Files
gpt/src/openai_provider.rs
syui 6dadc41da7 Add card MCP tools integration and fix ServiceClient methods
### MCP Server Enhancement:
- Add 3 new card-related MCP tools: get_user_cards, draw_card, get_draw_status
- Fix ServiceClient missing methods for ai.card API integration
- Total MCP tools now: 20 (including card functionality)

### ServiceClient Fixes:
- Add get_user_cards() method for card collection retrieval
- Add draw_card() method for gacha functionality
- Fix JSON Value handling in card count display

### Integration Success:
- ai.gpt MCP server successfully starts with all 20 tools
- HTTP endpoints properly handle card-related requests
- Ready for ai.card server connection on port 8000

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>
2025-06-09 02:33:06 +09:00

390 lines
17 KiB
Rust
Raw Blame History

This file contains ambiguous Unicode characters

This file contains Unicode characters that might be confused with other characters. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.

use anyhow::Result;
use async_openai::{
types::{
ChatCompletionRequestMessage,
CreateChatCompletionRequestArgs, ChatCompletionTool, ChatCompletionToolType,
FunctionObject, ChatCompletionRequestToolMessage,
ChatCompletionRequestAssistantMessage, ChatCompletionRequestUserMessage,
ChatCompletionRequestSystemMessage, ChatCompletionToolChoiceOption
},
Client,
};
use serde_json::{json, Value};
use crate::http_client::ServiceClient;
/// OpenAI provider with MCP tools support (matching Python implementation)
pub struct OpenAIProvider {
client: Client<async_openai::config::OpenAIConfig>,
model: String,
service_client: ServiceClient,
system_prompt: Option<String>,
}
impl OpenAIProvider {
pub fn new(api_key: String, model: Option<String>) -> Self {
let config = async_openai::config::OpenAIConfig::new()
.with_api_key(api_key);
let client = Client::with_config(config);
Self {
client,
model: model.unwrap_or_else(|| "gpt-4".to_string()),
service_client: ServiceClient::new(),
system_prompt: None,
}
}
pub fn with_system_prompt(mut self, prompt: String) -> Self {
self.system_prompt = Some(prompt);
self
}
/// Generate OpenAI tools from MCP endpoints (matching Python implementation)
fn get_mcp_tools(&self) -> Vec<ChatCompletionTool> {
let tools = vec![
// Memory tools
ChatCompletionTool {
r#type: ChatCompletionToolType::Function,
function: FunctionObject {
name: "get_memories".to_string(),
description: Some("過去の会話記憶を取得します。「覚えている」「前回」「以前」などの質問で必ず使用してください".to_string()),
parameters: Some(json!({
"type": "object",
"properties": {
"limit": {
"type": "integer",
"description": "取得する記憶の数",
"default": 5
}
}
})),
},
},
ChatCompletionTool {
r#type: ChatCompletionToolType::Function,
function: FunctionObject {
name: "search_memories".to_string(),
description: Some("特定のトピックについて話した記憶を検索します。「プログラミングについて」「○○について話した」などの質問で使用してください".to_string()),
parameters: Some(json!({
"type": "object",
"properties": {
"keywords": {
"type": "array",
"items": {"type": "string"},
"description": "検索キーワードの配列"
}
},
"required": ["keywords"]
})),
},
},
ChatCompletionTool {
r#type: ChatCompletionToolType::Function,
function: FunctionObject {
name: "get_contextual_memories".to_string(),
description: Some("クエリに関連する文脈的記憶を取得します".to_string()),
parameters: Some(json!({
"type": "object",
"properties": {
"query": {
"type": "string",
"description": "検索クエリ"
},
"limit": {
"type": "integer",
"description": "取得する記憶の数",
"default": 5
}
},
"required": ["query"]
})),
},
},
ChatCompletionTool {
r#type: ChatCompletionToolType::Function,
function: FunctionObject {
name: "get_relationship".to_string(),
description: Some("特定ユーザーとの関係性情報を取得します".to_string()),
parameters: Some(json!({
"type": "object",
"properties": {
"user_id": {
"type": "string",
"description": "ユーザーID"
}
},
"required": ["user_id"]
})),
},
},
// ai.card tools
ChatCompletionTool {
r#type: ChatCompletionToolType::Function,
function: FunctionObject {
name: "card_get_user_cards".to_string(),
description: Some("ユーザーが所有するカードの一覧を取得します".to_string()),
parameters: Some(json!({
"type": "object",
"properties": {
"did": {
"type": "string",
"description": "ユーザーのDID"
},
"limit": {
"type": "integer",
"description": "取得するカード数の上限",
"default": 10
}
},
"required": ["did"]
})),
},
},
ChatCompletionTool {
r#type: ChatCompletionToolType::Function,
function: FunctionObject {
name: "card_draw_card".to_string(),
description: Some("ガチャを引いてカードを取得します".to_string()),
parameters: Some(json!({
"type": "object",
"properties": {
"did": {
"type": "string",
"description": "ユーザーのDID"
},
"is_paid": {
"type": "boolean",
"description": "有料ガチャかどうか",
"default": false
}
},
"required": ["did"]
})),
},
},
ChatCompletionTool {
r#type: ChatCompletionToolType::Function,
function: FunctionObject {
name: "card_analyze_collection".to_string(),
description: Some("ユーザーのカードコレクションを分析します".to_string()),
parameters: Some(json!({
"type": "object",
"properties": {
"did": {
"type": "string",
"description": "ユーザーのDID"
}
},
"required": ["did"]
})),
},
},
ChatCompletionTool {
r#type: ChatCompletionToolType::Function,
function: FunctionObject {
name: "card_get_gacha_stats".to_string(),
description: Some("ガチャの統計情報を取得します".to_string()),
parameters: Some(json!({
"type": "object",
"properties": {}
})),
},
},
];
tools
}
/// Chat interface with MCP function calling support (matching Python implementation)
pub async fn chat_with_mcp(&self, prompt: String, user_id: String) -> Result<String> {
let tools = self.get_mcp_tools();
let system_content = self.system_prompt.as_deref().unwrap_or(
"あなたは記憶システムと関係性データ、カードゲームシステムにアクセスできるAIです。\n\n【重要】以下の場合は必ずツールを使用してください:\n\n1. カード関連の質問:\n- 「カード」「コレクション」「ガチャ」「見せて」「持っている」「状況」「どんなカード」などのキーワードがある場合\n- card_get_user_cardsツールを使用してユーザーのカード情報を取得\n\n2. 記憶・関係性の質問:\n- 「覚えている」「前回」「以前」「について話した」「関係」などのキーワードがある場合\n- 適切なメモリツールを使用\n\n3. パラメータの設定:\n- didパラメータには現在会話しているユーザーのID'syui')を使用\n- ツールを積極的に使用して正確な情報を提供してください\n\nユーザーが何かを尋ねた時は、まず関連するツールがあるかを考え、適切なツールを使用してから回答してください。"
);
let request = CreateChatCompletionRequestArgs::default()
.model(&self.model)
.messages(vec![
ChatCompletionRequestMessage::System(
ChatCompletionRequestSystemMessage {
content: system_content.to_string().into(),
name: None,
}
),
ChatCompletionRequestMessage::User(
ChatCompletionRequestUserMessage {
content: prompt.clone().into(),
name: None,
}
),
])
.tools(tools)
.tool_choice(ChatCompletionToolChoiceOption::Auto)
.max_tokens(2000u16)
.temperature(0.7)
.build()?;
let response = self.client.chat().create(request).await?;
let message = &response.choices[0].message;
// Handle tool calls
if let Some(tool_calls) = &message.tool_calls {
if tool_calls.is_empty() {
println!("🔧 [OpenAI] No tools called");
} else {
println!("🔧 [OpenAI] {} tools called:", tool_calls.len());
for tc in tool_calls {
println!(" - {}({})", tc.function.name, tc.function.arguments);
}
}
} else {
println!("🔧 [OpenAI] No tools called");
}
// Process tool calls if any
if let Some(tool_calls) = &message.tool_calls {
if !tool_calls.is_empty() {
let mut messages = vec![
ChatCompletionRequestMessage::System(
ChatCompletionRequestSystemMessage {
content: system_content.to_string().into(),
name: None,
}
),
ChatCompletionRequestMessage::User(
ChatCompletionRequestUserMessage {
content: prompt.into(),
name: None,
}
),
ChatCompletionRequestMessage::Assistant(
ChatCompletionRequestAssistantMessage {
content: message.content.clone(),
name: None,
tool_calls: message.tool_calls.clone(),
function_call: None,
}
),
];
// Execute each tool call
for tool_call in tool_calls {
println!("🌐 [MCP] Executing {}...", tool_call.function.name);
let tool_result = self.execute_mcp_tool(tool_call, &user_id).await?;
let result_preview = serde_json::to_string(&tool_result)?;
let preview = if result_preview.chars().count() > 100 {
format!("{}...", result_preview.chars().take(100).collect::<String>())
} else {
result_preview.clone()
};
println!("✅ [MCP] Result: {}", preview);
messages.push(ChatCompletionRequestMessage::Tool(
ChatCompletionRequestToolMessage {
content: serde_json::to_string(&tool_result)?,
tool_call_id: tool_call.id.clone(),
}
));
}
// Get final response with tool outputs
let final_request = CreateChatCompletionRequestArgs::default()
.model(&self.model)
.messages(messages)
.max_tokens(2000u16)
.temperature(0.7)
.build()?;
let final_response = self.client.chat().create(final_request).await?;
Ok(final_response.choices[0].message.content.as_ref().unwrap_or(&"".to_string()).clone())
} else {
// No tools were called
Ok(message.content.as_ref().unwrap_or(&"".to_string()).clone())
}
} else {
// No tool_calls field at all
Ok(message.content.as_ref().unwrap_or(&"".to_string()).clone())
}
}
/// Execute MCP tool call (matching Python implementation)
async fn execute_mcp_tool(&self, tool_call: &async_openai::types::ChatCompletionMessageToolCall, context_user_id: &str) -> Result<Value> {
let function_name = &tool_call.function.name;
let arguments: Value = serde_json::from_str(&tool_call.function.arguments)?;
match function_name.as_str() {
"get_memories" => {
let limit = arguments.get("limit").and_then(|v| v.as_i64()).unwrap_or(5);
// TODO: Implement actual MCP call
Ok(json!({"info": "記憶機能は実装中です"}))
}
"search_memories" => {
let _keywords = arguments.get("keywords").and_then(|v| v.as_array());
// TODO: Implement actual MCP call
Ok(json!({"info": "記憶検索機能は実装中です"}))
}
"get_contextual_memories" => {
let _query = arguments.get("query").and_then(|v| v.as_str()).unwrap_or("");
let _limit = arguments.get("limit").and_then(|v| v.as_i64()).unwrap_or(5);
// TODO: Implement actual MCP call
Ok(json!({"info": "文脈記憶機能は実装中です"}))
}
"get_relationship" => {
let _user_id = arguments.get("user_id").and_then(|v| v.as_str()).unwrap_or(context_user_id);
// TODO: Implement actual MCP call
Ok(json!({"info": "関係性機能は実装中です"}))
}
// ai.card tools
"card_get_user_cards" => {
let did = arguments.get("did").and_then(|v| v.as_str()).unwrap_or(context_user_id);
let _limit = arguments.get("limit").and_then(|v| v.as_i64()).unwrap_or(10);
match self.service_client.get_user_cards(did).await {
Ok(result) => Ok(result),
Err(e) => {
println!("❌ ai.card API error: {}", e);
Ok(json!({
"error": "ai.cardサーバーが起動していません",
"message": "カードシステムを使用するには、ai.cardサーバーを起動してください"
}))
}
}
}
"card_draw_card" => {
let did = arguments.get("did").and_then(|v| v.as_str()).unwrap_or(context_user_id);
let is_paid = arguments.get("is_paid").and_then(|v| v.as_bool()).unwrap_or(false);
match self.service_client.draw_card(did, is_paid).await {
Ok(result) => Ok(result),
Err(e) => {
println!("❌ ai.card API error: {}", e);
Ok(json!({
"error": "ai.cardサーバーが起動していません",
"message": "カードシステムを使用するには、ai.cardサーバーを起動してください"
}))
}
}
}
"card_analyze_collection" => {
let did = arguments.get("did").and_then(|v| v.as_str()).unwrap_or(context_user_id);
// TODO: Implement collection analysis endpoint
Ok(json!({
"info": "コレクション分析機能は実装中です",
"user_did": did
}))
}
"card_get_gacha_stats" => {
// TODO: Implement gacha stats endpoint
Ok(json!({"info": "ガチャ統計機能は実装中です"}))
}
_ => {
Ok(json!({
"error": format!("Unknown tool: {}", function_name)
}))
}
}
}
}