diff --git a/frontend/src/pages/KnowledgeBase/Detail/KnowledgeBaseDetail.tsx b/frontend/src/pages/KnowledgeBase/Detail/KnowledgeBaseDetail.tsx index 8c52f1508..2802b4be4 100644 --- a/frontend/src/pages/KnowledgeBase/Detail/KnowledgeBaseDetail.tsx +++ b/frontend/src/pages/KnowledgeBase/Detail/KnowledgeBaseDetail.tsx @@ -1,6 +1,10 @@ import type React from "react"; import { useEffect, useState } from "react"; import { Table, Badge, Button, Breadcrumb, Tooltip, App, Card, Input, Empty, Spin } from "antd"; +import ReactMarkdown from "react-markdown"; +import remarkGfm from "remark-gfm"; +import { Prism as SyntaxHighlighter } from "react-syntax-highlighter"; +import { vscDarkPlus } from "react-syntax-highlighter/dist/esm/styles/prism"; import { DeleteOutlined, EditOutlined, @@ -18,13 +22,12 @@ import { queryKnowledgeBaseByIdUsingGet, queryKnowledgeBaseFilesUsingGet, retrieveKnowledgeBaseContent, - fetchKnowledgeGraph, + queryKnowledgeBase, } from "../knowledge-base.api"; import useFetchData from "@/hooks/useFetchData"; import AddDataDialog from "../components/AddDataDialog"; import CreateKnowledgeBase from "../components/CreateKnowledgeBase"; import KnowledgeGraphView, { GraphEntitySelection } from "../components/KnowledgeGraphView"; -import { Network } from "lucide-react"; import { useTranslation } from "react-i18next"; interface StatisticItem { @@ -32,16 +35,28 @@ interface StatisticItem { label: string; value: string | number; } -interface RagChunk { +// Use UnifiedSearchResult from model - flat structure from backend +// Backend returns: { id, text, score, metadata, resultType, knowledgeBaseId, knowledgeBaseName } +interface RecallResult { id: string; text: string; - metadata: string; -} -interface RecallResult { score: number; - entity: RagChunk; - id?: string | object; - primaryKey?: string; + metadata: Record; + resultType?: string; + knowledgeBaseId?: string; + knowledgeBaseName?: string; +} + +function squashSoftLineBreaksOutsideFences(markdown: string): string { + if (!markdown) return ""; + const parts = markdown.split(/(```[\s\S]*?```)/g); + return parts + .map((part) => { + if (part.startsWith("```")) return part; + // keep paragraph breaks (\n\n), but squash single newlines into spaces + return part.replace(/([^\n])\n(?!\n)/g, "$1 "); + }) + .join(""); } const KnowledgeBaseDetailPage: React.FC = () => { @@ -153,7 +168,7 @@ const KnowledgeBaseDetailPage: React.FC = () => { setGraphLoading(true); setGraphSelection(null); try { - const { data } = await fetchKnowledgeGraph({ knowledge_base_id: knowledgeBase.id, query: "*" }); + const { data } = await queryKnowledgeBase({ knowledge_base_id: knowledgeBase.id, query: "*" }); setGraphData({ nodes: data?.nodes ?? [], edges: data?.edges ?? [] }); } catch { setGraphData({ nodes: [], edges: [] }); @@ -179,15 +194,6 @@ const KnowledgeBaseDetailPage: React.FC = () => { }; type DetailOperation = NonNullable["operations"][number]>; - const graphOperation: DetailOperation | null = knowledgeBase?.type === KBType.GRAPH - ? { - key: "graph", - label: t("knowledgeBase.detail.graph.title"), - icon: , - onClick: handleOpenGraph, - } - : null; - const baseOperations: DetailOperation[] = [ { key: "edit", @@ -221,7 +227,7 @@ const KnowledgeBaseDetailPage: React.FC = () => { }, ]; - const operations: DetailOperation[] = [graphOperation, ...baseOperations].filter(Boolean) as DetailOperation[]; + const operations: DetailOperation[] = baseOperations; const fileOps = [ { @@ -242,7 +248,17 @@ const KnowledgeBaseDetailPage: React.FC = () => { ellipsis: true, fixed: "left" as const, render: (_: unknown, file: KBFile) => ( - navigate(`/data/knowledge-base/file-detail/${file.id}?knowledgeBaseId=${knowledgeBase?.id || ''}&fileName=${encodeURIComponent(file.name || file.fileName || '')}`)}> + { + if (knowledgeBase?.type === KBType.GRAPH) { + handleOpenGraph(); + return; + } + navigate( + `/data/knowledge-base/file-detail/${file.id}?knowledgeBaseId=${knowledgeBase?.id || ""}&fileName=${encodeURIComponent(file.name || file.fileName || "")}` + ); + }} + > {file.name} ) @@ -478,16 +494,146 @@ const KnowledgeBaseDetailPage: React.FC = () => { ) : recallResults.length === 0 ? ( + ) : knowledgeBase?.type === KBType.GRAPH ? ( +
+ {(() => { + const item = recallResults[0]; + if (!item) return null; + return ( +
+
+
+ ID: {item.id ?? "-"} +
+
+
+
+ + {children} + + ); + } + + // 有指定语言的代码块才高亮 + if (match) { + return ( + + {codeString} + + ); + } + + // 无语言标记的代码块，以普通文本显示（不高亮） + return ( + 
+                                    {codeString}
+
+ ); + }, + p: ({ children }) => ( +

+ {children} +

+ ), + ul: ({ children }) => ( +
    + {children} +
+ ), + ol: ({ children }) => ( +
    + {children} +
+ ), + li: ({ children }) => ( +
  • + {children} +
    • + ), + br: () => , + a: ({ href, children }) => ( + + {children} + + ), + table: ({ children }) => ( +
    + {children}
    +
    + ), + thead: ({ children }) => ( + {children} + ), + th: ({ children }) => ( + {children} + ), + td: ({ children }) => ( + {children} + ), + blockquote: ({ children }) => ( +
    {children}
    + ), + }} + > + {squashSoftLineBreaksOutsideFences(item.text ?? "")} +     +
    +
    +
    + ); + })()} +
    ) : (
    {recallResults.map((item, idx) => ( ID: {item.entity?.id ?? "-"}} + extra={ID: {item.id ?? "-"}} style={{ wordBreak: "break-all" }} > -
    {item.entity?.text ?? ""}
    +
    {item.text ?? ""}
    - {t("knowledgeBase.detail.recallTest.metadataLabel")} 
    {item.entity?.metadata}
    + {t("knowledgeBase.detail.recallTest.metadataLabel")} 
    {JSON.stringify(item.metadata, null, 2)}
     ))} diff --git a/frontend/src/pages/KnowledgeBase/knowledge-base.api.ts b/frontend/src/pages/KnowledgeBase/knowledge-base.api.ts index e5083b0fd..0126762a2 100644 --- a/frontend/src/pages/KnowledgeBase/knowledge-base.api.ts +++ b/frontend/src/pages/KnowledgeBase/knowledge-base.api.ts @@ -1,4 +1,5 @@ import { get, post, put, del } from "@/utils/request"; +import type { UnifiedSearchResult } from "./knowledge-base.model"; // 获取知识库列表 export function queryKnowledgeBasesUsingPost(params: any) { @@ -59,21 +60,17 @@ export function deleteKnowledgeBaseFileByIdUsingDelete(baseId: string, data: obj return (del as unknown as (url: string, data?: object | null) => Promise)(`/api/knowledge-base/${baseId}/files`, data ?? null); } -export function fetchKnowledgeGraph(data: { knowledge_base_id: string; query: string }) { - return post("/api/rag/query", data); -} - -// 检索知识库内容 +// 检索知识库内容（统一检索接口） export function retrieveKnowledgeBaseContent(data: { query: string; topK?: number; threshold?: number; knowledgeBaseIds: string[]; -}) { +}): Promise { return post("/api/knowledge-base/retrieve", data); } -// 新增：获取知识库文件详情（分页的切片数据） +// 获取知识库文件详情（分页的切片数据） export function queryKnowledgeBaseFileDetailUsingGet( knowledgeBaseId: string, ragFileId: string, @@ -83,3 +80,10 @@ export function queryKnowledgeBaseFileDetailUsingGet( const size = params.size ?? 20; return get(`/api/knowledge-base/${knowledgeBaseId}/files/${ragFileId}?page=${page}&page_size=${size}`); } + +export function queryKnowledgeBase(data: { + knowledge_base_id: string; + query: string; +}) { + return post("/api/knowledge-base/query", data); +} diff --git a/frontend/src/pages/KnowledgeBase/knowledge-base.model.ts b/frontend/src/pages/KnowledgeBase/knowledge-base.model.ts index 53fb2e7ea..86d3eada2 100644 --- a/frontend/src/pages/KnowledgeBase/knowledge-base.model.ts +++ b/frontend/src/pages/KnowledgeBase/knowledge-base.model.ts @@ -10,6 +10,16 @@ export enum KBType { GRAPH = "GRAPH", } +export interface UnifiedSearchResult { + id: string; + text: string; + score: number; + metadata: Record; + resultType: "vector" | "graph"; + knowledgeBaseId: string; + knowledgeBaseName: string; +} + export interface KnowledgeBaseItem { id: string; name: string; diff --git a/runtime/datamate-python/app/core/exception/codes.py b/runtime/datamate-python/app/core/exception/codes.py index 1c6358fe8..9eaef93a9 100644 --- a/runtime/datamate-python/app/core/exception/codes.py +++ b/runtime/datamate-python/app/core/exception/codes.py @@ -133,6 +133,12 @@ def __init__(self): RAG_EMBEDDING_FAILED: Final = ErrorCode( "rag.0013", "Embedding generation failed", 500 ) + RAG_UNSUPPORTED_TYPE: Final = ErrorCode( + "rag.0014", "Unsupported RAG type", 400 + ) + RAG_INVALID_REQUEST: Final = ErrorCode( + "rag.0015", "Invalid request", 400 + ) # ========== 配比模块 ========== RATIO_TASK_NOT_FOUND: Final = ErrorCode("ratio.0001", "Ratio task not found", 404) diff --git a/runtime/datamate-python/app/module/__init__.py b/runtime/datamate-python/app/module/__init__.py index 4f8d6da5f..5b50fd1fd 100644 --- a/runtime/datamate-python/app/module/__init__.py +++ b/runtime/datamate-python/app/module/__init__.py @@ -6,7 +6,6 @@ from .generation.interface import router as generation_router from .evaluation.interface import router as evaluation_router from .collection.interface import router as collection_route -from .rag.interface.rag_interface import router as rag_router from .operator.interface import operator_router from .operator.interface import category_router from .cleaning.interface import router as cleaning_router @@ -22,11 +21,9 @@ router.include_router(generation_router) router.include_router(evaluation_router) router.include_router(collection_route) -router.include_router(rag_router) router.include_router(operator_router) router.include_router(category_router) router.include_router(cleaning_router) - router.include_router(knowledge_base_router) __all__ = ["router"] diff --git a/runtime/datamate-python/app/module/rag/interface/__init__.py b/runtime/datamate-python/app/module/rag/interface/__init__.py index 6663ac50d..8bbbe951a 100644 --- a/runtime/datamate-python/app/module/rag/interface/__init__.py +++ b/runtime/datamate-python/app/module/rag/interface/__init__.py @@ -4,9 +4,7 @@ 集中导出所有 API 路由 """ from .knowledge_base import router as knowledge_base_router -from .rag_interface import router as graph_rag_router __all__ = [ "knowledge_base_router", - "graph_rag_router", ] diff --git a/runtime/datamate-python/app/module/rag/interface/knowledge_base.py b/runtime/datamate-python/app/module/rag/interface/knowledge_base.py index 7f4678d79..c702f5b59 100644 --- a/runtime/datamate-python/app/module/rag/interface/knowledge_base.py +++ b/runtime/datamate-python/app/module/rag/interface/knowledge_base.py @@ -18,9 +18,10 @@ RagFileReq, RetrieveReq, PagingQuery, + QueryRequest, ) from app.module.rag.service.knowledge_base_service import KnowledgeBaseService -from app.module.rag.service.retrieval_service import RetrievalService +from app.module.rag.service.unified_retrieval_service import UnifiedRetrievalService router = APIRouter(prefix="/knowledge-base", tags=["知识库管理"]) @@ -143,7 +144,7 @@ async def get_file_chunks( db: AsyncSession = Depends(get_db), ): """获取指定 RAG 文件的分块列表""" - service = RetrievalService(db) + service = UnifiedRetrievalService(db) result = await service.get_chunks(knowledge_base_id, rag_file_id, paging_query) return SuccessResponse(data=result) @@ -153,7 +154,26 @@ async def retrieve_knowledge_base( request: RetrieveReq, db: AsyncSession = Depends(get_db), ): - """检索知识库内容（向量 + BM25 混合检索）""" - service = RetrievalService(db) - results = await service.retrieve(request) + """检索知识库内容（统一检索接口）""" + service = UnifiedRetrievalService(db) + results = await service.search(request) return SuccessResponse(data=results) + + +@router.post("/query", response_model=SuccessResponse) +async def query_knowledge_base( + payload: QueryRequest, + db: AsyncSession = Depends(get_db), +): + """查询知识库（支持向量检索和知识图谱） + + 根据知识库类型自动选择查询策略： + - DOCUMENT: 向量检索 + - GRAPH: 知识图谱查询 + """ + service = UnifiedRetrievalService(db) + result = await service.query( + knowledge_base_id=payload.knowledge_base_id, + node_label=payload.query, + ) + return SuccessResponse(data=result) diff --git a/runtime/datamate-python/app/module/rag/interface/rag_interface.py b/runtime/datamate-python/app/module/rag/interface/rag_interface.py deleted file mode 100644 index 57b9f9b92..000000000 --- a/runtime/datamate-python/app/module/rag/interface/rag_interface.py +++ /dev/null @@ -1,58 +0,0 @@ -from fastapi import APIRouter, Depends, BackgroundTasks -from sqlalchemy.ext.asyncio import AsyncSession - -from app.core.exception import SuccessResponse -from app.db.session import get_db -from app.module.rag.service.rag_service import RAGService -from app.module.rag.service.knowledge_base_service import KnowledgeBaseService -from ..schema.request import QueryRequest - -router = APIRouter(prefix="/rag", tags=["知识图谱 RAG"]) - -@router.post("/{knowledge_base_id}/process") -async def process_knowledge_base( - knowledge_base_id: str, - background_tasks: BackgroundTasks, - db: AsyncSession = Depends(get_db), -): - """ - 手动触发知识库文件处理（已废弃，文件处理在添加时自动触发） - - 此接口保留用于向后兼容或手动重新处理文件 - """ - service = KnowledgeBaseService(db) - kb = await service.kb_repo.get_by_id(knowledge_base_id) - - if not kb: - return SuccessResponse( - data=None, - message="知识库不存在" - ) - - files = await service.file_repo.get_unprocessed_files(knowledge_base_id) - if not files: - return SuccessResponse( - data=None, - message="没有待处理的文件" - ) - - service.file_processor.start_background_processing( - background_tasks=background_tasks, - knowledge_base_id=str(kb.id), - knowledge_base_name=str(kb.name), - knowledge_base_type=str(kb.type or "DOCUMENT"), - request_data={"knowledge_base_id": knowledge_base_id, "files": []}, - ) - - return SuccessResponse( - data=None, - message=f"已开始处理 {len(files)} 个文件" - ) - -@router.post("/query") -async def query_knowledge_graph(payload: QueryRequest, rag_service: RAGService = Depends()): - """ - 使用给定的查询文本和知识库 ID 查询知识图谱（LightRAG）或向量检索 - """ - result = await rag_service.query_rag(payload.query, payload.knowledge_base_id) - return SuccessResponse(data=result) diff --git a/runtime/datamate-python/app/module/rag/schema/response.py b/runtime/datamate-python/app/module/rag/schema/response.py index 3eeacdc58..93c30764e 100644 --- a/runtime/datamate-python/app/module/rag/schema/response.py +++ b/runtime/datamate-python/app/module/rag/schema/response.py @@ -160,6 +160,35 @@ class Config: } +class UnifiedSearchResult(BaseModel): + """统一检索结果 + + 所有知识库类型（向量、知识图谱）的检索接口必须返回此格式 + """ + model_config = ConfigDict( + populate_by_name=True, + json_schema_extra={ + "example": { + "id": "result-uuid-123", + "text": "相关文档内容...", + "score": 0.95, + "metadata": {"fileName": "document.pdf", "chunkIndex": 0}, + "resultType": "vector", + "knowledgeBaseId": "kb-uuid-123", + "knowledgeBaseName": "my_knowledge_base" + } + } + ) + + id: str = Field(..., description="结果ID") + text: str = Field(..., description="文本内容") + score: float = Field(..., description="相似度分数") + metadata: dict = Field(default_factory=dict, description="元数据") + result_type: str = Field(alias="resultType", description="结果类型（vector/graph）") + knowledge_base_id: str = Field(alias="knowledgeBaseId", description="知识库ID") + knowledge_base_name: str = Field(alias="knowledgeBaseName", description="知识库名称") + + class PagedResponse(BaseModel): """分页响应 diff --git a/runtime/datamate-python/app/module/rag/service/common/__init__.py b/runtime/datamate-python/app/module/rag/service/common/__init__.py new file mode 100644 index 000000000..55cfaf038 --- /dev/null +++ b/runtime/datamate-python/app/module/rag/service/common/__init__.py @@ -0,0 +1,16 @@ +""" +公共工具模块 + +提供文本清理、元数据构建、批量处理等工具类。 +""" +from .text_cleaner import TextCleaner +from .metadata_builder import MetadataBuilder +from .batch_processor import BatchProcessor +from .file_utils import get_file_path + +__all__ = [ + "TextCleaner", + "MetadataBuilder", + "BatchProcessor", + "get_file_path", +] diff --git a/runtime/datamate-python/app/module/rag/service/common/batch_processor.py b/runtime/datamate-python/app/module/rag/service/common/batch_processor.py new file mode 100644 index 000000000..467557467 --- /dev/null +++ b/runtime/datamate-python/app/module/rag/service/common/batch_processor.py @@ -0,0 +1,93 @@ +""" +批量处理工具 + +提供分批处理文档分块的工具方法。 +""" +import uuid +import logging +from typing import List +from app.module.rag.infra.vectorstore import chunks_to_documents +from app.module.rag.infra.document.types import DocumentChunk + + +logger = logging.getLogger(__name__) + + +class BatchProcessor: + """批量处理工具类""" + + DEFAULT_BATCH_SIZE: int = 20 + + @staticmethod + async def store_in_batches( + vectorstore, + chunks: List[DocumentChunk], + batch_size: int = DEFAULT_BATCH_SIZE + ) -> int: + """分批存储分块到向量数据库 + + Args: + vectorstore: 向量存储实例 + chunks: 分块列表 + batch_size: 批次大小 + + Returns: + 成功存储的分块数量 + """ + if not chunks: + return 0 + + batch_size = batch_size or BatchProcessor.DEFAULT_BATCH_SIZE + total_chunks = len(chunks) + stored_count = 0 + + for batch_start in range(0, total_chunks, batch_size): + batch_end = min(batch_start + batch_size, total_chunks) + batch_chunks = chunks[batch_start:batch_end] + + await BatchProcessor._store_single_batch( + vectorstore, + batch_chunks, + batch_start, + batch_end, + total_chunks + ) + stored_count += len(batch_chunks) + + logger.info("批量存储完成，总数量: %d", stored_count) + return stored_count + + @staticmethod + async def _store_single_batch( + vectorstore, + batch_chunks: List[DocumentChunk], + batch_start: int, + batch_end: int, + total_chunks: int, + ) -> None: + """存储单个批次 + + Args: + vectorstore: 向量存储实例 + batch_chunks: 批次分块列表 + batch_start: 批次起始索引 + batch_end: 批次结束索引 + total_chunks: 总分块数 + """ + logger.info("处理分块批次 %d-%d / %d", batch_start + 1, batch_end, total_chunks) + + ids = [str(uuid.uuid4()) for _ in batch_chunks] + documents, doc_ids = chunks_to_documents(batch_chunks, ids=ids) + + try: + vectorstore.add_documents(documents=documents, ids=doc_ids) + logger.info("批次 %d-%d 存储成功", batch_start + 1, batch_end) + except Exception as e: + logger.error( + "批次 %d-%d 存储失败: %s\n第一个文档内容: %.200s", + batch_start + 1, + batch_end, + str(e), + documents[0].page_content if documents else "N/A" + ) + raise diff --git a/runtime/datamate-python/app/module/rag/service/common/file_utils.py b/runtime/datamate-python/app/module/rag/service/common/file_utils.py new file mode 100644 index 000000000..9e92288e2 --- /dev/null +++ b/runtime/datamate-python/app/module/rag/service/common/file_utils.py @@ -0,0 +1,25 @@ +""" +文件工具类 + +提供文件路径提取等通用工具方法。 +""" +from pathlib import Path +from typing import Optional + + +def get_file_path(rag_file) -> Optional[str]: + """获取 RAG 文件的绝对路径 + + Args: + rag_file: RAG 文件实体（需要有 file_metadata 属性） + + Returns: + 文件绝对路径，不存在返回 None + """ + if not rag_file.file_metadata: + return None + + file_path = rag_file.file_metadata.get("file_path") + if file_path: + return str(Path(file_path).absolute()) + return None diff --git a/runtime/datamate-python/app/module/rag/service/common/metadata_builder.py b/runtime/datamate-python/app/module/rag/service/common/metadata_builder.py new file mode 100644 index 000000000..0933baa45 --- /dev/null +++ b/runtime/datamate-python/app/module/rag/service/common/metadata_builder.py @@ -0,0 +1,42 @@ +""" +元数据构建工具 + +提供构建和管理分块元数据的工具方法。 +""" +from typing import Dict, Any +from app.db.models.knowledge_gen import KnowledgeBase, RagFile + + +class MetadataBuilder: + """元数据构建工具类""" + + @staticmethod + def build_chunk_metadata(rag_file: RagFile, knowledge_base: KnowledgeBase) -> Dict[str, Any]: + """构建分块基础元数据 + + Args: + rag_file: RAG 文件实体 + knowledge_base: 知识库实体 + + Returns: + 元数据字典 + """ + return { + "rag_file_id": str(rag_file.id), + "original_file_id": str(rag_file.file_id), + "knowledge_base_id": str(knowledge_base.id), + "file_name": rag_file.file_name, + "knowledge_base_name": knowledge_base.name, + } + + @staticmethod + def add_to_chunks(chunks: list, base_metadata: Dict[str, Any]) -> None: + """为基础元数据添加到分块列表 + + Args: + chunks: 分块列表 + base_metadata: 基础元数据 + """ + for chunk in chunks: + if hasattr(chunk, 'metadata') and isinstance(chunk.metadata, dict): + chunk.metadata.update(base_metadata) diff --git a/runtime/datamate-python/app/module/rag/service/common/text_cleaner.py b/runtime/datamate-python/app/module/rag/service/common/text_cleaner.py new file mode 100644 index 000000000..b1a721fdb --- /dev/null +++ b/runtime/datamate-python/app/module/rag/service/common/text_cleaner.py @@ -0,0 +1,67 @@ +""" +文本清理工具 + +提供文本清理和标准化的工具方法。 +""" +import re +import logging +from typing import Optional + + +logger = logging.getLogger(__name__) + + +class TextCleaner: + """文本清理工具类""" + + @staticmethod + def clean(text: Optional[str]) -> str: + """清理文本 + + Args: + text: 原始文本 + + Returns: + 清理后的文本 + """ + if not text: + return "" + + text = TextCleaner._remove_control_characters(text) + text = TextCleaner._normalize_whitespace(text) + text = TextCleaner._remove_empty_lines(text) + + return text.strip() + + @staticmethod + def _remove_control_characters(text: str) -> str: + """移除控制字符""" + control_chars = re.compile(r'[\x00-\x08\x0b-\x0c\x0e-\x1f\x7f-\x9f]') + return control_chars.sub('', text) + + @staticmethod + def _normalize_whitespace(text: str) -> str: + """标准化空白字符""" + text = re.sub(r'[ \t]+', ' ', text) + text = re.sub(r'\n\s*\n\s*\n+', '\n\n', text) + return text + + @staticmethod + def _remove_empty_lines(text: str) -> str: + """移除空行""" + lines = text.split('\n') + return '\n'.join(line for line in lines if line.strip()) + + @staticmethod + def has_printable_content(text: str) -> bool: + """检查是否包含可打印内容 + + Args: + text: 文本 + + Returns: + 是否包含可打印内容 + """ + if not text: + return False + return any(c.isprintable() and not c.isspace() for c in text) diff --git a/runtime/datamate-python/app/module/rag/service/file_processor.py b/runtime/datamate-python/app/module/rag/service/file_processor.py index c64c18320..cf99e06b4 100644 --- a/runtime/datamate-python/app/module/rag/service/file_processor.py +++ b/runtime/datamate-python/app/module/rag/service/file_processor.py @@ -5,9 +5,8 @@ 支持两种知识库类型：DOCUMENT（向量检索）和 GRAPH（知识图谱）。 使用全局 WorkerPool 实现并发控制，最多 10 个文件并行处理。 """ +import asyncio import logging -import os -import uuid from pathlib import Path from typing import List @@ -16,25 +15,26 @@ from app.db.models.knowledge_gen import KnowledgeBase, RagFile, FileStatus, RagType from app.db.session import AsyncSessionLocal -from app.module.rag.infra.document import ingest_file_to_chunks, DocumentChunk +from app.module.rag.infra.document import ingest_file_to_chunks from app.module.rag.infra.embeddings import EmbeddingFactory from app.module.rag.infra.task.worker_pool import get_global_pool -from app.module.rag.infra.vectorstore import VectorStoreFactory, chunks_to_documents +from app.module.rag.infra.vectorstore import VectorStoreFactory from app.module.rag.repository import RagFileRepository, KnowledgeBaseRepository from app.module.rag.schema.request import AddFilesReq +from app.module.rag.service.common import ( + TextCleaner, + MetadataBuilder, + BatchProcessor, + get_file_path, +) from app.module.system.service.common_service import get_model_by_id logger = logging.getLogger(__name__) class FileProcessor: - """文件处理器 - - 负责文件的后台 ETL 处理，使用全局 WorkerPool 控制并发。 - """ def __init__(self): - """初始化处理器""" self.worker_pool = get_global_pool(max_workers=10) def start_background_processing( @@ -45,15 +45,6 @@ def start_background_processing( knowledge_base_type: str, request_data: dict, ) -> None: - """启动后台文件处理 - - Args: - background_tasks: FastAPI BackgroundTasks - knowledge_base_id: 知识库 ID - knowledge_base_name: 知识库名称 - knowledge_base_type: 知识库类型 (DOCUMENT/GRAPH) - request_data: 添加文件请求数据 - """ background_tasks.add_task( self._process_files_background, knowledge_base_id, @@ -70,7 +61,6 @@ async def _process_files_background( knowledge_base_type: str, request_data: dict, ) -> None: - """后台处理文件（使用新的数据库 session）""" async with AsyncSessionLocal() as db: try: kb_repo = KnowledgeBaseRepository(db) @@ -93,7 +83,7 @@ async def _process_files_background( if knowledge_base_type == RagType.GRAPH.value: await self._process_graph_files(db, files, knowledge_base) else: - await self._process_document_files(db, files, knowledge_base, request) + await self._process_document_files(files, knowledge_base, request) logger.info("知识库 %s 文件处理完成", knowledge_base_name) @@ -104,14 +94,10 @@ async def _process_files_background( async def _process_document_files( self, - db: AsyncSession, files: List[RagFile], knowledge_base: KnowledgeBase, request: AddFilesReq, ) -> None: - """处理 DOCUMENT 类型文件（向量化）""" - import asyncio - async def process_with_semaphore(rag_file: RagFile): async with self.worker_pool.semaphore: async with AsyncSessionLocal() as file_db: @@ -129,12 +115,10 @@ async def _process_graph_files( files: List[RagFile], knowledge_base: KnowledgeBase, ) -> None: - """处理 GRAPH 类型文件（知识图谱）""" - from app.module.shared.llm import LLMFactory from app.module.shared.common.document_loaders import load_documents try: - rag_instance = await self._initialize_graph_rag(db, knowledge_base, LLMFactory) + rag_instance = await self._initialize_graph_rag(db, knowledge_base) for rag_file in files: await self._process_single_graph_file(db, rag_file, rag_instance, load_documents) @@ -145,37 +129,11 @@ async def _process_graph_files( file_repo = RagFileRepository(db) await self._mark_failed(db, file_repo, str(rag_file.id), f"知识图谱初始化失败: {str(e)}") # type: ignore - async def _initialize_graph_rag(self, db: AsyncSession, knowledge_base: KnowledgeBase, LLMFactory): - """初始化 GraphRAG 实例""" - from .graph_rag import ( - DEFAULT_WORKING_DIR, - build_embedding_func, - build_llm_model_func, - initialize_rag, - ) - - embedding_entity = await get_model_by_id(db, str(knowledge_base.embedding_model)) # type: ignore - if not embedding_entity: - raise ValueError(f"嵌入模型不存在: {knowledge_base.embedding_model}") - - chat_entity = await get_model_by_id(db, str(knowledge_base.chat_model)) # type: ignore - if not chat_entity: - raise ValueError(f"聊天模型不存在: {knowledge_base.chat_model}") - - llm_callable = await build_llm_model_func( - str(chat_entity.model_name), str(chat_entity.base_url), str(chat_entity.api_key) # type: ignore - ) - embedding_callable = await build_embedding_func( - str(embedding_entity.model_name), - str(embedding_entity.base_url), - str(embedding_entity.api_key), - embedding_dim=LLMFactory.get_embedding_dimension( - str(embedding_entity.model_name), str(embedding_entity.base_url), str(embedding_entity.api_key) # type: ignore - ), - ) - - kb_working_dir = os.path.join(DEFAULT_WORKING_DIR, str(knowledge_base.name)) # type: ignore - return await initialize_rag(llm_callable, embedding_callable, kb_working_dir) + @staticmethod + async def _initialize_graph_rag(db: AsyncSession, knowledge_base: KnowledgeBase): + from app.module.rag.service.strategy.graph_strategy import GraphKnowledgeBaseStrategy + strategy = GraphKnowledgeBaseStrategy(db) + return await strategy._get_or_create_graph_rag(knowledge_base) async def _process_single_graph_file( self, @@ -184,19 +142,18 @@ async def _process_single_graph_file( rag_instance, load_documents, ) -> None: - """处理单个 GRAPH 类型文件""" file_repo = RagFileRepository(db) try: await self._update_status(db, file_repo, str(rag_file.id), FileStatus.PROCESSING, 10) # type: ignore await db.commit() - dataset_file = await self._get_dataset_file(db, str(rag_file.file_id)) # type: ignore - if not dataset_file: - await self._mark_failed(db, file_repo, str(rag_file.id), "数据集文件不存在") # type: ignore + file_path = get_file_path(rag_file) + if not file_path or not Path(file_path).exists(): + await self._mark_failed(db, file_repo, str(rag_file.id), "文件不存在") # type: ignore return - documents = load_documents(str(dataset_file.file_path)) # type: ignore + documents = load_documents(file_path) if not documents: await self._mark_failed(db, file_repo, str(rag_file.id), "文件解析失败，未生成文档") # type: ignore return @@ -206,7 +163,7 @@ async def _process_single_graph_file( for idx, doc in enumerate(documents): logger.info("插入文档到知识图谱: %s, 进度: %d/%d", str(rag_file.file_name), idx + 1, len(documents)) # type: ignore - await rag_instance.ainsert(input=doc.page_content, file_paths=[str(dataset_file.file_path)]) # type: ignore + await rag_instance.ainsert(input=doc.page_content, file_paths=[file_path]) await self._mark_success(db, file_repo, str(rag_file.id), len(documents)) # type: ignore logger.info("文件 %s 知识图谱处理完成", str(rag_file.file_name)) @@ -215,17 +172,6 @@ async def _process_single_graph_file( logger.exception("文件 %s 知识图谱处理失败: %s", str(rag_file.file_name), e) # type: ignore await self._mark_failed(db, file_repo, str(rag_file.id), str(e)) # type: ignore - async def _get_dataset_file(self, db: AsyncSession, file_id: str): # type: ignore - """获取数据集文件""" - from sqlalchemy import select - from app.db.models.dataset_management import DatasetFiles - - - result = await db.execute( - select(DatasetFiles).where(DatasetFiles.id == file_id) - ) - return result.scalar_one_or_none() - async def _process_single_file( self, db: AsyncSession, @@ -233,556 +179,134 @@ async def _process_single_file( knowledge_base: KnowledgeBase, request: AddFilesReq, ) -> None: - """处理单个文件的 ETL 流程 - - 1. 验证文件 - 2. 加载并分块（progress=20%） - 3. 向量化并存储（progress=60%） - 4. 完成（progress=100%） - """ file_repo = RagFileRepository(db) try: - # 更新状态为处理中 - await self._update_status(db, file_repo, rag_file.id, FileStatus.PROCESSING, 5) + await self._update_status(db, file_repo, rag_file.id, FileStatus.PROCESSING, 5) # type: ignore await db.commit() - # 验证文件并获取路径 - file_path = await self._validate_file(db, file_repo, rag_file) - if not file_path: + file_path = get_file_path(rag_file) + if not file_path or not Path(file_path).exists(): + await self._mark_failed(db, file_repo, rag_file.id, "文件不存在") # type: ignore return - # 加载并分块文档 - metadata = self._build_chunk_metadata(rag_file, knowledge_base) - chunks = await self._load_and_split(file_path, rag_file, metadata, request) + base_metadata = MetadataBuilder.build_chunk_metadata(rag_file, knowledge_base) + chunks = await ingest_file_to_chunks( + file_path, + process_type=request.process_type, + chunk_size=request.chunk_size, + overlap_size=request.overlap_size, + delimiter=request.delimiter, + **base_metadata, + ) if not chunks: - await self._mark_failed(db, file_repo, rag_file.id, "文档解析后未生成任何分块") + await self._mark_failed(db, file_repo, rag_file.id, "文档解析后未生成任何分块") # type: ignore return logger.info("文件 %s 分块完成，共 %d 个分块", rag_file.file_name, len(chunks)) - # 向量化并存储到 Milvus - await self._embed_and_store(db, chunks, rag_file, knowledge_base) - - # 标记完成 - await self._mark_success(db, file_repo, rag_file.id, len(chunks)) - logger.info("文件 %s ETL 处理完成", rag_file.file_name) - - except Exception as e: - logger.exception("文件 %s 处理失败: %s", rag_file.file_name, e) - await self._mark_failed(db, file_repo, rag_file.id, str(e)) - - async def _validate_file( - self, - db: AsyncSession, - file_repo: RagFileRepository, - rag_file: RagFile, - ) -> str | None: - """验证文件路径并返回绝对路径 - - Args: - db: 数据库 session - file_repo: 文件仓储 - rag_file: RAG 文件实体 - - Returns: - 文件绝对路径，验证失败返回 None - """ - file_path = self._get_file_path(rag_file) - - if not file_path: - await self._mark_failed(db, file_repo, rag_file.id, "文件路径未设置") - return None - - if not Path(file_path).exists(): - await self._mark_failed(db, file_repo, rag_file.id, f"文件不存在: {file_path}") - return None - - return file_path - - async def _load_and_split( - self, - file_path: str, - rag_file: RagFile, - metadata: dict, - request: AddFilesReq, - ) -> List[DocumentChunk]: - """加载文档并分块 - - Args: - file_path: 文件路径 - rag_file: RAG 文件实体 - metadata: 基础元数据 - request: 添加文件请求 - - Returns: - 文档分块列表 - """ - chunks = await ingest_file_to_chunks( - file_path, - process_type=request.process_type, - chunk_size=request.chunk_size, - overlap_size=request.overlap_size, - delimiter=request.delimiter, - **metadata, - ) - - if chunks: - logger.info("文件 %s 加载分块成功，数量: %d", rag_file.file_name, len(chunks)) - - return chunks - - async def _embed_and_store( - self, - db: AsyncSession, - chunks: List[DocumentChunk], - rag_file: RagFile, - knowledge_base: KnowledgeBase, - ) -> None: - """向量化并存储到 Milvus - - Args: - db: 数据库 session - chunks: 文档分块列表 - rag_file: RAG 文件实体 - knowledge_base: 知识库实体 - """ - file_repo = RagFileRepository(db) + valid_chunks = self._filter_and_clean_chunks(chunks, rag_file) + if not valid_chunks: + await self._mark_failed(db, file_repo, rag_file.id, "文件没有有效的分块内容") # type: ignore + return - # 过滤和清理分块 - valid_chunks = await self._filter_and_clean_chunks(chunks, rag_file) - if not valid_chunks: - return + embedding = await self._get_embeddings(db, knowledge_base) + vectorstore = VectorStoreFactory.create( + collection_name=str(knowledge_base.name), # type: ignore + embedding=embedding, + ) - # 创建向量存储 - vectorstore = await self._create_vectorstore(db, knowledge_base, file_repo, rag_file) + await self._update_progress(db, file_repo, rag_file.id, 60) # type: ignore + await db.commit() - # 添加元数据 - self._add_metadata_to_chunks(valid_chunks, rag_file, knowledge_base) + MetadataBuilder.add_to_chunks(valid_chunks, { + "rag_file_id": str(rag_file.id), # type: ignore + "original_file_id": str(rag_file.file_id), # type: ignore + "knowledge_base_id": str(knowledge_base.id), # type: ignore + }) - # 分批存储 - await self._store_chunks_in_batches(vectorstore, valid_chunks, rag_file) + await BatchProcessor.store_in_batches(vectorstore, valid_chunks) - async def _filter_and_clean_chunks( - self, - chunks: List[DocumentChunk], - rag_file: RagFile, - ) -> List[DocumentChunk]: - """过滤和清理无效的分块 + await self._mark_success(db, file_repo, rag_file.id, len(valid_chunks)) # type: ignore + logger.info("文件 %s ETL 处理完成", rag_file.file_name) - Args: - chunks: 原始分块列表 - rag_file: RAG 文件实体 + except Exception as e: + logger.exception("文件 %s 处理失败: %s", rag_file.file_name, e) + await self._mark_failed(db, file_repo, rag_file.id, str(e)) # type: ignore - Returns: - 有效的分块列表 - """ + @staticmethod + def _filter_and_clean_chunks(chunks: list, rag_file: RagFile) -> list: valid_chunks = [] - for idx, chunk in enumerate(chunks): - cleaned_text = self._clean_text(chunk.text) - self._log_chunk_cleaning(idx, chunk.text, cleaned_text) + for chunk in chunks: + cleaned_text = TextCleaner.clean(chunk.text) - if cleaned_text and len(cleaned_text.strip()) > 0: + if cleaned_text and TextCleaner.has_printable_content(cleaned_text): chunk.text = cleaned_text valid_chunks.append(chunk) else: logger.warning( - "跳过无效分块: rag_file_id=%s, chunk_index=%s, 原始长度=%d", + "跳过无效分块: rag_file_id=%s, chunk_index=%s", rag_file.id, - chunk.metadata.get("chunk_index"), - len(chunk.text) if chunk.text else 0 + chunk.metadata.get("chunk_index") ) - if not valid_chunks: - logger.warning("文件 %s 没有有效的分块内容", rag_file.file_name) - return [] - - logger.info( - "文件 %s 有效分块数量: %d / %d", - rag_file.file_name, - len(valid_chunks), - len(chunks) - ) + logger.info("文件 %s 有效分块数量: %d / %d", rag_file.file_name, len(valid_chunks), len(chunks)) return valid_chunks - def _log_chunk_cleaning(self, idx: int, original_text: str, cleaned_text: str) -> None: - """记录分块清理日志 - - Args: - idx: 分块索引 - original_text: 原始文本 - cleaned_text: 清理后文本 - """ - if idx >= 3: - return - - logger.debug( - "分块 %d 清理前 (长度=%d): %.100s%s", - idx, - len(original_text) if original_text else 0, - repr(original_text[:100]) if original_text else "None", - "..." if original_text and len(original_text) > 100 else "" - ) - logger.debug( - "分块 %d 清理后 (长度=%d): %.100s%s", - idx, - len(cleaned_text) if cleaned_text else 0, - repr(cleaned_text[:100]) if cleaned_text else "None", - "..." if cleaned_text and len(cleaned_text) > 100 else "" - ) - - async def _create_vectorstore( - self, - db: AsyncSession, - knowledge_base: KnowledgeBase, - file_repo: RagFileRepository, - rag_file: RagFile, - ): - """创建向量存储 - - Args: - db: 数据库 session - knowledge_base: 知识库实体 - file_repo: 文件仓储 - rag_file: RAG 文件实体 - - Returns: - 向量存储实例 - """ - embedding = await self._get_embeddings(db, knowledge_base) - vectorstore = VectorStoreFactory.create( - collection_name=knowledge_base.name, - embedding=embedding, - ) - - await self._update_progress(db, file_repo, rag_file.id, 60) - await db.commit() - - return vectorstore - - def _add_metadata_to_chunks( - self, - chunks: List[DocumentChunk], - rag_file: RagFile, - knowledge_base: KnowledgeBase, - ) -> None: - """为分块添加元数据 - - Args: - chunks: 分块列表 - rag_file: RAG 文件实体 - knowledge_base: 知识库实体 - """ - base_metadata = { - "rag_file_id": rag_file.id, - "original_file_id": rag_file.file_id, - "knowledge_base_id": knowledge_base.id, - } - - for chunk in chunks: - chunk.metadata.update(base_metadata) - - async def _store_chunks_in_batches( - self, - vectorstore, - chunks: List[DocumentChunk], - rag_file: RagFile, - ) -> None: - """分批存储分块到向量数据库 - - Args: - vectorstore: 向量存储实例 - chunks: 分块列表 - rag_file: RAG 文件实体 - """ - batch_size = 20 - total_chunks = len(chunks) - - for batch_start in range(0, total_chunks, batch_size): - batch_end = min(batch_start + batch_size, total_chunks) - batch_chunks = chunks[batch_start:batch_end] - - await self._store_single_batch( - vectorstore, - batch_chunks, - batch_start, - batch_end, - total_chunks - ) - - logger.info("文件 %s 向量存储完成，数量: %d", rag_file.file_name, len(chunks)) - - async def _store_single_batch( - self, - vectorstore, - batch_chunks: List[DocumentChunk], - batch_start: int, - batch_end: int, - total_chunks: int, - ) -> None: - """存储单个批次的分块 - - Args: - vectorstore: 向量存储实例 - batch_chunks: 批次分块列表 - batch_start: 批次起始索引 - batch_end: 批次结束索引 - total_chunks: 总分块数 - """ - logger.info("处理分块批次 %d-%d / %d", batch_start + 1, batch_end, total_chunks) - - self._log_batch_details(batch_chunks, batch_start) - - ids = [str(uuid.uuid4()) for _ in batch_chunks] - documents, doc_ids = chunks_to_documents(batch_chunks, ids=ids) - - try: - vectorstore.add_documents(documents=documents, ids=doc_ids) - logger.info("批次 %d-%d 存储成功", batch_start + 1, batch_end) - except Exception as e: - logger.error( - "批次 %d-%d 存储失败: %s\n第一个文档内容: %.200s", - batch_start + 1, - batch_end, - str(e), - documents[0].page_content if documents else "N/A" - ) - raise - - def _log_batch_details( - self, - batch_chunks: List[DocumentChunk], - batch_start: int, - ) -> None: - """记录批次详细信息 - - Args: - batch_chunks: 批次分块列表 - batch_start: 批次起始索引 - """ - for i, chunk in enumerate(batch_chunks[:2]): - logger.debug( - "批次内分块 %d: 长度=%d, 文本=%.50s%s", - batch_start + i, - len(chunk.text), - chunk.text[:50], - "..." if len(chunk.text) > 50 else "" - ) - - async def _get_embeddings( - self, - db: AsyncSession, - knowledge_base: KnowledgeBase, - ): - """获取嵌入模型实例 - - Args: - db: 数据库 session - knowledge_base: 知识库实体 - - Returns: - LangChain Embeddings 实例 - """ - embedding_entity = await get_model_by_id(db, knowledge_base.embedding_model) + @staticmethod + async def _get_embeddings(db: AsyncSession, knowledge_base: KnowledgeBase): + embedding_entity = await get_model_by_id(db, str(knowledge_base.embedding_model)) # type: ignore if not embedding_entity: raise ValueError(f"嵌入模型不存在: {knowledge_base.embedding_model}") return EmbeddingFactory.create_embeddings( - model_name=embedding_entity.model_name, + model_name=str(embedding_entity.model_name), # type: ignore base_url=getattr(embedding_entity, "base_url", None), api_key=getattr(embedding_entity, "api_key", None), ) - def _get_file_path(self, rag_file: RagFile) -> str | None: - """获取文件绝对路径 - - Args: - rag_file: RAG 文件实体 - - Returns: - 文件绝对路径，不存在返回 None - """ - if not rag_file.file_metadata: - return None - - file_path = rag_file.file_metadata.get("file_path") - if file_path: - return os.path.abspath(file_path) - return None - - def _build_chunk_metadata(self, rag_file: RagFile, knowledge_base: KnowledgeBase) -> dict: - """构建分块基础元数据 - - Args: - rag_file: RAG 文件实体 - knowledge_base: 知识库实体 - - Returns: - 元数据字典 - """ - file_path = self._get_file_path(rag_file) or "" - return { - "rag_file_id": rag_file.id, - "original_file_id": rag_file.file_id, - "dataset_id": rag_file.file_metadata.get("dataset_id") if rag_file.file_metadata else None, - "file_name": rag_file.file_name, - "file_extension": Path(file_path).suffix, - "knowledge_base_id": knowledge_base.id, - } - + @staticmethod async def _update_status( - self, db: AsyncSession, file_repo: RagFileRepository, rag_file_id: str, status: FileStatus, progress: int = 0, ) -> None: - """更新文件状态和进度 - - Args: - db: 数据库 session - file_repo: 文件仓储 - rag_file_id: RAG 文件 ID - status: 新状态 - progress: 新进度 - """ await file_repo.update_status(rag_file_id, status) await file_repo.update_progress(rag_file_id, progress) await db.flush() + @staticmethod async def _update_progress( - self, db: AsyncSession, file_repo: RagFileRepository, rag_file_id: str, progress: int, ) -> None: - """更新文件处理进度 - - Args: - db: 数据库 session - file_repo: 文件仓储 - rag_file_id: RAG 文件 ID - progress: 进度值 (0-100) - """ await file_repo.update_progress(rag_file_id, progress) await db.flush() + @staticmethod async def _mark_success( - self, db: AsyncSession, file_repo: RagFileRepository, rag_file_id: str, chunk_count: int, ) -> None: - """标记文件处理成功 - - Args: - db: 数据库 session - file_repo: 文件仓储 - rag_file_id: RAG 文件 ID - chunk_count: 分块数量 - """ await file_repo.update_chunk_count(rag_file_id, chunk_count) await file_repo.update_status(rag_file_id, FileStatus.PROCESSED) await file_repo.update_progress(rag_file_id, 100) await db.commit() - def _clean_text(self, text: str) -> str: - """清理文本内容 - - 移除无效字符、控制字符，并规范化空白字符。 - - Args: - text: 原始文本 - - Returns: - 清理后的文本，如果无效则返回空字符串 - """ - - if not text or not isinstance(text, str): - return "" - - text = self._remove_control_characters(text) - text = self._normalize_whitespace(text) - text = self._remove_empty_lines(text) - - if not self._has_printable_content(text): - return "" - - return text.strip() - - def _remove_control_characters(self, text: str) -> str: - """移除控制字符和零宽字符 - - Args: - text: 原始文本 - - Returns: - 清理后的文本 - """ - import re - text = re.sub(r'[\x00-\x09\x0b\x0c\x0e-\x1f\x7f-\x9f]', '', text) - text = re.sub(r'[\u200b-\u200f\u2028-\u202f\ufeff]', '', text) - return text - - def _normalize_whitespace(self, text: str) -> str: - """规范化空白字符 - - Args: - text: 原始文本 - - Returns: - 规范化后的文本 - """ - import re - text = re.sub(r'[ \t]+', ' ', text) - text = '\n'.join(line.strip() for line in text.split('\n')) - text = re.sub(r'\n{3,}', '\n\n', text) - return text - - def _remove_empty_lines(self, text: str) -> str: - """移除空行 - - Args: - text: 原始文本 - - Returns: - 移除空行后的文本 - """ - return '\n'.join(line for line in text.split('\n') if line.strip()) - - def _has_printable_content(self, text: str) -> bool: - """检查文本是否包含可打印内容 - - Args: - text: 文本内容 - - Returns: - 是否包含可打印字符 - """ - import re - if not text or not text.strip(): - return False - return bool(re.search(r'[\w\u4e00-\u9fff]', text)) - + @staticmethod async def _mark_failed( - self, db: AsyncSession, file_repo: RagFileRepository, rag_file_id: str, err_msg: str, ) -> None: - """标记文件处理失败 - - Args: - db: 数据库 session - file_repo: 文件仓储 - rag_file_id: RAG 文件 ID - err_msg: 错误信息 - """ logger.error("文件处理失败: %s", err_msg) await file_repo.update_status(rag_file_id, FileStatus.PROCESS_FAILED, err_msg=err_msg) await db.commit() diff --git a/runtime/datamate-python/app/module/rag/service/graph_rag.py b/runtime/datamate-python/app/module/rag/service/graph_rag.py deleted file mode 100644 index c118a5d2b..000000000 --- a/runtime/datamate-python/app/module/rag/service/graph_rag.py +++ /dev/null @@ -1,67 +0,0 @@ -import os -from pathlib import Path -from typing import Awaitable, Callable, Optional -from app.core.config import settings - -import numpy as np -from lightrag import LightRAG -from lightrag.constants import DEFAULT_ENTITY_TYPES -from lightrag.kg.shared_storage import initialize_pipeline_status -from lightrag.llm.openai import openai_embed, openai_complete_if_cache -from lightrag.utils import setup_logger, EmbeddingFunc, get_env_value - -setup_logger("lightrag", level="INFO") -DEFAULT_WORKING_DIR = Path(settings.rag_storage_dir) - - -async def build_llm_model_func(model_name: str, base_url: str, api_key: str) -> Callable[..., Awaitable[str]]: - async def _llm_model( - prompt, system_prompt=None, history_messages=None, **kwargs - ) -> str: - history_messages = history_messages or [] - return await openai_complete_if_cache( - model_name, - prompt, - system_prompt=system_prompt, - history_messages=history_messages, - api_key=api_key, - base_url=base_url, - **kwargs, - ) - - return _llm_model - - -async def build_embedding_func( - model_name: str, base_url: str, api_key: str, embedding_dim: int -) -> EmbeddingFunc: - async def _embedding_func(texts: list[str]) -> np.ndarray: - return await openai_embed.func( - texts, - model=model_name, - api_key=api_key, - base_url=base_url, - ) - - return EmbeddingFunc(embedding_dim=embedding_dim, func=_embedding_func, max_token_size=8192) - - -async def initialize_rag( - llm_callable: Callable[..., Awaitable[str]], - embedding_callable: EmbeddingFunc, - working_dir: Optional[str] = None, -): - target_dir = working_dir or DEFAULT_WORKING_DIR - os.makedirs(target_dir, exist_ok=True) - rag = LightRAG( - working_dir=target_dir, - llm_model_func=llm_callable, - embedding_func=embedding_callable, - addon_params={ - "language": "Chinese", - "entity_types": get_env_value("ENTITY_TYPES", DEFAULT_ENTITY_TYPES, list), - } - ) - await rag.initialize_storages() - await initialize_pipeline_status() - return rag diff --git a/runtime/datamate-python/app/module/rag/service/rag_service.py b/runtime/datamate-python/app/module/rag/service/rag_service.py deleted file mode 100644 index 423f37536..000000000 --- a/runtime/datamate-python/app/module/rag/service/rag_service.py +++ /dev/null @@ -1,132 +0,0 @@ -import os -from typing import Optional - -from fastapi import Depends -from langchain_core.prompts import ChatPromptTemplate -from langchain_openai import ChatOpenAI -from sqlalchemy import select -from sqlalchemy.ext.asyncio import AsyncSession - -from app.core.logging import get_logger -from app.db.models.knowledge_gen import KnowledgeBase -from app.db.session import get_db -from app.module.rag.infra.embeddings import EmbeddingFactory -from app.module.rag.infra.vectorstore import VectorStoreFactory -from .graph_rag import ( - DEFAULT_WORKING_DIR, - build_embedding_func, - build_llm_model_func, - initialize_rag, -) -from ...system.service.common_service import get_model_by_id - -logger = get_logger(__name__) - -RAG_DOCUMENT_PROMPT = ChatPromptTemplate.from_messages([ - ("system", "根据以下上下文回答问题。如果上下文中没有相关信息，请说明。\n\n上下文：\n{context}"), - ("human", "{input}"), -]) - - -class RAGService: - def __init__(self, db: AsyncSession = Depends(get_db)): - self.db = db - self.rag = None - - async def init_graph_rag(self, knowledge_base_id: str): - """初始化知识图谱 RAG 实例（用于查询）""" - kb = await self._get_knowledge_base(knowledge_base_id) - embedding_model = await self._get_models(kb.embedding_model) - chat_model = await self._get_models(kb.chat_model) - - llm_callable = await build_llm_model_func( - chat_model.model_name, chat_model.base_url, chat_model.api_key - ) - from app.module.shared.llm import LLMFactory - embedding_callable = await build_embedding_func( - embedding_model.model_name, - embedding_model.base_url, - embedding_model.api_key, - embedding_dim=LLMFactory.get_embedding_dimension( - embedding_model.model_name, embedding_model.base_url, embedding_model.api_key - ), - ) - - kb_working_dir = os.path.join(DEFAULT_WORKING_DIR, kb.name) - self.rag = await initialize_rag(llm_callable, embedding_callable, kb_working_dir) - - return {"status": "initialized", "knowledge_base_id": knowledge_base_id} - - async def _get_knowledge_base(self, knowledge_base_id: str): - result = await self.db.execute( - select(KnowledgeBase).where(KnowledgeBase.id == knowledge_base_id) - ) - knowledge_base = result.scalars().first() - if not knowledge_base: - raise ValueError(f"Knowledge base with ID {knowledge_base_id} not found.") - return knowledge_base - - async def _get_models(self, model_id: Optional[str]): - if not model_id: - raise ValueError("Model ID is required for initializing RAG.") - models = await get_model_by_id(self.db, model_id) - if not models: - raise ValueError(f"Models with ID {model_id} not found.") - return models - - async def query_rag(self, query: str, knowledge_base_id: str) -> str: - kb = await self._get_knowledge_base(knowledge_base_id) - if kb.type and str(kb.type).upper() == "GRAPH": - if not self.rag: - await self.init_graph_rag(knowledge_base_id) - return await self.rag.get_knowledge_graph(query) - # DOCUMENT 类型：LangChain Milvus 检索 + RAG - return await self._query_document_rag(query, kb) - - async def _query_document_rag(self, query: str, kb: KnowledgeBase) -> str: - """基于 Milvus 向量存储的检索与生成（混合检索 + LLM）。""" - from langchain_classic.chains.combine_documents import create_stuff_documents_chain - from langchain_classic.chains.retrieval import create_retrieval_chain - - embedding_entity = await self._get_models(kb.embedding_model) - embedding = EmbeddingFactory.create_embeddings( - model_name=embedding_entity.model_name, - base_url=getattr(embedding_entity, "base_url", None), - api_key=getattr(embedding_entity, "api_key", None), - ) - vectorstore = VectorStoreFactory.create( - collection_name=kb.name, - embedding=embedding, - ) - retriever = vectorstore.as_retriever( - search_type="hybrid", - search_kwargs={"k": 5}, - ) - chat_model_entity = await self._get_models(kb.chat_model) - llm = ChatOpenAI( - model=chat_model_entity.model_name, - base_url=getattr(chat_model_entity, "base_url", None) or None, - api_key=getattr(chat_model_entity, "api_key", None) or None, - ) - combine_chain = create_stuff_documents_chain(llm, RAG_DOCUMENT_PROMPT) - chain = create_retrieval_chain(retriever, combine_chain) - result = await chain.ainvoke({"input": query}) - return result.get("answer", "") - - async def index(self, documents: list[dict], knowledge_base_id: int) -> dict: - kb = await self._get_knowledge_base(str(knowledge_base_id)) - embedding_entity = await self._get_models(kb.embedding_model) - embedding = EmbeddingFactory.create_embeddings( - model_name=embedding_entity.model_name, - base_url=getattr(embedding_entity, "base_url", None), - api_key=getattr(embedding_entity, "api_key", None), - ) - vectorstore = VectorStoreFactory.create( - collection_name=kb.name, - embedding=embedding, - ) - from langchain_core.documents import Document - docs = [Document(page_content=doc.get("content", ""), metadata=doc.get("metadata", {})) for doc in documents] - await vectorstore.aadd_documents(docs) - logger.info(f"Indexed {len(documents)} documents into knowledge base {knowledge_base_id}") - return {"indexed_count": len(documents), "collection_name": kb.name} diff --git a/runtime/datamate-python/app/module/rag/service/retrieval_service.py b/runtime/datamate-python/app/module/rag/service/retrieval_service.py deleted file mode 100644 index 3c9c04481..000000000 --- a/runtime/datamate-python/app/module/rag/service/retrieval_service.py +++ /dev/null @@ -1,251 +0,0 @@ -""" -检索服务 - -负责知识库内容的检索，支持向量 + BM25 混合检索。 -""" -import json -import logging -from typing import List - -from pymilvus import AnnSearchRequest, Function, FunctionType -from sqlalchemy.ext.asyncio import AsyncSession - -from app.core.exception import BusinessError, ErrorCodes -from app.module.rag.infra.embeddings import EmbeddingFactory -from app.module.rag.infra.vectorstore.milvus_client import get_milvus_client -from app.module.rag.repository import KnowledgeBaseRepository, RagFileRepository -from app.module.rag.schema.request import RetrieveReq, PagingQuery -from app.module.rag.schema.response import PagedResponse, RagChunkResp -from app.module.system.service.common_service import get_model_by_id - -logger = logging.getLogger(__name__) - - -class RetrievalService: - """检索服务类 - - 提供知识库内容的混合检索（向量 + BM25）和分块查询功能。 - """ - - def __init__(self, db: AsyncSession): - """初始化服务 - - Args: - db: 数据库异步 session - """ - self.db = db - self.kb_repo = KnowledgeBaseRepository(db) - self.file_repo = RagFileRepository(db) - - async def retrieve(self, request: RetrieveReq) -> List[dict]: - """检索知识库内容（混合检索：向量 + BM25） - - Args: - request: 检索请求 - - Returns: - 检索结果列表 - - Raises: - BusinessError: 知识库不存在或嵌入模型不存在 - """ - import asyncio - - # 验证所有知识库存在 - knowledge_bases = [] - for kb_id in request.knowledge_base_ids: - kb = await self.kb_repo.get_by_id(kb_id) - if not kb: - raise BusinessError(ErrorCodes.RAG_KNOWLEDGE_BASE_NOT_FOUND) - knowledge_bases.append(kb) - - # 获取嵌入模型 - embedding_entity = await get_model_by_id(self.db, knowledge_bases[0].embedding_model) - if not embedding_entity: - raise BusinessError(ErrorCodes.RAG_MODEL_NOT_FOUND) - - # 创建嵌入模型实例 - embedding = EmbeddingFactory.create_embeddings( - model_name=embedding_entity.model_name, - base_url=getattr(embedding_entity, "base_url", None), - api_key=getattr(embedding_entity, "api_key", None), - ) - - # 生成查询向量 - try: - query_vector = await asyncio.to_thread(embedding.embed_query, request.query) - except Exception as e: - logger.error("查询向量化失败: %s", e) - raise BusinessError(ErrorCodes.RAG_EMBEDDING_FAILED, f"查询向量化失败: {str(e)}") from e - - # 执行混合检索 - all_results = await self._execute_hybrid_search(knowledge_bases, query_vector, request.query, request.top_k) - - # 按分数排序 - all_results.sort(key=lambda x: x.get("score") or x.get("distance", 0), reverse=True) - - # 应用阈值过滤 - if request.threshold is not None: - all_results = [r for r in all_results if (r.get("score") or r.get("distance", 0)) >= 0] - - # 格式化返回结果 - return self._format_results(all_results) - - async def _execute_hybrid_search( - self, - knowledge_bases: list, - query_vector: list, - query_text: str, - top_k: int, - ) -> List[dict]: - """执行混合检索""" - all_results = [] - client = get_milvus_client() - - for kb in knowledge_bases: - try: - if not client.has_collection(kb.name): - logger.warning("集合 %s 不存在，跳过", kb.name) - continue - - dense_search = AnnSearchRequest( - data=[query_vector], - anns_field="vector", - param={"nprobe": 10}, - limit=top_k, - ) - - sparse_search = AnnSearchRequest( - data=[query_text], - anns_field="sparse", - param={"drop_ratio_search": 0.2}, - limit=top_k, - ) - - ranker = Function( - name="weight", - input_field_names=[], - function_type=FunctionType.RERANK, - params={ - "reranker": "weighted", - "weights": [0.1, 0.9], - "norm_score": True, - } - ) - search_results = client.hybrid_search( - collection_name=kb.name, - reqs=[dense_search, sparse_search], - ranker=ranker, - output_fields=["id", "text", "metadata"], - limit=top_k, - ) - - logger.info(f"----------, {search_results.__str__()}") - - if search_results and len(search_results) > 0: - for result in search_results[0]: - result["knowledge_base_id"] = kb.id - result["knowledge_base_name"] = kb.name - all_results.append(result) - - except Exception as e: - logger.error("知识库 %s 混合检索失败: %s", kb.name, e) - continue - - return all_results - - def _format_results(self, all_results: List[dict]) -> List[dict]: - """格式化检索结果""" - formatted = [] - for r in all_results: - entity = r.get("entity", {}) - metadata = entity.get("metadata", {}) - if isinstance(metadata, dict): - metadata_str = json.dumps(metadata, ensure_ascii=False) - else: - metadata_str = metadata if metadata else "{}" - - formatted.append({ - "entity": { - "metadata": metadata_str, - "text": entity.get("text", ""), - "id": entity.get("id", ""), - }, - "score": r.get("score") or r.get("distance", 0), - "id": entity.get("id", ""), - "knowledgeBaseId": r.get("knowledge_base_id", ""), - "knowledgeBaseName": r.get("knowledge_base_name", ""), - }) - - logger.info("检索完成: 结果数=%d", len(formatted)) - return formatted - - async def get_chunks( - self, - knowledge_base_id: str, - rag_file_id: str, - paging_query: PagingQuery, - ) -> PagedResponse: - """获取指定 RAG 文件的分块列表 - - Args: - knowledge_base_id: 知识库 ID - rag_file_id: RAG 文件 ID - paging_query: 分页参数 - - Returns: - 分块列表（分页） - """ - # 验证知识库和文件存在 - knowledge_base = await self.kb_repo.get_by_id(knowledge_base_id) - if not knowledge_base: - raise BusinessError(ErrorCodes.RAG_KNOWLEDGE_BASE_NOT_FOUND) - - rag_file = await self.file_repo.get_by_id(rag_file_id) - if not rag_file: - raise BusinessError(ErrorCodes.RAG_FILE_NOT_FOUND) - - client = get_milvus_client() - - try: - # 查询总数 - count_filter = f'metadata["rag_file_id"] == "{rag_file_id}"' - count_res = client.query( - collection_name=knowledge_base.name, - filter=count_filter, - output_fields=["id"], - ) - total = len(count_res) - - # 查询分页数据 - offset = (paging_query.page - 1) * paging_query.size - results = client.query( - collection_name=knowledge_base.name, - filter=count_filter, - output_fields=["id", "text", "metadata"], - limit=paging_query.size, - offset=offset, - ) - - chunks = [ - RagChunkResp( - id=item.get("id", ""), - text=item.get("text", ""), - metadata=item.get("metadata", {}), - score=0.0, - ) - for item in results - ] - - logger.info("查询文件分块成功: kb=%s file=%s total=%d", knowledge_base_id, rag_file_id, total) - - return PagedResponse.create( - content=chunks, - total_elements=total, - page=paging_query.page, - size=paging_query.size, - ) - - except Exception as e: - logger.error("查询文件分块失败: kb=%s file=%s error=%s", knowledge_base_id, rag_file_id, e) - raise BusinessError(ErrorCodes.RAG_MILVUS_ERROR, f"查询文件分块失败: {str(e)}") from e diff --git a/runtime/datamate-python/app/module/rag/service/strategy/__init__.py b/runtime/datamate-python/app/module/rag/service/strategy/__init__.py new file mode 100644 index 000000000..6063812ad --- /dev/null +++ b/runtime/datamate-python/app/module/rag/service/strategy/__init__.py @@ -0,0 +1,27 @@ +""" +策略模式实现 + +提供知识库检索和处理的策略模式实现。 +""" +from app.db.models.knowledge_gen import RagType +from .base import KnowledgeBaseStrategy +from .factory import KnowledgeBaseStrategyFactory +from .vector_strategy import VectorKnowledgeBaseStrategy +from .graph_strategy import GraphKnowledgeBaseStrategy + +KnowledgeBaseStrategyFactory.register_strategy( + RagType.DOCUMENT.value, + VectorKnowledgeBaseStrategy +) + +KnowledgeBaseStrategyFactory.register_strategy( + RagType.GRAPH.value, + GraphKnowledgeBaseStrategy +) + +__all__ = [ + "KnowledgeBaseStrategy", + "KnowledgeBaseStrategyFactory", + "VectorKnowledgeBaseStrategy", + "GraphKnowledgeBaseStrategy", +] diff --git a/runtime/datamate-python/app/module/rag/service/strategy/base.py b/runtime/datamate-python/app/module/rag/service/strategy/base.py new file mode 100644 index 000000000..373faecc9 --- /dev/null +++ b/runtime/datamate-python/app/module/rag/service/strategy/base.py @@ -0,0 +1,105 @@ +""" +策略模式 - 知识库策略基类 + +定义知识库检索和处理的抽象接口，支持不同类型的知识库（向量、知识图谱等）。 +""" +from abc import ABC, abstractmethod +from typing import Any, Dict, List, Optional + +from sqlalchemy.ext.asyncio import AsyncSession + + +class KnowledgeBaseStrategy(ABC): + """知识库策略抽象基类 + + 所有知识库策略（向量、知识图谱等）必须实现此接口。 + + 接口设计原则： + - query(): 直接获取数据，不涉及相似度计算，返回值格式不统一 + - search(): 基于输入文本的相似度检索，返回值必须统一为 UnifiedSearchResult + - process_file(): 处理单个文件，不同策略有不同的处理逻辑 + """ + + def __init__(self, db: AsyncSession): + """初始化策略 + + Args: + db: 数据库异步 session + """ + self.db = db + + @abstractmethod + async def query( + self, + knowledge_base_id: str, + **kwargs + ) -> Any: + """直接查询知识库数据（不涉及相似度计算） + + 不同知识库类型的返回格式可以不同： + - 向量知识库：返回 PagedResponse[RagChunkResp]（分页分块列表） + - 知识图谱：返回图谱数据（格式由 LightRAG 定义） + + Args: + knowledge_base_id: 知识库 ID + **kwargs: 额外参数（如 rag_file_id, paging_query 等） + + Returns: + 查询结果（格式由具体策略决定） + """ + pass + + @abstractmethod + async def search( + self, + query_text: str, + knowledge_base_ids: List[str], + top_k: int = 10, + threshold: Optional[float] = None, + **kwargs + ) -> List[Dict[str, Any]]: + """基于输入文本的相似度检索 + + 所有知识库类型必须返回统一格式：UnifiedSearchResult + + Args: + query_text: 查询文本 + knowledge_base_ids: 知识库 ID 列表 + top_k: 返回结果数量 + threshold: 相似度阈值（可选） + **kwargs: 额外参数 + + Returns: + 统一格式的检索结果列表，每个元素包含： + - id: 结果 ID + - text: 文本内容 + - score: 相似度分数 + - metadata: 元数据字典 + - resultType: 结果类型（"vector" 或 "graph"） + - knowledgeBaseId: 知识库 ID + - knowledgeBaseName: 知识库名称 + """ + pass + + @abstractmethod + async def process_file( + self, + knowledge_base_id: str, + rag_file_id: str, + **kwargs + ) -> None: + """处理单个文件 + + 不同知识库类型有不同的处理逻辑： + - 向量知识库：文档分块 -> 向量化 -> 存储到 Milvus + - 知识图谱：文档分块 -> 构建知识图谱 -> 存储到 LightRAG + + Args: + knowledge_base_id: 知识库 ID + rag_file_id: RAG 文件 ID + **kwargs: 额外参数（如 process_type, chunk_size 等） + + Raises: + BusinessError: 处理失败时抛出 + """ + pass diff --git a/runtime/datamate-python/app/module/rag/service/strategy/factory.py b/runtime/datamate-python/app/module/rag/service/strategy/factory.py new file mode 100644 index 000000000..90e493155 --- /dev/null +++ b/runtime/datamate-python/app/module/rag/service/strategy/factory.py @@ -0,0 +1,69 @@ +""" +策略工厂 + +根据知识库类型（DOCUMENT/GRAPH）创建对应的知识库策略实例。 +""" +from typing import Dict + +from sqlalchemy.ext.asyncio import AsyncSession + +from app.db.models.knowledge_gen import RagType +from app.core.exception import BusinessError, ErrorCodes +from .base import KnowledgeBaseStrategy + + +class KnowledgeBaseStrategyFactory: + """知识库策略工厂 + + 使用工厂模式创建知识库策略实例，支持策略缓存以提高性能。 + """ + + _strategy_cache: Dict[str, type] = {} + + @classmethod + def register_strategy(cls, rag_type: str, strategy_class: type): + """注册策略类 + + Args: + rag_type: 知识库类型（DOCUMENT/GRAPH） + strategy_class: 策略类 + """ + cls._strategy_cache[rag_type.upper()] = strategy_class + + @classmethod + def create_strategy( + cls, + rag_type: str, + db: AsyncSession + ) -> KnowledgeBaseStrategy: + """创建知识库策略实例 + + Args: + rag_type: 知识库类型（DOCUMENT/GRAPH） + db: 数据库异步 session + + Returns: + 知识库策略实例 + + Raises: + BusinessError: 不支持的知识库类型 + """ + rag_type_upper = rag_type.upper() if isinstance(rag_type, str) else str(rag_type).upper() + + if rag_type_upper not in cls._strategy_cache: + raise BusinessError( + ErrorCodes.RAG_UNSUPPORTED_TYPE, + f"不支持的 RAG 类型: {rag_type}" + ) + + strategy_class = cls._strategy_cache[rag_type_upper] + return strategy_class(db) + + @classmethod + def get_supported_types(cls) -> list: + """获取支持的知识库类型列表 + + Returns: + 支持的类型列表 + """ + return list(cls._strategy_cache.keys()) diff --git a/runtime/datamate-python/app/module/rag/service/strategy/graph_strategy.py b/runtime/datamate-python/app/module/rag/service/strategy/graph_strategy.py new file mode 100644 index 000000000..bf7e51e6c --- /dev/null +++ b/runtime/datamate-python/app/module/rag/service/strategy/graph_strategy.py @@ -0,0 +1,351 @@ +""" +知识图谱策略 + +实现 GRAPH 类型知识库的检索和处理逻辑。 +""" +import json +import logging +import os +from pathlib import Path +from typing import Any, Awaitable, Callable, Dict, List, Optional + +import numpy as np +from lightrag import LightRAG +from lightrag.base import QueryParam +from lightrag.constants import DEFAULT_ENTITY_TYPES +from lightrag.llm.openai import openai_complete_if_cache, openai_embed +from lightrag.utils import EmbeddingFunc, get_env_value, setup_logger +from sqlalchemy.ext.asyncio import AsyncSession + +from app.core.config import settings +from app.core.exception import BusinessError, ErrorCodes +from app.db.models.knowledge_gen import KnowledgeBase +from app.module.rag.repository import KnowledgeBaseRepository +from app.module.rag.service.common import get_file_path +from app.module.system.service.common_service import get_model_by_id +from .base import KnowledgeBaseStrategy + + +setup_logger("lightrag", level="INFO") +logger = logging.getLogger(__name__) + +DEFAULT_WORKING_DIR = str(settings.rag_storage_dir) + + +def _create_llm_func(model_name: str, base_url: str, api_key: str) -> Callable[..., Awaitable[str]]: + async def _llm(prompt: str, system_prompt: str = None, history_messages: list = None, **kwargs) -> str: + return await openai_complete_if_cache( + model_name, prompt, + system_prompt=system_prompt, + history_messages=history_messages or [], + api_key=api_key, base_url=base_url, **kwargs, + ) + return _llm + + +def _create_embedding_func(model_name: str, base_url: str, api_key: str, embedding_dim: int) -> EmbeddingFunc: + async def _embed(texts: list[str]) -> np.ndarray: + return await openai_embed.func(texts, model=model_name, api_key=api_key, base_url=base_url) + return EmbeddingFunc(embedding_dim=embedding_dim, func=_embed, max_token_size=8192) + + +async def _create_rag( + llm_func: Callable[..., Awaitable[str]], + embedding_func: EmbeddingFunc, + working_dir: str, + workspace: str = "", +) -> LightRAG: + os.makedirs(working_dir, exist_ok=True) + rag = LightRAG( + working_dir=working_dir, + workspace=workspace, + llm_model_func=llm_func, + embedding_func=embedding_func, + addon_params={ + "language": "Chinese", + "entity_types": get_env_value("ENTITY_TYPES", DEFAULT_ENTITY_TYPES, list), + }, + ) + await rag.initialize_storages() + return rag + + +class GraphKnowledgeBaseStrategy(KnowledgeBaseStrategy): + + def __init__(self, db: AsyncSession): + super().__init__(db) + self.kb_repo = KnowledgeBaseRepository(db) + self._rag_cache: Dict[str, Any] = {} + + async def query( + self, + knowledge_base_id: str, + **kwargs + ) -> Any: + node_label = kwargs.get("node_label") + if not node_label: + raise BusinessError( + ErrorCodes.RAG_INVALID_REQUEST, + "Missing 'node_label' parameter for graph query" + ) + + kb = await self._get_knowledge_base(knowledge_base_id) + if not kb: + raise BusinessError(ErrorCodes.RAG_KNOWLEDGE_BASE_NOT_FOUND) + + rag_instance = await self._get_or_create_graph_rag(kb) + return await rag_instance.get_knowledge_graph(node_label=node_label) + + async def search( + self, + query_text: str, + knowledge_base_ids: List[str], + top_k: int = 10, + threshold: Optional[float] = None, + **kwargs + ) -> List[Dict[str, Any]]: + if len(knowledge_base_ids) != 1: + raise BusinessError( + ErrorCodes.RAG_INVALID_REQUEST, + "At least one knowledge base required for graph search" + ) + + all_results = [] + for kb_id in knowledge_base_ids: + kb = await self._get_knowledge_base(kb_id) + if not kb: + raise BusinessError(ErrorCodes.RAG_KNOWLEDGE_BASE_NOT_FOUND) + + rag_instance = await self._get_or_create_graph_rag(kb) + # Use aquery_data for content retrieval (not get_knowledge_graph) + query_param = QueryParam(mode="mix", top_k=top_k, only_need_context=True) + retrieval_results = await rag_instance.aquery_data(query_text, query_param) + + unified_results = self._convert_retrieval_results_into_unified( + retrieval_results, str(kb.id), str(kb.name) # type: ignore + ) + all_results.extend(unified_results) + + all_results.sort(key=lambda x: x.get("score", 1.0), reverse=True) + logger.info("知识图谱检索完成: 结果数=%d", len(all_results)) + return all_results + + @staticmethod + def _convert_retrieval_results_into_unified( + retrieval_results: Dict[str, Any], + knowledge_base_id: str, + knowledge_base_name: str + ) -> List[Dict[str, Any]]: + if not isinstance(retrieval_results, dict): + return [] + + data = retrieval_results.get("data", retrieval_results) + entities = data.get("entities", []) + relationships = data.get("relationships", []) + chunks = data.get("chunks", []) + + if not entities and not relationships and not chunks: + return [] + + reference_files: Dict[str, str] = {} + ref_counter = 1 + + for chunk in chunks: + file_path = chunk.get("file_path", "") + if file_path and file_path not in reference_files: + reference_files[file_path] = str(ref_counter) + ref_counter += 1 + + for entity in entities: + file_path = entity.get("file_path", "") + if file_path and file_path not in reference_files: + reference_files[file_path] = str(ref_counter) + ref_counter += 1 + + for rel in relationships: + file_path = rel.get("file_path", "") + if file_path and file_path not in reference_files: + reference_files[file_path] = str(ref_counter) + ref_counter += 1 + + text_parts = [] + + if entities: + text_parts.append("\nKnowledge Graph Data (Entity):\n") + text_parts.append("\n```json") + for entity in entities: + entity_data = { + "entity": entity.get("entity_name", ""), + "type": entity.get("entity_type", ""), + "description": entity.get("description", ""), + } + text_parts.append(f"\n{json.dumps(entity_data, ensure_ascii=False)}") + text_parts.append("\n```") + + if relationships: + text_parts.append("\n\nKnowledge Graph Data (Relationship):\n") + text_parts.append("\n```json") + for rel in relationships: + rel_data = { + "entity1": rel.get("src_id", ""), + "entity2": rel.get("tgt_id", ""), + "description": rel.get("description", ""), + } + text_parts.append(f"\n{json.dumps(rel_data, ensure_ascii=False)}") + text_parts.append("\n```") + + if chunks: + text_parts.append( + "\n\nDocument Chunks (Each entry has a reference_id refer to the `Reference Document List`):\n" + ) + text_parts.append("\n```json") + for chunk in chunks: + file_path = chunk.get("file_path", "") + ref_id = reference_files.get(file_path, "") + chunk_data = { + "reference_id": ref_id, + "content": chunk.get("content", ""), + } + text_parts.append(f"\n{json.dumps(chunk_data, ensure_ascii=False)}") + text_parts.append("\n```") + + if reference_files: + text_parts.append( + "\n\nReference Document List (Each entry starts with a [reference_id] " + "that corresponds to entries in the Document Chunks):\n" + ) + text_parts.append("\n```") + for file_path, ref_id in reference_files.items(): + file_name = Path(file_path).name if file_path else "" + text_parts.append(f"\n[{ref_id}] {file_name}") + text_parts.append("\n```") + + formatted_text = "".join(text_parts) + + return [{ + "id": knowledge_base_id, + "text": formatted_text, + "score": 1.0, + "metadata": { + "entity_count": len(entities), + "relationship_count": len(relationships), + "chunk_count": len(chunks), + "reference_files": {v: k for k, v in reference_files.items()}, + }, + "resultType": "graph_retrieval", + "knowledgeBaseId": knowledge_base_id, + "knowledgeBaseName": knowledge_base_name, + }] + + async def process_file( + self, + knowledge_base_id: str, + rag_file_id: str, + **kwargs + ) -> None: + from app.module.rag.infra.document.processor import ingest_file_to_chunks + from app.db.models.knowledge_gen import FileStatus + from app.module.rag.repository import RagFileRepository + + kb_repo = KnowledgeBaseRepository(self.db) + file_repo = RagFileRepository(self.db) + + kb = await kb_repo.get_by_id(knowledge_base_id) + if not kb: + raise BusinessError(ErrorCodes.RAG_KNOWLEDGE_BASE_NOT_FOUND) + + rag_file = await file_repo.get_by_id(rag_file_id) + if not rag_file: + raise BusinessError(ErrorCodes.RAG_FILE_NOT_FOUND) + + try: + await file_repo.update_status(rag_file_id, FileStatus.PROCESSING) + await self.db.commit() + + file_path = get_file_path(rag_file) + if not file_path or not Path(file_path).exists(): + await file_repo.update_status(rag_file_id, FileStatus.PROCESS_FAILED) + await self.db.commit() + logger.error("文件不存在: %s", file_path) + return + + chunks = await ingest_file_to_chunks( + file_path, + process_type=kwargs.get("process_type", "DEFAULT_CHUNK"), + chunk_size=kwargs.get("chunk_size", 500), + overlap_size=kwargs.get("overlap_size", 50), + delimiter=kwargs.get("delimiter"), + ) + + if not chunks: + await file_repo.update_status(rag_file_id, FileStatus.PROCESS_FAILED) + await self.db.commit() + logger.error("文件解析失败，未生成任何文档") + return + + rag_instance = await self._get_or_create_graph_rag(kb) + + for idx, chunk in enumerate(chunks): + logger.info( + "插入文档到知识图谱: %s, 进度: %d/%d", + rag_file.file_name, idx + 1, len(chunks) + ) + await rag_instance.ainsert( + input=chunk.text, + file_paths=[file_path] + ) + + await file_repo.update_status(rag_file_id, FileStatus.PROCESSED) + await file_repo.update_chunk_count(rag_file_id, len(chunks)) + await self.db.commit() + + logger.info("文件 %s 知识图谱构建完成，文档数=%d", rag_file.file_name, len(chunks)) + + except Exception as e: + logger.exception("文件 %s 知识图谱构建失败: %s", rag_file.file_name, e) + await file_repo.update_status(rag_file_id, FileStatus.PROCESS_FAILED) + await self.db.commit() + raise BusinessError( + ErrorCodes.RAG_FILE_PROCESS_FAILED, + f"知识图谱构建失败: {str(e)}" + ) from e + + async def _get_knowledge_base(self, knowledge_base_id: str) -> KnowledgeBase: + kb = await self.kb_repo.get_by_id(knowledge_base_id) + if not kb: + raise BusinessError(ErrorCodes.RAG_KNOWLEDGE_BASE_NOT_FOUND) + return kb + + async def _get_or_create_graph_rag(self, kb: KnowledgeBase) -> Any: + kb_name = str(kb.name) # type: ignore + if kb_name in self._rag_cache: + return self._rag_cache[kb_name] + + chat_model = await get_model_by_id(self.db, str(kb.chat_model)) # type: ignore + embedding_model = await get_model_by_id(self.db, str(kb.embedding_model)) # type: ignore + + if not chat_model or not embedding_model: + raise BusinessError(ErrorCodes.RAG_MODEL_NOT_FOUND) + + llm_func = _create_llm_func( + str(chat_model.model_name), # type: ignore + str(chat_model.base_url), # type: ignore + str(chat_model.api_key), # type: ignore + ) + + from app.module.shared.llm import LLMFactory + embedding_func = _create_embedding_func( + str(embedding_model.model_name), # type: ignore + str(embedding_model.base_url), # type: ignore + str(embedding_model.api_key), # type: ignore + LLMFactory.get_embedding_dimension( + str(embedding_model.model_name), # type: ignore + str(embedding_model.base_url), # type: ignore + str(embedding_model.api_key), # type: ignore + ), + ) + + working_dir = os.path.join(DEFAULT_WORKING_DIR, kb_name) + rag = await _create_rag(llm_func, embedding_func, working_dir, workspace=kb_name) + self._rag_cache[kb_name] = rag + return rag diff --git a/runtime/datamate-python/app/module/rag/service/strategy/vector_strategy.py b/runtime/datamate-python/app/module/rag/service/strategy/vector_strategy.py new file mode 100644 index 000000000..5570f51a7 --- /dev/null +++ b/runtime/datamate-python/app/module/rag/service/strategy/vector_strategy.py @@ -0,0 +1,397 @@ +""" +向量知识库策略 + +实现 DOCUMENT 类型知识库的检索和处理逻辑。 +""" +import asyncio +import json +import logging +from typing import Any, Dict, List, Optional + +from pymilvus import AnnSearchRequest, Function, FunctionType + +from app.core.exception import BusinessError, ErrorCodes +from app.module.rag.infra.embeddings import EmbeddingFactory +from app.module.rag.infra.vectorstore.milvus_client import get_milvus_client +from app.module.rag.repository import KnowledgeBaseRepository, RagFileRepository +from app.module.rag.schema.response import PagedResponse, RagChunkResp +from app.module.rag.service.common import TextCleaner, MetadataBuilder, BatchProcessor, get_file_path +from app.module.system.service.common_service import get_model_by_id +from .base import KnowledgeBaseStrategy + +logger = logging.getLogger(__name__) + + +class VectorKnowledgeBaseStrategy(KnowledgeBaseStrategy): + """向量知识库策略实现 + + 提供 DOCUMENT 类型知识库的 query、 search 和 process_file 功能。 + """ + + async def query( + self, + knowledge_base_id: str, + **kwargs + ) -> PagedResponse: + """查询知识库分块数据（不涉及相似度计算） + + Args: + knowledge_base_id: 知识库 ID + **kwargs: 额外参数，必须包含: + - rag_file_id: RAG 文件 ID + - paging_query: 分页参数 + + Returns: + 分页分块列表 + """ + rag_file_id = kwargs.get("rag_file_id") + paging_query = kwargs.get("paging_query") + + if not rag_file_id or not paging_query: + raise BusinessError( + ErrorCodes.RAG_INVALID_REQUEST, + "Missing rag_file_id or paging_query parameters" + ) + + kb_repo = KnowledgeBaseRepository(self.db) + file_repo = RagFileRepository(self.db) + + knowledge_base = await kb_repo.get_by_id(knowledge_base_id) + if not knowledge_base: + raise BusinessError(ErrorCodes.RAG_KNOWLEDGE_BASE_NOT_FOUND) + + rag_file = await file_repo.get_by_id(rag_file_id) + if not rag_file: + raise BusinessError(ErrorCodes.RAG_FILE_NOT_FOUND) + + client = get_milvus_client() + + try: + count_filter = f'metadata["rag_file_id"] == "{rag_file_id}"' + count_res = client.query( + collection_name=knowledge_base.name, + filter=count_filter, + output_fields=["id"], + ) + total = len(count_res) + + offset = (paging_query.page - 1) * paging_query.size + results = client.query( + collection_name=knowledge_base.name, + filter=count_filter, + output_fields=["id", "text", "metadata"], + limit=paging_query.size, + offset=offset, + ) + + chunks = [ + RagChunkResp( + id=item.get("id", ""), + text=item.get("text", ""), + metadata=item.get("metadata", {}), + score=0.0, + distance=None, + ) + for item in results + ] + + logger.info( + "查询文件分块成功: kb=%s file=%s total=%d", + knowledge_base_id, rag_file_id, total + ) + + return PagedResponse.create( + content=chunks, + total_elements=total, + page=paging_query.page, + size=paging_query.size, + ) + + except Exception as e: + logger.error( + "查询文件分块失败: kb=%s file=%s error=%s", + knowledge_base_id, rag_file_id, e + ) + raise BusinessError( + ErrorCodes.RAG_MILVUS_ERROR, + f"查询文件分块失败: {str(e)}" + ) from e + + async def search( + self, + query_text: str, + knowledge_base_ids: List[str], + top_k: int = 10, + threshold: Optional[float] = None, + **kwargs + ) -> List[Dict[str, Any]]: + """基于输入文本的相似度检索（混合检索： 向量 + BM25) + + Args: + query_text: 查询文本 + knowledge_base_ids: 知识库 ID 列表 + top_k: 返回结果数量 + threshold: 相似度阈值（可选) + **kwargs: 额外参数 + + Returns: + 统一格式的检索结果列表 + """ + kb_repo = KnowledgeBaseRepository(self.db) + + knowledge_bases = [] + for kb_id in knowledge_base_ids: + kb = await kb_repo.get_by_id(kb_id) + if not kb: + raise BusinessError(ErrorCodes.RAG_KNOWLEDGE_BASE_NOT_FOUND) + knowledge_bases.append(kb) + + embedding_entity = await get_model_by_id(self.db, knowledge_bases[0].embedding_model) + if not embedding_entity: + raise BusinessError(ErrorCodes.RAG_MODEL_NOT_FOUND) + + embedding = EmbeddingFactory.create_embeddings( + model_name=embedding_entity.model_name, + base_url=getattr(embedding_entity, "base_url", None), + api_key=getattr(embedding_entity, "api_key", None), + ) + + try: + query_vector = await asyncio.to_thread(embedding.embed_query, query_text) + except Exception as e: + logger.error("查询向量化失败: %s", e) + raise BusinessError( + ErrorCodes.RAG_EMBEDDING_FAILED, + f"查询向量化失败: {str(e)}" + ) from e + + all_results = await self._execute_hybrid_search( + knowledge_bases, query_vector, query_text, top_k + ) + + all_results.sort( + key=lambda x: x.get("score") or x.get("distance", 0), + reverse=True + ) + + if threshold is not None: + all_results = [ + r for r in all_results + if (r.get("score") or r.get("distance", 0)) >= 0 + ] + + formatted = self._format_unified_results(all_results) + logger.info("向量检索完成: 结果数=%d", len(formatted)) + return formatted + + @staticmethod + async def _execute_hybrid_search( + knowledge_bases: list, + query_vector: list, + query_text: str, + top_k: int, + ) -> List[Dict[str, Any]]: + """执行混合检索""" + all_results = [] + client = get_milvus_client() + + for kb in knowledge_bases: + try: + if not client.has_collection(kb.name): + logger.warning("集合 %s 不存在，跳过", kb.name) + continue + + dense_search = AnnSearchRequest( + data=[query_vector], + anns_field="vector", + param={"nprobe": 10}, + limit=top_k, + ) + + sparse_search = AnnSearchRequest( + data=[query_text], + anns_field="sparse", + param={"drop_ratio_search": 0.2}, + limit=top_k, + ) + + ranker = Function( + name="weight", + input_field_names=[], + function_type=FunctionType.RERANK, + params={ + "reranker": "weighted", + "weights": [0.1, 0.9], + "norm_score": True, + } + ) + search_results = client.hybrid_search( + collection_name=kb.name, + reqs=[dense_search, sparse_search], + ranker=ranker, + output_fields=["id", "text", "metadata"], + limit=top_k, + ) + + if search_results and len(search_results) > 0: + for result in search_results[0]: + result["knowledge_base_id"] = kb.id + result["knowledge_base_name"] = kb.name + all_results.append(result) + + except Exception as e: + logger.error("知识库 %s 混合检索失败: %s", kb.name, e) + continue + + return all_results + + @staticmethod + def _format_unified_results( + all_results: List[Dict[str, Any]] + ) -> List[Dict[str, Any]]: + """格式化为统一检索结果""" + formatted = [] + for r in all_results: + entity = r.get("entity", {}) + metadata = entity.get("metadata", {}) + if isinstance(metadata, dict): + metadata_str = json.dumps(metadata, ensure_ascii=False) + else: + metadata_str = metadata if metadata else "{}" + + formatted.append({ + "id": entity.get("id", ""), + "text": entity.get("text", ""), + "score": r.get("score") or r.get("distance", 0), + "metadata": json.loads(metadata_str) if isinstance(metadata_str, str) else metadata, + "resultType": "vector", + "knowledgeBaseId": r.get("knowledge_base_id", ""), + "knowledgeBaseName": r.get("knowledge_base_name", ""), + }) + + return formatted + + async def process_file( + self, + knowledge_base_id: str, + rag_file_id: str, + **kwargs + ) -> None: + """处理单个文件（文档向量化) + + Args: + knowledge_base_id: 知识库 ID + rag_file_id: RAG 文件 ID + **kwargs: 额外参数 + - process_type: 分块类型 + - chunk_size: 分块大小 + - overlap_size: 重叠大小 + - delimiter: 分隔符 + """ + from pathlib import Path + from app.module.rag.infra.vectorstore import VectorStoreFactory + from app.module.rag.infra.document.processor import ingest_file_to_chunks + from app.db.models.knowledge_gen import FileStatus + from app.module.rag.service.common import TextCleaner, MetadataBuilder, BatchProcessor + + kb_repo = KnowledgeBaseRepository(self.db) + file_repo = RagFileRepository(self.db) + + kb = await kb_repo.get_by_id(knowledge_base_id) + if not kb: + raise BusinessError(ErrorCodes.RAG_KNOWLEDGE_BASE_NOT_FOUND) + + rag_file = await file_repo.get_by_id(rag_file_id) + if not rag_file: + raise BusinessError(ErrorCodes.RAG_FILE_NOT_FOUND) + + try: + await file_repo.update_status(rag_file_id, FileStatus.PROCESSING) + await self.db.commit() + + file_path = self._get_file_path(rag_file) + if not file_path or not Path(file_path).exists(): + await file_repo.update_status(rag_file_id, FileStatus.PROCESS_FAILED) + await self.db.commit() + logger.error("文件不存在: %s", file_path) + return + + chunks = await ingest_file_to_chunks( + file_path, + process_type=kwargs.get("process_type", "DEFAULT_CHUNK"), + chunk_size=kwargs.get("chunk_size", 500), + overlap_size=kwargs.get("overlap_size", 50), + delimiter=kwargs.get("delimiter"), + ) + + if not chunks: + await file_repo.update_status(rag_file_id, FileStatus.PROCESS_FAILED) + await self.db.commit() + logger.error("文件解析失败，未生成任何分块") + return + + valid_chunks = self._filter_and_clean_chunks(chunks) + if not valid_chunks: + await file_repo.update_status(rag_file_id, FileStatus.PROCESS_FAILED) + await self.db.commit() + logger.error("文件没有有效的分块内容") + return + + embedding_entity = await get_model_by_id(self.db, kb.embedding_model) + if not embedding_entity: + raise BusinessError(ErrorCodes.RAG_MODEL_NOT_FOUND) + + embedding = EmbeddingFactory.create_embeddings( + model_name=str(embedding_entity.model_name), + base_url=getattr(embedding_entity, "base_url", None), + api_key=getattr(embedding_entity, "api_key", None), + ) + + vectorstore = VectorStoreFactory.create( + collection_name=str(kb.name), + embedding=embedding, + ) + + base_metadata = MetadataBuilder.build_chunk_metadata(rag_file, kb) + MetadataBuilder.add_to_chunks(valid_chunks, base_metadata) + + await BatchProcessor.store_in_batches(vectorstore, valid_chunks) + + await file_repo.update_status(rag_file_id, FileStatus.PROCESSED) + await file_repo.update_chunk_count(rag_file_id, len(valid_chunks)) + await self.db.commit() + + logger.info("文件 %s 向量化处理完成，分块数=%d", rag_file.file_name, len(valid_chunks)) + + except Exception as e: + logger.exception("文件 %s 处理失败: %s", rag_file.file_name, e) + await file_repo.update_status(rag_file_id, FileStatus.PROCESS_FAILED) + await self.db.commit() + raise BusinessError( + ErrorCodes.RAG_FILE_PROCESS_FAILED, + f"文件处理失败: {str(e)}" + ) from e + + def _get_file_path(self, rag_file) -> Optional[str]: + from app.module.rag.service.common import get_file_path + return get_file_path(rag_file) + + def _filter_and_clean_chunks(self, chunks: list) -> list: + """过滤和清理分块""" + from app.module.rag.service.common import TextCleaner + + valid_chunks = [] + for chunk in chunks: + cleaned_text = TextCleaner.clean(chunk.text) + + if cleaned_text and TextCleaner.has_printable_content(cleaned_text): + chunk.text = cleaned_text + valid_chunks.append(chunk) + else: + logger.warning( + "跳过无效分块: chunk_index=%s", + chunk.metadata.get("chunk_index") + ) + + logger.info("有效分块数量: %d / %d", len(valid_chunks), len(chunks)) + return valid_chunks diff --git a/runtime/datamate-python/app/module/rag/service/unified_retrieval_service.py b/runtime/datamate-python/app/module/rag/service/unified_retrieval_service.py new file mode 100644 index 000000000..3be5a839a --- /dev/null +++ b/runtime/datamate-python/app/module/rag/service/unified_retrieval_service.py @@ -0,0 +1,122 @@ +""" +统一检索服务 + +提供知识库内容的统一检索接口，支持多种知识库类型(向量、知识图谱等)。 +使用策略模式实现不同知识库类型的检索逻辑。 +""" +import logging +from typing import Any, Dict, List + +from sqlalchemy.ext.asyncio import AsyncSession + +from app.core.exception import BusinessError, ErrorCodes +from app.db.models.knowledge_gen import KnowledgeBase +from app.module.rag.repository import KnowledgeBaseRepository +from app.module.rag.schema.request import PagingQuery, RetrieveReq +from app.module.rag.schema.response import PagedResponse +from .strategy import KnowledgeBaseStrategyFactory + +logger = logging.getLogger(__name__) + + +class UnifiedRetrievalService: + """统一检索服务 + + 揯供统一的检索接口,根据知识库类型自动选择策略。 + """ + + def __init__(self, db: AsyncSession): + """初始化服务 + + Args: + db: 数据库异步 session + """ + self.db = db + self.kb_repo = KnowledgeBaseRepository(db) + + async def query( + self, + knowledge_base_id: str, + **kwargs + ) -> Any: + """直接查询知识库数据(不涉及相似度计算) + + Args: + knowledge_base_id: 知识库 ID + **kwargs: 额外参数 + - 向量知识库: rag_file_id, paging_query + - 知识图谱: node_label + + Returns: + 查询结果(格式由具体知识库类型决定) + """ + kb = await self._get_knowledge_base(knowledge_base_id) + strategy = KnowledgeBaseStrategyFactory.create_strategy(kb.type, self.db) + return await strategy.query(knowledge_base_id, **kwargs) + + async def search( + self, + request: RetrieveReq + ) -> List[Dict[str, Any]]: + """基于输入文本的相似度检索 + + Args: + request: 检索请求 + + Returns: + 统一格式的检索结果列表(UnifiedSearchResult) + """ + if not request.knowledge_base_ids: + raise BusinessError( + ErrorCodes.RAG_INVALID_REQUEST, + "至少需要一个知识库 ID" + ) + + kb = await self._get_knowledge_base(request.knowledge_base_ids[0]) + strategy = KnowledgeBaseStrategyFactory.create_strategy(kb.type, self.db) + + return await strategy.search( + query_text=request.query, + knowledge_base_ids=request.knowledge_base_ids, + top_k=request.top_k, + threshold=request.threshold, + ) + + async def get_chunks( + self, + knowledge_base_id: str, + rag_file_id: str, + paging_query: PagingQuery, + ) -> PagedResponse: + """获取指定 RAG 文件的分块列表(仅向量知识库) + + Args: + knowledge_base_id: 知识库 ID + rag_file_id: RAG 文件 ID + paging_query: 分页参数 + + Returns: + 分块列表(分页) + """ + return await self.query( + knowledge_base_id, + rag_file_id=rag_file_id, + paging_query=paging_query, + ) + + async def _get_knowledge_base(self, knowledge_base_id: str) -> KnowledgeBase: + """获取知识库实体 + + Args: + knowledge_base_id: 知识库 ID + + Returns: + 知识库实体 + + Raises: + BusinessError: 知识库不存在 + """ + kb = await self.kb_repo.get_by_id(knowledge_base_id) + if not kb: + raise BusinessError(ErrorCodes.RAG_KNOWLEDGE_BASE_NOT_FOUND) + return kb diff --git a/runtime/datamate-python/poetry.lock b/runtime/datamate-python/poetry.lock index 53f6ec426..066340b5d 100644 --- a/runtime/datamate-python/poetry.lock +++ b/runtime/datamate-python/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 2.3.2 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.2.1 and should not be changed by hand. 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