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Feature big query connector (#15871)
### What problem does this PR solve? This PR adds Google BigQuery as a first-class data source connector in RAGFlow. It enables users to ingest and sync BigQuery data using the same row-to-document model used by relational database connectors: selected content columns become document text, metadata columns become document metadata, an optional ID column provides stable document IDs, and an optional timestamp column enables cursor-based incremental sync. The connector supports service-account JSON credentials, table mode, custom query mode, GoogleSQL queries, cursor-based incremental sync, deleted-row pruning support, configurable query limits such as `maximum_bytes_billed`, dry-run validation, batch loading, stable document IDs, and BigQuery-aware value serialization.
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@@ -1,5 +1,6 @@
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import Image from '@/components/image';
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import SvgIcon from '@/components/svg-icon';
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import { MarkdownRemarkPlugins } from '@/constants/markdown-remark-plugins';
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import { IReference, IReferenceChunk } from '@/interfaces/database/chat';
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import { citationMarkerReg } from '@/utils/citation-utils';
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import { getExtension } from '@/utils/document-util';
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@@ -10,7 +11,6 @@ import Markdown from 'react-markdown';
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import SyntaxHighlighter from 'react-syntax-highlighter';
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import rehypeKatex from 'rehype-katex';
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import rehypeRaw from 'rehype-raw';
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import { MarkdownRemarkPlugins } from '@/constants/markdown-remark-plugins';
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import { visitParents } from 'unist-util-visit-parents';
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import { useTranslation } from 'react-i18next';
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@@ -72,7 +72,11 @@ const MarkdownContent = ({
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text = t('chat.searching');
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}
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const nextText = replaceTextByOldReg(text);
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return pipe(replaceThinkToSection, replaceRetrievingToSection, preprocessLaTeX)(nextText);
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return pipe(
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replaceThinkToSection,
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replaceRetrievingToSection,
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preprocessLaTeX,
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)(nextText);
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}, [content, t]);
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useEffect(() => {
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