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Major refactor: Switch to complete local operation

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- Remove external AI API dependency (no more OpenAI/Claude API calls)
- Claude Code now does all interpretation and scoring locally
- Zero cost: No API fees
- Complete privacy: No data sent to external servers
- Simplified dependencies: Removed openai crate and ai-analysis feature

Changes:
- ai_interpreter.rs: Simplified to lightweight wrapper
- Cargo.toml: Removed ai-analysis feature and openai dependency
- mcp/base.rs: Updated create_memory_with_ai to accept interpreted_content and priority_score from Claude Code
- memory.rs: Added create_memory_with_interpretation() method
- Documentation: Updated README, QUICKSTART, USAGE to reflect local-only operation
- Added CHANGELOG.md to track changes

How it works now:
User → Claude Code (interprets & scores) → aigpt (stores) → game result

Benefits:
✅ 完全ローカル (Fully local)
✅ ゼロコスト (Zero cost)
✅ プライバシー保護 (Privacy protected)
✅ 高速 (Faster - no network latency)
✅ シンプル (Simpler - fewer dependencies)
This commit is contained in:
Claude
2025-11-05 15:55:59 +00:00
parent dbb86cebe5
commit a235f42c61
8 changed files with 441 additions and 141 deletions
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137
src/ai_interpreter.rs
View File

@@ -1,138 +1,31 @@
use anyhow::{Context, Result};
use anyhow::Result;
#[cfg(feature = "ai-analysis")]
use openai::{
chat::{ChatCompletion, ChatCompletionMessage, ChatCompletionMessageRole},
set_key,
};
pub struct AIInterpreter {
#[cfg(feature = "ai-analysis")]
api_key: Option<String>,
}
/// AIInterpreter - Claude Code による解釈を期待する軽量ラッパー
///
/// このモジュールは外部 AI API を呼び出しません。
/// 代わりに、Claude Code 自身がコンテンツを解釈し、スコアを計算することを期待します。
///
/// 完全にローカルで動作し、API コストはゼロです。
pub struct AIInterpreter;
impl AIInterpreter {
pub fn new() -> Self {
#[cfg(feature = "ai-analysis")]
{
let api_key = std::env::var("OPENAI_API_KEY").ok();
if let Some(ref key) = api_key {
set_key(key.clone());
}
AIInterpreter { api_key }
}
#[cfg(not(feature = "ai-analysis"))]
{
AIInterpreter {}
}
AIInterpreter
}
/// AI解釈: 元のコンテンツを解釈して新しい表現を生成
#[cfg(feature = "ai-analysis")]
pub async fn interpret_content(&self, content: &str) -> Result<String> {
if self.api_key.is_none() {
return Ok(content.to_string());
}
let messages = vec![
ChatCompletionMessage {
role: ChatCompletionMessageRole::System,
content: Some("あなたは記憶を解釈するAIです。与えられたテキストを解釈し、より深い意味や文脈を抽出してください。元のテキストの本質を保ちながら、新しい視点や洞察を加えてください。".to_string()),
name: None,
function_call: None,
},
ChatCompletionMessage {
role: ChatCompletionMessageRole::User,
content: Some(format!("以下のテキストを解釈してください:\n\n{}", content)),
name: None,
function_call: None,
},
];
let chat_completion = ChatCompletion::builder("gpt-3.5-turbo", messages.clone())
.create()
.await
.context("Failed to create chat completion")?;
let response = chat_completion
.choices
.first()
.and_then(|choice| choice.message.content.clone())
.unwrap_or_else(|| content.to_string());
Ok(response)
}
#[cfg(not(feature = "ai-analysis"))]
/// コンテンツをそのまま返す（Claude Code が解釈を担当）
pub async fn interpret_content(&self, content: &str) -> Result<String> {
Ok(content.to_string())
}
/// 心理判定: テキストの重要度を0.0-1.0のスコアで評価
#[cfg(feature = "ai-analysis")]
pub async fn calculate_priority_score(&self, content: &str, user_context: Option<&str>) -> Result<f32> {
if self.api_key.is_none() {
return Ok(0.5); // デフォルトスコア
}
let context_info = user_context
.map(|ctx| format!("\n\nユーザーコンテキスト: {}", ctx))
.unwrap_or_default();
let messages = vec![
ChatCompletionMessage {
role: ChatCompletionMessageRole::System,
content: Some(format!(
"あなたは記憶の重要度を評価するAIです。以下の基準で0.0-1.0のスコアをつけてください:\n\
- 感情的インパクト (0.0-0.25)\n\
- ユーザーとの関連性 (0.0-0.25)\n\
- 新規性・独自性 (0.0-0.25)\n\
- 実用性 (0.0-0.25)\n\n\
スコアのみを小数で返してください。例: 0.75{}", context_info
)),
name: None,
function_call: None,
},
ChatCompletionMessage {
role: ChatCompletionMessageRole::User,
content: Some(format!("以下のテキストの重要度を評価してください:\n\n{}", content)),
name: None,
function_call: None,
},
];
let chat_completion = ChatCompletion::builder("gpt-3.5-turbo", messages.clone())
.create()
.await
.context("Failed to create chat completion")?;
let response = chat_completion
.choices
.first()
.and_then(|choice| choice.message.content.clone())
.unwrap_or_else(|| "0.5".to_string());
// スコアを抽出（小数を含む数字）
let score = response
.trim()
.parse::<f32>()
.unwrap_or(0.5)
.min(1.0)
.max(0.0);
Ok(score)
}
#[cfg(not(feature = "ai-analysis"))]
/// デフォルトスコアを返す（Claude Code が実際のスコアを決定）
pub async fn calculate_priority_score(&self, _content: &str, _user_context: Option<&str>) -> Result<f32> {
Ok(0.5) // デフォルトスコア
Ok(0.5) // デフォルト値
}
/// AI解釈と心理判定を同時に実行
pub async fn analyze(&self, content: &str, user_context: Option<&str>) -> Result<(String, f32)> {
let interpreted = self.interpret_content(content).await?;
let score = self.calculate_priority_score(content, user_context).await?;
Ok((interpreted, score))
/// 解釈とスコアリングを Claude Code に委ねる
pub async fn analyze(&self, content: &str, _user_context: Option<&str>) -> Result<(String, f32)> {
Ok((content.to_string(), 0.5))
}
}

30
src/mcp/base.rs
View File

@@ -166,13 +166,23 @@ impl BaseMCPServer {
}),
json!({
"name": "create_memory_with_ai",
"description": "Create a new memory with AI interpretation and priority scoring (with game-style result!)",
"description": "Create a memory with psychological priority scoring! \n\nIMPORTANT: You (Claude) should:\n1. Interpret the user's content and extract deeper meaning\n2. Calculate priority_score (0.0-1.0) based on these criteria:\n - Emotional impact (0.0-0.25)\n - User relevance (0.0-0.25)\n - Novelty/uniqueness (0.0-0.25)\n - Practical utility (0.0-0.25)\n3. Provide the interpreted content and score to this tool\n4. The system will show a game-style result with rarity, type, and XP!",
"inputSchema": {
"type": "object",
"properties": {
"content": {
"type": "string",
"description": "Content of the memory"
"description": "Original user content"
},
"interpreted_content": {
"type": "string",
"description": "Your interpretation of the content (extract deeper meaning)"
},
"priority_score": {
"type": "number",
"description": "Priority score 0.0-1.0 (sum of 4 criteria, each 0.0-0.25)",
"minimum": 0.0,
"maximum": 1.0
},
"user_context": {
"type": "string",
@@ -183,7 +193,7 @@ impl BaseMCPServer {
"description": "Show game-style result (default: true)"
}
},
"required": ["content"]
"required": ["content", "interpreted_content", "priority_score"]
}
}),
json!({
@@ -379,10 +389,18 @@ impl BaseMCPServer {
// AI解釈付きメモリ作成
async fn tool_create_memory_with_ai(&mut self, arguments: &Value) -> Value {
let content = arguments["content"].as_str().unwrap_or("");
let interpreted_content = arguments["interpreted_content"].as_str().unwrap_or(content);
let priority_score = arguments["priority_score"].as_f64().unwrap_or(0.5) as f32;
let user_context = arguments["user_context"].as_str();
let game_mode = arguments["game_mode"].as_bool().unwrap_or(true);
match self.memory_manager.create_memory_with_ai(content, user_context).await {
// Claude Code から受け取った解釈とスコアでメモリを作成
match self.memory_manager.create_memory_with_interpretation(
content,
interpreted_content,
priority_score,
user_context
) {
Ok(id) => {
// 作成したメモリを取得して詳細情報を返す
if let Some(memory) = self.memory_manager.get_memory(&id) {
@@ -408,13 +426,13 @@ impl BaseMCPServer {
},
"game_result": result,
"shareable_text": shareable,
"message": "Memory created with AI interpretation and priority scoring"
"message": "Memory created with Claude Code's interpretation and priority scoring!"
})
} else {
json!({
"success": true,
"id": id,
"message": "Memory created with AI interpretation"
"message": "Memory created"
})
}
}

33
src/memory.rs
View File

@@ -128,7 +128,7 @@ impl MemoryManager {
Ok(id)
}
/// AI解釈と心理判定を使った記憶作成
/// AI解釈と心理判定を使った記憶作成（後方互換性のため残す）
pub async fn create_memory_with_ai(
&mut self,
content: &str,
@@ -163,6 +163,37 @@ impl MemoryManager {
Ok(id)
}
/// Claude Code から解釈とスコアを受け取ってメモリを作成
pub fn create_memory_with_interpretation(
&mut self,
content: &str,
interpreted_content: &str,
priority_score: f32,
user_context: Option<&str>,
) -> Result<String> {
let id = Uuid::new_v4().to_string();
let now = Utc::now();
let memory = Memory {
id: id.clone(),
content: content.to_string(),
interpreted_content: interpreted_content.to_string(),
priority_score: priority_score.max(0.0).min(1.0), // 0.0-1.0 に制限
user_context: user_context.map(|s| s.to_string()),
created_at: now,
updated_at: now,
};
self.memories.insert(id.clone(), memory);
// 容量制限チェック
self.prune_memories_if_needed()?;
self.save_data()?;
Ok(id)
}
pub fn update_memory(&mut self, id: &str, content: &str) -> Result<()> {
if let Some(memory) = self.memories.get_mut(id) {
memory.content = content.to_string();