Simplify AI memory pipeline

api/app/features/memory/llm_schemas.py

@@ -2,13 +2,10 @@
 
 from __future__ import annotations
 
-import json
-from typing import Any, TypeVar
+from typing import Any
 
 from pydantic import BaseModel, Field, field_validator
 
-TModel = TypeVar("TModel", bound=BaseModel)
-
 
 class ExtractedFactItem(BaseModel):
     fact_type: str = "event"
@@ -38,49 +35,6 @@ class EnrichmentPayload(BaseModel):
     facts: list[ExtractedFactItem] = Field(default_factory=list)
 
 
-class SessionSummaryPayload(BaseModel):
-    summary: str = ""
-
-
-class RollingSummaryPayload(BaseModel):
-    rolling_summary: str = ""
-
-
-class TimelineEventItem(BaseModel):
-    event_year: int | None = None
-    event_date: str | None = None
-    title: str = ""
-    description: str | None = None
-    source_fact_ids: list[str] = Field(default_factory=list)
-
-    @field_validator("source_fact_ids", mode="before")
-    @classmethod
-    def _coerce_sf(cls, v: object) -> list[str]:
-        if v is None:
-            return []
-        if isinstance(v, str):
-            return [v] if v else []
-        if isinstance(v, list):
-            return [str(x) for x in v if x]
-        return []
-
-
-class TimelineEventsPayload(BaseModel):
-    events: list[TimelineEventItem] = Field(default_factory=list)
-
-
-def parse_json_payload(raw: str, model: type[TModel]) -> TModel | None:
-    """解析 invoke_json_object 返回的 JSON 字符串。"""
-    from app.core.json_utils import extract_json_payload
-
-    try:
-        cleaned = extract_json_payload(raw)
-        data = json.loads(cleaned)
-        return model.model_validate(data)
-    except (json.JSONDecodeError, ValueError, TypeError):
-        return None
-
-
 def facts_payload_to_dicts(payload: FactsExtractionPayload) -> list[dict]:
     out: list[dict] = []
     for item in payload.facts:
@@ -95,21 +49,3 @@ def facts_payload_to_dicts(payload: FactsExtractionPayload) -> list[dict]:
 def enrichment_payload_to_fact_dicts(payload: EnrichmentPayload) -> list[dict]:
     """将 EnrichmentPayload.facts 转为与 extract_facts 一致的字典列表。"""
     return facts_payload_to_dicts(FactsExtractionPayload(facts=list(payload.facts)))
-
-
-def timeline_payload_to_dicts(payload: TimelineEventsPayload) -> list[dict]:
-    out: list[dict] = []
-    for ev in payload.events:
-        title = (ev.title or "").strip()
-        if not title:
-            continue
-        out.append(
-            {
-                "event_year": ev.event_year,
-                "event_date": ev.event_date,
-                "title": title,
-                "description": ev.description,
-                "source_fact_ids": ev.source_fact_ids or [],
-            }
-        )
-    return out[:20]