fix mcp
This commit is contained in:
@ -1,6 +1,7 @@
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"""AI Provider integration for response generation"""
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import os
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import json
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from typing import Optional, Dict, List, Any, Protocol
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from abc import abstractmethod
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import logging
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@ -128,9 +129,9 @@ Recent memories:
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class OpenAIProvider:
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"""OpenAI API provider"""
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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):
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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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# Try to get API key from config first
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config = Config()
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@ -139,6 +140,90 @@ class OpenAIProvider:
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raise ValueError("OpenAI API key not provided. Set it with: aigpt config set providers.openai.api_key YOUR_KEY")
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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 # For MCP function calling
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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 or not self.mcp_client.available:
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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_memories",
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"description": "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",
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"parameters": {
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"type": "object",
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"properties": {
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"limit": {
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"type": "integer",
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"description": "取得する記憶の数",
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"default": 5
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}
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}
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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": "search_memories",
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"description": "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",
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"parameters": {
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"type": "object",
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"properties": {
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"keywords": {
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"type": "array",
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"items": {"type": "string"},
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"description": "検索キーワードの配列"
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}
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},
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"required": ["keywords"]
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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_contextual_memories",
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"description": "クエリに関連する文脈的記憶を取得します",
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"parameters": {
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"type": "object",
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"properties": {
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"query": {
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"type": "string",
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"description": "検索クエリ"
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},
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"limit": {
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"type": "integer",
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"description": "取得する記憶の数",
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"default": 5
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}
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},
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"required": ["query"]
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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_relationship",
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"description": "特定ユーザーとの関係性情報を取得します",
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"parameters": {
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"type": "object",
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"properties": {
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"user_id": {
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"type": "string",
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"description": "ユーザーID"
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}
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},
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"required": ["user_id"]
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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 generate_response(
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self,
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@ -184,6 +269,127 @@ Recent memories:
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self.logger.error(f"OpenAI generation failed: {e}")
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return self._fallback_response(persona_state)
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async def chat_with_mcp(self, prompt: str, max_tokens: int = 2000, user_id: str = "user") -> str:
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"""Chat interface with MCP function calling support"""
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if not self.mcp_client or not self.mcp_client.available:
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return self.chat(prompt, max_tokens)
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try:
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# Prepare tools
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tools = self._get_mcp_tools()
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# Initial request with 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": "あなたは記憶システムと関係性データにアクセスできます。過去の会話、記憶、関係性について質問された時は、必ずツールを使用して正確な情報を取得してください。「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れば積極的にツールを使用してください。"},
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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=max_tokens,
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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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print(f"🔧 [OpenAI] {len(message.tool_calls)} tools called:")
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for tc in message.tool_calls:
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print(f" - {tc.function.name}({tc.function.arguments})")
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messages = [
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{"role": "system", "content": "必要に応じて利用可能なツールを使って、より正確で詳細な回答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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print(f"🌐 [MCP] Executing {tool_call.function.name}...")
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tool_result = await self._execute_mcp_tool(tool_call, user_id)
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print(f"✅ [MCP] Result: {str(tool_result)[:100]}...")
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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 with tool outputs
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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=max_tokens,
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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, context_user_id: str = "user") -> Dict[str, Any]:
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"""Execute MCP tool call"""
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try:
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import json
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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_memories":
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limit = arguments.get("limit", 5)
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return await self.mcp_client.get_memories(limit) or {"error": "記憶の取得に失敗しました"}
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elif function_name == "search_memories":
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keywords = arguments.get("keywords", [])
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return await self.mcp_client.search_memories(keywords) or {"error": "記憶の検索に失敗しました"}
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elif function_name == "get_contextual_memories":
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query = arguments.get("query", "")
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limit = arguments.get("limit", 5)
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return await self.mcp_client.get_contextual_memories(query, limit) or {"error": "文脈記憶の取得に失敗しました"}
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elif function_name == "get_relationship":
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# 引数のuser_idがない場合はコンテキストから取得
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user_id = arguments.get("user_id", context_user_id)
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if not user_id or user_id == "user":
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user_id = context_user_id
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# デバッグ用ログ
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print(f"🔍 [DEBUG] get_relationship called with user_id: '{user_id}' (context: '{context_user_id}')")
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result = await self.mcp_client.get_relationship(user_id)
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print(f"🔍 [DEBUG] MCP result: {result}")
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return result or {"error": "関係性の取得に失敗しました"}
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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 chat(self, prompt: str, max_tokens: int = 2000) -> str:
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"""Simple chat interface without MCP tools"""
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try:
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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": "user", "content": prompt}
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],
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max_tokens=max_tokens,
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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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def _fallback_response(self, persona_state: PersonaState) -> str:
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"""Fallback response based on mood"""
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mood_responses = {
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@ -196,7 +402,7 @@ Recent memories:
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return mood_responses.get(persona_state.current_mood, "I see.")
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def create_ai_provider(provider: str = "ollama", model: Optional[str] = None, **kwargs) -> AIProvider:
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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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# Get model from config if not provided
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@ -228,6 +434,6 @@ def create_ai_provider(provider: str = "ollama", model: Optional[str] = None, **
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model = config.get('providers.openai.default_model', 'gpt-4o-mini')
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except:
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model = 'gpt-4o-mini' # Fallback to default
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return OpenAIProvider(model=model, **kwargs)
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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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