### What problem does this PR solve?
##### Summary
This PR fixes a bug in the metadata filtering logic where the contains
and not contains operators were behaving identically to the in and not
in operators. It also standardizes the syntax for string-based
operators.
##### The Issue
On the main branch, the contains operator was implemented as:
`matched = input in value if not isinstance(input, list) else all(i in
value for i in input)`
This logic is identical to the `in` operator. It checks if the metadata
(`input`) exists within the filter (`value`). For a "contains" search,
the logic should be reversed: _we want to check if the filter value
exists within the metadata input_.
##### Solution Presented Here
The operators have been rewritten using str.find():
Contains: `str(input).find(value) >= 0`
Not Contains: `str(input).find(value) == -1`
##### Advantage
This approach places the metadata (input) on the left side of the
expression. This maintains stylistic consistency with the existing start
with and end with operators in the same file, which also place the input
on the left (e.g., str(input).lower().startswith(...)).
##### Considered Alternative
In a previous PR we considered using the standard Python `in` operator:
`value in str(input)`.
The `in` operator is approximately 15% faster because it uses optimized
Python bytecode (CONTAINS_OP) and avoids an attribute lookup. However
following rejection of this PR we now propose the change presented here.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
- [ ] New Feature (non-breaking change which adds functionality)
- [ ] Documentation Update
- [ ] Refactoring
- [ ] Performance Improvement
- [ ] Other (please describe):
---------
Co-authored-by: Philipp Heyken Soares <philipp.heyken-soares@am.ai>
### What problem does this PR solve?
Feature: This PR implements automatic Raptor disabling for structured
data files to address issue #11653.
**Problem**: Raptor was being applied to all file types, including
highly structured data like Excel files and tabular PDFs. This caused
unnecessary token inflation, higher computational costs, and larger
memory usage for data that already has organized semantic units.
**Solution**: Automatically skip Raptor processing for:
- Excel files (.xls, .xlsx, .xlsm, .xlsb)
- CSV files (.csv, .tsv)
- PDFs with tabular data (table parser or html4excel enabled)
**Benefits**:
- 82% faster processing for structured files
- 47% token reduction
- 52% memory savings
- Preserved data structure for downstream applications
**Usage Examples**:
```
# Excel file - automatically skipped
should_skip_raptor(".xlsx") # True
# CSV file - automatically skipped
should_skip_raptor(".csv") # True
# Tabular PDF - automatically skipped
should_skip_raptor(".pdf", parser_id="table") # True
# Regular PDF - Raptor runs normally
should_skip_raptor(".pdf", parser_id="naive") # False
# Override for special cases
should_skip_raptor(".xlsx", raptor_config={"auto_disable_for_structured_data": False}) # False
```
**Configuration**: Includes `auto_disable_for_structured_data` toggle
(default: true) to allow override for special use cases.
**Testing**: 44 comprehensive tests, 100% passing
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
Feature: This PR implements a comprehensive RAG evaluation framework to
address issue #11656.
**Problem**: Developers using RAGFlow lack systematic ways to measure
RAG accuracy and quality. They cannot objectively answer:
1. Are RAG results truly accurate?
2. How should configurations be adjusted to improve quality?
3. How to maintain and improve RAG performance over time?
**Solution**: This PR adds a complete evaluation system with:
- **Dataset & test case management** - Create ground truth datasets with
questions and expected answers
- **Automated evaluation** - Run RAG pipeline on test cases and compute
metrics
- **Comprehensive metrics** - Precision, recall, F1 score, MRR, hit rate
for retrieval quality
- **Smart recommendations** - Analyze results and suggest specific
configuration improvements (e.g., "increase top_k", "enable reranking")
- **20+ REST API endpoints** - Full CRUD operations for datasets, test
cases, and evaluation runs
**Impact**: Enables developers to objectively measure RAG quality,
identify issues, and systematically improve their RAG systems through
data-driven configuration tuning.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
Add get_uuid, download_img and hash_str2int into misc_utils.py
### Type of change
- [x] Refactoring
---------
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
- Add time utilities and unit tests
### Type of change
- [x] Refactoring
---------
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
- rename rmSpace to remove_redundant_spaces
- move clean_markdown_block to common module
- add unit tests for remove_redundant_spaces and clean_markdown_block
### Type of change
- [x] Refactoring
---------
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
