feat: add streaming learning assistant and knowledge base scaffolding
This commit is contained in:
刘金宝
@@ -0,0 +1,177 @@
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import hashlib
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from pathlib import Path
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from fastapi import UploadFile
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from sqlalchemy.orm import Session
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from app.core.config import settings
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from app.core.context import UserContext
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from app.core.exceptions import AppError
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from app.integrations.milvus_adapter import MilvusVectorStore
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from app.integrations.pdf_parser import PdfParser
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from app.repositories.knowledge_base_repository import KnowledgeBaseRepository
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from app.schemas.knowledge_admin import KnowledgeDocumentUploadResponse
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from app.services.document_chunk_service import DocumentChunkService
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from app.services.embedding_service import EmbeddingService
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from app.services.knowledge_space_service import KnowledgeSpaceService
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class DocumentIngestionService:
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"""知识入库服务：处理 PDF 上传、解析、分片、向量化和 Milvus 写入。"""
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def __init__(
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self,
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db: Session,
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*,
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parser: PdfParser | None = None,
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chunker: DocumentChunkService | None = None,
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embedding_service: EmbeddingService | None = None,
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vector_store: MilvusVectorStore | None = None,
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) -> None:
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self.db = db
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self.repo = KnowledgeBaseRepository(db)
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self.space_service = KnowledgeSpaceService(self.repo)
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self.parser = parser or PdfParser()
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self.chunker = chunker or DocumentChunkService()
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self.embedding_service = embedding_service or EmbeddingService()
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self.vector_store = vector_store or MilvusVectorStore()
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async def upload_pdf(
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self,
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ctx: UserContext,
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file: UploadFile,
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*,
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document_title: str | None,
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document_category: str,
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version: str,
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) -> KnowledgeDocumentUploadResponse:
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"""文档上传：内容管理员上传 PDF 后创建知识文档并触发构建任务。"""
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self.space_service.ensure_content_admin(ctx)
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space = self.space_service.get_or_create_space(ctx)
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content = await file.read()
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self._validate_pdf(file, content)
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file_sha256 = hashlib.sha256(content).hexdigest()
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existing = self.repo.get_document_by_hash(space.institution_id, file_sha256)
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if existing:
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task = self.repo.create_ingestion_task(existing) if existing.status == "failed" else None
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return KnowledgeDocumentUploadResponse(
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document_id=existing.id,
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task_id=task.id if task else None,
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duplicate=True,
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status=existing.status,
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parse_status=existing.parse_status,
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embedding_status=existing.embedding_status,
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chunk_count=existing.chunk_count,
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collection_name=space.collection_name,
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)
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storage_path = self._save_file(space.institution_id, file.filename or "knowledge.pdf", content)
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document = self.repo.create_document(
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institution_id=space.institution_id,
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uploaded_by=ctx.user_id,
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file_name=file.filename or storage_path.name,
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file_sha256=file_sha256,
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file_size=len(content),
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file_path=str(storage_path),
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document_title=document_title or Path(file.filename or "").stem or None,
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document_category=document_category,
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version=version,
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)
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task = self.repo.create_ingestion_task(document)
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if settings.knowledge_ingestion_sync:
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await self.ingest_document(document.id, task.id)
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else:
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self._enqueue_async_task(document.id, task.id)
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return KnowledgeDocumentUploadResponse(
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document_id=document.id,
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task_id=task.id,
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duplicate=False,
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status=document.status,
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parse_status=document.parse_status,
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embedding_status=document.embedding_status,
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chunk_count=document.chunk_count,
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collection_name=space.collection_name,
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)
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async def ingest_document(self, document_id: int, task_id: int | None = None) -> None:
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"""知识构建：把已上传 PDF 转换为 MySQL 分片和 Milvus 向量。"""
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document = self.repo.get_document(document_id)
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if not document:
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raise AppError("KNOWLEDGE_DOCUMENT_NOT_FOUND", "knowledge document not found", 404)
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task = self.repo.get_ingestion_task(task_id) if task_id else None
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try:
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if task:
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self.repo.update_task(task, status="running", progress=5, current_step="parse_pdf")
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document.status = "processing"
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document.parse_status = "running"
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self.db.flush()
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pages = self.parser.parse(document.file_path)
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space = self.repo.get_or_create_space(document.institution_id, None)
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drafts = self.chunker.build_chunks(pages)
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chunks = self.chunker.to_models(
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institution_id=document.institution_id,
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document_id=document.id,
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collection_name=space.collection_name,
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embedding_model=settings.embedding_model,
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drafts=drafts,
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)
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if task:
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self.repo.update_task(task, progress=35, current_step="embed_chunks")
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document.parse_status = "success"
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document.embedding_status = "running"
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self.db.flush()
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vectors, _embedding_latency_ms = await self.embedding_service.embed_texts([chunk.chunk_text for chunk in chunks])
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if task:
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self.repo.update_task(task, progress=75, current_step="write_vectors")
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self.vector_store.upsert_vectors(
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space.collection_name,
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[(chunk.chunk_uid, vector) for chunk, vector in zip(chunks, vectors)],
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)
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self.repo.replace_chunks(document, chunks)
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document.status = "ready"
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document.embedding_status = "success"
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if task:
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self.repo.update_task(task, status="success", progress=100, current_step="completed")
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except Exception as exc:
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document.status = "failed"
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document.error_message = str(exc)[:2000]
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document.parse_status = document.parse_status if document.parse_status == "success" else "failed"
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document.embedding_status = "failed"
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if task:
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self.repo.update_task(task, status="failed", progress=100, current_step="failed", error_message=str(exc)[:2000])
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if isinstance(exc, AppError):
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raise
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raise AppError("KNOWLEDGE_INGESTION_FAILED", "knowledge document ingestion failed", 500) from exc
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def _validate_pdf(self, file: UploadFile, content: bytes) -> None:
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"""上传校验：限制文件类型和大小，只允许 PDF 文档进入知识库。"""
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if not content:
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raise AppError("UPLOAD_FILE_EMPTY", "uploaded file is empty", 422)
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max_bytes = settings.knowledge_max_upload_mb * 1024 * 1024
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if len(content) > max_bytes:
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raise AppError("UPLOAD_FILE_TOO_LARGE", f"uploaded file exceeds {settings.knowledge_max_upload_mb}MB", 413)
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filename = (file.filename or "").lower()
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if not filename.endswith(".pdf") and file.content_type not in {"application/pdf", "application/octet-stream"}:
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raise AppError("UPLOAD_FILE_TYPE_INVALID", "only pdf file is supported", 422)
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if not content.startswith(b"%PDF"):
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raise AppError("UPLOAD_FILE_NOT_PDF", "uploaded file is not a valid pdf", 422)
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def _save_file(self, institution_id: int, filename: str, content: bytes) -> Path:
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"""文件保存：按机构隔离保存原始 PDF，供后续重建知识库。"""
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safe_name = "".join(ch if ch.isalnum() or ch in {".", "_", "-"} else "_" for ch in filename)
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storage_dir = Path(settings.knowledge_storage_dir) / "raw" / str(institution_id)
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storage_dir.mkdir(parents=True, exist_ok=True)
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target = storage_dir / f"{hashlib.sha256(content).hexdigest()[:16]}_{safe_name}"
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target.write_bytes(content)
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return target
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def _enqueue_async_task(self, document_id: int, task_id: int) -> None:
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"""异步投递：生产环境通过 Celery worker 执行 PDF 知识库构建。"""
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try:
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from app.tasks.knowledge_ingestion_tasks import ingest_knowledge_document
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ingest_knowledge_document.delay(document_id, task_id)
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except Exception as exc: # pragma: no cover - Celery 未运行时保留任务 queued 状态
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raise AppError("KNOWLEDGE_TASK_ENQUEUE_FAILED", "knowledge ingestion task enqueue failed", 500) from exc
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