ac436b87a2
- MemoryService 异步路径委托 MemoryIngestService / MemoryRetrievalService；富化派发经 MemoryEnrichmentScheduler - WebSocket pipeline 经 ChatTurnService 与显式 DTO 编排单轮对话；回忆录片段入队由 MemoirIngestScheduler 封装 - 新增 LlmGateway（LlmUseCase），各 agent、任务与适配器对齐 ports - 补充 memory 提示适配、runtime 类型、memory-retrieval 文档、ai-touchpoints 说明与扫描脚本及配套测试 Made-with: Cursor
365 lines
12 KiB
Python
365 lines
12 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
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- ingest 成功后：向 ``memory_idle`` 队列派发 LLM 富化（见 ``schedule_memory_enrichment``），不阻塞请求
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- retrieve: 委托 HybridRetriever 返回 evidence bundle（向量 chunks）
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Celery 侧使用 `ingest_transcript_sync` + `retrieve_evidence_sync`，与异步路径对齐见
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`api/docs/memory-retrieval.md`。
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"""
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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.conversation.lineage_schemas import (
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primary_user_message_id_from_lineage,
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)
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from app.features.memory.enrichment_scheduler import MemoryEnrichmentScheduler
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from app.features.memory.ingest_service import MemoryIngestService
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from app.features.memory.repo import (
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create_curation_action,
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set_chunk_excluded,
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set_memory_fact_status,
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)
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from app.features.memory.retrieval_service import MemoryRetrievalService
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from app.features.memory.schemas import EvidenceBundle
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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,
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user_id: str,
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conversation_id: str,
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transcript: str,
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*,
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lineage_json: dict | None = None,
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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.
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Returns source_id.
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"""
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service = MemoryIngestService(self._db, embedding_provider=self._embedding)
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return await service.ingest_transcript(
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user_id,
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conversation_id,
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transcript,
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lineage_json=lineage_json,
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)
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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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service = MemoryRetrievalService(self._db, embedding_provider=self._embedding)
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return await service.retrieve(user_id, query, top_k=top_k)
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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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*,
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lineage_json: dict | None = None,
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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, 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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)
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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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primary_mid = (
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primary_user_message_id_from_lineage(lineage_json) if lineage_json else None
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)
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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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lineage_json=lineage_json,
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primary_user_message_id=primary_mid,
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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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from app.core.config import settings
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vectors_written = 0
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embedding_available = False
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try:
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embedding_provider = get_embedding_provider()
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if embedding_provider is not None:
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embedding_available = embedding_provider.is_available()
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except Exception as e:
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logger.warning(
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"memory embedding provider 不可用(sync): {} exc_type={}",
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e,
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type(e).__name__,
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)
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embedding_provider = None
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# 向量写入在 SAVEPOINT 内；失败仅回滚本段，source/chunks 主体仍可由外层提交。
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# LLM enrichment 在 commit 后由 schedule_memory_enrichment 入 memory_idle 队列。
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try:
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with session.begin_nested():
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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 = embedding_provider.embed_texts_sync(texts)
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for (chunk_id, _), emb in zip(
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chunk_records, embeddings, strict=False
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):
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if emb:
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vectors_written += 1
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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={}",
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e,
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type(e).__name__,
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)
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session.commit()
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enrichment_task_id: str | None = None
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if settings.memory_enrichment_enabled:
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try:
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from app.tasks.memory_enrichment_tasks import schedule_memory_enrichment
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enrichment_task_id = schedule_memory_enrichment(
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user_id, source.id, memoir_correlation_id=None
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)
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except Exception as e:
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logger.warning(
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"memory enrichment 任务派发失败: {} exc_type={}",
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e,
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type(e).__name__,
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)
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logger.info(
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"event=memory_ingest_done user_id={} conversation_id={} source_id={} "
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"chunks={} vectors_written={} embedding_available={} enrichment_enabled={} enrichment_task_id={} sync=1",
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user_id,
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conversation_id,
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source.id,
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len(chunk_records),
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vectors_written,
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embedding_available,
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settings.memory_enrichment_enabled,
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enrichment_task_id,
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)
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return source.id
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def ingest_transcripts_batch_sync(
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session,
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user_id: str,
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items: list[tuple[str, str, dict | None]],
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) -> list[str]:
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"""
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Phase1 批量：多段 transcript 在同一会话内建 source/chunks，并单次 embed_texts_sync（在适配器 batch 限制内）。
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不 commit；不派发 enrichment（由调用方 commit 后 ``schedule_enrichment_for_sources``）。
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items: (conversation_id, transcript, lineage_json)
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返回与有效 items 顺序一致的 source_id 列表。
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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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)
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source_ids: list[str] = []
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all_chunk_records: list[tuple[str, str]] = []
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for conversation_id, transcript, lineage_json in items:
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text = (transcript or "").strip()
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if not text:
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continue
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primary_mid = (
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primary_user_message_id_from_lineage(lineage_json) if lineage_json else None
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)
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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=text,
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conversation_id=conversation_id or None,
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lineage_json=lineage_json,
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primary_user_message_id=primary_mid,
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)
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session.flush()
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chunks_text = chunk_transcript(text)
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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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all_chunk_records.append((chunk.id, content))
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source_ids.append(source.id)
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embedding_provider = None
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try:
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embedding_provider = get_embedding_provider()
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except Exception as e:
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logger.warning(
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"memory embedding provider 不可用(batch sync): {} exc_type={}",
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e,
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type(e).__name__,
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)
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vectors_written = 0
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try:
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with session.begin_nested():
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if all_chunk_records and embedding_provider is not None:
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texts = [content for _, content in all_chunk_records]
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embeddings = embedding_provider.embed_texts_sync(texts)
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for (chunk_id, _), emb in zip(
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all_chunk_records, embeddings, strict=False
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):
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if emb:
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vectors_written += 1
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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(batch sync): {} exc_type={}",
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e,
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type(e).__name__,
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)
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emb_ok = (
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embedding_provider.is_available() if embedding_provider is not None else False
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)
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logger.info(
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"event=memory_ingest_batch_done user_id={} sources={} chunks={} "
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"vectors_written={} embedding_available={}",
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user_id,
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len(source_ids),
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len(all_chunk_records),
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vectors_written,
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emb_ok,
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)
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return source_ids
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def schedule_enrichment_for_sources(
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user_id: str,
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source_ids: list[str],
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*,
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memoir_correlation_id: str | None = None,
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) -> None:
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"""After successful ingest commit, enqueue LLM enrichment for each source (memory_idle queue)."""
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MemoryEnrichmentScheduler().schedule_many(
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user_id,
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source_ids,
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memoir_correlation_id=memoir_correlation_id,
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)
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