### What problem does this PR solve?
#### Summary
This PR enhances the Semi-automatic metadata filtering mode by allowing
users to explicitly pre-define operators (e.g., contains, =, >, etc.)
for selected metadata keys. While the LLM still dynamically extracts the
filter value from the user's query, it is now strictly constrained to
use the operator specified in the UI configuration.
Using this feature is optional. By default the operator selection is set
to "automatic" resulting in the LLM choosing the operator (as
presently).
#### Rationale & Use Case
This enhancement was driven by a concrete challenge I encountered while
working with technical documentation.
In my specific use case, I was trying to filter for software versions
within a technical manual. In this dataset, a single document chunk
often applies to multiple software versions. These versions are stored
as a combined string within the metadata for each chunk.
When using the standard semi-automatic filter, the LLM would
inconsistently choose between the contains and equals operators. When it
chose equals, it would exclude every chunk that applied to more than one
version, even if the version I was searching for was clearly included in
that metadata string. This led to incomplete and frustrating retrieval
results.
By extending the semi-automatic filter to allow pre-defining the
operator for a specific key, I was able to force the use of contains for
the version field. This change immediately led to significantly improved
and more reliable results in my case.
I believe this functionality will be equally useful for others dealing
with "tagged" or multi-value metadata where the relationship between the
query and the field is known, but the specific value needs to remain
dynamic.
#### Key Changes
##### Backend & Core Logic
- `common/metadata_utils.py`: Updated apply_meta_data_filter to support
a mixed data structure for semi_auto (handling both legacy string arrays
and the new object-based format {"key": "...", "op": "..."}).
- `rag/prompts/generator.py`: Extended gen_meta_filter to accept and
pass operator constraints to the LLM.
- `rag/prompts/meta_filter.md`: Updated the system prompt to instruct
the LLM to strictly respect provided operator constraints.
##### Frontend
- `web/src/components/metadata-filter/metadata-semi-auto-fields.tsx`:
Enhanced the UI to include an operator dropdown for each selected
metadata key, utilizing existing operator constants.
- `web/src/components/metadata-filter/index.tsx`: Updated the validation
schema to accommodate the new state structure.
#### Test Plan
- Backward Compatibility: Verified that existing semi-auto filters
stored as simple strings still function correctly.
- Prompt Verification: Confirmed that constraints are correctly rendered
in the LLM system prompt when specified.
- Added unit tests as
`test/unit_test/common/test_apply_semi_auto_meta_data_filter.py`
- Manual End-to-End:
- Configured a "Semi-automatic" filter for a "Version" key with the
"contains" operator.
- Asked a version-specific query.
- Result
<img width="1173" height="704" alt="Screenshot 2026-02-02 145359"
src="https://github.com/user-attachments/assets/510a6a61-a231-4dc2-a7fe-cdfc07219132"
/>
### Type of change
- [ ] Bug Fix (non-breaking change which fixes an issue)
- [x] 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?
Put document metadata in ES/Infinity.
Index name of meta data: ragflow_doc_meta_{tenant_id}
### Type of change
- [x] Refactoring
### What problem does this PR solve?
PDF vision figure parser supports reading context.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
- Simplified and consolidated extraction rules
- Emphasized strict evidence-based extraction only
- Strengthened enum handling and hallucination prevention
- Clarified output requirements for empty results
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Manage message and use in agent.
Issue #4213
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
Fix LLM tool does not exist in multiple retrieval case
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
pr:#12117
change:remove duplicate tool_meta
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Fix only one of multiple retrieval tools is effective
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
- Add license
- Fix IDE warnings
### Type of change
- [x] Refactoring
---------
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
Co-authored-by: Kevin Hu <kevinhu.sh@gmail.com>
### What problem does this PR solve?
Make RAGFlow more asynchronous 2. #11551, #11579, #11619.
### Type of change
- [x] Refactoring
- [x] Performance Improvement
### What problem does this PR solve?
Make RAGFlow more asynchronous 2. #11551, #11579, #11619.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
- [x] Refactoring
- [x] Performance Improvement
### What problem does this PR solve?
optimize meta filter generation for better structure handling
### 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?
issue:
#10495
change:
fix empty references in agent conversation
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Feat: Add prompts for toc relevance check according to #10436
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
**Adds a new feature that enables the LLM to extract a structured table
of contents (TOC) directly from plain text.**
_This implementation prioritizes efficiency over reasoning — the model
runs in a strictly deterministic mode (thinking disabled) to minimize
latency.
As a result, overall performance may be less optimal, but the extraction
speed and consistency are guaranteed._
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
Fix broken imports
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
---------
Signed-off-by: jinhai <haijin.chn@gmail.com>
