### What problem does this PR solve?
Fix AttributeError when calling llm.chat() in resume parser. LLMBundle
only has async_chat method, not chat method. Use `_run_coroutine_sync`
wrapper to call async_chat synchronously.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
Core optimizations (refer to arXiv:2510.09722):
1. PDF text fusion: Metadata + OCR dual-path extraction and fusion
2. Page-aware reconstruction: YOLOv10 page segmentation + hierarchical
sorting + line number indexing
3. Parallel task decomposition: Basic information/work
experience/educational background three-way parallel LLM extraction
4. Index pointer mechanism: LLM returns a range of line numbers instead
of generating the full text, reducing the illusion of full text.
---------
Co-authored-by: Aron.Yao <yaowei@yaoweideMacBook-Pro.local>
Co-authored-by: Aron.Yao <yaowei@192.168.1.68>
Co-authored-by: Yingfeng <yingfeng.zhang@gmail.com>
## Summary
When using MinerU, docling, TCADP, or paddleocr as the PDF parser with
the General (naive) chunk method, the user-configured `chunk_token_num`
is **unconditionally overwritten to 0** at
[rag/app/naive.py#L858-L859](https://github.com/infiniflow/ragflow/blob/main/rag/app/naive.py#L858-L859),
effectively disabling chunk merging regardless of what the user sets in
the UI.
### Problem
A user sets `chunk_token_num = 2048` in the dataset configuration UI,
expecting small parser blocks to be merged into larger chunks. However,
this line:
```python
if name in ["tcadp", "docling", "mineru", "paddleocr"]:
parser_config["chunk_token_num"] = 0
```
silently overrides the user's setting. As a result, every MinerU output
block becomes its own chunk. For short documents (e.g. a 3-page PDF fund
factsheet parsed by MinerU), this produces **47 tiny chunks** — some as
small as 11 characters (`"July 2025"`) or 15 characters (`"CIES
Eligible"`).
This severely degrades retrieval quality: vector embeddings of such
short fragments have minimal semantic value, and keyword search produces
excessive noise.
### Fix
Only apply the `chunk_token_num = 0` override when the user has **not**
explicitly configured a positive value:
```python
if name in ["tcadp", "docling", "mineru", "paddleocr"]:
if int(parser_config.get("chunk_token_num", 0)) <= 0:
parser_config["chunk_token_num"] = 0
```
This preserves the original default behavior (no merging) while
respecting the user's explicit configuration.
### Before / After (MinerU, 3-page PDF, chunk_token_num=2048)
| | Before | After |
|---|---|---|
| Chunks produced | 47 | ~8 (merged by token limit) |
| Smallest chunk | 11 chars | ~500 chars |
| User setting respected | No | Yes |
## Test plan
- [ ] Parse a PDF with MinerU and `chunk_token_num = 2048` → verify
chunks are merged up to token limit
- [ ] Parse a PDF with MinerU and `chunk_token_num = 0` (or default) →
verify original behavior (no merging)
- [ ] Parse a PDF with DeepDOC parser → verify no change in behavior
(not affected by this code path)
- [ ] Repeat with docling/paddleocr if available
### What problem does this PR solve?
Fix: add soft limit for graph rag size #13258 Q2
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
---------
Co-authored-by: Yingfeng <yingfeng.zhang@gmail.com>
### What problem does this PR solve?
When using OceanBase as the document storage engine, parsing and
inserting chunks with chunk_data (e.g., table parser row data) fails
with the following error:
```
[ERROR][Exception]: Insert chunk error: ['Unconsumed column names: chunk_data']
This happens because the chunk_data column was recently introduced but was omitted from the EXTRA_COLUMNS list in
rag/utils/ob_conn.py
```
As a result, the automatic schema migration for existing OceanBase
tables does not append the missing chunk_data column, causing the
underlying pyobvector or SQLAlchemy to raise an unconsumed column names
error during data insertion.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What is the solution?
Added column_chunk_data to the EXTRA_COLUMNS list in
```
rag/utils/ob_conn.py
```
This ensures that the OceanBase connection wrapper can correctly detect
the missing column and automatically alter existing chunk tables to
include the chunk_data field during initialization.
### What problem does this PR solve?
Feat: add preprocess parameters for ingestion pipeline
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
This PR adds comprehensive **Right-to-Left (RTL) language support**,
primarily targeting Arabic and other RTL scripts (Hebrew, Persian, Urdu,
etc.).
Previously, RTL content had multiple rendering issues:
- Incorrect sentence splitting for Arabic punctuation in citation logic
- Misaligned text in chat messages and markdown components
- Improper positioning of blockquotes and “think” sections
- Incorrect table alignment
- Citation placement ambiguity in RTL prompts
- UI layout inconsistencies when mixing LTR and RTL text
This PR introduces backend and frontend improvements to properly detect,
render, and style RTL content while preserving existing LTR behavior.
#### Backend
- Updated sentence boundary regex in `rag/nlp/search.py` to include
Arabic punctuation:
- `،` (comma)
- `؛` (semicolon)
- `؟` (question mark)
- `۔` (Arabic full stop)
- Ensures citation insertion works correctly in RTL sentences.
- Updated citation prompt instructions to clarify citation placement
rules for RTL languages.
#### Frontend
- Introduced a new utility: `text-direction.ts`
- Detects text direction based on Unicode ranges.
- Supports Arabic, Hebrew, Syriac, Thaana, and related scripts.
- Provides `getDirAttribute()` for automatic `dir` assignment.
- Applied dynamic `dir` attributes across:
- Markdown rendering
- Chat messages
- Search results
- Tables
- Hover cards and reference popovers
- Added proper RTL styling in LESS:
- Text alignment adjustments
- Blockquote border flipping
- Section indentation correction
- Table direction switching
- Use of `<bdi>` for figure labels to prevent bidirectional conflicts
#### DevOps / Environment
- Added Windows backend launch script with retry handling.
- Updated dependency metadata.
- Adjusted development-only React debugging behavior.
---
### Type of change
- [x] Bug Fix (non-breaking change which fixes RTL rendering and
citation issues)
- [x] New Feature (non-breaking change which adds RTL detection and
dynamic direction handling)
---------
Co-authored-by: 6ba3i <isbaaoui09@gmail.com>
Co-authored-by: Ahmad Intisar <ahmadintisar@Ahmads-MacBook-M4-Pro.local>
Co-authored-by: Ahmad Intisar <168020872+ahmadintisar@users.noreply.github.com>
Co-authored-by: Liu An <asiro@qq.com>
### What problem does this PR solve?
Properly close detached PIL image on JPEG save failure in encode_image.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
**Summary**
This PR tackles a significant memory bottleneck when processing
image-heavy Word documents. Previously, our pipeline eagerly decoded
DOCX images into `PIL.Image` objects, which caused high peak memory
usage. To solve this, I've introduced a **lazy-loading approach**:
images are now stored as raw blobs and only decoded exactly when and
where they are consumed.
This successfully reduces the memory footprint while keeping the parsing
output completely identical to before.
**What's Changed**
Instead of a dry file-by-file list, here is the logical breakdown of the
updates:
* **The Core Abstraction (`lazy_image.py`)**: Introduced `LazyDocxImage`
along with helper APIs to handle lazy decoding, image-type checks, and
NumPy compatibility. It also supports `.close()` and detached PIL access
to ensure safe lifecycle management and prevent memory leaks.
* **Pipeline Integration (`naive.py`, `figure_parser.py`, etc.)**:
Updated the general DOCX picture extraction to return these new lazy
images. Downstream consumers (like the figure/VLM flow and base64
encoding paths) now decode images right at the use site using detached
PIL instances, avoiding shared-instance side effects.
* **Compatibility Hooks (`operators.py`, `book.py`, etc.)**: Added
necessary compatibility conversions so these lazy images flow smoothly
through existing merging, filtering, and presentation steps without
breaking.
**Scope & What is Intentionally Left Out**
To keep this PR focused, I have restricted these changes strictly to the
**general Word pipeline** and its downstream consumers.
The `QA` and `manual` Word parsing pipelines are explicitly **not
modified** in this PR. They can be safely migrated to this new lazy-load
model in a subsequent, standalone PR.
**Design Considerations**
I briefly considered adding image compression during processing, but
decided against it to avoid any potential quality degradation in the
derived outputs. I also held off on a massive pipeline re-architecture
to avoid overly invasive changes right now.
**Validation & Testing**
I've tested this to ensure no regressions:
* Compared identical DOCX inputs before and after this branch: chunk
counts, extracted text, table HTML, and image descriptions match
perfectly.
* **Confirmed a noticeable drop in peak memory usage when processing
image-dense documents.** For a 30MB Word document containing 243 1080p
screenshots, memory consumption is reduced by approximately 1.5GB.
**Breaking Changes**
None.
### What problem does this PR solve?
The SeaFile connector currently synchronises the entire account — every
library
visible to the authenticated user. This is impractical for users who
only need
a subset of their data indexed, especially on large SeaFile instances
with many
shared libraries.
This PR introduces granular sync scope support, allowing users to choose
between
syncing their entire account, a single library, or a specific directory
within a
library. It also adds support for SeaFile library-scoped API tokens
(`/api/v2.1/via-repo-token/` endpoints), enabling tighter access control
without
exposing account-level credentials.
### 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):
### Test
```
from seafile_connector import SeaFileConnector
import logging
import os
logging.basicConfig(level=logging.DEBUG)
URL = os.environ.get("SEAFILE_URL", "https://seafile.example.com")
TOKEN = os.environ.get("SEAFILE_TOKEN", "")
REPO_ID = os.environ.get("SEAFILE_REPO_ID", "")
SYNC_PATH = os.environ.get("SEAFILE_SYNC_PATH", "/Documents")
REPO_TOKEN = os.environ.get("SEAFILE_REPO_TOKEN", "")
def _test_scope(scope, repo_id=None, sync_path=None):
print(f"\n{'='*50}")
print(f"Testing scope: {scope}")
print(f"{'='*50}")
creds = {"seafile_token": TOKEN} if TOKEN else {}
if REPO_TOKEN and scope in ("library", "directory"):
creds["repo_token"] = REPO_TOKEN
connector = SeaFileConnector(
seafile_url=URL,
batch_size=5,
sync_scope=scope,
include_shared = False,
repo_id=repo_id,
sync_path=sync_path,
)
connector.load_credentials(creds)
connector.validate_connector_settings()
count = 0
for batch in connector.load_from_state():
for doc in batch:
count += 1
print(f" [{count}] {doc.semantic_identifier} "
f"({doc.size_bytes} bytes, {doc.extension})")
print(f"\n-> {scope} scope: {count} document(s) found.\n")
# 1. Account scope
if TOKEN:
_test_scope("account")
else:
print("\nSkipping account scope (set SEAFILE_TOKEN)")
# 2. Library scope
if REPO_ID and (TOKEN or REPO_TOKEN):
_test_scope("library", repo_id=REPO_ID)
else:
print("\nSkipping library scope (set SEAFILE_REPO_ID + token)")
# 3. Directory scope
if REPO_ID and SYNC_PATH and (TOKEN or REPO_TOKEN):
_test_scope("directory", repo_id=REPO_ID, sync_path=SYNC_PATH)
else:
print("\nSkipping directory scope (set SEAFILE_REPO_ID + SEAFILE_SYNC_PATH + token)")
```
### What problem does this PR solve?
Refer to issue: #13236
The base url for GPUStack chat model requires `/v1` suffix. For the
other model type like `Embedding` or `Rerank`, the `/v1` suffix is not
required and will be appended in code.
So keep the same logic for chat model as other model type.
### Type of change
- [X] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
This PR adds [Avian](https://avian.io) as a new LLM provider to RAGFlow.
Avian provides an OpenAI-compatible API with competitive pricing,
offering access to models like DeepSeek V3.2, Kimi K2.5, GLM-5, and
MiniMax M2.5.
**Provider details:**
- API Base URL: `https://api.avian.io/v1`
- Auth: Bearer token via API key
- OpenAI-compatible (chat completions, streaming, function calling)
- Models:
- `deepseek/deepseek-v3.2` — 164K context, $0.26/$0.38 per 1M tokens
- `moonshotai/kimi-k2.5` — 131K context, $0.45/$2.20 per 1M tokens
- `z-ai/glm-5` — 131K context, $0.30/$2.55 per 1M tokens
- `minimax/minimax-m2.5` — 1M context, $0.30/$1.10 per 1M tokens
**Changes:**
- `rag/llm/chat_model.py` — Add `AvianChat` class extending `Base`
- `rag/llm/__init__.py` — Register in `SupportedLiteLLMProvider`,
`FACTORY_DEFAULT_BASE_URL`, `LITELLM_PROVIDER_PREFIX`
- `conf/llm_factories.json` — Add Avian factory with model definitions
- `web/src/constants/llm.ts` — Add to `LLMFactory` enum, `IconMap`,
`APIMapUrl`
- `web/src/components/svg-icon.tsx` — Register SVG icon
- `web/src/assets/svg/llm/avian.svg` — Provider icon
- `docs/references/supported_models.mdx` — Add to supported models table
This follows the same pattern as other OpenAI-compatible providers
(e.g., n1n #12680, TokenPony).
cc @KevinHuSh @JinHai-CN
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
- [x] Documentation Update
### What problem does this PR solve?
Feat: optimize ingestion pipeline with preprocess
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
Actual behavior
When using OceanBase as storage, the list_chunk sorting is abnormal. The
following is the SQL statement.
SELECT id, content_with_weight, important_kwd, question_kwd, img_id,
available_int, position_int, doc_type_kwd, create_timestamp_flt,
create_time, array_to_string(page_num_int, ',') AS page_num_int_sort,
array_to_string(top_int, ',') AS top_int_sort FROM
rag_store_284250730805059584 WHERE doc_id = '' AND kb_id IN ('') ORDER
BY page_num_int_sort ASC, top_int_sort ASC, create_timestamp_flt DESC
LIMIT 0, 20
<img width="1610" height="740" alt="image"
src="https://github.com/user-attachments/assets/84e14c30-a97f-4e8f-8c8c-6ccac915d97d"
/>
Co-authored-by: Aron.Yao <yaowei@yaoweideMacBook-Pro.local>
## Summary
Fixes MinIO SSL/TLS support in two places: the MinIO **client**
connection and the **health check** used by the Admin/Service Health
dashboard. Both now respect the `secure` and `verify` settings from the
MinIO configuration.
Closes#13158Closes#13159
---
## Problem
**#13158 – MinIO client:** The client in `rag/utils/minio_conn.py` was
hardcoded with `secure=False`, so RAGFlow could not connect to MinIO
over HTTPS even when `secure: true` was set in config. There was also no
way to disable certificate verification for self-signed certs.
**#13159 – MinIO health check:** In `api/utils/health_utils.py`, the
MinIO liveness check always used `http://` for the health URL. When
MinIO was configured with SSL, the health check failed and the dashboard
showed "timeout" even though MinIO was reachable over HTTPS.
---
## Solution
### MinIO client (`rag/utils/minio_conn.py`)
- Read `MINIO.secure` (default `false`) and pass it into the `Minio()`
constructor so HTTPS is used when configured.
- Add `_build_minio_http_client()` that reads `MINIO.verify` (default
`true`). When `verify` is false, return an `urllib3.PoolManager` with
`cert_reqs=ssl.CERT_NONE` and pass it as `http_client` to `Minio()` so
self-signed certificates are accepted.
- Support string values for `secure` and `verify` (e.g. `"true"`,
`"false"`).
### MinIO health check (`api/utils/health_utils.py`)
- Add `_minio_scheme_and_verify()` to derive URL scheme (http/https) and
the `verify` flag from `MINIO.secure` and `MINIO.verify`.
- Update `check_minio_alive()` to use the correct scheme, pass `verify`
into `requests.get(..., verify=verify)`, and use `timeout=10`.
### Config template (`docker/service_conf.yaml.template`)
- Add commented optional MinIO keys `secure` and `verify` (and env vars
`MINIO_SECURE`, `MINIO_VERIFY`) so deployers know they can enable HTTPS
and optional cert verification.
### Tests
- **`test/unit_test/utils/test_health_utils_minio.py`** – Tests for
`_minio_scheme_and_verify()` and `check_minio_alive()` (scheme, verify,
status codes, timeout, errors).
- **`test/unit_test/utils/test_minio_conn_ssl.py`** – Tests for
`_build_minio_http_client()` (verify true/false/missing, string values,
`CERT_NONE` when verify is false).
---
## Testing
- Unit tests added/updated as above; run with the project's test runner.
- Manually: configure MinIO with HTTPS and `secure: true` (and
optionally `verify: false` for self-signed); confirm client operations
work and the Service Health dashboard shows MinIO as alive instead of
timeout.
### What problem does this PR solve?
Refact: switch from oogle-generativeai to google-genai #13132
Refact: commnet out unused pywencai.
### Type of change
- [x] Refactoring
### What problem does this PR solve?
Fix error when extracting the graph.
A string is expected, but a tuple was provided.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
When match_expressions contains coroutine objects (from GraphRAG's
Dealer.get_vector()), the code cannot identify this type because it only
checks for MatchTextExpr, MatchDenseExpr, or FusionExpr.
As a result:
score_func remains initialized as an empty string ""
This empty string is appended to the output list
The output list is passed to Infinity SDK's table_instance.output()
method
Infinity's SQL parser (via sqlglot) fails to parse the empty string,
throwing a ParseError
### What problem does this PR solve?
Fix parameter of calling self.dataStore.get() and warning info during
parser
https://github.com/infiniflow/ragflow/issues/13036
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Adjust highlight parsing, add row-count SQL override, tweak retrieval
thresholding, and update tests with engine-aware skips/utilities.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Some Excel files have abnormal `max_row` metadata (e.g.,
`max_row=1,048,534` with only 300 actual data rows). This causes:
- `row_number()` returns incorrect count, creating 350+ tasks instead of
1
- `list(ws.rows)` iterates through millions of empty rows, causing
system hang
This PR uses binary search to find the actual last row with data.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] Performance Improvement
Co-authored-by: Cursor <cursoragent@cursor.com>
### What problem does this PR solve?
Fix: correct llm_id for graphrag #13030
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Fix ingestion pipeline
Only 1 file is acceptable for ingestion pipeline.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Update stepfun list.
Add TTS and Sequence2Text functionalities.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
Add support `doubao-embedding-vision` model.
`doubao-embedding-large-text` is deprecated.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
This PR adds MySQL and PostgreSQL as data source connectors, allowing
users to import data directly from relational databases into RAGFlow for
RAG workflows.
Many users store their knowledge in databases (product catalogs,
documentation, FAQs, etc.) and currently have no way to sync this data
into RAGFlow without exporting to files first. This feature lets them
connect directly to their databases, run SQL queries, and automatically
create documents from the results.
Closes#763Closes#11560
### 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):
### What this PR does
**New capabilities:**
- Connect to MySQL and PostgreSQL databases
- Run custom SQL queries to extract data
- Map database columns to document content (vectorized) and metadata
(searchable)
- Support incremental sync using a timestamp column
- Full frontend UI with connection form and tooltips
**Files changed:**
Backend:
- `common/constants.py` - Added MYSQL/POSTGRESQL to FileSource enum
- `common/data_source/config.py` - Added to DocumentSource enum
- `common/data_source/rdbms_connector.py` - New connector (368 lines)
- `common/data_source/__init__.py` - Exported the connector
- `rag/svr/sync_data_source.py` - Added MySQL and PostgreSQL sync
classes
- `pyproject.toml` - Added mysql-connector-python dependency
Frontend:
- `web/src/pages/user-setting/data-source/constant/index.tsx` - Form
fields
- `web/src/locales/en.ts` - English translations
- `web/src/assets/svg/data-source/mysql.svg` - MySQL icon
- `web/src/assets/svg/data-source/postgresql.svg` - PostgreSQL icon
### Testing done
Tested with MySQL 8.0 and PostgreSQL 16:
- Connection validation works correctly
- Full sync imports all query results as documents
- Incremental sync only fetches rows updated since last sync
- Custom SQL queries filter data as expected
- Invalid credentials show clear error messages
- Lint checks pass (`ruff check` returns no errors)
---------
Co-authored-by: mkdev11 <YOUR_GITHUB_ID+MkDev11@users.noreply.github.com>
### What problem does this PR solve?
Add OceanBase memory store and extracting base class `OBConnectionBase`.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
---------
Co-authored-by: Cursor <cursoragent@cursor.com>
### What problem does this PR solve?
Fix: docx parser output consistent
> File "/home/bxy/ragflow/rag/flow/parser/parser.py", line 506, in _word
> sections, tbls = docx_parser(name, binary=blob)
> ^^^^^^^^^^^^^^
> ValueError: too many values to unpack (expected 2)
>
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
This PR adds **Seafile** as a new data source connector for RAGFlow.
[Seafile](https://www.seafile.com/) is an open-source, self-hosted file
sync and share platform widely used by enterprises, universities, and
organizations that require data sovereignty and privacy. Users who store
documents in Seafile currently have no way to index and search their
content through RAGFlow.
This connector enables RAGFlow users to:
- Connect to self-hosted Seafile servers via API token
- Index documents from personal and shared libraries
- Support incremental polling for updated files
- Seamlessly integrate Seafile-stored documents into their RAG pipelines
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### Changes included
- `SeaFileConnector` implementing `LoadConnector` and `PollConnector`
interfaces
- Support for API token
- Recursive file traversal across libraries
- Time-based filtering for incremental updates
- Seafile logo (sourced from Simple Icons, CC0)
- Connector configuration and registration
### Testing
- Tested against self-hosted Seafile Community Edition
- Verified authentication (token)
- Verified document ingestion from personal and shared libraries
- Verified incremental polling with time filters
### 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?
Fixed 12787 with syntax error in generated MySql json path expression
https://github.com/infiniflow/ragflow/issues/12787
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
#### What was fixed:
- Changed line 237 in ob_conn.py from value_str = get_value_str(value)
if value else "" to value_str = get_value_str(value)
- This fixes the bug where falsy but valid values (0, False, "", [], {})
were being converted to empty strings, causing invalid SQL syntax
#### What was tested:
- Comprehensive unit tests covering all edge cases
- Regression tests specifically for the bug scenario
---------
Co-authored-by: Kevin Hu <kevinhu.sh@gmail.com>
## What problem does this PR solve?
This PR implements parsing support for legacy PowerPoint files (`.ppt`,
97-2003 format).
Currently, parsing these files fails because `python-pptx` **natively
lacks support** for the legacy OLE2 binary format.
## **Context:**
I originally using `aspose-slides` for this purpose. However, since
`aspose-slides` is **no longer a project dependency**, I implemented a
fallback mechanism using the existing `tika-server` to ensure
compatibility and stability.
## **Key Changes:**
- **Fallback Logic**: Modified `rag/app/presentation.py` to catch
`python-pptx` failures and automatically fall back to Tika parsing.
- **No New Dependencies**: Utilizes the `tika` service that is already
part of the RAGFlow stack.
- **Note**: Since Tika focuses on text extraction, this implementation
extracts text content but does not generate slide thumbnails .
## 🧪 Test / Verification Results
### 1. Before (The Issue)
I have verified the fix using a legacy `.ppt` file (`math(1).ppt`,
~8MB).
<img width="963" height="970" alt="image"
src="https://github.com/user-attachments/assets/468c4ba8-f90b-4d7b-b969-9c5f5e42c474"
/>
### 2. After (The Fix)
With this PR, the system detects the failure in python-pptx and
successfully falls back to Tika. The text is extracted correctly.
<img width="1467" height="1121" alt="image"
src="https://github.com/user-attachments/assets/fa0fed3b-b923-4c86-ba2c-24b3ce6ee7a6"
/>
**Type of change**
- [x] New Feature (non-breaking change which adds functionality)
Signed-off-by: evilhero <2278596667@qq.com>
Co-authored-by: Yingfeng <yingfeng.zhang@gmail.com>
### What problem does this PR solve?
close#12770
This PR adds OceanBase as a storage backend for the Table Parser. It
enables dynamic table schema storage via JSON and implements OceanBase
SQL execution for text-to-SQL retrieval.
### 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):
### Changes
- Table Parser stores row data into `chunk_data` when doc engine is
OceanBase. (table.py)
- OceanBase table schema adds `chunk_data` JSON column and migrates if
needed.
- Implemented OceanBase `sql()` to execute text-to-SQL results.
(ob_conn.py)
- Add `DOC_ENGINE_OCEANBASE` flag for engine detection (setting.py)
### Test
1. Set `DOC_ENGINE=oceanbase` (e.g. in `docker/.env`)
<img width="1290" height="783" alt="doc_engine_ob"
src="https://github.com/user-attachments/assets/7d1c609f-7bf2-4b2e-b4cc-4243e72ad4f1"
/>
2. Upload an Excel file to Knowledge Base.(for test, we use as below)
<img width="786" height="930" alt="excel"
src="https://github.com/user-attachments/assets/bedf82f2-cd00-426b-8f4d-6978a151231a"
/>
3. Choose **Table** as parsing method.
<img width="2550" height="1134" alt="parse_excel"
src="https://github.com/user-attachments/assets/aba11769-02be-4905-97e1-e24485e24cd0"
/>
4.Ask a natural language query in chat.
<img width="2550" height="1134" alt="query"
src="https://github.com/user-attachments/assets/26a910a6-e503-4ac7-b66a-f5754bbb0e91"
/>
**What problem does this PR solve?**
When loading JSON mapping/schema files, the code used
json.load(open(path)) without closing the file. The file handle stayed
open until garbage collection, which can leak file descriptors under
load (e.g. repeated reconnects or migrations).
**Type of change**
[x] Bug Fix (non-breaking change which fixes an issue)
**Change**
Replaced json.load(open(...)) with a context manager so the file is
closed after loading:
with open(fp_mapping, "r") as f: ... = json.load(f)
**Files updated**
rag/utils/opensearch_conn.py – mapping load (1 place)
common/doc_store/es_conn_base.py – mapping load + doc_meta_mapping load
(2 places)
common/doc_store/infinity_conn_base.py – schema loads in _migrate_db,
doc metadata table creation, and SQL field mapping (4 places)
Behavior is unchanged; only resource handling is fixed.
Co-authored-by: Gittensor Miner <miner@gittensor.io>
## Description
This PR implements comprehensive OceanBase performance monitoring and
health check functionality as requested in issue #12772. The
implementation follows the existing ES/Infinity health check patterns
and provides detailed metrics for operations teams.
## Problem
Currently, RAGFlow lacks detailed health monitoring for OceanBase when
used as the document engine. Operations teams need visibility into:
- Connection status and latency
- Storage space usage
- Query throughput (QPS)
- Slow query statistics
- Connection pool utilization
## Solution
### 1. Enhanced OBConnection Class (`rag/utils/ob_conn.py`)
Added comprehensive performance monitoring methods:
- `get_performance_metrics()` - Main method returning all performance
metrics
- `_get_storage_info()` - Retrieves database storage usage
- `_get_connection_pool_stats()` - Gets connection pool statistics
- `_get_slow_query_count()` - Counts queries exceeding threshold
- `_estimate_qps()` - Estimates queries per second
- Enhanced `health()` method with connection status
### 2. Health Check Utilities (`api/utils/health_utils.py`)
Added two new functions following ES/Infinity patterns:
- `get_oceanbase_status()` - Returns OceanBase status with health and
performance metrics
- `check_oceanbase_health()` - Comprehensive health check with detailed
metrics
### 3. API Endpoint (`api/apps/system_app.py`)
Added new endpoint:
- `GET /v1/system/oceanbase/status` - Returns OceanBase health status
and performance metrics
### 4. Comprehensive Unit Tests
(`test/unit_test/utils/test_oceanbase_health.py`)
Added 340+ lines of unit tests covering:
- Health check success/failure scenarios
- Performance metrics retrieval
- Error handling and edge cases
- Connection pool statistics
- Storage information retrieval
- QPS estimation
- Slow query detection
## Metrics Provided
- **Connection Status**: connected/disconnected
- **Latency**: Query latency in milliseconds
- **Storage**: Used and total storage space
- **QPS**: Estimated queries per second
- **Slow Queries**: Count of queries exceeding threshold
- **Connection Pool**: Active connections, max connections, pool size
## Testing
- All unit tests pass
- Error handling tested for connection failures
- Edge cases covered (missing tables, connection errors)
- Follows existing code patterns and conventions
## Code Statistics
- **Total Lines Changed**: 665+ lines
- **New Code**: ~600 lines
- **Test Coverage**: 340+ lines of comprehensive tests
- **Files Modified**: 3
- **Files Created**: 1 (test file)
## Acceptance Criteria Met
✅ `/system/oceanbase/status` API returns OceanBase health status
✅ Monitoring metrics accurately reflect OceanBase running status
✅ Clear error messages when health checks fail
✅ Response time optimized (metrics cached where possible)
✅ Follows existing ES/Infinity health check patterns
✅ Comprehensive test coverage
## Related Files
- `rag/utils/ob_conn.py` - OceanBase connection class
- `api/utils/health_utils.py` - Health check utilities
- `api/apps/system_app.py` - System API endpoints
- `test/unit_test/utils/test_oceanbase_health.py` - Unit tests
Fixes#12772
---------
Co-authored-by: Daniel <daniel@example.com>
## What problem does this PR solve?
This PR addresses three specific issues to improve agent reliability and
model support:
1. **`codeExec` Output Limitation**: Previously, the `codeExec` tool was
strictly limited to returning `string` types. I updated the output
constraint to `object` to support structured data (Dicts, Lists, etc.)
required for complex downstream tasks.
2. **`codeExec` Error Handling**: Improved the execution logic so that
when runtime errors occur, the tool captures the exception and returns
the error message as the output instead of causing the process to abort
or fail silently.
3. **Spark Model Configuration**:
- Added support for the `MAX-32k` model variant.
- Fixed the `Spark-Lite` mapping from `general` to `lite` to match the
latest API specifications.
## Type of change
- [x] Bug Fix (fixes execution logic and model mapping)
- [x] New Feature / Enhancement (adds model support and improves tool
flexibility)
## Key Changes
### `agent/tools/code_exec.py`
- Changed the output type definition from `string` to `object`.
- Refactored the execution flow to gracefully catch exceptions and
return error messages as part of the tool output.
### `rag/llm/chat_model.py`
- Added `"Spark-Max-32K": "max-32k"` to the model list.
- Updated `"Spark-Lite"` value from `"general"` to `"lite"`.
## Checklist
- [x] My code follows the style guidelines of this project.
- [x] I have performed a self-review of my own code.
Signed-off-by: evilhero <2278596667@qq.com>
