### What problem does this PR solve?
The table file parser (CSV/Excel) currently treats all columns
identically — every column is both vectorized (embedded in chunk text)
and stored as filterable metadata. There's no way for users to control
which columns should be searchable by semantic meaning versus which
should only be filterable attributes.
For example, when ingesting a news articles CSV with columns like title,
content, country, category, source, etc., the embedding includes
metadata fields like country: Brazil and source: Reuters in the chunk
text, which dilutes the semantic quality of the embedding without adding
retrieval value.
The RDBMS connector (MySQL/PostgreSQL) already supports content_columns
/ metadata_columns, but this capability was missing for file-based table
ingestion.
This PR adds column-level control (vectorize / metadata / both) for the
table file parser, following RAGFlow's existing patterns.
Backward compatible: Datasets without table_column_roles or with
table_column_mode: auto behave exactly as before (all columns = both).
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
Add validation logic for parser_config.
Refactor the processing flow. Before change, validation logics and
update logics are mixed up - some validation logis executes followed by
some update logic executes and then another such
"validation-and-then-update" which is not good. After change, all
validation logic executes firstly. Update logic will be executed after
ALL validation logic executed.
Validation logic for parameters (that come from front end) will be
checked using Pydantic. For validation logic that depends on data from
DB, they will be in separate methods.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] Refactoring
