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### 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.
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"