fix gpt
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290
api/app/ai_provider.py
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290
api/app/ai_provider.py
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@ -0,0 +1,290 @@
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"""AI Provider integration for ai.card"""
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import os
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import json
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from typing import Optional, Dict, List, Any
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from abc import ABC, abstractmethod
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import logging
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import httpx
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from openai import OpenAI
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import ollama
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class AIProvider(ABC):
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"""Base class for AI providers"""
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@abstractmethod
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async def chat(self, prompt: str, system_prompt: Optional[str] = None) -> str:
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"""Generate a response based on prompt"""
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pass
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class OllamaProvider(AIProvider):
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"""Ollama AI provider for ai.card"""
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def __init__(self, model: str = "qwen3", host: Optional[str] = None):
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self.model = model
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self.host = host or os.getenv('OLLAMA_HOST', 'http://127.0.0.1:11434')
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if not self.host.startswith('http'):
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self.host = f'http://{self.host}'
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self.client = ollama.Client(host=self.host, timeout=60.0)
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self.logger = logging.getLogger(__name__)
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self.logger.info(f"OllamaProvider initialized with host: {self.host}, model: {self.model}")
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async def chat(self, prompt: str, system_prompt: Optional[str] = None) -> str:
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"""Simple chat interface"""
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try:
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messages = []
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if system_prompt:
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messages.append({"role": "system", "content": system_prompt})
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messages.append({"role": "user", "content": prompt})
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response = self.client.chat(
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model=self.model,
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messages=messages,
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options={
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"num_predict": 2000,
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"temperature": 0.7,
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"top_p": 0.9,
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},
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stream=False
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)
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return response['message']['content']
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except Exception as e:
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self.logger.error(f"Ollama chat failed: {e}")
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return "I'm having trouble connecting to the AI model."
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class OpenAIProvider(AIProvider):
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"""OpenAI API provider with MCP function calling support"""
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def __init__(self, model: str = "gpt-4o-mini", api_key: Optional[str] = None, mcp_client=None):
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self.model = model
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self.api_key = api_key or os.getenv("OPENAI_API_KEY")
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if not self.api_key:
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raise ValueError("OpenAI API key not provided")
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self.client = OpenAI(api_key=self.api_key)
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self.logger = logging.getLogger(__name__)
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self.mcp_client = mcp_client
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def _get_mcp_tools(self) -> List[Dict[str, Any]]:
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"""Generate OpenAI tools from MCP endpoints"""
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if not self.mcp_client:
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return []
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tools = [
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{
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"type": "function",
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"function": {
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"name": "get_user_cards",
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"description": "ユーザーが所有するカードの一覧を取得します",
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"parameters": {
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"type": "object",
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"properties": {
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"did": {
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"type": "string",
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"description": "ユーザーのDID"
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},
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"limit": {
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"type": "integer",
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"description": "取得するカード数の上限",
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"default": 10
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}
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},
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"required": ["did"]
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}
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}
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},
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{
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"type": "function",
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"function": {
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"name": "draw_card",
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"description": "ガチャを引いてカードを取得します",
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"parameters": {
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"type": "object",
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"properties": {
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"did": {
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"type": "string",
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"description": "ユーザーのDID"
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},
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"is_paid": {
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"type": "boolean",
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"description": "有料ガチャかどうか",
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"default": False
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}
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},
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"required": ["did"]
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}
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}
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},
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{
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"type": "function",
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"function": {
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"name": "get_card_details",
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"description": "特定のカードの詳細情報を取得します",
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"parameters": {
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"type": "object",
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"properties": {
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"card_id": {
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"type": "integer",
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"description": "カードID"
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}
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},
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"required": ["card_id"]
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}
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}
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},
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{
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"type": "function",
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"function": {
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"name": "analyze_card_collection",
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"description": "ユーザーのカードコレクションを分析します",
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"parameters": {
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"type": "object",
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"properties": {
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"did": {
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"type": "string",
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"description": "ユーザーのDID"
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}
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},
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"required": ["did"]
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}
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}
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},
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{
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"type": "function",
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"function": {
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"name": "get_gacha_stats",
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"description": "ガチャの統計情報を取得します",
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"parameters": {
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"type": "object",
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"properties": {}
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}
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}
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}
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]
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return tools
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async def chat(self, prompt: str, system_prompt: Optional[str] = None) -> str:
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"""Simple chat interface without MCP tools"""
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try:
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messages = []
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if system_prompt:
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messages.append({"role": "system", "content": system_prompt})
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messages.append({"role": "user", "content": prompt})
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response = self.client.chat.completions.create(
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model=self.model,
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messages=messages,
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max_tokens=2000,
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temperature=0.7
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)
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return response.choices[0].message.content
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except Exception as e:
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self.logger.error(f"OpenAI chat failed: {e}")
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return "I'm having trouble connecting to the AI model."
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async def chat_with_mcp(self, prompt: str, did: str = "user") -> str:
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"""Chat interface with MCP function calling support"""
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if not self.mcp_client:
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return await self.chat(prompt)
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try:
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tools = self._get_mcp_tools()
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response = self.client.chat.completions.create(
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model=self.model,
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messages=[
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{"role": "system", "content": "あなたはai.cardシステムのアシスタントです。カードゲームの情報、ガチャ、コレクション分析などについて質問されたら、必要に応じてツールを使用して正確な情報を提供してください。"},
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{"role": "user", "content": prompt}
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],
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tools=tools,
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tool_choice="auto",
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max_tokens=2000,
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temperature=0.7
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)
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message = response.choices[0].message
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# Handle tool calls
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if message.tool_calls:
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messages = [
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{"role": "system", "content": "カードゲームシステムのツールを使って正確な情報を提供してください。"},
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{"role": "user", "content": prompt},
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{
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"role": "assistant",
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"content": message.content,
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"tool_calls": [tc.model_dump() for tc in message.tool_calls]
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}
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]
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# Execute each tool call
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for tool_call in message.tool_calls:
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tool_result = await self._execute_mcp_tool(tool_call, did)
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messages.append({
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"role": "tool",
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"tool_call_id": tool_call.id,
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"name": tool_call.function.name,
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"content": json.dumps(tool_result, ensure_ascii=False)
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})
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# Get final response
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final_response = self.client.chat.completions.create(
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model=self.model,
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messages=messages,
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max_tokens=2000,
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temperature=0.7
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)
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return final_response.choices[0].message.content
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else:
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return message.content
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except Exception as e:
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self.logger.error(f"OpenAI MCP chat failed: {e}")
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return f"申し訳ありません。エラーが発生しました: {e}"
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async def _execute_mcp_tool(self, tool_call, default_did: str = "user") -> Dict[str, Any]:
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"""Execute MCP tool call"""
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try:
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function_name = tool_call.function.name
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arguments = json.loads(tool_call.function.arguments)
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if function_name == "get_user_cards":
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did = arguments.get("did", default_did)
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limit = arguments.get("limit", 10)
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return await self.mcp_client.get_user_cards(did, limit)
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elif function_name == "draw_card":
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did = arguments.get("did", default_did)
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is_paid = arguments.get("is_paid", False)
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return await self.mcp_client.draw_card(did, is_paid)
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elif function_name == "get_card_details":
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card_id = arguments.get("card_id")
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return await self.mcp_client.get_card_details(card_id)
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elif function_name == "analyze_card_collection":
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did = arguments.get("did", default_did)
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return await self.mcp_client.analyze_card_collection(did)
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elif function_name == "get_gacha_stats":
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return await self.mcp_client.get_gacha_stats()
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else:
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return {"error": f"未知のツール: {function_name}"}
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except Exception as e:
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return {"error": f"ツール実行エラー: {str(e)}"}
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def create_ai_provider(provider: str = "ollama", model: Optional[str] = None, mcp_client=None, **kwargs) -> AIProvider:
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"""Factory function to create AI providers"""
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if provider == "ollama":
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model = model or "qwen3"
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return OllamaProvider(model=model, **kwargs)
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elif provider == "openai":
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model = model or "gpt-4o-mini"
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return OpenAIProvider(model=model, mcp_client=mcp_client, **kwargs)
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else:
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raise ValueError(f"Unknown provider: {provider}")
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@ -37,6 +37,10 @@ class AICardMcpServer:
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self.server = FastMCP("aicard")
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self._register_mcp_tools()
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def get_app(self) -> FastAPI:
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"""Get the FastAPI app instance"""
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return self.app
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def _register_mcp_tools(self):
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"""Register all MCP tools"""
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