refactor(api): TOML 配置 SSOT、统一错误契约、Auth/事务加固与可观测性 (#33)

配置 SSOT(TOML + .env)
统一错误契约
Auth 与事务边界
Redis / Celery 可靠性:业务 Redis(DB/0)与 Celery broker/backend(DB/1)显式拆分;连接池、sync client
可观测性(OpenTelemetry + LGTM)
This commit is contained in:
Sully
2026-05-22 13:44:50 +08:00
committed by GitHub
parent f09ae248f9
commit 53e0065e3e
298 changed files with 15247 additions and 4344 deletions

View File

@@ -5,6 +5,8 @@ from __future__ import annotations
from sqlalchemy.ext.asyncio import AsyncSession
from app.core.config import settings
from app.core.db import transactional
from app.core.errors import BadRequestError
from app.core.logging import get_logger
from app.features.conversation.lineage_schemas import (
primary_user_message_id_from_lineage,
@@ -22,8 +24,10 @@ from app.features.memory.enrichment_scheduler import (
from app.features.memory.repo import (
create_chunk,
create_source,
get_transcript_source_by_segment_id,
)
from app.ports.embedding import EmbeddingProvider
from app.features.memory.constants import memory
logger = get_logger(__name__)
@@ -53,34 +57,32 @@ class MemoryIngestService:
lineage_json: dict | None = None,
) -> str:
if not transcript or not transcript.strip():
raise ValueError("transcript cannot be empty")
raise BadRequestError("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(
async with transactional(self._db):
source = await create_source(
self._db,
source_id=source.id,
user_id=user_id,
content=content,
chunk_index=i,
source_type="transcript",
raw_text=transcript.strip(),
conversation_id=conversation_id,
lineage_json=lineage_json,
primary_user_message_id=primary_mid,
)
chunk_records.append((chunk.id, content))
await self._db.flush()
await self._db.commit()
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))
embedding_result = await MemoryEmbeddingService(
self._db,
@@ -108,7 +110,7 @@ class MemoryIngestService:
embedding_result.get("status"),
emb_ok,
embedding_task_id,
settings.memory_enrichment_enabled,
memory.enrichment_enabled,
enrichment_task_id,
)
return source.id
@@ -116,50 +118,63 @@ class MemoryIngestService:
async def ingest_transcripts_batch(
self,
user_id: str,
items: list[tuple[str, str, dict | None]],
items: list[tuple[str, str, dict | None, str | None]],
*,
memoir_correlation_id: str | None = None,
) -> list[str]:
"""
Batch ingest transcript items through the async memory path.
items: (conversation_id, transcript, lineage_json). Empty transcripts are skipped.
items: (conversation_id, transcript, lineage_json, segment_id).
Empty transcripts are skipped. When segment_id is set and a transcript
source already exists for the user, returns the existing source id.
"""
source_ids: list[str] = []
chunk_records: list[tuple[str, str]] = []
new_source_ids: list[str] = []
for conversation_id, transcript, lineage_json in items:
text = (transcript or "").strip()
if not text:
continue
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=text,
conversation_id=conversation_id or None,
lineage_json=lineage_json,
primary_user_message_id=primary_mid,
)
source_ids.append(source.id)
for i, content in enumerate(chunk_transcript(text)):
chunk = await create_chunk(
self._db,
source_id=source.id,
user_id=user_id,
content=content,
chunk_index=i,
async with transactional(self._db):
for conversation_id, transcript, lineage_json, segment_id in items:
text = (transcript or "").strip()
if not text:
continue
sid = (segment_id or "").strip() or None
if sid:
existing = await get_transcript_source_by_segment_id(
self._db,
user_id=user_id,
segment_id=sid,
)
if existing is not None:
source_ids.append(existing.id)
continue
primary_mid = (
primary_user_message_id_from_lineage(lineage_json)
if lineage_json
else None
)
chunk_records.append((chunk.id, content))
source = await create_source(
self._db,
user_id=user_id,
source_type="transcript",
raw_text=text,
conversation_id=conversation_id or None,
segment_id=sid,
lineage_json=lineage_json,
primary_user_message_id=primary_mid,
)
source_ids.append(source.id)
new_source_ids.append(source.id)
await self._db.flush()
await self._db.commit()
for i, content in enumerate(chunk_transcript(text)):
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))
vectors_written = 0
embedding_retry_task_ids: list[str] = []
@@ -168,7 +183,7 @@ class MemoryIngestService:
self._db,
embedding_provider=self._embedding,
)
for source_id in source_ids:
for source_id in new_source_ids:
result = await embedding_service.embed_source(user_id, source_id)
vectors_written += int(result.get("vectors_written") or 0)
status = str(result.get("status") or "unknown")
@@ -185,7 +200,7 @@ class MemoryIngestService:
emb_ok = self._embedding.is_available() if self._embedding else False
task_ids = self._enrichment_scheduler.schedule_many(
user_id,
source_ids,
new_source_ids,
memoir_correlation_id=memoir_correlation_id,
)
@@ -200,7 +215,7 @@ class MemoryIngestService:
emb_ok,
embedding_statuses,
len(embedding_retry_task_ids),
settings.memory_enrichment_enabled,
memory.enrichment_enabled,
len(task_ids),
)
return source_ids