feat(api): 收敛对话与记忆流程边界，引入 LLM 网关与专用服务

- 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

api/app/features/memory/ingest_service.py

@@ -0,0 +1,110 @@
+"""Memory ingest service boundary."""
+
+from __future__ import annotations
+
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.core.config import settings
+from app.core.logging import get_logger
+from app.features.conversation.lineage_schemas import (
+    primary_user_message_id_from_lineage,
+)
+from app.features.memory.chunker import chunk_transcript
+from app.features.memory.enrichment_scheduler import (
+    MemoryEnrichmentRequest,
+    MemoryEnrichmentScheduler,
+)
+from app.features.memory.repo import (
+    create_chunk,
+    create_source,
+    update_chunk_embedding,
+)
+from app.ports.embedding import EmbeddingProvider
+
+logger = get_logger(__name__)
+
+
+class MemoryIngestService:
+    """Creates memory sources/chunks and schedules post-commit enrichment."""
+
+    def __init__(
+        self,
+        db: AsyncSession,
+        *,
+        embedding_provider: EmbeddingProvider | None = None,
+        enrichment_scheduler: MemoryEnrichmentScheduler | None = None,
+    ) -> None:
+        self._db = db
+        self._embedding = embedding_provider
+        self._enrichment_scheduler = enrichment_scheduler or MemoryEnrichmentScheduler()
+
+    async def ingest_transcript(
+        self,
+        user_id: str,
+        conversation_id: str,
+        transcript: str,
+        *,
+        lineage_json: dict | None = None,
+    ) -> str:
+        if not transcript or not transcript.strip():
+            raise ValueError("transcript cannot be empty")
+
+        primary_mid = (
+            primary_user_message_id_from_lineage(lineage_json) if lineage_json else None
+        )
+        source = await create_source(
+            self._db,
+            user_id=user_id,
+            source_type="transcript",
+            raw_text=transcript.strip(),
+            conversation_id=conversation_id,
+            lineage_json=lineage_json,
+            primary_user_message_id=primary_mid,
+        )
+
+        chunk_records: list[tuple[str, str]] = []
+        for i, content in enumerate(chunk_transcript(transcript.strip())):
+            chunk = await create_chunk(
+                self._db,
+                source_id=source.id,
+                user_id=user_id,
+                content=content,
+                chunk_index=i,
+            )
+            chunk_records.append((chunk.id, content))
+
+        await self._db.flush()
+
+        vectors_written = 0
+        if self._embedding and chunk_records:
+            texts = [content for _, content in chunk_records]
+            embeddings = await self._embedding.embed_texts(texts)
+            for (chunk_id, _), emb in zip(
+                chunk_records, embeddings, strict=False
+            ):
+                if emb:
+                    vectors_written += 1
+                    await update_chunk_embedding(self._db, chunk_id, emb)
+
+        await self._db.commit()
+        emb_ok = self._embedding.is_available() if self._embedding else False
+        enrichment_task_id = self._enrichment_scheduler.schedule(
+            MemoryEnrichmentRequest(user_id=user_id, source_id=source.id)
+        )
+
+        logger.info(
+            "event=memory_ingest_done user_id={} conversation_id={} source_id={} "
+            "chunks={} vectors_written={} embedding_available={} enrichment_enabled={} enrichment_task_id={}",
+            user_id,
+            conversation_id,
+            source.id,
+            len(chunk_records),
+            vectors_written,
+            emb_ok,
+            settings.memory_enrichment_enabled,
+            enrichment_task_id,
+        )
+        return source.id
+
+
+__all__ = ["MemoryIngestService"]