## Problem
When users configure auto-metadata for a dataset, parsing crashes with:
```
KeyError: 'properties' in gen_metadata → schema["properties"]
```
## Root Cause
Pydantic `AutoMetadataField` defaults `enum` and `description` to `None`
when the frontend omits these fields:
```python
class AutoMetadataField(Base):
enum: Annotated[list[str] | None, Field(default=None)]
description: Annotated[str | None, Field(default=None)]
```
These `None` values propagate through the call chain and cause two
crashes:
### What problem does this PR solve?
Table parser metadata aggregation previously only ran when
`table_column_mode` was set to `manual`. In auto mode (default), all
columns default to `"both"` role, meaning they should also be aggregated
into document-level metadata for UI/chat filters. Additionally, the task
snapshot could be stale — `table_column_names` are written to KB
`parser_config` during `chunk()` but the task may have been created
before that.
Changes:
- Renames `aggregate_table_manual_doc_metadata` →
`aggregate_table_doc_metadata`
- Supports both `"manual"` and `"auto"` `table_column_mode` (defaults to
`"auto"`)
- Reloads `table_column_names` from KB DB when missing from task
snapshot
- Removes the manual-only guard in `task_executor` and refactored
`post_processor`
- Updates all tests with new function name and adds auto mode test cases
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Python implementation of the Go-based model_provider API suite.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
---------
Co-authored-by: bill <yibie_jingnian@163.com>
### What problem does this PR solve?
1. Break huge function into smaller pieces
2. Add unit test for the smaller pieces function
3. Layer-ed design
a. infra layer - task_context.py, recording_context.py,
write_operation_interceptor.py, ...
b. service layer - *_service.py
c. business layer - task_handler.py
4. Default behavior: use "refactor-ed version" - can switch to original
version by change env variable
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] Refactoring
- [x] Performance Improvement
---------
Co-authored-by: Liu An <asiro@qq.com>
Co-authored-by: Zhichang Yu <yuzhichang@gmail.com>
### 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)
