Files
playchat/backend/app/core/memory.py
T
colcaroli 234ebba103 Add web search knowledge base, message tagging, and word-memory tip topics
Introduces websearch.py (Tavily) + knowledge.py to ground chat responses
in search results, tagging.py to tag user and assistant messages via LLM
and pick related topics for the Discord useless-tips job, and
word_memory.py to track recent conversation nouns (surfaced in the new
WordPool.vue sidebar) used to pick tip topics close to a related tag.
2026-06-20 18:06:06 +09:00

55 lines
2.3 KiB
Python

from sqlalchemy.orm import Session as DBSession
from app.db.models import Memory, Message, Session
from app.core.config import MEMORY_SUMMARIZE_THRESHOLD
from app.core import llm
from app.core import knowledge
from app.core.tagging import tag_memory
def get_context_messages(db: DBSession, session_id: str) -> list[dict]:
"""蓄積知識・長期メモリ(要約)・短期メモリ(最近のメッセージ)を結合してコンテキストを返す。"""
memories = db.query(Memory).filter(Memory.session_id == session_id).order_by(Memory.created_at).all()
messages = db.query(Message).filter(Message.session_id == session_id).order_by(Message.created_at).all()
context: list[dict] = []
knowledge_context = knowledge.get_knowledge_context(db)
if knowledge_context:
context.append({"role": "system", "content": knowledge_context})
if memories:
combined_summary = "\n\n".join(m.summary for m in memories)
context.append({
"role": "system",
"content": f"以下はこの会話の過去の要約です:\n\n{combined_summary}",
})
context.extend({"role": m.role, "content": m.content} for m in messages)
return context
async def maybe_summarize(db: DBSession, session: Session) -> None:
"""メッセージ数が閾値を超えたら古いメッセージを要約してMemoryに保存し、元メッセージを削除する。"""
messages = db.query(Message).filter(Message.session_id == session.id).order_by(Message.created_at).all()
if len(messages) < MEMORY_SUMMARIZE_THRESHOLD:
return
# 古いメッセージ(閾値の半分)を要約対象にする
half = MEMORY_SUMMARIZE_THRESHOLD // 2
to_summarize = messages[:half]
summary_prompt = [
{"role": "system", "content": "以下の会話を簡潔に要約してください。重要な事実・決定・文脈を保持してください。"},
*[{"role": m.role, "content": m.content} for m in to_summarize],
]
summary_text = await llm.complete(summary_prompt, model=session.model, session_id=session.id)
memory = Memory(session_id=session.id, summary=summary_text)
db.add(memory)
for msg in to_summarize:
db.delete(msg)
db.commit()
db.refresh(memory)
await tag_memory(db, memory)