### What problem does this PR solve?
Adds first-class support for **Mistral OCR** (`POST /v1/ocr`) as a
document parser, and fixes the long-standing bug where selecting
`mistral-ocr-latest` fails with `Can't find model for
<tenant>/image2text/mistral-ocr-latest`.
`mistral-ocr-latest` is Mistral's dedicated document-OCR endpoint, not a
vision-chat (`image2text`) model, but the catalog tagged it `image2text`
— so it resolved to the `CvModel` registry, which has no `Mistral`
entry, and there was no `OcrModel` entry either. This PR registers it
correctly and wires it end to end.
Closes#17056Closes#5782Closes#7075
**What it does**
1. **`MistralParser` + `MistralOcrModel`**
(`deepdoc/parser/mistral_parser.py`, `rag/llm/ocr_model.py`) — a proper
`OcrModel` factory `Mistral OCR`, mirroring the SoMark cloud-OCR
template. Tables stay inline as HTML; the page range maps to Mistral's
native `pages` selector (absolute page indices, billed per selected
page, so multi-task documents do not re-OCR the whole file); documents
over the inline limit go through the `/v1/files` signed-URL flow with
cleanup.
2. **Removes the `image2text` mis-tag** for `mistral-ocr-latest` from
the `Mistral` factory in `conf/llm_factories.json` (it now lives only in
the `Mistral OCR` factory, typed `ocr`). This is what closes the `Can't
find model` path.
3. **`MistralCV`** (`rag/llm/cv_model.py`) — a thin `GptV4` subclass
over Mistral's OpenAI-compatible endpoint, registering a `Mistral` entry
in the `CvModel` registry so Mistral vision models (`pixtral-*`) become
usable as `image2text` at all.
4. **Figure description** — Mistral-OCR-extracted figures are captioned
using the tenant's configured `image2text` model (any provider),
matching MinerU/deepdoc behaviour.
5. **Wires the parser into every chunking method** (`naive`, `paper`,
`book`, `laws`, `manual`, `one`, `presentation`) and the `rag/flow` DAG
path. This also fixes a related latent gap where those chunkers
forwarded only `mineru_llm_name`, so any model-based OCR provider
selected on a non-`naive` method silently fell through.
**Notes on the API contract** (verified against the live Mistral API):
`pages` is a selector (returns absolute `index`, bills only the
requested pages); `include_blocks: true` returns per-block bounding
boxes usable for chunk highlighting and figure cropping; large files use
`POST /v1/files` → signed URL → OCR → `DELETE`.
**Testing**: new unit tests cover the response→sections contract (both
the 2-tuple `naive` path and the typed 3-tuple DAG path), the
position-tag rescale, the HTTP client incl. upload failure/cleanup
paths, `parse_pdf` page-range threading, registry registration, env
config, the suffix normalization, the factory catalog entry, `MistralCV`
registration, and figure-description injection. Verified end to end
against the live Mistral API on real PDFs (table extraction,
page-selector cost avoidance, figure captioning).
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] New Feature (non-breaking change which adds functionality)
## Summary
Add image parsing capability to PaddleOCR integration, building on top
of #15967 (async Job API migration).
## Changes
### `deepdoc/parser/paddleocr_parser.py`
- Add `parse_image()` method that uses the same async Job API flow as
`parse_pdf()`
- Extracts text from `layoutParsingResults` → `prunedResult` →
`parsing_res_list`
- Returns concatenated block content as a single string
### `rag/llm/ocr_model.py`
- Add `parse_image()` wrapper to `PaddleOCROcrModel` with availability
check and logging
## Relationship to other PRs
- **Depends on**: #15967 (async Job API migration) — this PR is based on
that branch
- **Replaces**: #14826 (original image processing PR based on old sync
API)
## Notes
This PR uses `base_url` and the async Job API (submit → poll → fetch)
consistent with #15967, rather than the old `api_url` + sync POST
pattern from #14826.
## Summary
Migrate PaddleOCR integration from the deprecated synchronous HTTP API
to the new asynchronous Job API (`submit → poll → fetch`), aligning with
PaddleOCR 3.6.0+ architecture.
## Changes
### Python (`deepdoc/parser/paddleocr_parser.py`)
- Replace synchronous `requests.post()` with async Job API flow (submit
→ poll → fetch)
- Authentication: `token {token}` → `Bearer {token}`
- File transfer: base64 JSON body → multipart file upload
- Polling: exponential backoff (initial 3s, ×1.5, max 15s, timeout
controlled by `request_timeout`)
- Result: fetch full JSONL from result URL, preserving `prunedResult`
with bbox info for crop functionality
- Rename `api_url` → `base_url` (backward compatible: `api_url` still
accepted as fallback)
### Python (`rag/llm/ocr_model.py`)
- Prefer `paddleocr_base_url` / `PADDLEOCR_BASE_URL`, fallback to
`paddleocr_api_url` / `PADDLEOCR_API_URL`
### Go (`internal/entity/models/paddleocr.go`)
- Add `Client-Platform: ragflow` header to submit and poll requests
- Change polling from fixed 3s to exponential backoff (initial 3s, ×1.5,
max 15s)
### Python (`common/constants.py`)
- Add `PADDLEOCR_BASE_URL` to env keys and default config
## Backward Compatibility
- Old env var `PADDLEOCR_API_URL` still works (used as fallback)
- Frontend field `paddleocr_api_url` still works (backend reads it as
fallback)
- No user-facing configuration changes required for existing setups
## Why not use the `paddleocr` SDK package directly?
RAGFlow's `_transfer_to_sections()` relies on `prunedResult` (containing
`block_bbox`, `block_label`, `parsing_res_list`) from the raw API
response for PDF crop functionality. The SDK's public `parse_document()`
API only returns `DocParsingResult` with `markdown_text`, discarding the
bbox data. Therefore we implement the async Job API flow directly via
HTTP, following the same logic as the SDK internally.
### What problem does this PR solve?
Add PaddleOCR as a new PDF parser.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
Only support MinerU-API now, still need to complete frontend for
pipeline to allow the configuration of MinerU options.
### Type of change
- [x] Refactoring
我已在下面的评论中用中文重复说明。
### What problem does this PR solve?
## Summary
This PR enhances the MinerU document parser with additional
configuration options, giving users more control over PDF parsing
behavior and improving support for multilingual documents.
## Changes
### Backend (`deepdoc/parser/mineru_parser.py`)
- Added configurable parsing options:
- **Parse Method**: `auto`, `txt`, or `ocr` — allows users to choose the
extraction strategy
- **Formula Recognition**: Toggle for enabling/disabling formula
extraction (useful to disable for Cyrillic documents where it may cause
issues)
- **Table Recognition**: Toggle for enabling/disabling table extraction
- Added language code mapping (`LANGUAGE_TO_MINERU_MAP`) to translate
RAGFlow language settings to MinerU-compatible language codes for better
OCR accuracy
- Improved parser configuration handling to pass these options through
the processing pipeline
### Frontend (`web/`)
- Created new `MinerUOptionsFormField` component that conditionally
renders when MinerU is selected as the layout recognition engine
- Added UI controls for:
- Parse method selection (dropdown)
- Formula recognition toggle (switch)
- Table recognition toggle (switch)
- Added i18n translations for English and Chinese
- Integrated the options into both the dataset creation dialog and
dataset settings page
### Integration
- Updated `rag/app/naive.py` to forward MinerU options to the parser
- Updated task service to handle the new configuration parameters
## Why
MinerU is a powerful document parser, but the default settings don't
work well for all document types. This PR allows users to:
1. Choose the best parsing method for their documents
2. Disable formula recognition for Cyrillic/non-Latin scripts where it
causes issues
3. Control table extraction based on document needs
4. Benefit from automatic language detection for better OCR results
## Testing
- [x] Tested MinerU parsing with different parse methods
- [x] Verified UI renders correctly when MinerU is selected/deselected
- [x] Confirmed settings persist correctly in dataset configuration
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] New Feature (non-breaking change which adds functionality)
- [ ] Documentation Update
- [x] Refactoring
- [ ] Performance Improvement
- [ ] Other (please describe):
---------
Co-authored-by: user210 <user210@rt>
Co-authored-by: Kevin Hu <kevinhu.sh@gmail.com>
### What problem does this PR solve?
Fix pipeline ignore MinerU backend config and vllm module is missing.
#11944, #11947.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Treat MinerU as an OCR model.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
- [x] Refactoring
