bb16d3a5c9
访谈与阶段 - 新增 app/agents/stage_constants.py：集中 CHAT_STAGES、章节分类/顺序、阶段到默认 memoir 类别等，与 MemoirState 默认槽位顺序对齐；减少散落在 prompts 内的重复常量。 - 新增 app/agents/chat/prompt_context.py：以 ChatPromptContext 汇总 guided 系统提示所需字段（阶段、槽位、轮次、人设、记忆证据、回复长度模式、背景声线、职业等），统一走 get_guided_conversation_prompt。 - 大幅收敛 app/agents/chat/prompts_conversation.py；调整 prompts.py、stage_prompts.py、stage_detection.py；同步 interview_agent、profile_agent、helpers 与 state_schema，使对话侧构造提示的方式一致、可测。 回忆录流水线 - memoir/prompts.py 删除已迁至 stage_constants / 独立模板的大段常量与图片占位相关逻辑；classification / extraction / fidelity / narrative agents 与 orchest（全量历史仍可用于计数，注入模型时按轮次与字符上限截断）、image_prompt_fallback_disabled。 - dependencies 增加 get_llm_provider_fast（LRU 缓存，可与默认共用密钥与 base_url）。 任务与编排 - memoir_tasks：prepare_batches 注入 llm_fast；开启独立快档模型时打结构化日志。 - chapter_cover_tasks、story_image_tasks：与图片 prompt / JSON 工具路径或策略变更对齐（import 与行为一致）。 - story_pipeline_sync 等小处同步。 其它核心 - langchain_llm、text_normalize 随上述调用链微调。 开发者体验 - .cursor/settings.json：启用 redis-development、postman 插件。 测试 - 新增 test_image_prompt_policy：覆盖「禁止回退」等图片 prompt 策略。 - 更新 test_interview_prompts、test_interview_reply_length、test_experience_regressions、test_json_and_memory_utils，匹配新常量位置、json_utils 与对话/长度行为。
253 lines
8.1 KiB
Python
253 lines
8.1 KiB
Python
"""
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MemoryService — conversation / memoir 的统一门面。
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- ingest_transcript: transcript -> memory_sources, chunks, embedding, FTS
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- ingest 后可选：LLM 富化（session/rolling 摘要、事实、时间线）
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- retrieve: 委托 HybridRetriever 返回 evidence bundle（FTS + 可选向量 RRF）
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Celery 侧使用 `ingest_transcript_sync` + `retrieve_evidence_sync`，与异步路径差异见
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`api/docs/memory-retrieval.md`。
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"""
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import asyncio
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.core.logging import get_logger
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from app.features.memory.chunker import chunk_transcript
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from app.features.memory.schemas import EvidenceBundle
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from app.features.memory.repo import (
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create_chunk,
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create_curation_action,
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create_source,
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set_chunk_excluded,
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set_memory_fact_status,
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update_chunk_embedding,
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update_chunk_fts,
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)
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from app.ports.embedding import EmbeddingProvider
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logger = get_logger(__name__)
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class MemoryService:
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def __init__(
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self,
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db: AsyncSession,
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*,
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embedding_provider: EmbeddingProvider | None = None,
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):
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self._db = db
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self._embedding = embedding_provider
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async def ingest_transcript(
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self, user_id: str, conversation_id: str, transcript: str
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) -> str:
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"""
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Ingest conversation transcript into memory.
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Creates MemorySource, chunks, populates embedding + FTS.
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Returns source_id.
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"""
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if not transcript or not transcript.strip():
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raise ValueError("transcript cannot be empty")
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source = await create_source(
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self._db,
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user_id=user_id,
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source_type="transcript",
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raw_text=transcript.strip(),
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conversation_id=conversation_id,
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)
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chunks_text = chunk_transcript(transcript.strip())
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chunk_records = []
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for i, content in enumerate(chunks_text):
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chunk = await create_chunk(
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self._db,
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source_id=source.id,
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user_id=user_id,
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content=content,
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chunk_index=i,
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)
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chunk_records.append((chunk.id, content))
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await self._db.flush()
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# FTS: populate content_tsv
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for chunk_id, _ in chunk_records:
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await update_chunk_fts(self._db, chunk_id)
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# Embedding: 若有 provider 则写入
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if self._embedding and chunk_records:
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texts = [c for _, c in chunk_records]
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embeddings = await self._embedding.embed_texts(texts)
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for (chunk_id, _), emb in zip(chunk_records, embeddings):
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if emb:
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await update_chunk_embedding(self._db, chunk_id, emb)
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try:
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from app.core.config import settings
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from app.core.dependencies import get_llm_provider_fast
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from app.features.memory.enrichment import enrich_memory_after_ingest_async
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if settings.memory_enrichment_enabled:
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llm = get_llm_provider_fast().langchain_llm
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await enrich_memory_after_ingest_async(
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self._db, user_id, source.id, llm
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)
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except Exception as e:
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logger.warning(
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"memory enrichment 跳过: {} exc_type={}", e, type(e).__name__
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)
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await self._db.commit()
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return source.id
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async def retrieve(
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self, user_id: str, query: str, *, top_k: int = 10
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) -> EvidenceBundle:
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"""Retrieve relevant evidence. 委托 HybridRetriever。"""
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from app.features.memory.retriever import HybridRetriever
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retriever = HybridRetriever(self._db, embedding_provider=self._embedding)
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raw = await retriever.retrieve(user_id=user_id, query=query, top_k=top_k)
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return EvidenceBundle.model_validate(raw)
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async def exclude_chunk(
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self, user_id: str, chunk_id: str, *, reason: str = ""
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) -> bool:
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ok = await set_chunk_excluded(self._db, chunk_id, user_id, True)
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if not ok:
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return False
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await create_curation_action(
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self._db,
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user_id=user_id,
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action_type="exclude",
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target_type="chunk",
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target_id=chunk_id,
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details={"reason": reason} if reason else None,
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)
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await self._db.commit()
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return True
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async def restore_chunk(self, user_id: str, chunk_id: str) -> bool:
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ok = await set_chunk_excluded(self._db, chunk_id, user_id, False)
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if not ok:
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return False
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await create_curation_action(
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self._db,
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user_id=user_id,
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action_type="restore",
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target_type="chunk",
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target_id=chunk_id,
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details=None,
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)
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await self._db.commit()
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return True
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async def confirm_fact(self, user_id: str, fact_id: str) -> bool:
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ok = await set_memory_fact_status(self._db, fact_id, user_id, "confirmed")
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if not ok:
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return False
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await create_curation_action(
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self._db,
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user_id=user_id,
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action_type="confirm",
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target_type="fact",
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target_id=fact_id,
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details=None,
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)
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await self._db.commit()
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return True
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async def reject_fact(
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self, user_id: str, fact_id: str, *, reason: str = ""
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) -> bool:
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ok = await set_memory_fact_status(self._db, fact_id, user_id, "rejected")
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if not ok:
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return False
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await create_curation_action(
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self._db,
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user_id=user_id,
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action_type="reject",
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target_type="fact",
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target_id=fact_id,
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details={"reason": reason} if reason else None,
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)
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await self._db.commit()
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return ok
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def ingest_transcript_sync(
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session,
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user_id: str,
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conversation_id: str,
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transcript: str,
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) -> str:
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"""
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Sync transcript ingest for Celery tasks.
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Creates source + chunks + FTS, and best-effort populates embeddings.
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Returns source_id.
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"""
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from app.core.dependencies import get_embedding_provider
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from app.features.memory.chunker import chunk_transcript
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from app.features.memory.repo import (
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create_chunk_sync,
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create_source_sync,
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update_chunk_embedding_sync,
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update_chunk_fts_sync,
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)
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if not transcript or not transcript.strip():
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raise ValueError("transcript cannot be empty")
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source = create_source_sync(
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session,
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user_id=user_id,
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source_type="transcript",
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raw_text=transcript.strip(),
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conversation_id=conversation_id,
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)
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session.flush()
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chunks_text = chunk_transcript(transcript.strip())
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chunk_records: list[tuple[str, str]] = []
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for i, content in enumerate(chunks_text):
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chunk = create_chunk_sync(
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session,
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source_id=source.id,
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user_id=user_id,
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content=content,
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chunk_index=i,
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)
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session.flush()
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chunk_records.append((chunk.id, content))
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update_chunk_fts_sync(session, chunk.id)
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try:
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embedding_provider = get_embedding_provider()
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if chunk_records and embedding_provider is not None:
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texts = [content for _, content in chunk_records]
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embeddings = asyncio.run(embedding_provider.embed_texts(texts))
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for (chunk_id, _), emb in zip(chunk_records, embeddings):
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if emb:
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update_chunk_embedding_sync(session, chunk_id, emb)
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except Exception as e:
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logger.warning(
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"memory embedding 跳过(sync): {} exc_type={}", e, type(e).__name__
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)
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try:
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from app.core.config import settings
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from app.features.memory.enrichment import enrich_memory_after_ingest_sync
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if settings.memory_enrichment_enabled:
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enrich_memory_after_ingest_sync(session, user_id, source.id, llm=None)
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except Exception as e:
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logger.warning(
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"memory enrichment 跳过(sync): {} exc_type={}", e, type(e).__name__
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)
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session.commit()
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return source.id
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