a3f61fcc0f
- DB: segments 用户输入文本（Alembic 0002） - Chat: 阶段检测/阶段提示/回复限制，编排与访谈/画像 prompts 调整 - Memoir: 忠实度检查 agent，叙事与分类等链路更新 - Core: agent 日志、Alembic 启动、LangChain/日志/配置等 - Story: time_hints；Memory 检索与相关测试 - Expo: 助手头像、会话页与消息拆分、实时会话与文案/i18n - Docs/scripts/tests: 迁移脚本、LLM JSON/记忆检索文档、新增单测
133 lines
4.0 KiB
Python
133 lines
4.0 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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- 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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from sqlalchemy.ext.asyncio import AsyncSession
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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,
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create_source,
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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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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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await self._db.commit()
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return source.id
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async def retrieve(self, user_id: str, query: str, *, top_k: int = 10) -> dict:
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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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return await retriever.retrieve(user_id=user_id, query=query, top_k=top_k)
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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. Skips embedding (async).
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Returns source_id.
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"""
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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_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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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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update_chunk_fts_sync(session, chunk.id)
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session.commit()
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return source.id
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