: Vec<String>,
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pub llm_integration: bool,
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}
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#[derive(Debug, Serialize, Deserialize)]
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pub struct StateTransition {
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pub states: Vec<String>,
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pub transitions: String,
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}
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#[derive(Debug, Serialize, Deserialize)]
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pub struct Extensions {
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pub persistence: Persistence,
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pub api: Api,
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pub scheduler: Scheduler,
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}
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#[derive(Debug, Serialize, Deserialize)]
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pub struct Persistence {
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pub database: String,
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pub storage_items: Vec<String>,
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}
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#[derive(Debug, Serialize, Deserialize)]
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pub struct Api {
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pub llm: String,
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pub mode: String,
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pub external_event_trigger: bool,
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}
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#[derive(Debug, Serialize, Deserialize)]
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pub struct Scheduler {
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pub async_event_loop: bool,
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pub interval_check: i32,
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pub time_decay_check: bool,
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}
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File diff suppressed because one or more lines are too long
@ -1,46 +0,0 @@
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use serde::{Deserialize, Serialize};
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use std::fs::File;
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use std::io::{BufReader, Write};
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use std::time::{SystemTime, UNIX_EPOCH};
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mod model;
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use model::RelationalAutonomousAI;
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fn load_config(path: &str) -> std::io::Result<RelationalAutonomousAI> {
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let file = File::open(path)?;
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let reader = BufReader::new(file);
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let config: RelationalAutonomousAI = serde_json::from_reader(reader)?;
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Ok(config)
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}
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fn save_config(config: &RelationalAutonomousAI, path: &str) -> std::io::Result<()> {
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let mut file = File::create(path)?;
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let json = serde_json::to_string_pretty(config)?;
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file.write_all(json.as_bytes())?;
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Ok(())
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}
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fn should_send_message(config: &RelationalAutonomousAI) -> bool {
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// 簡易な送信条件: relationshipが高く、daily_luckが0.8以上
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config.core_components.relationship.parameters.contains(&"trust".to_string())
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&& config.core_components.environment.daily_luck.range[1] >= 0.8
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}
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fn main() -> std::io::Result<()> {
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let path = "config.json";
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let mut config = load_config(path)?;
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if should_send_message(&config) {
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println!("💌 メッセージを送信できます: {:?}", config.core_components.personality.r#type);
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// ステート変化の例: メッセージ送信後に記録用トランジションを追加
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config.core_components.state_transition.transitions.push("message_sent".to_string());
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save_config(&config, path)?;
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} else {
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println!("😶 まだ送信条件に達していません。");
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}
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Ok(())
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}
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