feat: add durable document indexing pipeline
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@@ -0,0 +1,125 @@
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import asyncio
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import logging
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from pathlib import Path
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from sqlalchemy.ext.asyncio import async_sessionmaker
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from kbqa.api.errors import AppError
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from kbqa.config import Settings
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from kbqa.documents.models import Chunk
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from kbqa.documents.repository import DocumentRepository
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from kbqa.indexing.loaders import load_document
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from kbqa.indexing.splitter import split_pages
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from kbqa.rag.embeddings import EmbeddingProvider
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from kbqa.rag.types import PreparedChunk
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from kbqa.rag.vector_store import MilvusVectorStore
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logger = logging.getLogger(__name__)
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class IndexWorker:
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def __init__(
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self,
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settings: Settings,
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session_factory: async_sessionmaker,
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embeddings: EmbeddingProvider,
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vector_store: MilvusVectorStore,
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) -> None:
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self.settings = settings
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self.session_factory = session_factory
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self.embeddings = embeddings
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self.vector_store = vector_store
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self.queue: asyncio.Queue[str] = asyncio.Queue()
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self._task: asyncio.Task[None] | None = None
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async def start(self) -> None:
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async with self.session_factory() as session:
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pending_ids = await DocumentRepository(session).requeue_unfinished()
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for job_id in pending_ids:
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self.queue.put_nowait(job_id)
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self._task = asyncio.create_task(self._run(), name="kbqa-index-worker")
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async def stop(self) -> None:
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if self._task is None:
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return
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self._task.cancel()
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try:
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await self._task
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except asyncio.CancelledError:
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pass
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self._task = None
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def enqueue(self, job_id: str) -> None:
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self.queue.put_nowait(job_id)
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async def _run(self) -> None:
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while True:
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job_id = await self.queue.get()
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try:
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await self._process(job_id)
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except asyncio.CancelledError:
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raise
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except Exception:
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logger.exception("Unexpected indexing worker failure job_id=%s", job_id)
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finally:
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self.queue.task_done()
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async def _process(self, job_id: str) -> None:
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async with self.session_factory() as session:
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repository = DocumentRepository(session)
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job = await repository.claim_job(job_id)
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if job is None:
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return
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document = await repository.get(job.document_id)
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if document is None:
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return
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document_id = document.id
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stored_path = document.stored_path
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file_type = document.file_type
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try:
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await asyncio.to_thread(self.vector_store.delete_document, document_id)
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async with self.session_factory() as session:
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await DocumentRepository(session).replace_chunks(document_id, [])
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pages = await asyncio.to_thread(load_document, Path(stored_path), file_type)
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prepared = split_pages(
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document_id,
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pages,
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self.settings.chunk_size,
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self.settings.chunk_overlap,
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)
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if not prepared:
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raise AppError("EMPTY_DOCUMENT", "文档分块结果为空", 415)
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orm_chunks = [
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Chunk(
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id=chunk.id,
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document_id=chunk.document_id,
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order_index=chunk.order_index,
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content=chunk.content,
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page_number=chunk.page_number,
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char_count=len(chunk.content),
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)
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for chunk in prepared
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]
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async with self.session_factory() as session:
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await DocumentRepository(session).replace_chunks(document_id, orm_chunks)
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await self._embed_and_upsert(prepared)
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async with self.session_factory() as session:
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await DocumentRepository(session).mark_indexed(document_id, len(prepared))
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except asyncio.CancelledError:
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raise
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except AppError as exc:
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await self._mark_failed(document_id, exc.code, exc.message)
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except Exception:
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logger.exception("Document indexing failed document_id=%s", document_id)
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await self._mark_failed(document_id, "INDEXING_FAILED", "文档索引失败,请重试")
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async def _embed_and_upsert(self, chunks: list[PreparedChunk]) -> None:
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batch_size = 20
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for start in range(0, len(chunks), batch_size):
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batch = chunks[start : start + batch_size]
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vectors = await self.embeddings.embed_documents([chunk.content for chunk in batch])
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await asyncio.to_thread(self.vector_store.upsert, batch, vectors)
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async def _mark_failed(self, document_id: str, code: str, message: str) -> None:
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async with self.session_factory() as session:
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await DocumentRepository(session).mark_failed(document_id, code, message)
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