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
106 lines
3.4 KiB
Python
106 lines
3.4 KiB
Python
"""Hybrid retriever — metadata filter + FTS + vector retrieval + score fusion."""
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.features.memory.repo import (
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get_facts_for_user,
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get_timeline_events_for_user,
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search_chunks_fts,
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search_chunks_vector,
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)
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from app.ports.embedding import EmbeddingProvider
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def _rrf_merge(
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fts_items: list[dict], vector_items: list[dict], k: int = 60
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) -> list[dict]:
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"""Reciprocal Rank Fusion. Merge FTS and vector results by id."""
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scores: dict[str, float] = {}
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for rank, item in enumerate(fts_items):
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cid = item["id"]
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scores[cid] = scores.get(cid, 0) + 1 / (k + rank + 1)
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for rank, item in enumerate(vector_items):
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cid = item["id"]
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scores[cid] = scores.get(cid, 0) + 1 / (k + rank + 1)
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all_items = {x["id"]: x for x in fts_items + vector_items}
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sorted_ids = sorted(scores.keys(), key=lambda i: scores[i], reverse=True)
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return [all_items[i] for i in sorted_ids]
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class HybridRetriever:
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"""Combine FTS, vector, and metadata filter into evidence bundle."""
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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 retrieve(self, user_id: str, query: str, *, top_k: int = 10) -> dict:
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"""
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Return evidence bundle:
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{relevant_chunks, relevant_summaries, relevant_facts, timeline_hints, relevant_stories}
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`relevant_summaries` / `relevant_stories` 当前多为占位空列表；叙事 prompt 仅应依赖
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已实现填充的字段（见 `format_evidence_chunks_for_prompt`）。
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"""
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fts_chunks = await search_chunks_fts(
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self._db, user_id=user_id, query=query, limit=top_k * 2
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)
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vector_chunks: list[dict] = []
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if self._embedding and query.strip():
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q_emb = await self._embedding.embed_text(query.strip())
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if q_emb:
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vector_chunks = await search_chunks_vector(
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self._db, user_id=user_id, query_embedding=q_emb, limit=top_k * 2
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)
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merged = _rrf_merge(fts_chunks, vector_chunks)[:top_k]
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relevant_chunks = [
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{
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"id": c["id"],
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"content": c["content"],
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"chunk_index": c.get("chunk_index", 0),
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}
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for c in merged
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]
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facts = await get_facts_for_user(self._db, user_id=user_id, limit=top_k)
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relevant_facts = [
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{
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"id": f.id,
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"fact_type": f.fact_type,
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"subject": f.subject,
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"predicate": f.predicate,
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"object_json": f.object_json,
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}
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for f in facts
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]
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events = await get_timeline_events_for_user(
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self._db, user_id=user_id, limit=top_k
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)
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timeline_hints = [
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{
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"id": e.id,
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"event_year": e.event_year,
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"event_date": e.event_date,
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"title": e.title,
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"description": e.description,
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}
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for e in events
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]
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return {
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"relevant_chunks": relevant_chunks,
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"relevant_summaries": [],
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"relevant_facts": relevant_facts,
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"timeline_hints": timeline_hints,
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"relevant_stories": [],
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}
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