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Merge branch 'feat/improve-agent-prompt'

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penghanyuan
2026-03-01 10:12:23 +01:00
parent a69d5c625c
commit c1e2fb31a0
11 changed files with 644 additions and 65 deletions
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23
api/tasks/memoir_tasks.py
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@@ -14,7 +14,7 @@ from sqlalchemy import select
from sqlalchemy.orm import Session
from database.database import SessionLocal
from database.models import Book, Chapter, Segment, MemoirState
from database.models import Book, Chapter, Segment, MemoirState, User
from services.llm_service import llm_service
from agents.state_schema import MemoirStateSchema, SlotData, default_state
from agents.prompts.memory_prompts import (
@@ -23,6 +23,7 @@ from agents.prompts.memory_prompts import (
get_state_extraction_prompt,
STAGE_TO_ORDER,
)
from agents.prompts.profile_prompts import format_user_profile_context
logger = logging.getLogger(__name__)
@@ -179,9 +180,21 @@ def process_memoir_segments(self, user_id: str, segment_ids: List[str]):
logger.warning(f"未找到段落: {segment_ids}")
return {"status": "no_segments"}
# 获取用户状态
# 获取用户状态和资料
state = _get_or_create_state_sync(user_id, db)
llm = llm_service.get_llm()
user_obj = db.get(User, user_id)
user_profile = ""
user_birth_year = None
if user_obj:
user_birth_year = user_obj.birth_year
user_profile = format_user_profile_context(
birth_year=user_obj.birth_year,
birth_place=user_obj.birth_place,
grew_up_place=user_obj.grew_up_place,
occupation=user_obj.occupation,
)
# 按阶段分组处理
stage_to_segments: Dict[str, List[Segment]] = {}
@@ -257,7 +270,9 @@ def process_memoir_segments(self, user_id: str, segment_ids: List[str]):
title_prompt = get_creative_title_prompt(
stage=stage,
emotion="neutral",
slots=slot_snippets
slots=slot_snippets,
user_profile=user_profile,
birth_year=user_birth_year,
)
title_response = llm.invoke(title_prompt)
title = title_response.content.strip().strip('"')
@@ -267,6 +282,8 @@ def process_memoir_segments(self, user_id: str, segment_ids: List[str]):
slots=slot_snippets,
new_content=combined_text,
existing_content=existing_content,
user_profile=user_profile,
birth_year=user_birth_year,
)
narrative_response = llm.invoke(narrative_prompt)
new_narrative = narrative_response.content.strip()