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### 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>
Install front-end dependencies
npm install
Launch front-end
npm run dev
The following output confirms a successful launch of the system:
Login to RAGFlow web UI
Open your browser and navigate to:
http://localhost:9222 or http://[YOUR_MACHINE_IP]:9222
Replace [YOUR_MACHINE_IP] with your actual machine IP address (e.g., http://192.168.1.49:9222).
Login to RAGFlow web admin UI
Open your browser and navigate to:
http://localhost:9222/admin or http://[YOUR_MACHINE_IP]:9222/admin
Replace [YOUR_MACHINE_IP] with your actual machine IP address (e.g., http://192.168.1.49:9222/admin).
Shutdown front-end
Ctrl + C or
kill -f "umi dev"