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ragflow/web/src/components/fallback-component/index.tsx

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import React from 'react';
import { useTranslation } from 'react-i18next';
Feat/configurable metadata display (#13464) ### What problem does this PR solve? Currently, RAGFlow's Search and Chat interfaces display only raw vectorized text chunks during retrieval, without contextual information about their source documents. Users cannot see document titles, page numbers, upload dates, or custom metadata fields that would help them understand and trust the retrieved results. This PR introduces an **optional metadata display feature** that enriches retrieved chunks with document-level metadata in both the Search tab and Chatbot interface. **Key improvements:** - **Search results**: Display document metadata as styled badges beneath chunk snippets - **Chat citations**: Show metadata in citation popovers and reference lists for better source context - **LLM context**: Metadata is injected into the LLM prompt to enable more accurate, citation-aware responses - **External API support**: Applications using RAGFlow's SDK retrieval endpoints (`/v1/retrieval`, `/v1/searchbots/retrieval_test`) can opt-in via request parameters - **User control**: Multi-select dropdown UI allows users to choose which metadata fields to display **Implementation approach:** - ✅ Reuses existing `DocMetadataService` infrastructure (no new database tables or indices) - ✅ Settings stored in existing JSON configuration fields (`search_config.reference_metadata`, `prompt_config.reference_metadata`) - ✅ No database migrations required - ✅ Disabled by default (fully opt-in and backward-compatible) - ✅ Dynamic metadata field selection populated from actual document metadata keys - ✅ Fixed critical bug where Python's builtin `set()` was shadowed by a route handler function **Modified endpoints (all backward-compatible):** - `POST /v1/retrieval` (Public SDK) - `POST /v1/searchbots/retrieval_test` (Searchbots) - `POST /v1/chunk/retrieval_test` (UI/Internal) - Chat completions endpoints (via `extra_body.reference_metadata` or `prompt_config`) ### Type of change - [x] New Feature (non-breaking change which adds functionality) ###Images - <img width="879" height="1275" alt="image" src="https://github.com/user-attachments/assets/95b2d731-31ae-45a1-b081-bf5893f52aeb" /> <br><br> <br><br> <img width="1532" height="362" alt="image" src="https://github.com/user-attachments/assets/9cebc65b-b7a7-459f-b25e-3b13fa9b638e" /> <br><br> <br><br> <img width="2586" height="1320" alt="image" src="https://github.com/user-attachments/assets/2153d493-d899-461f-a7a9-041391e07776" /> --------- Co-authored-by: Cursor Agent <cursoragent@cursor.com> Co-authored-by: Attili-sys <Attili-sys@users.noreply.github.com> Co-authored-by: Ahmad Intisar <ahmadintisar@Ahmads-MacBook-M4-Pro.local>
2026-04-30 18:13:27 +03:00
import { isRouteErrorResponse, useRouteError } from 'react-router';
interface FallbackComponentProps {
error?: Error;
reset?: () => void;
}
const FallbackComponent: React.FC<FallbackComponentProps> = ({
Feat/configurable metadata display (#13464) ### What problem does this PR solve? Currently, RAGFlow's Search and Chat interfaces display only raw vectorized text chunks during retrieval, without contextual information about their source documents. Users cannot see document titles, page numbers, upload dates, or custom metadata fields that would help them understand and trust the retrieved results. This PR introduces an **optional metadata display feature** that enriches retrieved chunks with document-level metadata in both the Search tab and Chatbot interface. **Key improvements:** - **Search results**: Display document metadata as styled badges beneath chunk snippets - **Chat citations**: Show metadata in citation popovers and reference lists for better source context - **LLM context**: Metadata is injected into the LLM prompt to enable more accurate, citation-aware responses - **External API support**: Applications using RAGFlow's SDK retrieval endpoints (`/v1/retrieval`, `/v1/searchbots/retrieval_test`) can opt-in via request parameters - **User control**: Multi-select dropdown UI allows users to choose which metadata fields to display **Implementation approach:** - ✅ Reuses existing `DocMetadataService` infrastructure (no new database tables or indices) - ✅ Settings stored in existing JSON configuration fields (`search_config.reference_metadata`, `prompt_config.reference_metadata`) - ✅ No database migrations required - ✅ Disabled by default (fully opt-in and backward-compatible) - ✅ Dynamic metadata field selection populated from actual document metadata keys - ✅ Fixed critical bug where Python's builtin `set()` was shadowed by a route handler function **Modified endpoints (all backward-compatible):** - `POST /v1/retrieval` (Public SDK) - `POST /v1/searchbots/retrieval_test` (Searchbots) - `POST /v1/chunk/retrieval_test` (UI/Internal) - Chat completions endpoints (via `extra_body.reference_metadata` or `prompt_config`) ### Type of change - [x] New Feature (non-breaking change which adds functionality) ###Images - <img width="879" height="1275" alt="image" src="https://github.com/user-attachments/assets/95b2d731-31ae-45a1-b081-bf5893f52aeb" /> <br><br> <br><br> <img width="1532" height="362" alt="image" src="https://github.com/user-attachments/assets/9cebc65b-b7a7-459f-b25e-3b13fa9b638e" /> <br><br> <br><br> <img width="2586" height="1320" alt="image" src="https://github.com/user-attachments/assets/2153d493-d899-461f-a7a9-041391e07776" /> --------- Co-authored-by: Cursor Agent <cursoragent@cursor.com> Co-authored-by: Attili-sys <Attili-sys@users.noreply.github.com> Co-authored-by: Ahmad Intisar <ahmadintisar@Ahmads-MacBook-M4-Pro.local>
2026-04-30 18:13:27 +03:00
error: errorProp,
reset,
}) => {
const { t } = useTranslation();
Feat/configurable metadata display (#13464) ### What problem does this PR solve? Currently, RAGFlow's Search and Chat interfaces display only raw vectorized text chunks during retrieval, without contextual information about their source documents. Users cannot see document titles, page numbers, upload dates, or custom metadata fields that would help them understand and trust the retrieved results. This PR introduces an **optional metadata display feature** that enriches retrieved chunks with document-level metadata in both the Search tab and Chatbot interface. **Key improvements:** - **Search results**: Display document metadata as styled badges beneath chunk snippets - **Chat citations**: Show metadata in citation popovers and reference lists for better source context - **LLM context**: Metadata is injected into the LLM prompt to enable more accurate, citation-aware responses - **External API support**: Applications using RAGFlow's SDK retrieval endpoints (`/v1/retrieval`, `/v1/searchbots/retrieval_test`) can opt-in via request parameters - **User control**: Multi-select dropdown UI allows users to choose which metadata fields to display **Implementation approach:** - ✅ Reuses existing `DocMetadataService` infrastructure (no new database tables or indices) - ✅ Settings stored in existing JSON configuration fields (`search_config.reference_metadata`, `prompt_config.reference_metadata`) - ✅ No database migrations required - ✅ Disabled by default (fully opt-in and backward-compatible) - ✅ Dynamic metadata field selection populated from actual document metadata keys - ✅ Fixed critical bug where Python's builtin `set()` was shadowed by a route handler function **Modified endpoints (all backward-compatible):** - `POST /v1/retrieval` (Public SDK) - `POST /v1/searchbots/retrieval_test` (Searchbots) - `POST /v1/chunk/retrieval_test` (UI/Internal) - Chat completions endpoints (via `extra_body.reference_metadata` or `prompt_config`) ### Type of change - [x] New Feature (non-breaking change which adds functionality) ###Images - <img width="879" height="1275" alt="image" src="https://github.com/user-attachments/assets/95b2d731-31ae-45a1-b081-bf5893f52aeb" /> <br><br> <br><br> <img width="1532" height="362" alt="image" src="https://github.com/user-attachments/assets/9cebc65b-b7a7-459f-b25e-3b13fa9b638e" /> <br><br> <br><br> <img width="2586" height="1320" alt="image" src="https://github.com/user-attachments/assets/2153d493-d899-461f-a7a9-041391e07776" /> --------- Co-authored-by: Cursor Agent <cursoragent@cursor.com> Co-authored-by: Attili-sys <Attili-sys@users.noreply.github.com> Co-authored-by: Ahmad Intisar <ahmadintisar@Ahmads-MacBook-M4-Pro.local>
2026-04-30 18:13:27 +03:00
const routeError = useRouteError();
const error =
errorProp ?? (routeError instanceof Error ? routeError : undefined);
let routeErrorDataStr = '';
if (isRouteErrorResponse(routeError)) {
if (typeof routeError.data === 'string') {
routeErrorDataStr = routeError.data;
} else if (routeError.data == null) {
routeErrorDataStr = 'no body';
} else {
try {
routeErrorDataStr = JSON.stringify(routeError.data);
} catch {
routeErrorDataStr = String(routeError.data);
}
}
}
const errorMessage = isRouteErrorResponse(routeError)
? `${routeError.status} ${routeError.statusText}${routeErrorDataStr ? `: ${routeErrorDataStr}` : ''}`
: (error?.toString() ?? (routeError ? String(routeError) : undefined));
return (
<div style={{ padding: '20px', textAlign: 'center' }}>
<h2>{t('error_boundary.title', 'Something went wrong')}</h2>
<p>
{t(
'error_boundary.description',
'Sorry, an error occurred while loading the page.',
)}
</p>
Feat/configurable metadata display (#13464) ### What problem does this PR solve? Currently, RAGFlow's Search and Chat interfaces display only raw vectorized text chunks during retrieval, without contextual information about their source documents. Users cannot see document titles, page numbers, upload dates, or custom metadata fields that would help them understand and trust the retrieved results. This PR introduces an **optional metadata display feature** that enriches retrieved chunks with document-level metadata in both the Search tab and Chatbot interface. **Key improvements:** - **Search results**: Display document metadata as styled badges beneath chunk snippets - **Chat citations**: Show metadata in citation popovers and reference lists for better source context - **LLM context**: Metadata is injected into the LLM prompt to enable more accurate, citation-aware responses - **External API support**: Applications using RAGFlow's SDK retrieval endpoints (`/v1/retrieval`, `/v1/searchbots/retrieval_test`) can opt-in via request parameters - **User control**: Multi-select dropdown UI allows users to choose which metadata fields to display **Implementation approach:** - ✅ Reuses existing `DocMetadataService` infrastructure (no new database tables or indices) - ✅ Settings stored in existing JSON configuration fields (`search_config.reference_metadata`, `prompt_config.reference_metadata`) - ✅ No database migrations required - ✅ Disabled by default (fully opt-in and backward-compatible) - ✅ Dynamic metadata field selection populated from actual document metadata keys - ✅ Fixed critical bug where Python's builtin `set()` was shadowed by a route handler function **Modified endpoints (all backward-compatible):** - `POST /v1/retrieval` (Public SDK) - `POST /v1/searchbots/retrieval_test` (Searchbots) - `POST /v1/chunk/retrieval_test` (UI/Internal) - Chat completions endpoints (via `extra_body.reference_metadata` or `prompt_config`) ### Type of change - [x] New Feature (non-breaking change which adds functionality) ###Images - <img width="879" height="1275" alt="image" src="https://github.com/user-attachments/assets/95b2d731-31ae-45a1-b081-bf5893f52aeb" /> <br><br> <br><br> <img width="1532" height="362" alt="image" src="https://github.com/user-attachments/assets/9cebc65b-b7a7-459f-b25e-3b13fa9b638e" /> <br><br> <br><br> <img width="2586" height="1320" alt="image" src="https://github.com/user-attachments/assets/2153d493-d899-461f-a7a9-041391e07776" /> --------- Co-authored-by: Cursor Agent <cursoragent@cursor.com> Co-authored-by: Attili-sys <Attili-sys@users.noreply.github.com> Co-authored-by: Ahmad Intisar <ahmadintisar@Ahmads-MacBook-M4-Pro.local>
2026-04-30 18:13:27 +03:00
{errorMessage && (
<details open className="mt-4 whitespace-pre-wrap">
<summary>{t('error_boundary.details', 'Error details')}</summary>
Feat/configurable metadata display (#13464) ### What problem does this PR solve? Currently, RAGFlow's Search and Chat interfaces display only raw vectorized text chunks during retrieval, without contextual information about their source documents. Users cannot see document titles, page numbers, upload dates, or custom metadata fields that would help them understand and trust the retrieved results. This PR introduces an **optional metadata display feature** that enriches retrieved chunks with document-level metadata in both the Search tab and Chatbot interface. **Key improvements:** - **Search results**: Display document metadata as styled badges beneath chunk snippets - **Chat citations**: Show metadata in citation popovers and reference lists for better source context - **LLM context**: Metadata is injected into the LLM prompt to enable more accurate, citation-aware responses - **External API support**: Applications using RAGFlow's SDK retrieval endpoints (`/v1/retrieval`, `/v1/searchbots/retrieval_test`) can opt-in via request parameters - **User control**: Multi-select dropdown UI allows users to choose which metadata fields to display **Implementation approach:** - ✅ Reuses existing `DocMetadataService` infrastructure (no new database tables or indices) - ✅ Settings stored in existing JSON configuration fields (`search_config.reference_metadata`, `prompt_config.reference_metadata`) - ✅ No database migrations required - ✅ Disabled by default (fully opt-in and backward-compatible) - ✅ Dynamic metadata field selection populated from actual document metadata keys - ✅ Fixed critical bug where Python's builtin `set()` was shadowed by a route handler function **Modified endpoints (all backward-compatible):** - `POST /v1/retrieval` (Public SDK) - `POST /v1/searchbots/retrieval_test` (Searchbots) - `POST /v1/chunk/retrieval_test` (UI/Internal) - Chat completions endpoints (via `extra_body.reference_metadata` or `prompt_config`) ### Type of change - [x] New Feature (non-breaking change which adds functionality) ###Images - <img width="879" height="1275" alt="image" src="https://github.com/user-attachments/assets/95b2d731-31ae-45a1-b081-bf5893f52aeb" /> <br><br> <br><br> <img width="1532" height="362" alt="image" src="https://github.com/user-attachments/assets/9cebc65b-b7a7-459f-b25e-3b13fa9b638e" /> <br><br> <br><br> <img width="2586" height="1320" alt="image" src="https://github.com/user-attachments/assets/2153d493-d899-461f-a7a9-041391e07776" /> --------- Co-authored-by: Cursor Agent <cursoragent@cursor.com> Co-authored-by: Attili-sys <Attili-sys@users.noreply.github.com> Co-authored-by: Ahmad Intisar <ahmadintisar@Ahmads-MacBook-M4-Pro.local>
2026-04-30 18:13:27 +03:00
{errorMessage}
</details>
)}
<div style={{ marginTop: '16px' }}>
<button
onClick={() => window.location.reload()}
style={{
marginRight: '12px',
padding: '8px 16px',
backgroundColor: '#1890ff',
color: 'white',
border: 'none',
borderRadius: '4px',
cursor: 'pointer',
}}
>
{t('error_boundary.reload', 'Reload Page')}
</button>
{reset && (
<button
onClick={reset}
style={{
padding: '8px 16px',
backgroundColor: '#52c41a',
color: 'white',
border: 'none',
borderRadius: '4px',
cursor: 'pointer',
}}
>
{t('error_boundary.retry', 'Retry')}
</button>
)}
</div>
</div>
);
};
export default FallbackComponent;