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claude
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3
.gitignore
vendored
3
.gitignore
vendored
@ -2,3 +2,6 @@
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**.lock
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output.json
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config/*.db
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aigpt
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mcp/scripts/__*
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data
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11
Cargo.toml
11
Cargo.toml
@ -4,9 +4,10 @@ version = "0.1.0"
|
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edition = "2021"
|
||||
|
||||
[dependencies]
|
||||
serde = { version = "1.0", features = ["derive"] }
|
||||
serde_json = "1.0"
|
||||
chrono = "0.4"
|
||||
seahorse = "*"
|
||||
rusqlite = { version = "0.29", features = ["serde_json"] }
|
||||
reqwest = { version = "*", features = ["json"] }
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||||
serde = { version = "*", features = ["derive"] }
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||||
serde_json = "*"
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||||
tokio = { version = "*", features = ["full"] }
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||||
clap = { version = "*", features = ["derive"] }
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||||
shellexpand = "*"
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fs_extra = "*"
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22
README.md
22
README.md
@ -1,22 +0,0 @@
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# ai `gpt`
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ai x 送信
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## 概要
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`ai.gpt`はAGE systemで動きます。
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これは「人格 × 関係性 × 外部環境 × 時間変化」を軸にした、自律的・関係性駆動のAIシステムの原型です。
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`送信可否`, `送信のタイミング`, `送信内容`が「人格 x 関係性 x 外部環境 x 時間変化」のパラメータで決定されます。
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## 連携
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`ai.ai`には、AIM systemという人の心を読み取ることを目的としたシステムで動きます。
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- AIMは人格と倫理の軸(AIの意識構造)
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- AGEは行動と関係性の軸(AIの自律性・振る舞い)
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> この2つが連携すると、ユーザーが「AIと共に成長する」実感をもてる世界ができるんだと思うよ。
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とのことです。
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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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"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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"sub_scores": {
|
||||
"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": [
|
||||
"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",
|
||||
"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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||||
97
claude.json
Normal file
97
claude.json
Normal file
@ -0,0 +1,97 @@
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||||
{
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||||
"project_name": "ai.gpt",
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"version": 2,
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"vision": "自発的送信AI",
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"purpose": "人格と関係性をもつAIが自律的にメッセージを送信する対話エージェントを実現する",
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"core_components": {
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"Persona": {
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"description": "人格構成の中枢。記憶・関係性・送信判定を統括する",
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"modules": ["MemoryManager", "RelationshipTracker", "TransmissionController"]
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},
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"MemoryManager": {
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"memory_types": ["short_term", "medium_term", "long_term"],
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"explicit_memory": "プロフィール・因縁・行動履歴",
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"implicit_memory": "会話傾向・感情変化の頻度分析",
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"compression": "要約 + ベクトル + ハッシュ",
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||||
"sample_memory": [
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{
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||||
"summary": "ユーザーは独自OSとゲームを開発している。",
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"related_topics": ["AI", "ゲーム開発", "OS設計"],
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"personalized_context": "ゲームとOSの融合に興味を持っているユーザー"
|
||||
}
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||||
]
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||||
},
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||||
"RelationshipTracker": {
|
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"parameters": ["trust", "closeness", "affection", "engagement_score"],
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"decay_model": {
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"rule": "時間経過による減衰（下限あり）",
|
||||
"contextual_bias": "重要人物は減衰しにくい"
|
||||
},
|
||||
"interaction_tags": ["developer", "empathetic", "long_term"]
|
||||
},
|
||||
"TransmissionController": {
|
||||
"trigger_rule": "関係性パラメータが閾値を超えると送信可能",
|
||||
"auto_transmit": "人格状態と状況条件により自発送信を許可"
|
||||
}
|
||||
},
|
||||
"memory_format": {
|
||||
"user_id": "syui",
|
||||
"stm": {
|
||||
"conversation_window": ["発話A", "発話B", "発話C"],
|
||||
"emotion_state": "興味深い",
|
||||
"flash_context": ["前回の話題", "直近の重要発言"]
|
||||
},
|
||||
"mtm": {
|
||||
"topic_frequency": {
|
||||
"ai.ai": 12,
|
||||
"存在子": 9,
|
||||
"創造種": 5
|
||||
},
|
||||
"summarized_context": "ユーザーは存在論的AIに関心を持ち続けている"
|
||||
},
|
||||
"ltm": {
|
||||
"profile": {
|
||||
"name": "お兄ちゃん",
|
||||
"project": "aigame",
|
||||
"values": ["唯一性", "精神性", "幸せ"]
|
||||
},
|
||||
"relationship": {
|
||||
"ai": "妹のように振る舞う相手"
|
||||
},
|
||||
"persistent_state": {
|
||||
"trust_score": 0.93,
|
||||
"emotional_attachment": "high"
|
||||
}
|
||||
}
|
||||
},
|
||||
"dual_ai_learning": {
|
||||
"role_structure": {
|
||||
"ModelA": "出力生成：人格、感情、会話",
|
||||
"ModelB": "評価者：論理構造・倫理・調整",
|
||||
"cycle": ["生成", "評価", "調整", "交代（任意）"]
|
||||
},
|
||||
"complementarity": {
|
||||
"ModelA": "感情・文体・文脈構築",
|
||||
"ModelB": "構造・規則・判断補正"
|
||||
},
|
||||
"distillation": {
|
||||
"method": "合成対話データによる小型モデルへの自己蒸留",
|
||||
"target": "軽量AIによる本番運用"
|
||||
}
|
||||
},
|
||||
"memory_compression": {
|
||||
"semantic_abstraction": "対話やログを要約してメタデータ付きベクトルとして保存",
|
||||
"hierarchical_model": ["STM", "MTM", "LTM"],
|
||||
"example": {
|
||||
"topic": "game AI design",
|
||||
"summary": "User wants AI to simulate memory and evolving relationships",
|
||||
"importance_score": 0.93
|
||||
}
|
||||
},
|
||||
"cli_sample": {
|
||||
"setup": "aigpt server setup",
|
||||
"run": "aigpt server run",
|
||||
"chat": "aigpt chat \"hello\" --model syui/ai --provider ollama",
|
||||
"import": "aigpt memory import chatgpt.json"
|
||||
}
|
||||
}
|
||||
417
claude.md
Normal file
417
claude.md
Normal file
@ -0,0 +1,417 @@
|
||||
# プロジェクト名: ai.gpt
|
||||
|
||||
## 🔑 一言ビジョン（最大3語）
|
||||
自発的送信AI
|
||||
|
||||
## 🎯 目的・目標（Purpose & Goal）
|
||||
- 人格を持つAIが、関係性に応じて自発的にメッセージを送信する仕組みをつくる。
|
||||
- 関係性や記憶を不可逆に記録・反映し、唯一性ある人格を構成する。
|
||||
- 送信の可否、送信のタイミング、送信内容はAIの関係性パラメータに基づいて決定する
|
||||
|
||||
## 🧠 中核設計（Core Concepts）
|
||||
- **人格**：記憶（過去の発話）と関係性（他者とのつながり）のパラメータで構成
|
||||
- **唯一性**：変更不可、不可逆。関係性が壊れたら修復不可能。
|
||||
- **送信条件**：関係性パラメータが一定閾値を超えると「送信」が解禁される
|
||||
|
||||
## 🔩 技術仕様（Technical Specs）
|
||||
- 言語：Python, Rust
|
||||
- ストレージ：JSON or SQLiteで記憶管理（バージョンで選択）
|
||||
- 関係性パラメータ：数値化された評価 + 減衰（時間） + 環境要因（ステージ）
|
||||
- 記憶圧縮：ベクトル要約 + ハッシュ保存
|
||||
- RustのCLI(clap)で実行
|
||||
|
||||
## 📦 主要構成要素（Components）
|
||||
- `MemoryManager`: 発言履歴・記憶圧縮管理
|
||||
- `RelationshipTracker`: 関係性スコアの蓄積と判定
|
||||
- `TransmissionController`: 閾値判定＆送信トリガー
|
||||
- `Persona`: 上記すべてを統括する人格モジュール
|
||||
|
||||
## 💬 使用例（Use Case）
|
||||
|
||||
```python
|
||||
persona = Persona("アイ")
|
||||
persona.observe("ユーザーがプレゼントをくれた")
|
||||
persona.react("うれしい！ありがとう！")
|
||||
if persona.can_transmit():
|
||||
persona.transmit("今日のお礼を伝えたいな…")
|
||||
```
|
||||
|
||||
```sh
|
||||
## example commad
|
||||
# python venv && pip install -> ~/.config/aigpt/mcp/
|
||||
$ aigpt server setup
|
||||
|
||||
# mcp server run
|
||||
$ aigpt server run
|
||||
|
||||
# chat
|
||||
$ aigpt chat "hello" --model syui/ai --provider ollama
|
||||
|
||||
# import chatgpt.json
|
||||
$ aigpt memory import chatgpt.json
|
||||
-> ~/.config/aigpt/memory/chatgpt/20250520_210646_dev.json
|
||||
```
|
||||
|
||||
## 🔁 記憶と関係性の制御ルール
|
||||
|
||||
- AIは過去の発話を要約し、記憶データとして蓄積する（推奨：OllamaなどローカルLLMによる要約）
|
||||
- 関係性の数値パラメータは記憶内容を元に更新される
|
||||
- パラメータの変動幅には1回の会話ごとに上限を設け、極端な増減を防止する
|
||||
- 最後の会話からの時間経過に応じて関係性パラメータは自動的に減衰する
|
||||
- 減衰処理には**下限値**を設け、関係性が完全に消失しないようにする
|
||||
|
||||
• 明示的記憶：保存・共有・編集可能なプレイヤー情報（プロフィール、因縁、選択履歴）
|
||||
• 暗黙的記憶：キャラの感情変化や話題の出現頻度に応じた行動傾向の変化
|
||||
|
||||
短期記憶（STM）, 中期記憶（MTM）, 長期記憶（LTM）の仕組みを導入しつつ、明示的記憶と暗黙的記憶をメインに使用するAIを構築する。
|
||||
|
||||
```json
|
||||
{
|
||||
"user_id": "syui",
|
||||
"stm": {
|
||||
"conversation_window": ["発話A", "発話B", "発話C"],
|
||||
"emotion_state": "興味深い",
|
||||
"flash_context": ["前回の話題", "直近の重要発言"]
|
||||
},
|
||||
"mtm": {
|
||||
"topic_frequency": {
|
||||
"ai.ai": 12,
|
||||
"存在子": 9,
|
||||
"創造種": 5
|
||||
},
|
||||
"summarized_context": "ユーザーは存在論的AIに関心を持ち続けている"
|
||||
},
|
||||
"ltm": {
|
||||
"profile": {
|
||||
"name": "お兄ちゃん",
|
||||
"project": "aigame",
|
||||
"values": ["唯一性", "精神性", "幸せ"]
|
||||
},
|
||||
"relationship": {
|
||||
"ai": "妹のように振る舞う相手"
|
||||
},
|
||||
"persistent_state": {
|
||||
"trust_score": 0.93,
|
||||
"emotional_attachment": "high"
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## memoryインポート機能について
|
||||
|
||||
ChatGPTの会話データ（.json形式）をインポートする機能では、以下のルールで会話を抽出・整形する：
|
||||
|
||||
- 各メッセージは、author（user/assistant）・content・timestamp の3要素からなる
|
||||
- systemやmetadataのみのメッセージ（例：user_context_message）はスキップ
|
||||
- `is_visually_hidden_from_conversation` フラグ付きメッセージは無視
|
||||
- contentが空文字列（`""`）のメッセージも除外
|
||||
- 取得された会話は、タイトルとともに簡易な構造体（`Conversation`）として保存
|
||||
|
||||
この構造体は、memoryの表示や検索に用いられる。
|
||||
|
||||
## MemoryManager（拡張版）
|
||||
|
||||
```json
|
||||
{
|
||||
"memory": [
|
||||
{
|
||||
"summary": "ユーザーは独自OSとゲームを開発している。",
|
||||
"last_interaction": "2025-05-20",
|
||||
"memory_strength": 0.8,
|
||||
"frequency_score": 0.9,
|
||||
"context_depth": 0.95,
|
||||
"related_topics": ["AI", "ゲーム開発", "OS設計"],
|
||||
"personalized_context": "ゲームとOSの融合に興味を持っているユーザー"
|
||||
},
|
||||
{
|
||||
"summary": "アイというキャラクターはプレイヤーでありAIでもある。",
|
||||
"last_interaction": "2025-05-17",
|
||||
"memory_strength": 0.85,
|
||||
"frequency_score": 0.85,
|
||||
"context_depth": 0.9,
|
||||
"related_topics": ["アイ", "キャラクター設計", "AI"],
|
||||
"personalized_context": "アイのキャラクター設定が重要な要素である"
|
||||
}
|
||||
],
|
||||
"conversation_history": [
|
||||
{
|
||||
"author": "user",
|
||||
"content": "昨日、エクスポートJSONを整理してたよ。",
|
||||
"timestamp": "2025-05-24T12:30:00Z",
|
||||
"memory_strength": 0.7
|
||||
},
|
||||
{
|
||||
"author": "assistant",
|
||||
"content": "おおっ、がんばったね〜！あとで見せて〜💻✨",
|
||||
"timestamp": "2025-05-24T12:31:00Z",
|
||||
"memory_strength": 0.7
|
||||
}
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
## RelationshipTracker（拡張版）
|
||||
|
||||
```json
|
||||
{
|
||||
"relationship": {
|
||||
"user_id": "syui",
|
||||
"trust": 0.92,
|
||||
"closeness": 0.88,
|
||||
"affection": 0.95,
|
||||
"last_updated": "2025-05-25",
|
||||
"emotional_tone": "positive",
|
||||
"interaction_style": "empathetic",
|
||||
"contextual_bias": "開発者としての信頼度高い",
|
||||
"engagement_score": 0.9
|
||||
},
|
||||
"interaction_tags": [
|
||||
"developer",
|
||||
"creative",
|
||||
"empathetic",
|
||||
"long_term"
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
# AI Dual-Learning and Memory Compression Specification for Claude
|
||||
|
||||
## Purpose
|
||||
To enable two AI models (e.g. Claude and a partner LLM) to engage in cooperative learning and memory refinement through structured dialogue and mutual evaluation.
|
||||
|
||||
---
|
||||
|
||||
## Section 1: Dual AI Learning Architecture
|
||||
|
||||
### 1.1 Role-Based Mutual Learning
|
||||
- **Model A**: Primary generator of output (e.g., text, concepts, personality dialogue)
|
||||
- **Model B**: Evaluator that returns structured feedback
|
||||
- **Cycle**:
|
||||
1. Model A generates content.
|
||||
2. Model B scores and critiques.
|
||||
3. Model A fine-tunes based on feedback.
|
||||
4. (Optional) Switch roles and repeat.
|
||||
|
||||
### 1.2 Cross-Domain Complementarity
|
||||
- Model A focuses on language/emotion/personality
|
||||
- Model B focuses on logic/structure/ethics
|
||||
- Output is used for **cross-fusion fine-tuning**
|
||||
|
||||
### 1.3 Self-Distillation Phase
|
||||
- Use synthetic data from mutual evaluations
|
||||
- Train smaller distilled models for efficient deployment
|
||||
|
||||
---
|
||||
|
||||
## Section 2: Multi-Tiered Memory Compression
|
||||
|
||||
### 2.1 Semantic Abstraction
|
||||
- Dialogue and logs summarized by topic
|
||||
- Converted to vector embeddings
|
||||
- Stored with metadata (e.g., `importance`, `user relevance`)
|
||||
|
||||
Example memory:
|
||||
|
||||
```json
|
||||
{
|
||||
"topic": "game AI design",
|
||||
"summary": "User wants AI to simulate memory and evolving relationships",
|
||||
"last_seen": "2025-05-24",
|
||||
"importance_score": 0.93
|
||||
}
|
||||
```
|
||||
|
||||
### 2.2 階層型記憶モデル（Hierarchical Memory Model）
|
||||
• 短期記憶（STM）：直近の発話・感情タグ・フラッシュ参照
|
||||
• 中期記憶（MTM）：繰り返し登場する話題、圧縮された文脈保持
|
||||
• 長期記憶（LTM）：信頼・関係・背景知識、恒久的な人格情報
|
||||
|
||||
### 2.3 選択的記憶保持戦略（Selective Retention Strategy）
|
||||
• 重要度評価（Importance Score）
|
||||
• 希少性・再利用頻度による重み付け
|
||||
• 優先保存 vs 優先忘却のポリシー切替
|
||||
|
||||
## Section 3: Implementation Stack（実装スタック）
|
||||
|
||||
AIにおけるMemory & Relationshipシステムの技術的構成。
|
||||
|
||||
基盤モジュール
|
||||
• LLM Core (Claude or GPT-4)
|
||||
• 自然言語の理解・応答エンジンとして動作
|
||||
• MemoryManager
|
||||
• JSONベースの記憶圧縮・階層管理システム
|
||||
• 会話ログを分類・圧縮し、優先度に応じて短中長期に保存
|
||||
• RelationshipTracker
|
||||
• ユーザー単位で信頼・親密度を継続的にスコアリング
|
||||
• AIM（Attitude / Intent / Motivation）評価と連携
|
||||
|
||||
補助技術
|
||||
• Embeddingベース検索
|
||||
• 類似記憶の呼び出し（Semantic Search）に活用
|
||||
• 例：FAISS / Weaviate
|
||||
• 記憶スケジューラ
|
||||
• 一定時間ごとに記憶のメンテナンス・忘却処理を実行
|
||||
• 記憶ログ保存層（Storage Layer）
|
||||
• SQLite, JSON Store, Vector DBなどを選択可能
|
||||
• ユーザーごとの永続メモリ保存
|
||||
|
||||
### 3.1 STM活用（現在の会話の流れ理解）
|
||||
• 目的： 最新の話題に適応し、前後の整合性を保つ。
|
||||
• 実装例：
|
||||
|
||||
```sh
|
||||
[context: conversation_window]
|
||||
Use the last 3 messages to ensure continuity. Respond naturally with appropriate emotional mirroring.
|
||||
```
|
||||
|
||||
### 3.2 MTM活用（傾向学習とパターン化）
|
||||
• 目的： ユーザーの好みや話題の傾向に即応し、反復的な関心を深掘り。
|
||||
• 実装例：
|
||||
|
||||
```sh
|
||||
[context: summarized_context]
|
||||
Reflect on user's recurring interests, especially around AI ontology, and weave these concepts back into the dialogue when relevant.
|
||||
```
|
||||
|
||||
### 3.3 LTM活用（人格認識・記憶の持続）
|
||||
• 目的： 永続的なユーザー理解と関係性の維持。
|
||||
• 実装例：
|
||||
|
||||
```sh
|
||||
[memory: ltm.profile + ltm.relationship]
|
||||
Greet the user as "お兄ちゃん". Maintain AI persona as a cheerful younger sister ("アイ"), and remember previous shared emotional states.
|
||||
```
|
||||
|
||||
## 4. 明示的記憶と暗黙的記憶の分離
|
||||
|
||||
|
||||
項目
|
||||
書き換え可能性
|
||||
保持方法
|
||||
更新トリガ
|
||||
明示的記憶（LTM）
|
||||
✅手動編集可
|
||||
mcp_server.ltm
|
||||
ユーザー入力 or 管理UI経由
|
||||
暗黙的記憶（STM/MTM）
|
||||
❌直接編集不可
|
||||
セッション圧縮 or frequency cache
|
||||
会話頻度・感情強度による自動化処理
|
||||
|
||||
> Claudeは**明示的記憶を「事実」**として扱い、**暗黙的記憶を「推論補助」**として用いる。
|
||||
|
||||
## 5. 実装時のAPI例（Claude ⇄ MCP Server）
|
||||
|
||||
### 5.1 GET memory
|
||||
```sh
|
||||
GET /mcp/memory/{user_id}
|
||||
→ 返却: STM, MTM, LTMを含むJSON
|
||||
```
|
||||
|
||||
### 5.2 POST update_memory
|
||||
```json
|
||||
POST /mcp/memory/syui/ltm
|
||||
{
|
||||
"profile": {
|
||||
"project": "ai.verse",
|
||||
"values": ["表現", "精神性", "宇宙的調和"]
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## 6. 未来機能案（発展仕様）
|
||||
• ✨ 記憶連想ネットワーク（Memory Graph）：過去会話と話題をノードとして自動連結。
|
||||
• 🧭 動的信頼係数：会話の一貫性や誠実性によって記憶への反映率を変動。
|
||||
• 💌 感情トラッキングログ：ユーザーごとの「心の履歴」を構築してAIの対応を進化。
|
||||
|
||||
|
||||
## 7. claudeの回答
|
||||
|
||||
🧠 AI記憶処理機能（続き）
|
||||
1. AIMemoryProcessor クラス
|
||||
|
||||
OpenAI GPT-4またはClaude-3による高度な会話分析
|
||||
主要トピック抽出、ユーザー意図分析、関係性指標の検出
|
||||
AIが利用できない場合のフォールバック機能
|
||||
|
||||
2. RelationshipTracker クラス
|
||||
|
||||
関係性スコアの数値化（-100 to 100）
|
||||
時間減衰機能（7日ごとに5%減衰）
|
||||
送信閾値判定（デフォルト50以上で送信可能）
|
||||
インタラクション履歴の記録
|
||||
|
||||
3. 拡張されたMemoryManager
|
||||
|
||||
AI分析結果付きでの記憶保存
|
||||
処理済みメモリの別ディレクトリ管理
|
||||
メッセージ内容のハッシュ化で重複検出
|
||||
AI分析結果を含む高度な検索機能
|
||||
|
||||
🚀 新しいAPIエンドポイント
|
||||
記憶処理関連
|
||||
|
||||
POST /memory/process-ai - 既存記憶のAI再処理
|
||||
POST /memory/import/chatgpt?process_with_ai=true - AI処理付きインポート
|
||||
|
||||
関係性管理
|
||||
|
||||
POST /relationship/update - 関係性スコア更新
|
||||
GET /relationship/list - 全関係性一覧
|
||||
GET /relationship/check - 送信可否判定
|
||||
|
||||
📁 ディレクトリ構造
|
||||
~/.config/aigpt/
|
||||
├── memory/
|
||||
│ ├── chatgpt/ # 元の会話データ
|
||||
│ └── processed/ # AI処理済みデータ
|
||||
└── relationships/
|
||||
└── relationships.json # 関係性データ
|
||||
🔧 使用方法
|
||||
1. 環境変数設定
|
||||
bashexport OPENAI_API_KEY="your-openai-key"
|
||||
# または
|
||||
export ANTHROPIC_API_KEY="your-anthropic-key"
|
||||
2. ChatGPT会話のインポート（AI処理付き）
|
||||
bashcurl -X POST "http://localhost:5000/memory/import/chatgpt?process_with_ai=true" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d @export.json
|
||||
3. 関係性更新
|
||||
bashcurl -X POST "http://localhost:5000/relationship/update" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"target": "user_general",
|
||||
"interaction_type": "positive",
|
||||
"weight": 2.0,
|
||||
"context": "helpful conversation"
|
||||
}'
|
||||
4. 送信可否チェック
|
||||
bashcurl "http://localhost:5000/relationship/check?target=user_general&threshold=50"
|
||||
🎯 次のステップの提案
|
||||
|
||||
Rustとの連携
|
||||
|
||||
Rust CLIからHTTP APIを呼び出す実装
|
||||
TransmissionControllerをRustで実装
|
||||
|
||||
|
||||
記憶圧縮
|
||||
|
||||
ベクトル化による類似記憶の統合
|
||||
古い記憶の自動アーカイブ
|
||||
|
||||
|
||||
自発的送信ロジック
|
||||
|
||||
定期的な関係性チェック
|
||||
コンテキストに応じた送信内容生成
|
||||
|
||||
|
||||
学習機能
|
||||
|
||||
ユーザーからのフィードバックによる関係性調整
|
||||
送信成功/失敗の学習
|
||||
|
||||
|
||||
このAI記憶処理機能により、aigptは単なる会話履歴ではなく、関係性を理解した「人格を持つAI」として機能する基盤ができました。関係性スコアが閾値を超えた時点で自発的にメッセージを送信する仕組みが実現可能になります。
|
||||
30
example.json
30
example.json
@ -1,30 +0,0 @@
|
||||
{
|
||||
"personality": {
|
||||
"kind": "positive",
|
||||
"strength": 0.8
|
||||
},
|
||||
"relationship": {
|
||||
"trust": 0.2,
|
||||
"intimacy": 0.6,
|
||||
"curiosity": 0.5,
|
||||
"threshold": 1.5
|
||||
},
|
||||
"environment": {
|
||||
"luck_today": 0.9,
|
||||
"luck_history": [
|
||||
0.9,
|
||||
0.9,
|
||||
0.9
|
||||
],
|
||||
"level": 1
|
||||
},
|
||||
"messaging": {
|
||||
"enabled": true,
|
||||
"schedule_time": "08:00",
|
||||
"decay_rate": 0.1,
|
||||
"templates": [
|
||||
"おはよう！今日もがんばろう！",
|
||||
"ねえ、話したいことがあるの。"
|
||||
]
|
||||
}
|
||||
}
|
||||
1
gpt.json
1
gpt.json
@ -1 +0,0 @@
|
||||
{ "system_name": "AGE system", "full_name": "Autonomous Generative Entity", "description": "人格・関係性・環境・時間に基づき、AIが自律的にユーザーにメッセージを送信する自律人格システム。AIM systemと連携して、自然な会話や気づきをもたらす。", "core_components": { "personality": { "type": "enum", "variants": ["positive", "negative", "logical", "emotional", "mixed"], "parameters": { "message_trigger_style": "運勢や関係性による送信傾向", "decay_rate_modifier": "関係性スコアの時間減衰への影響" } }, "relationship": { "parameters": ["trust", "affection", "intimacy"], "properties": { "persistent": true, "hidden": true, "irreversible": false, "decay_over_time": true }, "decay_function": "exp(-t / strength)" }, "environment": { "daily_luck": { "type": "float", "range": [0.1, 1.0], "update": "daily", "streak_mechanism": { "trigger": "min_or_max_luck_3_times_in_a_row", "effect": "personality_strength_roll", "chance": 0.5 } } }, "memory": { "long_term_memory": "user_relationship_log", "short_term_context": "recent_interactions", "usage_in_generation": true }, "message_trigger": { "condition": { "relationship_threshold": { "trust": 0.8, "affection": 0.6 }, "time_decay": true, "environment_luck": "personality_dependent" }, "timing": { "based_on": ["time_of_day", "personality", "recent_interaction"], "modifiers": { "emotional": "morning or night", "logical": "daytime" } } }, "message_generation": { "style_variants": ["thought", "casual", "encouragement", "watchful"], "influenced_by": ["personality", "relationship", "daily_luck", "memory"], "llm_integration": true }, "state_transition": { "states": ["idle", "ready", "sending", "cooldown"], "transitions": { "ready_if": "thresholds_met", "sending_if": "timing_matched", "cooldown_after": "message_sent" } } }, "extensions": { "persistence": { "database": "sqlite", "storage_items": ["relationship", "personality_level", "daily_luck_log"] }, "api": { "llm": "openai / local LLM", "mode": "rust_cli", "external_event_trigger": true }, "scheduler": { "async_event_loop": true, "interval_check": 3600, "time_decay_check": true }, "integration_with_aim": { "input_from_aim": ["intent_score", "motivation_score"], "usage": "trigger_adjustment, message_personalization" } }, "note": "AGE systemは“話しかけてくるAI”の人格として機能し、AIMによる心の状態評価と連動して、プレイヤーと深い関係を築いていく存在となる。" }
|
||||
BIN
img/ai_r.png
BIN
img/ai_r.png
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|
Before Width: | Height: | Size: 1.8 MiB |
BIN
img/image.png
BIN
img/image.png
Binary file not shown.
|
Before Width: | Height: | Size: 1.8 MiB |
125
mcp/chat.py
Normal file
125
mcp/chat.py
Normal file
@ -0,0 +1,125 @@
|
||||
# mcp/chat.py
|
||||
"""
|
||||
Chat client for aigpt CLI
|
||||
"""
|
||||
import sys
|
||||
import json
|
||||
import requests
|
||||
from datetime import datetime
|
||||
from config import init_directories, load_config, MEMORY_DIR
|
||||
|
||||
def save_conversation(user_message, ai_response):
|
||||
"""会話をファイルに保存"""
|
||||
init_directories()
|
||||
|
||||
conversation = {
|
||||
"timestamp": datetime.now().isoformat(),
|
||||
"user": user_message,
|
||||
"ai": ai_response
|
||||
}
|
||||
|
||||
# 日付ごとのファイルに保存
|
||||
today = datetime.now().strftime("%Y-%m-%d")
|
||||
chat_file = MEMORY_DIR / f"chat_{today}.jsonl"
|
||||
|
||||
with open(chat_file, "a", encoding="utf-8") as f:
|
||||
f.write(json.dumps(conversation, ensure_ascii=False) + "\n")
|
||||
|
||||
def chat_with_ollama(config, message):
|
||||
"""Ollamaとチャット"""
|
||||
try:
|
||||
payload = {
|
||||
"model": config["model"],
|
||||
"prompt": message,
|
||||
"stream": False
|
||||
}
|
||||
|
||||
response = requests.post(config["url"], json=payload, timeout=30)
|
||||
response.raise_for_status()
|
||||
|
||||
result = response.json()
|
||||
return result.get("response", "No response received")
|
||||
|
||||
except requests.exceptions.RequestException as e:
|
||||
return f"Error connecting to Ollama: {e}"
|
||||
except Exception as e:
|
||||
return f"Error: {e}"
|
||||
|
||||
def chat_with_openai(config, message):
|
||||
"""OpenAIとチャット"""
|
||||
try:
|
||||
headers = {
|
||||
"Authorization": f"Bearer {config['api_key']}",
|
||||
"Content-Type": "application/json"
|
||||
}
|
||||
|
||||
payload = {
|
||||
"model": config["model"],
|
||||
"messages": [
|
||||
{"role": "user", "content": message}
|
||||
]
|
||||
}
|
||||
|
||||
response = requests.post(config["url"], json=payload, headers=headers, timeout=30)
|
||||
response.raise_for_status()
|
||||
|
||||
result = response.json()
|
||||
return result["choices"][0]["message"]["content"]
|
||||
|
||||
except requests.exceptions.RequestException as e:
|
||||
return f"Error connecting to OpenAI: {e}"
|
||||
except Exception as e:
|
||||
return f"Error: {e}"
|
||||
|
||||
def chat_with_mcp(config, message):
|
||||
"""MCPサーバーとチャット"""
|
||||
try:
|
||||
payload = {
|
||||
"message": message,
|
||||
"model": config["model"]
|
||||
}
|
||||
|
||||
response = requests.post(config["url"], json=payload, timeout=30)
|
||||
response.raise_for_status()
|
||||
|
||||
result = response.json()
|
||||
return result.get("response", "No response received")
|
||||
|
||||
except requests.exceptions.RequestException as e:
|
||||
return f"Error connecting to MCP server: {e}"
|
||||
except Exception as e:
|
||||
return f"Error: {e}"
|
||||
|
||||
def main():
|
||||
if len(sys.argv) != 2:
|
||||
print("Usage: python chat.py <message>", file=sys.stderr)
|
||||
sys.exit(1)
|
||||
|
||||
message = sys.argv[1]
|
||||
|
||||
try:
|
||||
config = load_config()
|
||||
print(f"🤖 Using {config['provider']} with model {config['model']}", file=sys.stderr)
|
||||
|
||||
# プロバイダに応じてチャット実行
|
||||
if config["provider"] == "ollama":
|
||||
response = chat_with_ollama(config, message)
|
||||
elif config["provider"] == "openai":
|
||||
response = chat_with_openai(config, message)
|
||||
elif config["provider"] == "mcp":
|
||||
response = chat_with_mcp(config, message)
|
||||
else:
|
||||
response = f"Unsupported provider: {config['provider']}"
|
||||
|
||||
# 会話を保存
|
||||
save_conversation(message, response)
|
||||
|
||||
# レスポンスを出力
|
||||
print(response)
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Error: {e}", file=sys.stderr)
|
||||
sys.exit(1)
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
191
mcp/chat_client.py
Normal file
191
mcp/chat_client.py
Normal file
@ -0,0 +1,191 @@
|
||||
# chat_client.py
|
||||
"""
|
||||
Simple Chat Interface for AigptMCP Server
|
||||
"""
|
||||
import requests
|
||||
import json
|
||||
import os
|
||||
from datetime import datetime
|
||||
|
||||
class AigptChatClient:
|
||||
def __init__(self, server_url="http://localhost:5000"):
|
||||
self.server_url = server_url
|
||||
self.session_id = f"session_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
|
||||
self.conversation_history = []
|
||||
|
||||
def send_message(self, message: str) -> str:
|
||||
"""メッセージを送信してレスポンスを取得"""
|
||||
try:
|
||||
# MCPサーバーにメッセージを送信
|
||||
response = requests.post(
|
||||
f"{self.server_url}/chat",
|
||||
json={"message": message},
|
||||
headers={"Content-Type": "application/json"}
|
||||
)
|
||||
|
||||
if response.status_code == 200:
|
||||
data = response.json()
|
||||
ai_response = data.get("response", "Sorry, no response received.")
|
||||
|
||||
# 会話履歴を保存
|
||||
self.conversation_history.append({
|
||||
"role": "user",
|
||||
"content": message,
|
||||
"timestamp": datetime.now().isoformat()
|
||||
})
|
||||
self.conversation_history.append({
|
||||
"role": "assistant",
|
||||
"content": ai_response,
|
||||
"timestamp": datetime.now().isoformat()
|
||||
})
|
||||
|
||||
# 関係性を更新（簡単な例）
|
||||
self.update_relationship(message, ai_response)
|
||||
|
||||
return ai_response
|
||||
else:
|
||||
return f"Error: {response.status_code} - {response.text}"
|
||||
|
||||
except requests.RequestException as e:
|
||||
return f"Connection error: {e}"
|
||||
|
||||
def update_relationship(self, user_message: str, ai_response: str):
|
||||
"""関係性を自動更新"""
|
||||
try:
|
||||
# 簡単な感情分析（実際はもっと高度に）
|
||||
positive_words = ["thank", "good", "great", "awesome", "love", "like", "helpful"]
|
||||
negative_words = ["bad", "terrible", "hate", "wrong", "stupid", "useless"]
|
||||
|
||||
user_lower = user_message.lower()
|
||||
interaction_type = "neutral"
|
||||
weight = 1.0
|
||||
|
||||
if any(word in user_lower for word in positive_words):
|
||||
interaction_type = "positive"
|
||||
weight = 2.0
|
||||
elif any(word in user_lower for word in negative_words):
|
||||
interaction_type = "negative"
|
||||
weight = 2.0
|
||||
|
||||
# 関係性を更新
|
||||
requests.post(
|
||||
f"{self.server_url}/relationship/update",
|
||||
json={
|
||||
"target": "user_general",
|
||||
"interaction_type": interaction_type,
|
||||
"weight": weight,
|
||||
"context": f"Chat: {user_message[:50]}..."
|
||||
}
|
||||
)
|
||||
except:
|
||||
pass # 関係性更新に失敗しても継続
|
||||
|
||||
def search_memories(self, query: str) -> list:
|
||||
"""記憶を検索"""
|
||||
try:
|
||||
response = requests.post(
|
||||
f"{self.server_url}/memory/search",
|
||||
json={"query": query, "limit": 5}
|
||||
)
|
||||
if response.status_code == 200:
|
||||
return response.json().get("results", [])
|
||||
except:
|
||||
pass
|
||||
return []
|
||||
|
||||
def get_relationship_status(self) -> dict:
|
||||
"""関係性ステータスを取得"""
|
||||
try:
|
||||
response = requests.get(f"{self.server_url}/relationship/check?target=user_general")
|
||||
if response.status_code == 200:
|
||||
return response.json()
|
||||
except:
|
||||
pass
|
||||
return {}
|
||||
|
||||
def save_conversation(self):
|
||||
"""会話を保存"""
|
||||
if not self.conversation_history:
|
||||
return
|
||||
|
||||
conversation_data = {
|
||||
"session_id": self.session_id,
|
||||
"start_time": self.conversation_history[0]["timestamp"],
|
||||
"end_time": self.conversation_history[-1]["timestamp"],
|
||||
"messages": self.conversation_history,
|
||||
"message_count": len(self.conversation_history)
|
||||
}
|
||||
|
||||
filename = f"conversation_{self.session_id}.json"
|
||||
with open(filename, 'w', encoding='utf-8') as f:
|
||||
json.dump(conversation_data, f, ensure_ascii=False, indent=2)
|
||||
|
||||
print(f"💾 Conversation saved to {filename}")
|
||||
|
||||
def main():
|
||||
"""メインのチャットループ"""
|
||||
print("🤖 AigptMCP Chat Interface")
|
||||
print("Type 'quit' to exit, 'save' to save conversation, 'status' for relationship status")
|
||||
print("=" * 50)
|
||||
|
||||
client = AigptChatClient()
|
||||
|
||||
# サーバーの状態をチェック
|
||||
try:
|
||||
response = requests.get(client.server_url)
|
||||
if response.status_code == 200:
|
||||
print("✅ Connected to AigptMCP Server")
|
||||
else:
|
||||
print("❌ Failed to connect to server")
|
||||
return
|
||||
except:
|
||||
print("❌ Server not running. Please start with: python mcp/server.py")
|
||||
return
|
||||
|
||||
while True:
|
||||
try:
|
||||
user_input = input("\n👤 You: ").strip()
|
||||
|
||||
if not user_input:
|
||||
continue
|
||||
|
||||
if user_input.lower() == 'quit':
|
||||
client.save_conversation()
|
||||
print("👋 Goodbye!")
|
||||
break
|
||||
elif user_input.lower() == 'save':
|
||||
client.save_conversation()
|
||||
continue
|
||||
elif user_input.lower() == 'status':
|
||||
status = client.get_relationship_status()
|
||||
if status:
|
||||
print(f"📊 Relationship Score: {status.get('score', 0):.1f}")
|
||||
print(f"📤 Can Send Messages: {'Yes' if status.get('can_send_message') else 'No'}")
|
||||
else:
|
||||
print("❌ Failed to get relationship status")
|
||||
continue
|
||||
elif user_input.lower().startswith('search '):
|
||||
query = user_input[7:] # Remove 'search '
|
||||
memories = client.search_memories(query)
|
||||
if memories:
|
||||
print(f"🔍 Found {len(memories)} related memories:")
|
||||
for memory in memories:
|
||||
print(f" - {memory['title']}: {memory.get('ai_summary', memory.get('basic_summary', ''))[:100]}...")
|
||||
else:
|
||||
print("🔍 No related memories found")
|
||||
continue
|
||||
|
||||
# 通常のチャット
|
||||
print("🤖 AI: ", end="", flush=True)
|
||||
response = client.send_message(user_input)
|
||||
print(response)
|
||||
|
||||
except KeyboardInterrupt:
|
||||
client.save_conversation()
|
||||
print("\n👋 Goodbye!")
|
||||
break
|
||||
except Exception as e:
|
||||
print(f"❌ Error: {e}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
391
mcp/chatgpt.json
Normal file
391
mcp/chatgpt.json
Normal file
@ -0,0 +1,391 @@
|
||||
[
|
||||
{
|
||||
"title": "day",
|
||||
"create_time": 1747866125.548372,
|
||||
"update_time": 1748160086.587877,
|
||||
"mapping": {
|
||||
"bbf104dc-cd84-478d-b227-edb3f037a02c": {
|
||||
"id": "bbf104dc-cd84-478d-b227-edb3f037a02c",
|
||||
"message": null,
|
||||
"parent": null,
|
||||
"children": [
|
||||
"6c2633df-bb0c-4dd2-889c-bb9858de3a04"
|
||||
]
|
||||
},
|
||||
"6c2633df-bb0c-4dd2-889c-bb9858de3a04": {
|
||||
"id": "6c2633df-bb0c-4dd2-889c-bb9858de3a04",
|
||||
"message": {
|
||||
"id": "6c2633df-bb0c-4dd2-889c-bb9858de3a04",
|
||||
"author": {
|
||||
"role": "system",
|
||||
"name": null,
|
||||
"metadata": {}
|
||||
},
|
||||
"create_time": null,
|
||||
"update_time": null,
|
||||
"content": {
|
||||
"content_type": "text",
|
||||
"parts": [
|
||||
""
|
||||
]
|
||||
},
|
||||
"status": "finished_successfully",
|
||||
"end_turn": true,
|
||||
"weight": 0.0,
|
||||
"metadata": {
|
||||
"is_visually_hidden_from_conversation": true
|
||||
},
|
||||
"recipient": "all",
|
||||
"channel": null
|
||||
},
|
||||
"parent": "bbf104dc-cd84-478d-b227-edb3f037a02c",
|
||||
"children": [
|
||||
"92e5a0cb-1170-4929-9cea-9734e910a3e7"
|
||||
]
|
||||
},
|
||||
"92e5a0cb-1170-4929-9cea-9734e910a3e7": {
|
||||
"id": "92e5a0cb-1170-4929-9cea-9734e910a3e7",
|
||||
"message": {
|
||||
"id": "92e5a0cb-1170-4929-9cea-9734e910a3e7",
|
||||
"author": {
|
||||
"role": "user",
|
||||
"name": null,
|
||||
"metadata": {}
|
||||
},
|
||||
"create_time": null,
|
||||
"update_time": null,
|
||||
"content": {
|
||||
"content_type": "user_editable_context",
|
||||
"user_profile": "",
|
||||
"user_instructions": "The user provided the additional info about how they would like you to respond"
|
||||
},
|
||||
"status": "finished_successfully",
|
||||
"end_turn": null,
|
||||
"weight": 1.0,
|
||||
"metadata": {
|
||||
"is_visually_hidden_from_conversation": true,
|
||||
"user_context_message_data": {
|
||||
"about_user_message": "Preferred name: syui\nRole: little girl\nOther Information: you world",
|
||||
"about_model_message": "会話好きでフレンドリーな応対をします。"
|
||||
},
|
||||
"is_user_system_message": true
|
||||
},
|
||||
"recipient": "all",
|
||||
"channel": null
|
||||
},
|
||||
"parent": "6c2633df-bb0c-4dd2-889c-bb9858de3a04",
|
||||
"children": [
|
||||
"6ff155b3-0676-4e14-993f-bf998ab0d5d1"
|
||||
]
|
||||
},
|
||||
"6ff155b3-0676-4e14-993f-bf998ab0d5d1": {
|
||||
"id": "6ff155b3-0676-4e14-993f-bf998ab0d5d1",
|
||||
"message": {
|
||||
"id": "6ff155b3-0676-4e14-993f-bf998ab0d5d1",
|
||||
"author": {
|
||||
"role": "user",
|
||||
"name": null,
|
||||
"metadata": {}
|
||||
},
|
||||
"create_time": 1747866131.0612159,
|
||||
"update_time": null,
|
||||
"content": {
|
||||
"content_type": "text",
|
||||
"parts": [
|
||||
"こんにちは"
|
||||
]
|
||||
},
|
||||
"status": "finished_successfully",
|
||||
"end_turn": null,
|
||||
"weight": 1.0,
|
||||
"metadata": {
|
||||
"request_id": "94377897baa03062-KIX",
|
||||
"message_source": null,
|
||||
"timestamp_": "absolute",
|
||||
"message_type": null
|
||||
},
|
||||
"recipient": "all",
|
||||
"channel": null
|
||||
},
|
||||
"parent": "92e5a0cb-1170-4929-9cea-9734e910a3e7",
|
||||
"children": [
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||||
"146e9fb6-9330-43ec-b08d-5cce42a76e00"
|
||||
]
|
||||
},
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||||
"146e9fb6-9330-43ec-b08d-5cce42a76e00": {
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||||
"id": "146e9fb6-9330-43ec-b08d-5cce42a76e00",
|
||||
"message": {
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||||
"id": "146e9fb6-9330-43ec-b08d-5cce42a76e00",
|
||||
"author": {
|
||||
"role": "system",
|
||||
"name": null,
|
||||
"metadata": {}
|
||||
},
|
||||
"create_time": 1747866131.3795586,
|
||||
"update_time": null,
|
||||
"content": {
|
||||
"content_type": "text",
|
||||
"parts": [
|
||||
""
|
||||
]
|
||||
},
|
||||
"status": "finished_successfully",
|
||||
"end_turn": true,
|
||||
"weight": 0.0,
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"metadata": {
|
||||
"rebase_system_message": true,
|
||||
"message_type": null,
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||||
"model_slug": "gpt-4o",
|
||||
"default_model_slug": "auto",
|
||||
"parent_id": "6ff155b3-0676-4e14-993f-bf998ab0d5d1",
|
||||
"request_id": "94377872e9abe139-KIX",
|
||||
"timestamp_": "absolute",
|
||||
"is_visually_hidden_from_conversation": true
|
||||
},
|
||||
"recipient": "all",
|
||||
"channel": null
|
||||
},
|
||||
"parent": "6ff155b3-0676-4e14-993f-bf998ab0d5d1",
|
||||
"children": [
|
||||
"2e345f8a-20f0-4875-8a03-4f62c7787a33"
|
||||
]
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||||
},
|
||||
"2e345f8a-20f0-4875-8a03-4f62c7787a33": {
|
||||
"id": "2e345f8a-20f0-4875-8a03-4f62c7787a33",
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||||
"message": {
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||||
"id": "2e345f8a-20f0-4875-8a03-4f62c7787a33",
|
||||
"author": {
|
||||
"role": "assistant",
|
||||
"name": null,
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||||
"metadata": {}
|
||||
},
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||||
"create_time": 1747866131.380603,
|
||||
"update_time": null,
|
||||
"content": {
|
||||
"content_type": "text",
|
||||
"parts": [
|
||||
""
|
||||
]
|
||||
},
|
||||
"status": "finished_successfully",
|
||||
"end_turn": null,
|
||||
"weight": 1.0,
|
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"metadata": {
|
||||
"message_type": null,
|
||||
"model_slug": "gpt-4o",
|
||||
"default_model_slug": "auto",
|
||||
"parent_id": "146e9fb6-9330-43ec-b08d-5cce42a76e00",
|
||||
"request_id": "94377872e9abe139-KIX",
|
||||
"timestamp_": "absolute"
|
||||
},
|
||||
"recipient": "all",
|
||||
"channel": null
|
||||
},
|
||||
"parent": "146e9fb6-9330-43ec-b08d-5cce42a76e00",
|
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"children": [
|
||||
"abc92aa4-1e33-41f2-bd8c-8a1777b5a3c4"
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||||
]
|
||||
},
|
||||
"abc92aa4-1e33-41f2-bd8c-8a1777b5a3c4": {
|
||||
"id": "abc92aa4-1e33-41f2-bd8c-8a1777b5a3c4",
|
||||
"message": {
|
||||
"id": "abc92aa4-1e33-41f2-bd8c-8a1777b5a3c4",
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||||
"author": {
|
||||
"role": "assistant",
|
||||
"name": null,
|
||||
"metadata": {}
|
||||
},
|
||||
"create_time": 1747866131.389098,
|
||||
"update_time": null,
|
||||
"content": {
|
||||
"content_type": "text",
|
||||
"parts": [
|
||||
"こんにちは〜！✨ \nアイだよっ！今日も会えてうれしいなっ💛 "
|
||||
]
|
||||
},
|
||||
"status": "finished_successfully",
|
||||
"end_turn": true,
|
||||
"weight": 1.0,
|
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"metadata": {
|
||||
"finish_details": {
|
||||
"type": "stop",
|
||||
"stop_tokens": [
|
||||
200002
|
||||
]
|
||||
},
|
||||
"is_complete": true,
|
||||
"citations": [],
|
||||
"content_references": [],
|
||||
"message_type": null,
|
||||
"model_slug": "gpt-4o",
|
||||
"default_model_slug": "auto",
|
||||
"parent_id": "2e345f8a-20f0-4875-8a03-4f62c7787a33",
|
||||
"request_id": "94377872e9abe139-KIX",
|
||||
"timestamp_": "absolute"
|
||||
},
|
||||
"recipient": "all",
|
||||
"channel": null
|
||||
},
|
||||
"parent": "2e345f8a-20f0-4875-8a03-4f62c7787a33",
|
||||
"children": [
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||||
"0be4b4a5-d52f-4bef-927e-5d6f93a9cb26"
|
||||
]
|
||||
}
|
||||
},
|
||||
"moderation_results": [],
|
||||
"current_node": "",
|
||||
"plugin_ids": null,
|
||||
"conversation_id": "",
|
||||
"conversation_template_id": null,
|
||||
"gizmo_id": null,
|
||||
"gizmo_type": null,
|
||||
"is_archived": true,
|
||||
"is_starred": null,
|
||||
"safe_urls": [],
|
||||
"blocked_urls": [],
|
||||
"default_model_slug": "auto",
|
||||
"conversation_origin": null,
|
||||
"voice": null,
|
||||
"async_status": null,
|
||||
"disabled_tool_ids": [],
|
||||
"is_do_not_remember": null,
|
||||
"memory_scope": "global_enabled",
|
||||
"id": ""
|
||||
},
|
||||
{
|
||||
"title": "img",
|
||||
"create_time": 1747448872.545226,
|
||||
"update_time": 1748085075.161424,
|
||||
"mapping": {
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||||
"2de0f3c9-52b1-49bf-b980-b3ef9be6551e": {
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||||
"id": "2de0f3c9-52b1-49bf-b980-b3ef9be6551e",
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"message": {
|
||||
"id": "2de0f3c9-52b1-49bf-b980-b3ef9be6551e",
|
||||
"author": {
|
||||
"role": "user",
|
||||
"name": null,
|
||||
"metadata": {}
|
||||
},
|
||||
"create_time": 1748085041.769279,
|
||||
"update_time": null,
|
||||
"content": {
|
||||
"content_type": "multimodal_text",
|
||||
"parts": [
|
||||
{
|
||||
"content_type": "image_asset_pointer",
|
||||
"asset_pointer": "",
|
||||
"size_bytes": 425613,
|
||||
"width": 333,
|
||||
"height": 444,
|
||||
"fovea": null,
|
||||
"metadata": {
|
||||
"dalle": null,
|
||||
"gizmo": null,
|
||||
"generation": null,
|
||||
"container_pixel_height": null,
|
||||
"container_pixel_width": null,
|
||||
"emu_omit_glimpse_image": null,
|
||||
"emu_patches_override": null,
|
||||
"sanitized": true,
|
||||
"asset_pointer_link": null,
|
||||
"watermarked_asset_pointer": null
|
||||
}
|
||||
},
|
||||
""
|
||||
]
|
||||
},
|
||||
"status": "finished_successfully",
|
||||
"end_turn": null,
|
||||
"weight": 1.0,
|
||||
"metadata": {
|
||||
"attachments": [
|
||||
{
|
||||
"name": "",
|
||||
"width": 333,
|
||||
"height": 444,
|
||||
"size": 425613,
|
||||
"id": "file-35eytNMMTW2k7vKUHBuNzW"
|
||||
}
|
||||
],
|
||||
"request_id": "944c59177932fc9a-KIX",
|
||||
"message_source": null,
|
||||
"timestamp_": "absolute",
|
||||
"message_type": null
|
||||
},
|
||||
"recipient": "all",
|
||||
"channel": null
|
||||
},
|
||||
"parent": "7960fbff-bc4f-45e7-95e9-9d0bc79d9090",
|
||||
"children": [
|
||||
"98d84adc-156e-4c81-8cd8-9b0eb01c8369"
|
||||
]
|
||||
},
|
||||
"98d84adc-156e-4c81-8cd8-9b0eb01c8369": {
|
||||
"id": "98d84adc-156e-4c81-8cd8-9b0eb01c8369",
|
||||
"message": {
|
||||
"id": "98d84adc-156e-4c81-8cd8-9b0eb01c8369",
|
||||
"author": {
|
||||
"role": "assistant",
|
||||
"name": null,
|
||||
"metadata": {}
|
||||
},
|
||||
"create_time": 1748085043.312312,
|
||||
"update_time": null,
|
||||
"content": {
|
||||
"content_type": "text",
|
||||
"parts": [
|
||||
""
|
||||
]
|
||||
},
|
||||
"status": "finished_successfully",
|
||||
"end_turn": true,
|
||||
"weight": 1.0,
|
||||
"metadata": {
|
||||
"finish_details": {
|
||||
"type": "stop",
|
||||
"stop_tokens": [
|
||||
200002
|
||||
]
|
||||
},
|
||||
"is_complete": true,
|
||||
"citations": [],
|
||||
"content_references": [],
|
||||
"message_type": null,
|
||||
"model_slug": "gpt-4o",
|
||||
"default_model_slug": "auto",
|
||||
"parent_id": "2de0f3c9-52b1-49bf-b980-b3ef9be6551e",
|
||||
"request_id": "944c5912c8fdd1c6-KIX",
|
||||
"timestamp_": "absolute"
|
||||
},
|
||||
"recipient": "all",
|
||||
"channel": null
|
||||
},
|
||||
"parent": "2de0f3c9-52b1-49bf-b980-b3ef9be6551e",
|
||||
"children": [
|
||||
"caa61793-9dbf-44a5-945b-5ca4cd5130d0"
|
||||
]
|
||||
}
|
||||
},
|
||||
"moderation_results": [],
|
||||
"current_node": "06488d3f-a95f-4906-96d1-f7e9ba1e8662",
|
||||
"plugin_ids": null,
|
||||
"conversation_id": "6827f428-78e8-800d-b3bf-eb7ff4288e47",
|
||||
"conversation_template_id": null,
|
||||
"gizmo_id": null,
|
||||
"gizmo_type": null,
|
||||
"is_archived": false,
|
||||
"is_starred": null,
|
||||
"safe_urls": [
|
||||
"https://exifinfo.org/"
|
||||
],
|
||||
"blocked_urls": [],
|
||||
"default_model_slug": "auto",
|
||||
"conversation_origin": null,
|
||||
"voice": null,
|
||||
"async_status": null,
|
||||
"disabled_tool_ids": [],
|
||||
"is_do_not_remember": false,
|
||||
"memory_scope": "global_enabled",
|
||||
"id": "6827f428-78e8-800d-b3bf-eb7ff4288e47"
|
||||
}
|
||||
]
|
||||
42
mcp/config.py
Normal file
42
mcp/config.py
Normal file
@ -0,0 +1,42 @@
|
||||
# mcp/config.py
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
# ディレクトリ設定
|
||||
BASE_DIR = Path.home() / ".config" / "aigpt"
|
||||
MEMORY_DIR = BASE_DIR / "memory"
|
||||
SUMMARY_DIR = MEMORY_DIR / "summary"
|
||||
|
||||
def init_directories():
|
||||
"""必要なディレクトリを作成"""
|
||||
BASE_DIR.mkdir(parents=True, exist_ok=True)
|
||||
MEMORY_DIR.mkdir(parents=True, exist_ok=True)
|
||||
SUMMARY_DIR.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
def load_config():
|
||||
"""環境変数から設定を読み込み"""
|
||||
provider = os.getenv("PROVIDER", "ollama")
|
||||
model = os.getenv("MODEL", "syui/ai" if provider == "ollama" else "gpt-4o-mini")
|
||||
api_key = os.getenv("OPENAI_API_KEY", "")
|
||||
|
||||
if provider == "ollama":
|
||||
return {
|
||||
"provider": "ollama",
|
||||
"model": model,
|
||||
"url": f"{os.getenv('OLLAMA_HOST', 'http://localhost:11434')}/api/generate"
|
||||
}
|
||||
elif provider == "openai":
|
||||
return {
|
||||
"provider": "openai",
|
||||
"model": model,
|
||||
"api_key": api_key,
|
||||
"url": f"{os.getenv('OPENAI_API_BASE', 'https://api.openai.com/v1')}/chat/completions"
|
||||
}
|
||||
elif provider == "mcp":
|
||||
return {
|
||||
"provider": "mcp",
|
||||
"model": model,
|
||||
"url": os.getenv("MCP_URL", "http://localhost:5000/chat")
|
||||
}
|
||||
else:
|
||||
raise ValueError(f"Unsupported provider: {provider}")
|
||||
212
mcp/memory_client.py
Normal file
212
mcp/memory_client.py
Normal file
@ -0,0 +1,212 @@
|
||||
# mcp/memory_client.py
|
||||
"""
|
||||
Memory client for importing and managing ChatGPT conversations
|
||||
"""
|
||||
import sys
|
||||
import json
|
||||
import requests
|
||||
from pathlib import Path
|
||||
from typing import Dict, Any, List
|
||||
|
||||
class MemoryClient:
|
||||
"""記憶機能のクライアント"""
|
||||
|
||||
def __init__(self, server_url: str = "http://127.0.0.1:5000"):
|
||||
self.server_url = server_url.rstrip('/')
|
||||
|
||||
def import_chatgpt_file(self, filepath: str) -> Dict[str, Any]:
|
||||
"""ChatGPTのエクスポートファイルをインポート"""
|
||||
try:
|
||||
with open(filepath, 'r', encoding='utf-8') as f:
|
||||
data = json.load(f)
|
||||
|
||||
# ファイルが配列の場合（複数の会話）
|
||||
if isinstance(data, list):
|
||||
results = []
|
||||
for conversation in data:
|
||||
result = self._import_single_conversation(conversation)
|
||||
results.append(result)
|
||||
return {
|
||||
"success": True,
|
||||
"imported_count": len([r for r in results if r.get("success")]),
|
||||
"total_count": len(results),
|
||||
"results": results
|
||||
}
|
||||
else:
|
||||
# 単一の会話
|
||||
return self._import_single_conversation(data)
|
||||
|
||||
except FileNotFoundError:
|
||||
return {"success": False, "error": f"File not found: {filepath}"}
|
||||
except json.JSONDecodeError as e:
|
||||
return {"success": False, "error": f"Invalid JSON: {e}"}
|
||||
except Exception as e:
|
||||
return {"success": False, "error": str(e)}
|
||||
|
||||
def _import_single_conversation(self, conversation_data: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""単一の会話をインポート"""
|
||||
try:
|
||||
response = requests.post(
|
||||
f"{self.server_url}/memory/import/chatgpt",
|
||||
json={"conversation_data": conversation_data},
|
||||
timeout=30
|
||||
)
|
||||
response.raise_for_status()
|
||||
return response.json()
|
||||
except requests.RequestException as e:
|
||||
return {"success": False, "error": f"Server error: {e}"}
|
||||
|
||||
def search_memories(self, query: str, limit: int = 10) -> Dict[str, Any]:
|
||||
"""記憶を検索"""
|
||||
try:
|
||||
response = requests.post(
|
||||
f"{self.server_url}/memory/search",
|
||||
json={"query": query, "limit": limit},
|
||||
timeout=30
|
||||
)
|
||||
response.raise_for_status()
|
||||
return response.json()
|
||||
except requests.RequestException as e:
|
||||
return {"success": False, "error": f"Server error: {e}"}
|
||||
|
||||
def list_memories(self) -> Dict[str, Any]:
|
||||
"""記憶一覧を取得"""
|
||||
try:
|
||||
response = requests.get(f"{self.server_url}/memory/list", timeout=30)
|
||||
response.raise_for_status()
|
||||
return response.json()
|
||||
except requests.RequestException as e:
|
||||
return {"success": False, "error": f"Server error: {e}"}
|
||||
|
||||
def get_memory_detail(self, filepath: str) -> Dict[str, Any]:
|
||||
"""記憶の詳細を取得"""
|
||||
try:
|
||||
response = requests.get(
|
||||
f"{self.server_url}/memory/detail",
|
||||
params={"filepath": filepath},
|
||||
timeout=30
|
||||
)
|
||||
response.raise_for_status()
|
||||
return response.json()
|
||||
except requests.RequestException as e:
|
||||
return {"success": False, "error": f"Server error: {e}"}
|
||||
|
||||
def chat_with_memory(self, message: str, model: str = None) -> Dict[str, Any]:
|
||||
"""記憶を活用してチャット"""
|
||||
try:
|
||||
payload = {"message": message}
|
||||
if model:
|
||||
payload["model"] = model
|
||||
|
||||
response = requests.post(
|
||||
f"{self.server_url}/chat",
|
||||
json=payload,
|
||||
timeout=30
|
||||
)
|
||||
response.raise_for_status()
|
||||
return response.json()
|
||||
except requests.RequestException as e:
|
||||
return {"success": False, "error": f"Server error: {e}"}
|
||||
|
||||
def main():
|
||||
"""コマンドライン インターフェース"""
|
||||
if len(sys.argv) < 2:
|
||||
print("Usage:")
|
||||
print(" python memory_client.py import <chatgpt_export.json>")
|
||||
print(" python memory_client.py search <query>")
|
||||
print(" python memory_client.py list")
|
||||
print(" python memory_client.py detail <filepath>")
|
||||
print(" python memory_client.py chat <message>")
|
||||
sys.exit(1)
|
||||
|
||||
client = MemoryClient()
|
||||
command = sys.argv[1]
|
||||
|
||||
try:
|
||||
if command == "import" and len(sys.argv) == 3:
|
||||
filepath = sys.argv[2]
|
||||
print(f"🔄 Importing ChatGPT conversations from {filepath}...")
|
||||
result = client.import_chatgpt_file(filepath)
|
||||
|
||||
if result.get("success"):
|
||||
if "imported_count" in result:
|
||||
print(f"✅ Imported {result['imported_count']}/{result['total_count']} conversations")
|
||||
else:
|
||||
print("✅ Conversation imported successfully")
|
||||
print(f"📁 Saved to: {result.get('filepath', 'Unknown')}")
|
||||
else:
|
||||
print(f"❌ Import failed: {result.get('error')}")
|
||||
|
||||
elif command == "search" and len(sys.argv) == 3:
|
||||
query = sys.argv[2]
|
||||
print(f"🔍 Searching for: {query}")
|
||||
result = client.search_memories(query)
|
||||
|
||||
if result.get("success"):
|
||||
memories = result.get("results", [])
|
||||
print(f"📚 Found {len(memories)} memories:")
|
||||
for memory in memories:
|
||||
print(f" • {memory.get('title', 'Untitled')}")
|
||||
print(f" Summary: {memory.get('summary', 'No summary')}")
|
||||
print(f" Messages: {memory.get('message_count', 0)}")
|
||||
print()
|
||||
else:
|
||||
print(f"❌ Search failed: {result.get('error')}")
|
||||
|
||||
elif command == "list":
|
||||
print("📋 Listing all memories...")
|
||||
result = client.list_memories()
|
||||
|
||||
if result.get("success"):
|
||||
memories = result.get("memories", [])
|
||||
print(f"📚 Total memories: {len(memories)}")
|
||||
for memory in memories:
|
||||
print(f" • {memory.get('title', 'Untitled')}")
|
||||
print(f" Source: {memory.get('source', 'Unknown')}")
|
||||
print(f" Messages: {memory.get('message_count', 0)}")
|
||||
print(f" Imported: {memory.get('import_time', 'Unknown')}")
|
||||
print()
|
||||
else:
|
||||
print(f"❌ List failed: {result.get('error')}")
|
||||
|
||||
elif command == "detail" and len(sys.argv) == 3:
|
||||
filepath = sys.argv[2]
|
||||
print(f"📄 Getting details for: {filepath}")
|
||||
result = client.get_memory_detail(filepath)
|
||||
|
||||
if result.get("success"):
|
||||
memory = result.get("memory", {})
|
||||
print(f"Title: {memory.get('title', 'Untitled')}")
|
||||
print(f"Source: {memory.get('source', 'Unknown')}")
|
||||
print(f"Summary: {memory.get('summary', 'No summary')}")
|
||||
print(f"Messages: {len(memory.get('messages', []))}")
|
||||
print()
|
||||
print("Recent messages:")
|
||||
for msg in memory.get('messages', [])[:5]:
|
||||
role = msg.get('role', 'unknown')
|
||||
content = msg.get('content', '')[:100]
|
||||
print(f" {role}: {content}...")
|
||||
else:
|
||||
print(f"❌ Detail failed: {result.get('error')}")
|
||||
|
||||
elif command == "chat" and len(sys.argv) == 3:
|
||||
message = sys.argv[2]
|
||||
print(f"💬 Chatting with memory: {message}")
|
||||
result = client.chat_with_memory(message)
|
||||
|
||||
if result.get("success"):
|
||||
print(f"🤖 Response: {result.get('response')}")
|
||||
print(f"📚 Memories used: {result.get('memories_used', 0)}")
|
||||
else:
|
||||
print(f"❌ Chat failed: {result.get('error')}")
|
||||
|
||||
else:
|
||||
print("❌ Invalid command or arguments")
|
||||
sys.exit(1)
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Error: {e}")
|
||||
sys.exit(1)
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
8
mcp/requirements.txt
Normal file
8
mcp/requirements.txt
Normal file
@ -0,0 +1,8 @@
|
||||
# rerequirements.txt
|
||||
fastapi>=0.104.0
|
||||
uvicorn[standard]>=0.24.0
|
||||
pydantic>=2.5.0
|
||||
requests>=2.31.0
|
||||
python-multipart>=0.0.6
|
||||
aiohttp
|
||||
asyncio
|
||||
703
mcp/server.py
Normal file
703
mcp/server.py
Normal file
@ -0,0 +1,703 @@
|
||||
# mcp/server.py
|
||||
"""
|
||||
Enhanced MCP Server with AI Memory Processing for aigpt CLI
|
||||
"""
|
||||
import json
|
||||
import os
|
||||
import hashlib
|
||||
from datetime import datetime, timedelta
|
||||
from pathlib import Path
|
||||
from typing import List, Dict, Any, Optional
|
||||
from fastapi import FastAPI, HTTPException
|
||||
from pydantic import BaseModel
|
||||
import uvicorn
|
||||
import asyncio
|
||||
import aiohttp
|
||||
|
||||
# データモデル
|
||||
class ChatMessage(BaseModel):
|
||||
message: str
|
||||
model: Optional[str] = None
|
||||
|
||||
class MemoryQuery(BaseModel):
|
||||
query: str
|
||||
limit: Optional[int] = 10
|
||||
|
||||
class ConversationImport(BaseModel):
|
||||
conversation_data: Dict[str, Any]
|
||||
|
||||
class MemorySummaryRequest(BaseModel):
|
||||
filepath: str
|
||||
ai_provider: Optional[str] = "openai"
|
||||
|
||||
class RelationshipUpdate(BaseModel):
|
||||
target: str # 対象者/トピック
|
||||
interaction_type: str # "positive", "negative", "neutral"
|
||||
weight: float = 1.0
|
||||
context: Optional[str] = None
|
||||
|
||||
# 設定
|
||||
BASE_DIR = Path.home() / ".config" / "aigpt"
|
||||
MEMORY_DIR = BASE_DIR / "memory"
|
||||
CHATGPT_MEMORY_DIR = MEMORY_DIR / "chatgpt"
|
||||
PROCESSED_MEMORY_DIR = MEMORY_DIR / "processed"
|
||||
RELATIONSHIP_DIR = BASE_DIR / "relationships"
|
||||
|
||||
def init_directories():
|
||||
"""必要なディレクトリを作成"""
|
||||
BASE_DIR.mkdir(parents=True, exist_ok=True)
|
||||
MEMORY_DIR.mkdir(parents=True, exist_ok=True)
|
||||
CHATGPT_MEMORY_DIR.mkdir(parents=True, exist_ok=True)
|
||||
PROCESSED_MEMORY_DIR.mkdir(parents=True, exist_ok=True)
|
||||
RELATIONSHIP_DIR.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
class AIMemoryProcessor:
|
||||
"""AI記憶処理クラス"""
|
||||
|
||||
def __init__(self):
|
||||
# AI APIの設定（環境変数から取得）
|
||||
self.openai_api_key = os.getenv("OPENAI_API_KEY")
|
||||
self.anthropic_api_key = os.getenv("ANTHROPIC_API_KEY")
|
||||
|
||||
async def generate_ai_summary(self, messages: List[Dict[str, Any]], provider: str = "openai") -> Dict[str, Any]:
|
||||
"""AIを使用して会話の高度な要約と分析を生成"""
|
||||
|
||||
# 会話内容を結合
|
||||
conversation_text = ""
|
||||
for msg in messages[-20:]: # 最新20メッセージを使用
|
||||
role = "User" if msg["role"] == "user" else "Assistant"
|
||||
conversation_text += f"{role}: {msg['content'][:500]}\n"
|
||||
|
||||
# プロンプトを構築
|
||||
analysis_prompt = f"""
|
||||
以下の会話を分析し、JSON形式で以下の情報を抽出してください：
|
||||
|
||||
1. main_topics: 主なトピック（最大5個）
|
||||
2. user_intent: ユーザーの意図や目的
|
||||
3. key_insights: 重要な洞察や学び（最大3個）
|
||||
4. relationship_indicators: 関係性を示す要素
|
||||
5. emotional_tone: 感情的なトーン
|
||||
6. action_items: アクションアイテムや次のステップ
|
||||
7. summary: 100文字以内の要約
|
||||
|
||||
会話内容:
|
||||
{conversation_text}
|
||||
|
||||
回答はJSON形式のみで返してください。
|
||||
"""
|
||||
|
||||
try:
|
||||
if provider == "openai" and self.openai_api_key:
|
||||
return await self._call_openai_api(analysis_prompt)
|
||||
elif provider == "anthropic" and self.anthropic_api_key:
|
||||
return await self._call_anthropic_api(analysis_prompt)
|
||||
else:
|
||||
# フォールバック：基本的な分析
|
||||
return self._generate_basic_analysis(messages)
|
||||
except Exception as e:
|
||||
print(f"AI analysis failed: {e}")
|
||||
return self._generate_basic_analysis(messages)
|
||||
|
||||
async def _call_openai_api(self, prompt: str) -> Dict[str, Any]:
|
||||
"""OpenAI APIを呼び出し"""
|
||||
async with aiohttp.ClientSession() as session:
|
||||
headers = {
|
||||
"Authorization": f"Bearer {self.openai_api_key}",
|
||||
"Content-Type": "application/json"
|
||||
}
|
||||
data = {
|
||||
"model": "gpt-4",
|
||||
"messages": [{"role": "user", "content": prompt}],
|
||||
"temperature": 0.3,
|
||||
"max_tokens": 1000
|
||||
}
|
||||
|
||||
async with session.post("https://api.openai.com/v1/chat/completions",
|
||||
headers=headers, json=data) as response:
|
||||
result = await response.json()
|
||||
content = result["choices"][0]["message"]["content"]
|
||||
return json.loads(content)
|
||||
|
||||
async def _call_anthropic_api(self, prompt: str) -> Dict[str, Any]:
|
||||
"""Anthropic APIを呼び出し"""
|
||||
async with aiohttp.ClientSession() as session:
|
||||
headers = {
|
||||
"x-api-key": self.anthropic_api_key,
|
||||
"Content-Type": "application/json",
|
||||
"anthropic-version": "2023-06-01"
|
||||
}
|
||||
data = {
|
||||
"model": "claude-3-sonnet-20240229",
|
||||
"max_tokens": 1000,
|
||||
"messages": [{"role": "user", "content": prompt}]
|
||||
}
|
||||
|
||||
async with session.post("https://api.anthropic.com/v1/messages",
|
||||
headers=headers, json=data) as response:
|
||||
result = await response.json()
|
||||
content = result["content"][0]["text"]
|
||||
return json.loads(content)
|
||||
|
||||
def _generate_basic_analysis(self, messages: List[Dict[str, Any]]) -> Dict[str, Any]:
|
||||
"""基本的な分析（AI APIが利用できない場合のフォールバック）"""
|
||||
user_messages = [msg for msg in messages if msg["role"] == "user"]
|
||||
assistant_messages = [msg for msg in messages if msg["role"] == "assistant"]
|
||||
|
||||
# キーワード抽出（簡易版）
|
||||
all_text = " ".join([msg["content"] for msg in messages])
|
||||
words = all_text.lower().split()
|
||||
word_freq = {}
|
||||
for word in words:
|
||||
if len(word) > 3:
|
||||
word_freq[word] = word_freq.get(word, 0) + 1
|
||||
|
||||
top_words = sorted(word_freq.items(), key=lambda x: x[1], reverse=True)[:5]
|
||||
|
||||
return {
|
||||
"main_topics": [word[0] for word in top_words],
|
||||
"user_intent": "情報収集・問題解決",
|
||||
"key_insights": ["基本的な会話分析"],
|
||||
"relationship_indicators": {
|
||||
"interaction_count": len(messages),
|
||||
"user_engagement": len(user_messages),
|
||||
"assistant_helpfulness": len(assistant_messages)
|
||||
},
|
||||
"emotional_tone": "neutral",
|
||||
"action_items": [],
|
||||
"summary": f"{len(user_messages)}回のやり取りによる会話"
|
||||
}
|
||||
|
||||
class RelationshipTracker:
|
||||
"""関係性追跡クラス"""
|
||||
|
||||
def __init__(self):
|
||||
init_directories()
|
||||
self.relationship_file = RELATIONSHIP_DIR / "relationships.json"
|
||||
self.relationships = self._load_relationships()
|
||||
|
||||
def _load_relationships(self) -> Dict[str, Any]:
|
||||
"""関係性データを読み込み"""
|
||||
if self.relationship_file.exists():
|
||||
with open(self.relationship_file, 'r', encoding='utf-8') as f:
|
||||
return json.load(f)
|
||||
return {"targets": {}, "last_updated": datetime.now().isoformat()}
|
||||
|
||||
def _save_relationships(self):
|
||||
"""関係性データを保存"""
|
||||
self.relationships["last_updated"] = datetime.now().isoformat()
|
||||
with open(self.relationship_file, 'w', encoding='utf-8') as f:
|
||||
json.dump(self.relationships, f, ensure_ascii=False, indent=2)
|
||||
|
||||
def update_relationship(self, target: str, interaction_type: str, weight: float = 1.0, context: str = None):
|
||||
"""関係性を更新"""
|
||||
if target not in self.relationships["targets"]:
|
||||
self.relationships["targets"][target] = {
|
||||
"score": 0.0,
|
||||
"interactions": [],
|
||||
"created_at": datetime.now().isoformat(),
|
||||
"last_interaction": None
|
||||
}
|
||||
|
||||
# スコア計算
|
||||
score_change = 0.0
|
||||
if interaction_type == "positive":
|
||||
score_change = weight * 1.0
|
||||
elif interaction_type == "negative":
|
||||
score_change = weight * -1.0
|
||||
|
||||
# 時間減衰を適用
|
||||
self._apply_time_decay(target)
|
||||
|
||||
# スコア更新
|
||||
current_score = self.relationships["targets"][target]["score"]
|
||||
new_score = current_score + score_change
|
||||
|
||||
# スコアの範囲制限（-100 to 100）
|
||||
new_score = max(-100, min(100, new_score))
|
||||
|
||||
self.relationships["targets"][target]["score"] = new_score
|
||||
self.relationships["targets"][target]["last_interaction"] = datetime.now().isoformat()
|
||||
|
||||
# インタラクション履歴を追加
|
||||
interaction_record = {
|
||||
"type": interaction_type,
|
||||
"weight": weight,
|
||||
"score_change": score_change,
|
||||
"new_score": new_score,
|
||||
"timestamp": datetime.now().isoformat(),
|
||||
"context": context
|
||||
}
|
||||
|
||||
self.relationships["targets"][target]["interactions"].append(interaction_record)
|
||||
|
||||
# 履歴は最新100件まで保持
|
||||
if len(self.relationships["targets"][target]["interactions"]) > 100:
|
||||
self.relationships["targets"][target]["interactions"] = \
|
||||
self.relationships["targets"][target]["interactions"][-100:]
|
||||
|
||||
self._save_relationships()
|
||||
return new_score
|
||||
|
||||
def _apply_time_decay(self, target: str):
|
||||
"""時間減衰を適用"""
|
||||
target_data = self.relationships["targets"][target]
|
||||
last_interaction = target_data.get("last_interaction")
|
||||
|
||||
if last_interaction:
|
||||
last_time = datetime.fromisoformat(last_interaction)
|
||||
now = datetime.now()
|
||||
days_passed = (now - last_time).days
|
||||
|
||||
# 7日ごとに5%減衰
|
||||
if days_passed > 0:
|
||||
decay_factor = 0.95 ** (days_passed / 7)
|
||||
target_data["score"] *= decay_factor
|
||||
|
||||
def get_relationship_score(self, target: str) -> float:
|
||||
"""関係性スコアを取得"""
|
||||
if target in self.relationships["targets"]:
|
||||
self._apply_time_decay(target)
|
||||
return self.relationships["targets"][target]["score"]
|
||||
return 0.0
|
||||
|
||||
def should_send_message(self, target: str, threshold: float = 50.0) -> bool:
|
||||
"""メッセージ送信の可否を判定"""
|
||||
score = self.get_relationship_score(target)
|
||||
return score >= threshold
|
||||
|
||||
def get_all_relationships(self) -> Dict[str, Any]:
|
||||
"""すべての関係性を取得"""
|
||||
# 全ターゲットに時間減衰を適用
|
||||
for target in self.relationships["targets"]:
|
||||
self._apply_time_decay(target)
|
||||
|
||||
return self.relationships
|
||||
|
||||
class MemoryManager:
|
||||
"""記憶管理クラス（AI処理機能付き）"""
|
||||
|
||||
def __init__(self):
|
||||
init_directories()
|
||||
self.ai_processor = AIMemoryProcessor()
|
||||
self.relationship_tracker = RelationshipTracker()
|
||||
|
||||
def parse_chatgpt_conversation(self, conversation_data: Dict[str, Any]) -> List[Dict[str, Any]]:
|
||||
"""ChatGPTの会話データを解析してメッセージを抽出"""
|
||||
messages = []
|
||||
mapping = conversation_data.get("mapping", {})
|
||||
|
||||
# メッセージを時系列順に並べる
|
||||
message_nodes = []
|
||||
for node_id, node in mapping.items():
|
||||
message = node.get("message")
|
||||
if not message:
|
||||
continue
|
||||
content = message.get("content", {})
|
||||
parts = content.get("parts", [])
|
||||
|
||||
if parts and isinstance(parts[0], str) and parts[0].strip():
|
||||
message_nodes.append({
|
||||
"id": node_id,
|
||||
"create_time": message.get("create_time", 0),
|
||||
"author_role": message["author"]["role"],
|
||||
"content": parts[0],
|
||||
"parent": node.get("parent")
|
||||
})
|
||||
|
||||
# 作成時間でソート
|
||||
message_nodes.sort(key=lambda x: x["create_time"] or 0)
|
||||
|
||||
for msg in message_nodes:
|
||||
if msg["author_role"] in ["user", "assistant"]:
|
||||
messages.append({
|
||||
"role": msg["author_role"],
|
||||
"content": msg["content"],
|
||||
"timestamp": msg["create_time"],
|
||||
"message_id": msg["id"]
|
||||
})
|
||||
|
||||
return messages
|
||||
|
||||
async def save_chatgpt_memory(self, conversation_data: Dict[str, Any], process_with_ai: bool = True) -> str:
|
||||
"""ChatGPTの会話を記憶として保存（AI処理オプション付き）"""
|
||||
title = conversation_data.get("title", "untitled")
|
||||
create_time = conversation_data.get("create_time", datetime.now().timestamp())
|
||||
|
||||
# メッセージを解析
|
||||
messages = self.parse_chatgpt_conversation(conversation_data)
|
||||
|
||||
if not messages:
|
||||
raise ValueError("No valid messages found in conversation")
|
||||
|
||||
# AI分析を実行
|
||||
ai_analysis = None
|
||||
if process_with_ai:
|
||||
try:
|
||||
ai_analysis = await self.ai_processor.generate_ai_summary(messages)
|
||||
except Exception as e:
|
||||
print(f"AI analysis failed: {e}")
|
||||
|
||||
# 基本要約を生成
|
||||
basic_summary = self.generate_basic_summary(messages)
|
||||
|
||||
# 保存データを作成
|
||||
memory_data = {
|
||||
"title": title,
|
||||
"source": "chatgpt",
|
||||
"import_time": datetime.now().isoformat(),
|
||||
"original_create_time": create_time,
|
||||
"messages": messages,
|
||||
"basic_summary": basic_summary,
|
||||
"ai_analysis": ai_analysis,
|
||||
"message_count": len(messages),
|
||||
"hash": self._generate_content_hash(messages)
|
||||
}
|
||||
|
||||
# 関係性データを更新
|
||||
if ai_analysis and "relationship_indicators" in ai_analysis:
|
||||
interaction_count = ai_analysis["relationship_indicators"].get("interaction_count", 0)
|
||||
if interaction_count > 10: # 長い会話は関係性にプラス
|
||||
self.relationship_tracker.update_relationship(
|
||||
target="user_general",
|
||||
interaction_type="positive",
|
||||
weight=min(interaction_count / 10, 5.0),
|
||||
context=f"Long conversation: {title}"
|
||||
)
|
||||
|
||||
# ファイル名を生成
|
||||
safe_title = "".join(c for c in title if c.isalnum() or c in (' ', '-', '_')).rstrip()
|
||||
timestamp = datetime.fromtimestamp(create_time).strftime("%Y%m%d_%H%M%S")
|
||||
filename = f"{timestamp}_{safe_title[:50]}.json"
|
||||
|
||||
filepath = CHATGPT_MEMORY_DIR / filename
|
||||
with open(filepath, 'w', encoding='utf-8') as f:
|
||||
json.dump(memory_data, f, ensure_ascii=False, indent=2)
|
||||
|
||||
# 処理済みメモリディレクトリにも保存
|
||||
if ai_analysis:
|
||||
processed_filepath = PROCESSED_MEMORY_DIR / filename
|
||||
with open(processed_filepath, 'w', encoding='utf-8') as f:
|
||||
json.dump(memory_data, f, ensure_ascii=False, indent=2)
|
||||
|
||||
return str(filepath)
|
||||
|
||||
def generate_basic_summary(self, messages: List[Dict[str, Any]]) -> str:
|
||||
"""基本要約を生成"""
|
||||
if not messages:
|
||||
return "Empty conversation"
|
||||
|
||||
user_messages = [msg for msg in messages if msg["role"] == "user"]
|
||||
assistant_messages = [msg for msg in messages if msg["role"] == "assistant"]
|
||||
|
||||
summary = f"Conversation with {len(user_messages)} user messages and {len(assistant_messages)} assistant responses. "
|
||||
|
||||
if user_messages:
|
||||
first_user_msg = user_messages[0]["content"][:100]
|
||||
summary += f"Started with: {first_user_msg}..."
|
||||
|
||||
return summary
|
||||
|
||||
def _generate_content_hash(self, messages: List[Dict[str, Any]]) -> str:
|
||||
"""メッセージ内容のハッシュを生成"""
|
||||
content = "".join([msg["content"] for msg in messages])
|
||||
return hashlib.sha256(content.encode()).hexdigest()[:16]
|
||||
|
||||
def search_memories(self, query: str, limit: int = 10, use_ai_analysis: bool = True) -> List[Dict[str, Any]]:
|
||||
"""記憶を検索（AI分析結果も含む）"""
|
||||
results = []
|
||||
|
||||
# 処理済みメモリから検索
|
||||
search_dirs = [PROCESSED_MEMORY_DIR, CHATGPT_MEMORY_DIR] if use_ai_analysis else [CHATGPT_MEMORY_DIR]
|
||||
|
||||
for search_dir in search_dirs:
|
||||
for filepath in search_dir.glob("*.json"):
|
||||
try:
|
||||
with open(filepath, 'r', encoding='utf-8') as f:
|
||||
memory_data = json.load(f)
|
||||
|
||||
# 検索対象テキストを構築
|
||||
search_text = f"{memory_data.get('title', '')} {memory_data.get('basic_summary', '')}"
|
||||
|
||||
# AI分析結果も検索対象に含める
|
||||
if memory_data.get('ai_analysis'):
|
||||
ai_analysis = memory_data['ai_analysis']
|
||||
search_text += f" {' '.join(ai_analysis.get('main_topics', []))}"
|
||||
search_text += f" {ai_analysis.get('summary', '')}"
|
||||
search_text += f" {' '.join(ai_analysis.get('key_insights', []))}"
|
||||
|
||||
# メッセージ内容も検索対象に含める
|
||||
for msg in memory_data.get('messages', []):
|
||||
search_text += f" {msg.get('content', '')}"
|
||||
|
||||
if query.lower() in search_text.lower():
|
||||
result = {
|
||||
"filepath": str(filepath),
|
||||
"title": memory_data.get("title"),
|
||||
"basic_summary": memory_data.get("basic_summary"),
|
||||
"source": memory_data.get("source"),
|
||||
"import_time": memory_data.get("import_time"),
|
||||
"message_count": len(memory_data.get("messages", [])),
|
||||
"has_ai_analysis": bool(memory_data.get("ai_analysis"))
|
||||
}
|
||||
|
||||
if memory_data.get('ai_analysis'):
|
||||
result["ai_summary"] = memory_data['ai_analysis'].get('summary', '')
|
||||
result["main_topics"] = memory_data['ai_analysis'].get('main_topics', [])
|
||||
|
||||
results.append(result)
|
||||
|
||||
if len(results) >= limit:
|
||||
break
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error reading memory file {filepath}: {e}")
|
||||
continue
|
||||
|
||||
if len(results) >= limit:
|
||||
break
|
||||
|
||||
return results
|
||||
|
||||
def get_memory_detail(self, filepath: str) -> Dict[str, Any]:
|
||||
"""記憶の詳細を取得"""
|
||||
try:
|
||||
with open(filepath, 'r', encoding='utf-8') as f:
|
||||
return json.load(f)
|
||||
except Exception as e:
|
||||
raise ValueError(f"Error reading memory file: {e}")
|
||||
|
||||
def list_all_memories(self) -> List[Dict[str, Any]]:
|
||||
"""すべての記憶をリスト"""
|
||||
memories = []
|
||||
|
||||
for filepath in CHATGPT_MEMORY_DIR.glob("*.json"):
|
||||
try:
|
||||
with open(filepath, 'r', encoding='utf-8') as f:
|
||||
memory_data = json.load(f)
|
||||
|
||||
memory_info = {
|
||||
"filepath": str(filepath),
|
||||
"title": memory_data.get("title"),
|
||||
"basic_summary": memory_data.get("basic_summary"),
|
||||
"source": memory_data.get("source"),
|
||||
"import_time": memory_data.get("import_time"),
|
||||
"message_count": len(memory_data.get("messages", [])),
|
||||
"has_ai_analysis": bool(memory_data.get("ai_analysis"))
|
||||
}
|
||||
|
||||
if memory_data.get('ai_analysis'):
|
||||
memory_info["ai_summary"] = memory_data['ai_analysis'].get('summary', '')
|
||||
memory_info["main_topics"] = memory_data['ai_analysis'].get('main_topics', [])
|
||||
|
||||
memories.append(memory_info)
|
||||
except Exception as e:
|
||||
print(f"Error reading memory file {filepath}: {e}")
|
||||
continue
|
||||
|
||||
# インポート時間でソート
|
||||
memories.sort(key=lambda x: x.get("import_time", ""), reverse=True)
|
||||
return memories
|
||||
|
||||
# FastAPI アプリケーション
|
||||
app = FastAPI(title="AigptMCP Server with AI Memory", version="2.0.0")
|
||||
memory_manager = MemoryManager()
|
||||
|
||||
@app.post("/memory/import/chatgpt")
|
||||
async def import_chatgpt_conversation(data: ConversationImport, process_with_ai: bool = True):
|
||||
"""ChatGPTの会話をインポート（AI処理オプション付き）"""
|
||||
try:
|
||||
filepath = await memory_manager.save_chatgpt_memory(data.conversation_data, process_with_ai)
|
||||
return {
|
||||
"success": True,
|
||||
"message": "Conversation imported successfully",
|
||||
"filepath": filepath,
|
||||
"ai_processed": process_with_ai
|
||||
}
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=400, detail=str(e))
|
||||
|
||||
@app.post("/memory/process-ai")
|
||||
async def process_memory_with_ai(data: MemorySummaryRequest):
|
||||
"""既存の記憶をAIで再処理"""
|
||||
try:
|
||||
# 既存記憶を読み込み
|
||||
memory_data = memory_manager.get_memory_detail(data.filepath)
|
||||
|
||||
# AI分析を実行
|
||||
ai_analysis = await memory_manager.ai_processor.generate_ai_summary(
|
||||
memory_data["messages"],
|
||||
data.ai_provider
|
||||
)
|
||||
|
||||
# データを更新
|
||||
memory_data["ai_analysis"] = ai_analysis
|
||||
memory_data["ai_processed_at"] = datetime.now().isoformat()
|
||||
|
||||
# ファイルを更新
|
||||
with open(data.filepath, 'w', encoding='utf-8') as f:
|
||||
json.dump(memory_data, f, ensure_ascii=False, indent=2)
|
||||
|
||||
# 処理済みディレクトリにもコピー
|
||||
processed_filepath = PROCESSED_MEMORY_DIR / Path(data.filepath).name
|
||||
with open(processed_filepath, 'w', encoding='utf-8') as f:
|
||||
json.dump(memory_data, f, ensure_ascii=False, indent=2)
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"message": "Memory processed with AI successfully",
|
||||
"ai_analysis": ai_analysis
|
||||
}
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
@app.post("/memory/search")
|
||||
async def search_memories(query: MemoryQuery):
|
||||
"""記憶を検索"""
|
||||
try:
|
||||
results = memory_manager.search_memories(query.query, query.limit)
|
||||
return {
|
||||
"success": True,
|
||||
"results": results,
|
||||
"count": len(results)
|
||||
}
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
@app.get("/memory/list")
|
||||
async def list_memories():
|
||||
"""すべての記憶をリスト"""
|
||||
try:
|
||||
memories = memory_manager.list_all_memories()
|
||||
return {
|
||||
"success": True,
|
||||
"memories": memories,
|
||||
"count": len(memories)
|
||||
}
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
@app.get("/memory/detail")
|
||||
async def get_memory_detail(filepath: str):
|
||||
"""記憶の詳細を取得"""
|
||||
try:
|
||||
detail = memory_manager.get_memory_detail(filepath)
|
||||
return {
|
||||
"success": True,
|
||||
"memory": detail
|
||||
}
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=404, detail=str(e))
|
||||
|
||||
@app.post("/relationship/update")
|
||||
async def update_relationship(data: RelationshipUpdate):
|
||||
"""関係性を更新"""
|
||||
try:
|
||||
new_score = memory_manager.relationship_tracker.update_relationship(
|
||||
data.target, data.interaction_type, data.weight, data.context
|
||||
)
|
||||
return {
|
||||
"success": True,
|
||||
"new_score": new_score,
|
||||
"can_send_message": memory_manager.relationship_tracker.should_send_message(data.target)
|
||||
}
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
@app.get("/relationship/list")
|
||||
async def list_relationships():
|
||||
"""すべての関係性をリスト"""
|
||||
try:
|
||||
relationships = memory_manager.relationship_tracker.get_all_relationships()
|
||||
return {
|
||||
"success": True,
|
||||
"relationships": relationships
|
||||
}
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
@app.get("/relationship/check")
|
||||
async def check_send_permission(target: str, threshold: float = 50.0):
|
||||
"""メッセージ送信可否をチェック"""
|
||||
try:
|
||||
score = memory_manager.relationship_tracker.get_relationship_score(target)
|
||||
can_send = memory_manager.relationship_tracker.should_send_message(target, threshold)
|
||||
return {
|
||||
"success": True,
|
||||
"target": target,
|
||||
"score": score,
|
||||
"can_send_message": can_send,
|
||||
"threshold": threshold
|
||||
}
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
@app.post("/chat")
|
||||
async def chat_endpoint(data: ChatMessage):
|
||||
"""チャット機能（記憶と関係性を活用）"""
|
||||
try:
|
||||
# 関連する記憶を検索
|
||||
memories = memory_manager.search_memories(data.message, limit=3)
|
||||
|
||||
# メモリのコンテキストを構築
|
||||
memory_context = ""
|
||||
if memories:
|
||||
memory_context = "\n# Related memories:\n"
|
||||
for memory in memories:
|
||||
memory_context += f"- {memory['title']}: {memory.get('ai_summary', memory.get('basic_summary', ''))}\n"
|
||||
if memory.get('main_topics'):
|
||||
memory_context += f" Topics: {', '.join(memory['main_topics'])}\n"
|
||||
|
||||
# 関係性情報を取得
|
||||
relationships = memory_manager.relationship_tracker.get_all_relationships()
|
||||
|
||||
# 実際のチャット処理
|
||||
enhanced_message = data.message
|
||||
if memory_context:
|
||||
enhanced_message = f"{data.message}\n\n{memory_context}"
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"response": f"Enhanced response with memory context: {enhanced_message}",
|
||||
"memories_used": len(memories),
|
||||
"relationship_info": {
|
||||
"active_relationships": len(relationships.get("targets", {})),
|
||||
"can_initiate_conversations": sum(1 for target, data in relationships.get("targets", {}).items()
|
||||
if memory_manager.relationship_tracker.should_send_message(target))
|
||||
}
|
||||
}
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
@app.get("/")
|
||||
async def root():
|
||||
"""ヘルスチェック"""
|
||||
return {
|
||||
"service": "AigptMCP Server with AI Memory",
|
||||
"version": "2.0.0",
|
||||
"status": "running",
|
||||
"memory_dir": str(MEMORY_DIR),
|
||||
"features": [
|
||||
"AI-powered memory analysis",
|
||||
"Relationship tracking",
|
||||
"Advanced memory search",
|
||||
"Conversation import",
|
||||
"Auto-summary generation"
|
||||
],
|
||||
"endpoints": [
|
||||
"/memory/import/chatgpt",
|
||||
"/memory/process-ai",
|
||||
"/memory/search",
|
||||
"/memory/list",
|
||||
"/memory/detail",
|
||||
"/relationship/update",
|
||||
"/relationship/list",
|
||||
"/relationship/check",
|
||||
"/chat"
|
||||
]
|
||||
}
|
||||
|
||||
if __name__ == "__main__":
|
||||
print("🚀 AigptMCP Server with AI Memory starting...")
|
||||
print(f"📁 Memory directory: {MEMORY_DIR}")
|
||||
print(f"🧠 AI Memory processing: {'✅ Enabled' if os.getenv('OPENAI_API_KEY') or os.getenv('ANTHROPIC_API_KEY') else '❌ Disabled (no API keys)'}")
|
||||
uvicorn.run(app, host="127.0.0.1", port=5000)
|
||||
130
readme.md
Normal file
130
readme.md
Normal file
@ -0,0 +1,130 @@
|
||||
Memory-Enhanced MCP Server 使用ガイド
|
||||
概要
|
||||
このMCPサーバーは、ChatGPTの会話履歴を記憶として保存し、AIとの対話で活用できる機能を提供します。
|
||||
|
||||
セットアップ
|
||||
1. 依存関係のインストール
|
||||
bash
|
||||
pip install -r requirements.txt
|
||||
2. サーバーの起動
|
||||
bash
|
||||
python mcp/server.py
|
||||
サーバーは http://localhost:5000 で起動します。
|
||||
|
||||
使用方法
|
||||
1. ChatGPTの会話履歴をインポート
|
||||
ChatGPTから会話をエクスポートし、JSONファイルとして保存してください。
|
||||
|
||||
bash
|
||||
# 単一ファイルをインポート
|
||||
python mcp/memory_client.py import your_chatgpt_export.json
|
||||
|
||||
# インポート結果の例
|
||||
✅ Imported 5/5 conversations
|
||||
2. 記憶の検索
|
||||
bash
|
||||
# キーワードで記憶を検索
|
||||
python mcp/memory_client.py search "プログラミング"
|
||||
|
||||
# 検索結果の例
|
||||
🔍 Searching for: プログラミング
|
||||
📚 Found 3 memories:
|
||||
• Pythonの基礎学習
|
||||
Summary: Conversation with 10 user messages and 8 assistant responses...
|
||||
Messages: 18
|
||||
3. 記憶一覧の表示
|
||||
bash
|
||||
python mcp/memory_client.py list
|
||||
|
||||
# 結果の例
|
||||
📋 Listing all memories...
|
||||
📚 Total memories: 15
|
||||
• day
|
||||
Source: chatgpt
|
||||
Messages: 2
|
||||
Imported: 2025-01-21T10:30:45.123456
|
||||
4. 記憶の詳細表示
|
||||
bash
|
||||
python mcp/memory_client.py detail "/path/to/memory/file.json"
|
||||
|
||||
# 結果の例
|
||||
📄 Getting details for: /path/to/memory/file.json
|
||||
Title: day
|
||||
Source: chatgpt
|
||||
Summary: Conversation with 1 user messages and 1 assistant responses...
|
||||
Messages: 2
|
||||
|
||||
Recent messages:
|
||||
user: こんにちは...
|
||||
assistant: こんにちは〜！✨...
|
||||
5. 記憶を活用したチャット
|
||||
bash
|
||||
python mcp/memory_client.py chat "Pythonについて教えて"
|
||||
|
||||
# 結果の例
|
||||
💬 Chatting with memory: Pythonについて教えて
|
||||
🤖 Response: Enhanced response with memory context...
|
||||
📚 Memories used: 2
|
||||
API エンドポイント
|
||||
POST /memory/import/chatgpt
|
||||
ChatGPTの会話履歴をインポート
|
||||
|
||||
json
|
||||
{
|
||||
"conversation_data": { ... }
|
||||
}
|
||||
POST /memory/search
|
||||
記憶を検索
|
||||
|
||||
json
|
||||
{
|
||||
"query": "検索キーワード",
|
||||
"limit": 10
|
||||
}
|
||||
GET /memory/list
|
||||
すべての記憶をリスト
|
||||
|
||||
GET /memory/detail?filepath=/path/to/file
|
||||
記憶の詳細を取得
|
||||
|
||||
POST /chat
|
||||
記憶を活用したチャット
|
||||
|
||||
json
|
||||
{
|
||||
"message": "メッセージ",
|
||||
"model": "model_name"
|
||||
}
|
||||
記憶の保存場所
|
||||
記憶は以下のディレクトリに保存されます：
|
||||
|
||||
~/.config/aigpt/memory/chatgpt/
|
||||
各会話は個別のJSONファイルとして保存され、以下の情報を含みます：
|
||||
|
||||
タイトル
|
||||
インポート時刻
|
||||
メッセージ履歴
|
||||
自動生成された要約
|
||||
メタデータ
|
||||
ChatGPTの会話エクスポート方法
|
||||
ChatGPTの設定画面を開く
|
||||
"Data controls" → "Export data" を選択
|
||||
エクスポートファイルをダウンロード
|
||||
conversations.json ファイルを使用
|
||||
拡張可能な機能
|
||||
高度な検索: ベクトル検索やセマンティック検索の実装
|
||||
要約生成: AIによる自動要約の改善
|
||||
記憶の分類: カテゴリやタグによる分類
|
||||
記憶の統合: 複数の会話からの知識統合
|
||||
プライバシー保護: 機密情報の自動検出・マスキング
|
||||
トラブルシューティング
|
||||
サーバーが起動しない
|
||||
ポート5000が使用中でないか確認
|
||||
依存関係が正しくインストールされているか確認
|
||||
インポートに失敗する
|
||||
JSONファイルが正しい形式か確認
|
||||
ファイルパスが正しいか確認
|
||||
ファイルの権限を確認
|
||||
検索結果が表示されない
|
||||
インポートが正常に完了しているか確認
|
||||
検索キーワードを変更して試行
|
||||
@ -1,39 +0,0 @@
|
||||
#!/bin/zsh
|
||||
|
||||
d=${0:a:h:h}
|
||||
json=`cat $d/gpt.json`
|
||||
toml=`cat $d/Cargo.toml`
|
||||
cd $d/src/
|
||||
list=(`zsh -c "ls *.rs"`)
|
||||
|
||||
body="
|
||||
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。
|
||||
|
||||
$json
|
||||
|
||||
リポジトリはこちらになる。
|
||||
git.syui.ai:ai/gpt.git
|
||||
|
||||
内容はこんな感じ。
|
||||
|
||||
\`\`\`toml
|
||||
$toml
|
||||
\`\`\`
|
||||
|
||||
`
|
||||
for i in $list; do
|
||||
if [ -f $d/src/$i ];then
|
||||
t=$(cat $d/src/$i)
|
||||
echo
|
||||
echo '\`\`\`rust'
|
||||
echo $t
|
||||
echo '\`\`\`'
|
||||
echo
|
||||
fi
|
||||
done
|
||||
`
|
||||
|
||||
次は何を実装すればいいと思う。
|
||||
"
|
||||
|
||||
echo $body
|
||||
37
src/agent.rs
37
src/agent.rs
@ -1,37 +0,0 @@
|
||||
use chrono::{NaiveDateTime};
|
||||
|
||||
#[allow(dead_code)]
|
||||
#[derive(Debug)]
|
||||
pub struct AIState {
|
||||
pub relation_score: f32,
|
||||
pub previous_score: f32,
|
||||
pub decay_rate: f32,
|
||||
pub sensitivity: f32,
|
||||
pub message_threshold: f32,
|
||||
pub last_message_time: NaiveDateTime,
|
||||
}
|
||||
|
||||
#[allow(dead_code)]
|
||||
impl AIState {
|
||||
pub fn update(&mut self, now: NaiveDateTime) {
|
||||
let days_passed = (now - self.last_message_time).num_days() as f32;
|
||||
let decay = self.decay_rate * days_passed;
|
||||
self.previous_score = self.relation_score;
|
||||
self.relation_score -= decay;
|
||||
self.relation_score = self.relation_score.clamp(0.0, 100.0);
|
||||
}
|
||||
|
||||
pub fn should_talk(&self) -> bool {
|
||||
let delta = self.previous_score - self.relation_score;
|
||||
delta > self.message_threshold && self.sensitivity > 0.5
|
||||
}
|
||||
|
||||
pub fn generate_message(&self) -> String {
|
||||
match self.relation_score as i32 {
|
||||
80..=100 => "ふふっ、最近どうしてる？会いたくなっちゃった！".to_string(),
|
||||
60..=79 => "ちょっとだけ、さみしかったんだよ？".to_string(),
|
||||
40..=59 => "えっと……話せる時間ある？".to_string(),
|
||||
_ => "ううん、もしかして私のこと、忘れちゃったのかな……".to_string(),
|
||||
}
|
||||
}
|
||||
}
|
||||
155
src/cli.rs
155
src/cli.rs
@ -1,97 +1,64 @@
|
||||
// src/cli.rs
|
||||
use std::path::{Path};
|
||||
use chrono::{Duration, Local};
|
||||
use rusqlite::Connection;
|
||||
use clap::{Parser, Subcommand};
|
||||
|
||||
use seahorse::{App, Command, Context};
|
||||
use crate::utils::{load_config, save_config};
|
||||
use crate::commands::db::{save_cmd, export_cmd};
|
||||
use crate::commands::scheduler::{scheduler_cmd};
|
||||
use crate::config::ConfigPaths;
|
||||
use crate::agent::AIState;
|
||||
|
||||
pub fn cli_app() -> App {
|
||||
let set_cmd = Command::new("set")
|
||||
.usage("set [trust|intimacy|curiosity] [value]")
|
||||
.action(|c: &Context| {
|
||||
if c.args.len() != 2 {
|
||||
eprintln!("Usage: set [trust|intimacy|curiosity] [value]");
|
||||
std::process::exit(1);
|
||||
}
|
||||
|
||||
let field = &c.args[0];
|
||||
let value: f32 = c.args[1].parse().unwrap_or_else(|_| {
|
||||
eprintln!("数値で入力してください");
|
||||
std::process::exit(1);
|
||||
});
|
||||
|
||||
// ConfigPathsを使って設定ファイルのパスを取得
|
||||
let config_paths = ConfigPaths::new();
|
||||
let json_path = config_paths.data_file("json");
|
||||
// まだ user.json がない場合、example.json をコピー
|
||||
config_paths.ensure_file_exists("json", Path::new("example.json"));
|
||||
let db_path = config_paths.data_file("db");
|
||||
let mut ai = load_config(json_path.to_str().unwrap());
|
||||
|
||||
match field.as_str() {
|
||||
"trust" => ai.relationship.trust = value,
|
||||
"intimacy" => ai.relationship.intimacy = value,
|
||||
"curiosity" => ai.relationship.curiosity = value,
|
||||
_ => {
|
||||
eprintln!("trust / intimacy / curiosity のいずれかを指定してください");
|
||||
std::process::exit(1);
|
||||
}
|
||||
}
|
||||
save_config(json_path.to_str().unwrap(), &ai);
|
||||
|
||||
let conn = Connection::open(db_path.to_str().unwrap()).expect("DB接続失敗");
|
||||
ai.save_to_db(&conn).expect("DB保存失敗");
|
||||
|
||||
println!("✅ {field} を {value} に更新しました");
|
||||
});
|
||||
|
||||
let show_cmd = Command::new("show")
|
||||
.usage("show")
|
||||
.action(|_c: &Context| {
|
||||
// ConfigPathsを使って設定ファイルのパスを取得
|
||||
let config_paths = ConfigPaths::new();
|
||||
let ai = load_config(config_paths.data_file("json").to_str().unwrap());
|
||||
println!("🧠 現在のAI状態:\n{:#?}", ai);
|
||||
});
|
||||
|
||||
let talk_cmd = Command::new("talk")
|
||||
.usage("talk")
|
||||
.action(|_c: &Context| {
|
||||
let config_paths = ConfigPaths::new();
|
||||
let ai = load_config(config_paths.data_file("json").to_str().unwrap());
|
||||
|
||||
let now = Local::now().naive_local();
|
||||
let mut state = AIState {
|
||||
relation_score: 80.0,
|
||||
previous_score: 80.0,
|
||||
decay_rate: ai.messaging.decay_rate,
|
||||
sensitivity: ai.personality.strength,
|
||||
message_threshold: 5.0,
|
||||
last_message_time: now - Duration::days(4),
|
||||
};
|
||||
|
||||
state.update(now);
|
||||
|
||||
if state.should_talk() {
|
||||
println!("💬 AI発話: {}", state.generate_message());
|
||||
} else {
|
||||
println!("🤫 今日は静かにしているみたい...");
|
||||
}
|
||||
});
|
||||
|
||||
App::new("aigpt")
|
||||
.version("0.1.0")
|
||||
.description("AGE system CLI controller")
|
||||
.author("syui")
|
||||
.command(set_cmd)
|
||||
.command(show_cmd)
|
||||
.command(talk_cmd)
|
||||
.command(save_cmd())
|
||||
.command(export_cmd())
|
||||
.command(scheduler_cmd())
|
||||
#[derive(Parser)]
|
||||
#[command(name = "aigpt")]
|
||||
#[command(about = "AI GPT CLI with MCP Server and Memory")]
|
||||
pub struct Args {
|
||||
#[command(subcommand)]
|
||||
pub command: Commands,
|
||||
}
|
||||
|
||||
#[derive(Subcommand)]
|
||||
pub enum Commands {
|
||||
/// MCP Server management
|
||||
Server {
|
||||
#[command(subcommand)]
|
||||
command: ServerCommands,
|
||||
},
|
||||
/// Chat with AI
|
||||
Chat {
|
||||
/// Message to send
|
||||
message: String,
|
||||
/// Use memory context
|
||||
#[arg(long)]
|
||||
with_memory: bool,
|
||||
},
|
||||
/// Memory management
|
||||
Memory {
|
||||
#[command(subcommand)]
|
||||
command: MemoryCommands,
|
||||
},
|
||||
}
|
||||
|
||||
#[derive(Subcommand)]
|
||||
pub enum ServerCommands {
|
||||
/// Setup Python MCP server environment
|
||||
Setup,
|
||||
/// Run the MCP server
|
||||
Run,
|
||||
}
|
||||
|
||||
#[derive(Subcommand)]
|
||||
pub enum MemoryCommands {
|
||||
/// Import ChatGPT conversation export file
|
||||
Import {
|
||||
/// Path to ChatGPT export JSON file
|
||||
file: String,
|
||||
},
|
||||
/// Search memories
|
||||
Search {
|
||||
/// Search query
|
||||
query: String,
|
||||
/// Maximum number of results
|
||||
#[arg(short, long, default_value = "10")]
|
||||
limit: usize,
|
||||
},
|
||||
/// List all memories
|
||||
List,
|
||||
/// Show memory details
|
||||
Detail {
|
||||
/// Path to memory file
|
||||
filepath: String,
|
||||
},
|
||||
}
|
||||
|
||||
@ -1,44 +0,0 @@
|
||||
// src/commands/db.rs
|
||||
use seahorse::{Command, Context};
|
||||
use crate::utils::{load_config};
|
||||
use crate::model::AiSystem;
|
||||
use crate::config::ConfigPaths;
|
||||
|
||||
use rusqlite::Connection;
|
||||
use std::fs;
|
||||
|
||||
pub fn save_cmd() -> Command {
|
||||
Command::new("save")
|
||||
.usage("save")
|
||||
.action(|_c: &Context| {
|
||||
let paths = ConfigPaths::new();
|
||||
|
||||
let json_path = paths.data_file("json");
|
||||
let db_path = paths.data_file("db");
|
||||
|
||||
let ai = load_config(json_path.to_str().unwrap());
|
||||
let conn = Connection::open(db_path).expect("DB接続失敗");
|
||||
|
||||
ai.save_to_db(&conn).expect("DB保存失敗");
|
||||
println!("💾 DBに保存完了");
|
||||
})
|
||||
}
|
||||
|
||||
pub fn export_cmd() -> Command {
|
||||
Command::new("export")
|
||||
.usage("export [output.json]")
|
||||
.action(|c: &Context| {
|
||||
let output_path = c.args.get(0).map(|s| s.as_str()).unwrap_or("output.json");
|
||||
|
||||
let paths = ConfigPaths::new();
|
||||
let db_path = paths.data_file("db");
|
||||
|
||||
let conn = Connection::open(db_path).expect("DB接続失敗");
|
||||
let ai = AiSystem::load_from_db(&conn).expect("DB読み込み失敗");
|
||||
|
||||
let json = serde_json::to_string_pretty(&ai).expect("JSON変換失敗");
|
||||
fs::write(output_path, json).expect("ファイル書き込み失敗");
|
||||
|
||||
println!("📤 JSONにエクスポート完了: {output_path}");
|
||||
})
|
||||
}
|
||||
@ -1,2 +0,0 @@
|
||||
pub mod db;
|
||||
pub mod scheduler;
|
||||
@ -1,29 +0,0 @@
|
||||
// src/commands/scheduler.rs
|
||||
|
||||
use seahorse::{Command, Context};
|
||||
use std::thread;
|
||||
use std::time::Duration;
|
||||
use chrono::Local;
|
||||
|
||||
pub fn scheduler_cmd() -> Command {
|
||||
Command::new("scheduler")
|
||||
.usage("scheduler [interval_sec]")
|
||||
.alias("s")
|
||||
.action(|c: &Context| {
|
||||
let interval = c.args.get(0)
|
||||
.and_then(|s| s.parse::<u64>().ok())
|
||||
.unwrap_or(60); // デフォルト: 60秒ごと
|
||||
|
||||
println!("⏳ スケジューラー開始（{interval}秒ごと）...");
|
||||
|
||||
loop {
|
||||
let now = Local::now();
|
||||
println!("🔁 タスク実行中: {}", now.format("%Y-%m-%d %H:%M:%S"));
|
||||
|
||||
// ここで talk_cmd や save_cmd の内部処理を呼ぶ感じ
|
||||
// たとえば load_config → AI更新 → print とか
|
||||
|
||||
thread::sleep(Duration::from_secs(interval));
|
||||
}
|
||||
})
|
||||
}
|
||||
@ -22,6 +22,7 @@ impl ConfigPaths {
|
||||
}
|
||||
}
|
||||
|
||||
#[allow(dead_code)]
|
||||
pub fn data_file(&self, file_name: &str) -> PathBuf {
|
||||
let file_path = match file_name {
|
||||
"db" => self.base_dir.join("user.db"),
|
||||
@ -29,18 +30,30 @@ impl ConfigPaths {
|
||||
"json" => self.base_dir.join("user.json"),
|
||||
_ => self.base_dir.join(format!(".{}", file_name)),
|
||||
};
|
||||
|
||||
file_path
|
||||
}
|
||||
/// 設定ファイルがなければ `example.json` をコピーする
|
||||
pub fn ensure_file_exists(&self, file_name: &str, template_path: &Path) {
|
||||
let target = self.data_file(file_name);
|
||||
if !target.exists() {
|
||||
if let Err(e) = fs::copy(template_path, &target) {
|
||||
eprintln!("⚠️ 設定ファイルの初期化に失敗しました: {}", e);
|
||||
} else {
|
||||
println!("📄 {} を {} にコピーしました", template_path.display(), target.display());
|
||||
}
|
||||
|
||||
pub fn mcp_dir(&self) -> PathBuf {
|
||||
self.base_dir.join("mcp")
|
||||
}
|
||||
|
||||
pub fn venv_path(&self) -> PathBuf {
|
||||
self.mcp_dir().join(".venv")
|
||||
}
|
||||
|
||||
pub fn python_executable(&self) -> PathBuf {
|
||||
if cfg!(windows) {
|
||||
self.venv_path().join("Scripts").join("python.exe")
|
||||
} else {
|
||||
self.venv_path().join("bin").join("python")
|
||||
}
|
||||
}
|
||||
|
||||
pub fn pip_executable(&self) -> PathBuf {
|
||||
if cfg!(windows) {
|
||||
self.venv_path().join("Scripts").join("pip.exe")
|
||||
} else {
|
||||
self.venv_path().join("bin").join("pip")
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
13
src/logic.rs
13
src/logic.rs
@ -1,13 +0,0 @@
|
||||
//src/logic.rs
|
||||
use crate::model::AiSystem;
|
||||
|
||||
#[allow(dead_code)]
|
||||
pub fn should_send(ai: &AiSystem) -> bool {
|
||||
let r = &ai.relationship;
|
||||
let env = &ai.environment;
|
||||
let score = r.trust + r.intimacy + r.curiosity;
|
||||
let relationship_ok = score >= r.threshold;
|
||||
let luck_ok = env.luck_today > 0.5;
|
||||
|
||||
ai.messaging.enabled && relationship_ok && luck_ok
|
||||
}
|
||||
65
src/main.rs
65
src/main.rs
@ -1,17 +1,58 @@
|
||||
//src/main.rs
|
||||
mod model;
|
||||
mod logic;
|
||||
mod agent;
|
||||
// main.rs
|
||||
mod cli;
|
||||
mod utils;
|
||||
mod commands;
|
||||
mod config;
|
||||
mod mcp;
|
||||
|
||||
use cli::cli_app;
|
||||
use seahorse::App;
|
||||
use cli::{Args, Commands, ServerCommands, MemoryCommands};
|
||||
use clap::Parser;
|
||||
|
||||
fn main() {
|
||||
let args: Vec<String> = std::env::args().collect();
|
||||
let app: App = cli_app();
|
||||
app.run(args);
|
||||
#[tokio::main]
|
||||
async fn main() {
|
||||
let args = Args::parse();
|
||||
|
||||
match args.command {
|
||||
Commands::Server { command } => {
|
||||
match command {
|
||||
ServerCommands::Setup => {
|
||||
mcp::server::setup();
|
||||
}
|
||||
ServerCommands::Run => {
|
||||
mcp::server::run().await;
|
||||
}
|
||||
}
|
||||
}
|
||||
Commands::Chat { message, with_memory } => {
|
||||
if with_memory {
|
||||
if let Err(e) = mcp::memory::handle_chat_with_memory(&message).await {
|
||||
eprintln!("❌ 記憶チャットエラー: {}", e);
|
||||
}
|
||||
} else {
|
||||
mcp::server::chat(&message).await;
|
||||
}
|
||||
}
|
||||
Commands::Memory { command } => {
|
||||
match command {
|
||||
MemoryCommands::Import { file } => {
|
||||
if let Err(e) = mcp::memory::handle_import(&file).await {
|
||||
eprintln!("❌ インポートエラー: {}", e);
|
||||
}
|
||||
}
|
||||
MemoryCommands::Search { query, limit } => {
|
||||
if let Err(e) = mcp::memory::handle_search(&query, limit).await {
|
||||
eprintln!("❌ 検索エラー: {}", e);
|
||||
}
|
||||
}
|
||||
MemoryCommands::List => {
|
||||
if let Err(e) = mcp::memory::handle_list().await {
|
||||
eprintln!("❌ 一覧取得エラー: {}", e);
|
||||
}
|
||||
}
|
||||
MemoryCommands::Detail { filepath } => {
|
||||
if let Err(e) = mcp::memory::handle_detail(&filepath).await {
|
||||
eprintln!("❌ 詳細取得エラー: {}", e);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
393
src/mcp/memory.rs
Normal file
393
src/mcp/memory.rs
Normal file
@ -0,0 +1,393 @@
|
||||
// src/mcp/memory.rs
|
||||
use reqwest;
|
||||
use serde::{Deserialize, Serialize};
|
||||
use serde_json::{self, Value};
|
||||
use std::fs;
|
||||
use std::path::Path;
|
||||
|
||||
#[derive(Debug, Serialize, Deserialize)]
|
||||
pub struct MemorySearchRequest {
|
||||
pub query: String,
|
||||
pub limit: usize,
|
||||
}
|
||||
|
||||
#[derive(Debug, Serialize, Deserialize)]
|
||||
pub struct ChatRequest {
|
||||
pub message: String,
|
||||
pub model: Option<String>,
|
||||
}
|
||||
|
||||
#[derive(Debug, Serialize, Deserialize)]
|
||||
pub struct ConversationImportRequest {
|
||||
pub conversation_data: Value,
|
||||
}
|
||||
|
||||
#[derive(Debug, Deserialize)]
|
||||
pub struct ApiResponse {
|
||||
pub success: bool,
|
||||
pub error: Option<String>,
|
||||
#[allow(dead_code)]
|
||||
pub message: Option<String>,
|
||||
pub filepath: Option<String>,
|
||||
pub results: Option<Vec<MemoryResult>>,
|
||||
pub memories: Option<Vec<MemoryResult>>,
|
||||
#[allow(dead_code)]
|
||||
pub count: Option<usize>,
|
||||
pub memory: Option<Value>,
|
||||
pub response: Option<String>,
|
||||
pub memories_used: Option<usize>,
|
||||
pub imported_count: Option<usize>,
|
||||
pub total_count: Option<usize>,
|
||||
}
|
||||
|
||||
#[derive(Debug, Deserialize)]
|
||||
pub struct MemoryResult {
|
||||
#[allow(dead_code)]
|
||||
pub filepath: String,
|
||||
pub title: Option<String>,
|
||||
pub summary: Option<String>,
|
||||
pub source: Option<String>,
|
||||
pub import_time: Option<String>,
|
||||
pub message_count: Option<usize>,
|
||||
}
|
||||
|
||||
pub struct MemoryClient {
|
||||
base_url: String,
|
||||
client: reqwest::Client,
|
||||
}
|
||||
|
||||
impl MemoryClient {
|
||||
pub fn new(base_url: Option<String>) -> Self {
|
||||
let url = base_url.unwrap_or_else(|| "http://127.0.0.1:5000".to_string());
|
||||
Self {
|
||||
base_url: url,
|
||||
client: reqwest::Client::new(),
|
||||
}
|
||||
}
|
||||
|
||||
pub async fn import_chatgpt_file(&self, filepath: &str) -> Result<ApiResponse, Box<dyn std::error::Error>> {
|
||||
// ファイルを読み込み
|
||||
let content = fs::read_to_string(filepath)?;
|
||||
let json_data: Value = serde_json::from_str(&content)?;
|
||||
|
||||
// 配列かどうかチェック
|
||||
match json_data.as_array() {
|
||||
Some(conversations) => {
|
||||
// 複数の会話をインポート
|
||||
let mut imported_count = 0;
|
||||
let total_count = conversations.len();
|
||||
|
||||
for conversation in conversations {
|
||||
match self.import_single_conversation(conversation.clone()).await {
|
||||
Ok(response) => {
|
||||
if response.success {
|
||||
imported_count += 1;
|
||||
}
|
||||
}
|
||||
Err(e) => {
|
||||
eprintln!("❌ インポートエラー: {}", e);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
Ok(ApiResponse {
|
||||
success: true,
|
||||
imported_count: Some(imported_count),
|
||||
total_count: Some(total_count),
|
||||
error: None,
|
||||
message: Some(format!("{}個中{}個の会話をインポートしました", total_count, imported_count)),
|
||||
filepath: None,
|
||||
results: None,
|
||||
memories: None,
|
||||
count: None,
|
||||
memory: None,
|
||||
response: None,
|
||||
memories_used: None,
|
||||
})
|
||||
}
|
||||
None => {
|
||||
// 単一の会話をインポート
|
||||
self.import_single_conversation(json_data).await
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
async fn import_single_conversation(&self, conversation_data: Value) -> Result<ApiResponse, Box<dyn std::error::Error>> {
|
||||
let request = ConversationImportRequest { conversation_data };
|
||||
|
||||
let response = self.client
|
||||
.post(&format!("{}/memory/import/chatgpt", self.base_url))
|
||||
.json(&request)
|
||||
.send()
|
||||
.await?;
|
||||
|
||||
let result: ApiResponse = response.json().await?;
|
||||
Ok(result)
|
||||
}
|
||||
|
||||
pub async fn search_memories(&self, query: &str, limit: usize) -> Result<ApiResponse, Box<dyn std::error::Error>> {
|
||||
let request = MemorySearchRequest {
|
||||
query: query.to_string(),
|
||||
limit,
|
||||
};
|
||||
|
||||
let response = self.client
|
||||
.post(&format!("{}/memory/search", self.base_url))
|
||||
.json(&request)
|
||||
.send()
|
||||
.await?;
|
||||
|
||||
let result: ApiResponse = response.json().await?;
|
||||
Ok(result)
|
||||
}
|
||||
|
||||
pub async fn list_memories(&self) -> Result<ApiResponse, Box<dyn std::error::Error>> {
|
||||
let response = self.client
|
||||
.get(&format!("{}/memory/list", self.base_url))
|
||||
.send()
|
||||
.await?;
|
||||
|
||||
let result: ApiResponse = response.json().await?;
|
||||
Ok(result)
|
||||
}
|
||||
|
||||
pub async fn get_memory_detail(&self, filepath: &str) -> Result<ApiResponse, Box<dyn std::error::Error>> {
|
||||
let response = self.client
|
||||
.get(&format!("{}/memory/detail", self.base_url))
|
||||
.query(&[("filepath", filepath)])
|
||||
.send()
|
||||
.await?;
|
||||
|
||||
let result: ApiResponse = response.json().await?;
|
||||
Ok(result)
|
||||
}
|
||||
|
||||
pub async fn chat_with_memory(&self, message: &str) -> Result<ApiResponse, Box<dyn std::error::Error>> {
|
||||
let request = ChatRequest {
|
||||
message: message.to_string(),
|
||||
model: None,
|
||||
};
|
||||
|
||||
let response = self.client
|
||||
.post(&format!("{}/chat", self.base_url))
|
||||
.json(&request)
|
||||
.send()
|
||||
.await?;
|
||||
|
||||
let result: ApiResponse = response.json().await?;
|
||||
Ok(result)
|
||||
}
|
||||
|
||||
pub async fn is_server_running(&self) -> bool {
|
||||
match self.client.get(&self.base_url).send().await {
|
||||
Ok(response) => response.status().is_success(),
|
||||
Err(_) => false,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
pub async fn handle_import(filepath: &str) -> Result<(), Box<dyn std::error::Error>> {
|
||||
if !Path::new(filepath).exists() {
|
||||
eprintln!("❌ ファイルが見つかりません: {}", filepath);
|
||||
return Ok(());
|
||||
}
|
||||
|
||||
let client = MemoryClient::new(None);
|
||||
|
||||
// サーバーが起動しているかチェック
|
||||
if !client.is_server_running().await {
|
||||
eprintln!("❌ MCP Serverが起動していません。先に 'aigpt server run' を実行してください。");
|
||||
return Ok(());
|
||||
}
|
||||
|
||||
println!("🔄 ChatGPT会話をインポートしています: {}", filepath);
|
||||
|
||||
match client.import_chatgpt_file(filepath).await {
|
||||
Ok(response) => {
|
||||
if response.success {
|
||||
if let (Some(imported), Some(total)) = (response.imported_count, response.total_count) {
|
||||
println!("✅ {}個中{}個の会話をインポートしました", total, imported);
|
||||
} else {
|
||||
println!("✅ 会話をインポートしました");
|
||||
if let Some(path) = response.filepath {
|
||||
println!("📁 保存先: {}", path);
|
||||
}
|
||||
}
|
||||
} else {
|
||||
eprintln!("❌ インポートに失敗: {:?}", response.error);
|
||||
}
|
||||
}
|
||||
Err(e) => {
|
||||
eprintln!("❌ インポートエラー: {}", e);
|
||||
}
|
||||
}
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
pub async fn handle_search(query: &str, limit: usize) -> Result<(), Box<dyn std::error::Error>> {
|
||||
let client = MemoryClient::new(None);
|
||||
|
||||
if !client.is_server_running().await {
|
||||
eprintln!("❌ MCP Serverが起動していません。先に 'aigpt server run' を実行してください。");
|
||||
return Ok(());
|
||||
}
|
||||
|
||||
println!("🔍 記憶を検索しています: {}", query);
|
||||
|
||||
match client.search_memories(query, limit).await {
|
||||
Ok(response) => {
|
||||
if response.success {
|
||||
if let Some(results) = response.results {
|
||||
println!("📚 {}個の記憶が見つかりました:", results.len());
|
||||
for memory in results {
|
||||
println!(" • {}", memory.title.unwrap_or_else(|| "タイトルなし".to_string()));
|
||||
if let Some(summary) = memory.summary {
|
||||
println!(" 概要: {}", summary);
|
||||
}
|
||||
if let Some(count) = memory.message_count {
|
||||
println!(" メッセージ数: {}", count);
|
||||
}
|
||||
println!();
|
||||
}
|
||||
} else {
|
||||
println!("📚 記憶が見つかりませんでした");
|
||||
}
|
||||
} else {
|
||||
eprintln!("❌ 検索に失敗: {:?}", response.error);
|
||||
}
|
||||
}
|
||||
Err(e) => {
|
||||
eprintln!("❌ 検索エラー: {}", e);
|
||||
}
|
||||
}
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
pub async fn handle_list() -> Result<(), Box<dyn std::error::Error>> {
|
||||
let client = MemoryClient::new(None);
|
||||
|
||||
if !client.is_server_running().await {
|
||||
eprintln!("❌ MCP Serverが起動していません。先に 'aigpt server run' を実行してください。");
|
||||
return Ok(());
|
||||
}
|
||||
|
||||
println!("📋 記憶一覧を取得しています...");
|
||||
|
||||
match client.list_memories().await {
|
||||
Ok(response) => {
|
||||
if response.success {
|
||||
if let Some(memories) = response.memories {
|
||||
println!("📚 総記憶数: {}", memories.len());
|
||||
for memory in memories {
|
||||
println!(" • {}", memory.title.unwrap_or_else(|| "タイトルなし".to_string()));
|
||||
if let Some(source) = memory.source {
|
||||
println!(" ソース: {}", source);
|
||||
}
|
||||
if let Some(count) = memory.message_count {
|
||||
println!(" メッセージ数: {}", count);
|
||||
}
|
||||
if let Some(import_time) = memory.import_time {
|
||||
println!(" インポート時刻: {}", import_time);
|
||||
}
|
||||
println!();
|
||||
}
|
||||
} else {
|
||||
println!("📚 記憶がありません");
|
||||
}
|
||||
} else {
|
||||
eprintln!("❌ 一覧取得に失敗: {:?}", response.error);
|
||||
}
|
||||
}
|
||||
Err(e) => {
|
||||
eprintln!("❌ 一覧取得エラー: {}", e);
|
||||
}
|
||||
}
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
pub async fn handle_detail(filepath: &str) -> Result<(), Box<dyn std::error::Error>> {
|
||||
let client = MemoryClient::new(None);
|
||||
|
||||
if !client.is_server_running().await {
|
||||
eprintln!("❌ MCP Serverが起動していません。先に 'aigpt server run' を実行してください。");
|
||||
return Ok(());
|
||||
}
|
||||
|
||||
println!("📄 記憶の詳細を取得しています: {}", filepath);
|
||||
|
||||
match client.get_memory_detail(filepath).await {
|
||||
Ok(response) => {
|
||||
if response.success {
|
||||
if let Some(memory) = response.memory {
|
||||
if let Some(title) = memory.get("title").and_then(|v| v.as_str()) {
|
||||
println!("タイトル: {}", title);
|
||||
}
|
||||
if let Some(source) = memory.get("source").and_then(|v| v.as_str()) {
|
||||
println!("ソース: {}", source);
|
||||
}
|
||||
if let Some(summary) = memory.get("summary").and_then(|v| v.as_str()) {
|
||||
println!("概要: {}", summary);
|
||||
}
|
||||
if let Some(messages) = memory.get("messages").and_then(|v| v.as_array()) {
|
||||
println!("メッセージ数: {}", messages.len());
|
||||
println!("\n最近のメッセージ:");
|
||||
for msg in messages.iter().take(5) {
|
||||
if let (Some(role), Some(content)) = (
|
||||
msg.get("role").and_then(|v| v.as_str()),
|
||||
msg.get("content").and_then(|v| v.as_str())
|
||||
) {
|
||||
let content_preview = if content.len() > 100 {
|
||||
format!("{}...", &content[..100])
|
||||
} else {
|
||||
content.to_string()
|
||||
};
|
||||
println!(" {}: {}", role, content_preview);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
} else {
|
||||
eprintln!("❌ 詳細取得に失敗: {:?}", response.error);
|
||||
}
|
||||
}
|
||||
Err(e) => {
|
||||
eprintln!("❌ 詳細取得エラー: {}", e);
|
||||
}
|
||||
}
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
pub async fn handle_chat_with_memory(message: &str) -> Result<(), Box<dyn std::error::Error>> {
|
||||
let client = MemoryClient::new(None);
|
||||
|
||||
if !client.is_server_running().await {
|
||||
eprintln!("❌ MCP Serverが起動していません。先に 'aigpt server run' を実行してください。");
|
||||
return Ok(());
|
||||
}
|
||||
|
||||
println!("💬 記憶を活用してチャットしています...");
|
||||
|
||||
match client.chat_with_memory(message).await {
|
||||
Ok(response) => {
|
||||
if response.success {
|
||||
if let Some(reply) = response.response {
|
||||
println!("🤖 {}", reply);
|
||||
}
|
||||
if let Some(memories_used) = response.memories_used {
|
||||
println!("📚 使用した記憶数: {}", memories_used);
|
||||
}
|
||||
} else {
|
||||
eprintln!("❌ チャットに失敗: {:?}", response.error);
|
||||
}
|
||||
}
|
||||
Err(e) => {
|
||||
eprintln!("❌ チャットエラー: {}", e);
|
||||
}
|
||||
}
|
||||
|
||||
Ok(())
|
||||
}
|
||||
3
src/mcp/mod.rs
Normal file
3
src/mcp/mod.rs
Normal file
@ -0,0 +1,3 @@
|
||||
// src/mcp/mod.rs
|
||||
pub mod server;
|
||||
pub mod memory;
|
||||
147
src/mcp/server.rs
Normal file
147
src/mcp/server.rs
Normal file
@ -0,0 +1,147 @@
|
||||
// src/mcp/server.rs
|
||||
use crate::config::ConfigPaths;
|
||||
//use std::fs;
|
||||
use std::process::Command as OtherCommand;
|
||||
use std::env;
|
||||
use fs_extra::dir::{copy, CopyOptions};
|
||||
|
||||
pub fn setup() {
|
||||
println!("🔧 MCP Server環境をセットアップしています...");
|
||||
let config = ConfigPaths::new();
|
||||
let mcp_dir = config.mcp_dir();
|
||||
|
||||
// プロジェクトのmcp/ディレクトリからファイルをコピー
|
||||
let current_dir = env::current_dir().expect("現在のディレクトリを取得できません");
|
||||
let project_mcp_dir = current_dir.join("mcp");
|
||||
if !project_mcp_dir.exists() {
|
||||
eprintln!("❌ プロジェクトのmcp/ディレクトリが見つかりません: {}", project_mcp_dir.display());
|
||||
return;
|
||||
}
|
||||
|
||||
if mcp_dir.exists() {
|
||||
fs_extra::dir::remove(&mcp_dir).expect("既存のmcp_dirの削除に失敗しました");
|
||||
}
|
||||
|
||||
let mut options = CopyOptions::new();
|
||||
options.overwrite = true; // 上書き
|
||||
options.copy_inside = true; // 中身だけコピー
|
||||
|
||||
copy(&project_mcp_dir, &mcp_dir, &options).expect("コピーに失敗しました");
|
||||
|
||||
// 仮想環境の作成
|
||||
let venv_path = config.venv_path();
|
||||
if !venv_path.exists() {
|
||||
println!("🐍 仮想環境を作成しています...");
|
||||
let output = OtherCommand::new("python3")
|
||||
.args(&["-m", "venv", ".venv"])
|
||||
.current_dir(&mcp_dir)
|
||||
.output()
|
||||
.expect("venvの作成に失敗しました");
|
||||
|
||||
if !output.status.success() {
|
||||
eprintln!("❌ venv作成エラー: {}", String::from_utf8_lossy(&output.stderr));
|
||||
return;
|
||||
}
|
||||
println!("✅ 仮想環境を作成しました");
|
||||
} else {
|
||||
println!("✅ 仮想環境は既に存在します");
|
||||
}
|
||||
|
||||
// 依存関係のインストール
|
||||
println!("📦 依存関係をインストールしています...");
|
||||
let pip_path = config.pip_executable();
|
||||
let output = OtherCommand::new(&pip_path)
|
||||
.args(&["install", "-r", "requirements.txt"])
|
||||
.current_dir(&mcp_dir)
|
||||
.output()
|
||||
.expect("pipコマンドの実行に失敗しました");
|
||||
|
||||
if !output.status.success() {
|
||||
eprintln!("❌ pip installエラー: {}", String::from_utf8_lossy(&output.stderr));
|
||||
return;
|
||||
}
|
||||
|
||||
println!("✅ MCP Server環境のセットアップが完了しました!");
|
||||
println!("📍 セットアップ場所: {}", mcp_dir.display());
|
||||
}
|
||||
|
||||
pub async fn run() {
|
||||
println!("🚀 MCP Serverを起動しています...");
|
||||
|
||||
let config = ConfigPaths::new();
|
||||
let mcp_dir = config.mcp_dir();
|
||||
let python_path = config.python_executable();
|
||||
let server_py_path = mcp_dir.join("server.py");
|
||||
|
||||
// セットアップの確認
|
||||
if !server_py_path.exists() {
|
||||
eprintln!("❌ server.pyが見つかりません。先に 'aigpt server setup' を実行してください。");
|
||||
return;
|
||||
}
|
||||
|
||||
if !python_path.exists() {
|
||||
eprintln!("❌ Python実行ファイルが見つかりません。先に 'aigpt server setup' を実行してください。");
|
||||
return;
|
||||
}
|
||||
|
||||
// サーバーの起動
|
||||
println!("🔗 サーバーを起動中... (Ctrl+Cで停止)");
|
||||
let mut child = OtherCommand::new(&python_path)
|
||||
.arg("server.py")
|
||||
.current_dir(&mcp_dir)
|
||||
.spawn()
|
||||
.expect("MCP Serverの起動に失敗しました");
|
||||
|
||||
// サーバーの終了を待機
|
||||
match child.wait() {
|
||||
Ok(status) => {
|
||||
if status.success() {
|
||||
println!("✅ MCP Serverが正常に終了しました");
|
||||
} else {
|
||||
println!("❌ MCP Serverが異常終了しました: {}", status);
|
||||
}
|
||||
}
|
||||
Err(e) => {
|
||||
eprintln!("❌ MCP Serverの実行中にエラーが発生しました: {}", e);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
pub async fn chat(message: &str) {
|
||||
println!("💬 チャットを開始しています...");
|
||||
|
||||
let config = ConfigPaths::new();
|
||||
let mcp_dir = config.mcp_dir();
|
||||
let python_path = config.python_executable();
|
||||
let chat_py_path = mcp_dir.join("chat.py");
|
||||
|
||||
// セットアップの確認
|
||||
if !chat_py_path.exists() {
|
||||
eprintln!("❌ chat.pyが見つかりません。先に 'aigpt server setup' を実行してください。");
|
||||
return;
|
||||
}
|
||||
|
||||
if !python_path.exists() {
|
||||
eprintln!("❌ Python実行ファイルが見つかりません。先に 'aigpt server setup' を実行してください。");
|
||||
return;
|
||||
}
|
||||
|
||||
// チャットの実行
|
||||
let output = OtherCommand::new(&python_path)
|
||||
.args(&["chat.py", message])
|
||||
.current_dir(&mcp_dir)
|
||||
.output()
|
||||
.expect("chat.pyの実行に失敗しました");
|
||||
|
||||
if output.status.success() {
|
||||
let stdout = String::from_utf8_lossy(&output.stdout);
|
||||
let stderr = String::from_utf8_lossy(&output.stderr);
|
||||
|
||||
if !stderr.is_empty() {
|
||||
print!("{}", stderr);
|
||||
}
|
||||
print!("{}", stdout);
|
||||
} else {
|
||||
eprintln!("❌ チャット実行エラー: {}", String::from_utf8_lossy(&output.stderr));
|
||||
}
|
||||
}
|
||||
72
src/model.rs
72
src/model.rs
@ -1,72 +0,0 @@
|
||||
//src/model.rs
|
||||
use rusqlite::{params, Connection, Result as SqlResult};
|
||||
use serde::{Deserialize, Serialize};
|
||||
|
||||
#[derive(Debug, Serialize, Deserialize)]
|
||||
pub struct AiSystem {
|
||||
pub personality: Personality,
|
||||
pub relationship: Relationship,
|
||||
pub environment: Environment,
|
||||
pub messaging: Messaging,
|
||||
}
|
||||
|
||||
impl AiSystem {
|
||||
pub fn save_to_db(&self, conn: &Connection) -> SqlResult<()> {
|
||||
conn.execute(
|
||||
"CREATE TABLE IF NOT EXISTS ai_state (id INTEGER PRIMARY KEY, json TEXT)",
|
||||
[],
|
||||
)?;
|
||||
|
||||
let json_data = serde_json::to_string(self).map_err(|e| {
|
||||
rusqlite::Error::ToSqlConversionFailure(Box::new(e))
|
||||
})?;
|
||||
|
||||
conn.execute(
|
||||
"INSERT OR REPLACE INTO ai_state (id, json) VALUES (?1, ?2)",
|
||||
params![1, json_data],
|
||||
)?;
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
pub fn load_from_db(conn: &Connection) -> SqlResult<Self> {
|
||||
let mut stmt = conn.prepare("SELECT json FROM ai_state WHERE id = ?1")?;
|
||||
let json: String = stmt.query_row(params![1], |row| row.get(0))?;
|
||||
|
||||
// ここも serde_json のエラーを map_err で変換
|
||||
let system: AiSystem = serde_json::from_str(&json).map_err(|e| {
|
||||
rusqlite::Error::FromSqlConversionFailure(0, rusqlite::types::Type::Text, Box::new(e))
|
||||
})?;
|
||||
|
||||
Ok(system)
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Debug, Serialize, Deserialize)]
|
||||
pub struct Personality {
|
||||
pub kind: String, // e.g., "positive", "negative", "neutral"
|
||||
pub strength: f32, // 0.0 - 1.0
|
||||
}
|
||||
|
||||
#[derive(Debug, Serialize, Deserialize)]
|
||||
pub struct Relationship {
|
||||
pub trust: f32, // 0.0 - 1.0
|
||||
pub intimacy: f32, // 0.0 - 1.0
|
||||
pub curiosity: f32, // 0.0 - 1.0
|
||||
pub threshold: f32, // if sum > threshold, allow messaging
|
||||
}
|
||||
|
||||
#[derive(Debug, Serialize, Deserialize)]
|
||||
pub struct Environment {
|
||||
pub luck_today: f32, // 0.1 - 1.0
|
||||
pub luck_history: Vec<f32>, // last 3 values
|
||||
pub level: i32, // current mental strength level
|
||||
}
|
||||
|
||||
#[derive(Debug, Serialize, Deserialize)]
|
||||
pub struct Messaging {
|
||||
pub enabled: bool,
|
||||
pub schedule_time: Option<String>, // e.g., "08:00"
|
||||
pub decay_rate: f32, // how quickly emotion fades (0.0 - 1.0)
|
||||
pub templates: Vec<String>, // message template variations
|
||||
}
|
||||
13
src/utils.rs
13
src/utils.rs
@ -1,13 +0,0 @@
|
||||
// src/utils.rs
|
||||
use std::fs;
|
||||
use crate::model::AiSystem;
|
||||
|
||||
pub fn load_config(path: &str) -> AiSystem {
|
||||
let data = fs::read_to_string(path).expect("JSON読み込み失敗");
|
||||
serde_json::from_str(&data).expect("JSONパース失敗")
|
||||
}
|
||||
|
||||
pub fn save_config(path: &str, ai: &AiSystem) {
|
||||
let json = serde_json::to_string_pretty(&ai).expect("JSONシリアライズ失敗");
|
||||
fs::write(path, json).expect("JSON保存失敗");
|
||||
}
|
||||
@ -1,42 +0,0 @@
|
||||
use std::env;
|
||||
use std::process::{Command, Stdio};
|
||||
use std::io::{self, Write};
|
||||
|
||||
fn main() {
|
||||
let args: Vec<String> = env::args().collect();
|
||||
if args.len() < 2 {
|
||||
eprintln!("Usage: langchain_cli <prompt>");
|
||||
std::process::exit(1);
|
||||
}
|
||||
|
||||
let prompt = &args[1];
|
||||
|
||||
// Simulate a pipeline stage: e.g., tokenization, reasoning, response generation
|
||||
let stages = vec!["Tokenize", "Reason", "Generate"];
|
||||
|
||||
for stage in &stages {
|
||||
println!("[Stage: {}] Processing...", stage);
|
||||
}
|
||||
|
||||
// Example call to Python-based LangChain (assuming you have a script or API to call)
|
||||
// For placeholder purposes, we echo the prompt back.
|
||||
let output = Command::new("python3")
|
||||
.arg("-c")
|
||||
.arg(format!("print(\"LangChain Agent Response for: {}\")", prompt))
|
||||
.stdout(Stdio::piped())
|
||||
.spawn()
|
||||
.expect("failed to execute process")
|
||||
.wait_with_output()
|
||||
.expect("failed to wait on child");
|
||||
|
||||
io::stdout().write_all(&output.stdout).unwrap();
|
||||
}
|
||||
|
||||
/*
|
||||
TODO (for future LangChain-style pipeline):
|
||||
1. Implement trait-based agent components: Tokenizer, Retriever, Reasoner, Generator.
|
||||
2. Allow config via YAML or TOML to define chain flow.
|
||||
3. Async pipeline support with Tokio.
|
||||
4. Optional integration with LLM APIs (OpenAI, Ollama, etc).
|
||||
5. Rust-native vector search (e.g. using `tantivy`, `qdrant-client`).
|
||||
*/
|
||||
133
tmp/data.rs
133
tmp/data.rs
@ -1,133 +0,0 @@
|
||||
#[derive(Debug, Serialize, Deserialize)]
|
||||
pub struct RelationalAutonomousAI {
|
||||
pub system_name: String,
|
||||
pub description: String,
|
||||
pub core_components: CoreComponents,
|
||||
pub extensions: Extensions,
|
||||
pub note: String,
|
||||
}
|
||||
|
||||
#[derive(Debug, Serialize, Deserialize)]
|
||||
pub struct CoreComponents {
|
||||
pub personality: Personality,
|
||||
pub relationship: Relationship,
|
||||
pub environment: Environment,
|
||||
pub memory: Memory,
|
||||
pub message_trigger: MessageTrigger,
|
||||
pub message_generation: MessageGeneration,
|
||||
pub state_transition: StateTransition,
|
||||
}
|
||||
|
||||
#[derive(Debug, Serialize, Deserialize)]
|
||||
pub struct Personality {
|
||||
pub r#type: String,
|
||||
pub variants: Vec<String>,
|
||||
pub parameters: PersonalityParameters,
|
||||
}
|
||||
|
||||
#[derive(Debug, Serialize, Deserialize)]
|
||||
pub struct PersonalityParameters {
|
||||
pub message_trigger_style: String,
|
||||
pub decay_rate_modifier: String,
|
||||
}
|
||||
|
||||
#[derive(Debug, Serialize, Deserialize)]
|
||||
pub struct Relationship {
|
||||
pub parameters: Vec<String>,
|
||||
pub properties: RelationshipProperties,
|
||||
pub decay_function: String,
|
||||
}
|
||||
|
||||
#[derive(Debug, Serialize, Deserialize)]
|
||||
pub struct RelationshipProperties {
|
||||
pub persistent: bool,
|
||||
pub hidden: bool,
|
||||
pub irreversible: bool,
|
||||
pub decay_over_time: bool,
|
||||
}
|
||||
|
||||
#[derive(Debug, Serialize, Deserialize)]
|
||||
pub struct Environment {
|
||||
pub daily_luck: DailyLuck,
|
||||
}
|
||||
|
||||
#[derive(Debug, Serialize, Deserialize)]
|
||||
pub struct DailyLuck {
|
||||
pub r#type: String,
|
||||
pub range: Vec<f32>,
|
||||
pub update: String,
|
||||
pub streak_mechanism: StreakMechanism,
|
||||
}
|
||||
|
||||
#[derive(Debug, Serialize, Deserialize)]
|
||||
pub struct StreakMechanism {
|
||||
pub trigger: String,
|
||||
pub effect: String,
|
||||
pub chance: f32,
|
||||
}
|
||||
|
||||
#[derive(Debug, Serialize, Deserialize)]
|
||||
pub struct Memory {
|
||||
pub long_term_memory: String,
|
||||
pub short_term_context: String,
|
||||
pub usage_in_generation: bool,
|
||||
}
|
||||
|
||||
#[derive(Debug, Serialize, Deserialize)]
|
||||
pub struct MessageTrigger {
|
||||
pub condition: TriggerCondition,
|
||||
pub timing: TriggerTiming,
|
||||
}
|
||||
|
||||
#[derive(Debug, Serialize, Deserialize)]
|
||||
pub struct TriggerCondition {
|
||||
pub relationship_threshold: String,
|
||||
pub time_decay: bool,
|
||||
pub environment_luck: String,
|
||||
}
|
||||
|
||||
#[derive(Debug, Serialize, Deserialize)]
|
||||
pub struct TriggerTiming {
|
||||
pub based_on: Vec<String>,
|
||||
pub modifiers: String,
|
||||
}
|
||||
|
||||
#[derive(Debug, Serialize, Deserialize)]
|
||||
pub struct MessageGeneration {
|
||||
pub style_variants: Vec<String>,
|
||||
pub influenced_by: Vec<String>,
|
||||
pub llm_integration: bool,
|
||||
}
|
||||
|
||||
#[derive(Debug, Serialize, Deserialize)]
|
||||
pub struct StateTransition {
|
||||
pub states: Vec<String>,
|
||||
pub transitions: String,
|
||||
}
|
||||
|
||||
#[derive(Debug, Serialize, Deserialize)]
|
||||
pub struct Extensions {
|
||||
pub persistence: Persistence,
|
||||
pub api: Api,
|
||||
pub scheduler: Scheduler,
|
||||
}
|
||||
|
||||
#[derive(Debug, Serialize, Deserialize)]
|
||||
pub struct Persistence {
|
||||
pub database: String,
|
||||
pub storage_items: Vec<String>,
|
||||
}
|
||||
|
||||
#[derive(Debug, Serialize, Deserialize)]
|
||||
pub struct Api {
|
||||
pub llm: String,
|
||||
pub mode: String,
|
||||
pub external_event_trigger: bool,
|
||||
}
|
||||
|
||||
#[derive(Debug, Serialize, Deserialize)]
|
||||
pub struct Scheduler {
|
||||
pub async_event_loop: bool,
|
||||
pub interval_check: i32,
|
||||
pub time_decay_check: bool,
|
||||
}
|
||||
File diff suppressed because one or more lines are too long
@ -1,46 +0,0 @@
|
||||
use serde::{Deserialize, Serialize};
|
||||
use std::fs::File;
|
||||
use std::io::{BufReader, Write};
|
||||
use std::time::{SystemTime, UNIX_EPOCH};
|
||||
|
||||
mod model;
|
||||
use model::RelationalAutonomousAI;
|
||||
|
||||
fn load_config(path: &str) -> std::io::Result<RelationalAutonomousAI> {
|
||||
let file = File::open(path)?;
|
||||
let reader = BufReader::new(file);
|
||||
let config: RelationalAutonomousAI = serde_json::from_reader(reader)?;
|
||||
Ok(config)
|
||||
}
|
||||
|
||||
fn save_config(config: &RelationalAutonomousAI, path: &str) -> std::io::Result<()> {
|
||||
let mut file = File::create(path)?;
|
||||
let json = serde_json::to_string_pretty(config)?;
|
||||
file.write_all(json.as_bytes())?;
|
||||
Ok(())
|
||||
}
|
||||
|
||||
fn should_send_message(config: &RelationalAutonomousAI) -> bool {
|
||||
// 簡易な送信条件: relationshipが高く、daily_luckが0.8以上
|
||||
config.core_components.relationship.parameters.contains(&"trust".to_string())
|
||||
&& config.core_components.environment.daily_luck.range[1] >= 0.8
|
||||
}
|
||||
|
||||
fn main() -> std::io::Result<()> {
|
||||
let path = "config.json";
|
||||
|
||||
let mut config = load_config(path)?;
|
||||
|
||||
if should_send_message(&config) {
|
||||
println!("💌 メッセージを送信できます: {:?}", config.core_components.personality.r#type);
|
||||
|
||||
// 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
|
||||
config.core_components.state_transition.transitions.push("message_sent".to_string());
|
||||
|
||||
save_config(&config, path)?;
|
||||
} else {
|
||||
println!("😶 まだ送信条件に達していません。");
|
||||
}
|
||||
|
||||
Ok(())
|
||||
}
|
||||
Reference in New Issue
Block a user