shinalok
06bcdb03ac
- Upgrade LLM to Qwen3-14B-4bit with Thinking mode (MlxChatModel as LangChain BaseChatModel) - Add LangGraph ReAct agent with tool calling loop (search_documents, web_search, get_current_date, remember/recall_user_info) - Add RAG pipeline: BAAI/bge-m3 embeddings + Qdrant vector store + semantic chunking (SemanticSplitter via cosine similarity) - Replace fixed-size RecursiveCharacterTextSplitter with meaning-based SemanticSplitter (numpy only, no extra deps) - Add Gradio Web UI (app.py): chat, document ingestion, document management tabs - Add multi-user support (user_id isolation in DB + per-user agent cache + dropdown selector) - Add conversation history restore from MySQL on agent init (Phase 11) - Add UserProfileRepository for persistent user profile (remember/recall tools) - Add thread-local DB connections to fix pymysql thread-safety with LangGraph ToolNode - Add Phase 14 voice interface: Whisper STT (microphone → text) + macOS TTS (say -v Yuna) - Enforce search_documents-first policy in system prompt and tool descriptions - Update ROADMAP2.md: Phase 14 완료, Phase 13 청킹 부분 완료 Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
67 lines
2.6 KiB
Python
67 lines
2.6 KiB
Python
from __future__ import annotations
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from services.db.mysql_service import DatabaseService
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class ConversationRepository:
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"""td_conversations / td_messages 테이블 접근을 담당하는 Repository."""
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def __init__(self, db: DatabaseService):
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self._db = db
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def create_conversation(self, user_id: str = "default") -> int:
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return self._db.execute_write(
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"INSERT INTO td_conversations (user_id) VALUES (%s)",
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(user_id,),
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)
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def get_latest_conversation_id(self, user_id: str = "default") -> int | None:
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rows = self._db.execute(
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"SELECT id FROM td_conversations WHERE user_id = %s ORDER BY created_at DESC LIMIT 1",
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(user_id,),
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)
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return rows[0]["id"] if rows else None
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def save_message(self, conversation_id: int, role: str, content: str) -> None:
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self._db.execute_write(
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"INSERT INTO td_messages (conversation_id, role, content) VALUES (%s, %s, %s)",
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(conversation_id, role, content),
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)
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def save_summary(self, conversation_id: int, summary: str) -> None:
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self._db.execute_write(
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"INSERT INTO td_messages (conversation_id, role, content) VALUES (%s, %s, %s)",
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(conversation_id, "summary", summary),
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)
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def get_latest_summary(self, conversation_id: int) -> tuple[int | None, str | None]:
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"""가장 최근 요약 메시지의 (id, content)를 반환. 없으면 (None, None)."""
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rows = self._db.execute(
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"""SELECT id, content FROM td_messages
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WHERE conversation_id = %s AND role = 'summary'
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ORDER BY created_at DESC LIMIT 1""",
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(conversation_id,),
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)
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if rows:
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return rows[0]["id"], rows[0]["content"]
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return None, None
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def load_turns_after(
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self, conversation_id: int, after_id: int | None, limit: int
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) -> list[dict]:
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"""요약 이후의 user/assistant 턴을 최근 limit개 반환."""
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if after_id is not None:
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rows = self._db.execute(
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"""SELECT role, content FROM td_messages
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WHERE conversation_id = %s AND id > %s AND role IN ('user', 'assistant')
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ORDER BY created_at DESC LIMIT %s""",
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(conversation_id, after_id, limit),
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)
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else:
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rows = self._db.execute(
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"""SELECT role, content FROM td_messages
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WHERE conversation_id = %s AND role IN ('user', 'assistant')
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ORDER BY created_at DESC LIMIT %s""",
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(conversation_id, limit),
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)
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return list(reversed(rows))
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