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.

common/data_source/__init__.py

@@ -47,6 +47,7 @@ from .imap_connector import ImapConnector
 from .zendesk_connector import ZendeskConnector
 from .seafile_connector import SeaFileConnector
 from .rdbms_connector import RDBMSConnector
+from .bigquery_connector import BigQueryConnector
 from .webdav_connector import WebDAVConnector
 from .rest_api_connector import RestAPIConnector
 from .config import BlobType, DocumentSource
@@ -94,6 +95,7 @@ __all__ = [
     "ZendeskConnector",
     "SeaFileConnector",
     "RDBMSConnector",
+    "BigQueryConnector",
     "WebDAVConnector",
     "DingTalkAITableConnector",
     "RestAPIConnector",