2025-12-09 18:54:14 +08:00
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# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import json
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import logging
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import os
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2025-12-17 12:58:48 +08:00
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from typing import Any, Optional
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2025-12-09 18:54:14 +08:00
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from deepdoc.parser.mineru_parser import MinerUParser
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Feat: add OpenDataLoader PDF parser backend (#14058) (#14097)
### What problem does this PR solve?
Closes #14058.
RAGFlow supports multiple PDF parsing backends (DeepDOC, MinerU,
Docling, TCADP, PaddleOCR). This PR adds **OpenDataLoader**
([opendataloader-project/opendataloader-pdf](https://github.com/opendataloader-project/opendataloader-pdf))
as a new optional backend, giving users a deterministic, local-first
alternative with competitive table extraction accuracy.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
- [x] Documentation Update
---
### Changes
#### Backend
- `deepdoc/parser/opendataloader_parser.py` — new `OpenDataLoaderParser`
class inheriting `RAGFlowPdfParser`. Implements `check_installation()`
(guards Python package + Java 11+ runtime), `parse_pdf()` with
JSON-first extraction (heading/paragraph/table/list/image/formula) and
Markdown fallback, position-tag generation compatible with the shared
`@@page\tx0\tx1\ty0\ty1##` format, and temp-dir lifecycle with cleanup.
- `rag/app/naive.py` — new `by_opendataloader()` wrapper, registered in
`PARSERS` dict, added to `chunk_token_num=0` override list.
- `rag/flow/parser/parser.py` — `"opendataloader"` branch in the
pipeline PDF handler + check validation list.
#### Infrastructure
- `docker/entrypoint.sh` — `ensure_opendataloader()` function: opt-in
via `USE_OPENDATALOADER=true`, skips gracefully if Java is not on PATH.
#### Frontend
- `web/src/components/layout-recognize-form-field.tsx` —
`OpenDataLoader` added to `ParseDocumentType` enum and parser dropdown.
Cascades automatically to the pipeline editor's Parser component.
#### Docs
- `docs/guides/dataset/select_pdf_parser.md` — added OpenDataLoader
entry and full env-var reference.
---
### Environment variables
| Variable | Default | Description |
|---|---|---|
| `USE_OPENDATALOADER` | `false` | Set `true` to install
`opendataloader-pdf` on container startup |
| `OPENDATALOADER_VERSION` | latest | Pin the PyPI release (e.g.
`==2.2.1`) |
| `OPENDATALOADER_HYBRID` | _(unset)_ | Enable hybrid AI mode (e.g.
`docling-fast`) |
| `OPENDATALOADER_IMAGE_OUTPUT` | _(unset)_ | `off` / `embedded` /
`external` |
| `OPENDATALOADER_OUTPUT_DIR` | _(tmp)_ | Persistent output dir; temp
dir used + cleaned if unset |
| `OPENDATALOADER_DELETE_OUTPUT` | `1` | `0` to retain intermediate
files for debugging |
| `OPENDATALOADER_SANITIZE` | _(unset)_ | `1` to filter prompt-injection
patterns from output |
---
### Dependencies
- **Runtime**: `opendataloader-pdf` (PyPI, Apache 2.0) — opt-in, not
added to `pyproject.toml` core deps. Installed by
`ensure_opendataloader()` at container startup when
`USE_OPENDATALOADER=true`.
- **System**: Java 11+ on PATH (JVM is the underlying engine). The
installer skips with a warning if `java` is not found.
---
### How to test
**Standalone parser:**
```bash
source .venv/bin/activate
uv pip install opendataloader-pdf
python3 -c "
import sys; sys.path.insert(0, '.')
from deepdoc.parser.opendataloader_parser import OpenDataLoaderParser
p = OpenDataLoaderParser()
print('available:', p.check_installation())
s, t = p.parse_pdf('path/to/test.pdf', parse_method='pipeline')
print(f'sections={len(s)} tables={len(t)}')
"
```
### Benchmark vs Docling
```
file parser secs sections tables
----------------------------------------------------------------------
text-heavy.pdf docling 45.29 148 10
text-heavy.pdf opendataloader 3.14 559 0
table-heavy.pdf docling 7.05 76 3
table-heavy.pdf opendataloader 3.71 90 0
complex.pdf docling 42.67 114 8
complex.pdf opendataloader 3.51 180 0
```
2026-04-24 18:33:02 +02:00
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from deepdoc.parser.opendataloader_parser import OpenDataLoaderParser
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from deepdoc.parser.paddleocr_parser import PaddleOCRParser
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2026-07-01 13:29:28 +08:00
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from deepdoc.parser.somark_parser import SoMarkParser
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class Base:
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def __init__(self, key: str | dict, model_name: str, **kwargs):
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self.model_name = model_name
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def parse_pdf(self, filepath: str, binary=None, **kwargs) -> tuple[Any, Any]:
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raise NotImplementedError("Please implement parse_pdf!")
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class MinerUOcrModel(Base, MinerUParser):
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_FACTORY_NAME = "MinerU"
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def __init__(self, key: str | dict, model_name: str, **kwargs):
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Base.__init__(self, key, model_name, **kwargs)
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raw_config = {}
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if key:
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try:
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raw_config = json.loads(key)
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except Exception:
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raw_config = {}
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# nested {"api_key": {...}} from UI
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# flat {"MINERU_*": "..."} payload auto-provisioned from env vars
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config = raw_config.get("api_key", raw_config)
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if not isinstance(config, dict):
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config = {}
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def _resolve_config(key: str, env_key: str, default=""):
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# lower-case keys (UI), upper-case MINERU_* (env auto-provision), env vars
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return config.get(key, config.get(env_key, os.environ.get(env_key, default)))
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self.mineru_api = _resolve_config("mineru_apiserver", "MINERU_APISERVER", "")
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self.mineru_output_dir = _resolve_config("mineru_output_dir", "MINERU_OUTPUT_DIR", "")
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self.mineru_backend = _resolve_config("mineru_backend", "MINERU_BACKEND", "pipeline")
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self.mineru_server_url = _resolve_config("mineru_server_url", "MINERU_SERVER_URL", "")
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self.mineru_delete_output = bool(int(_resolve_config("mineru_delete_output", "MINERU_DELETE_OUTPUT", 1)))
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# Redact sensitive config keys before logging
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redacted_config = {}
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for k, v in config.items():
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if any(sensitive_word in k.lower() for sensitive_word in ("key", "password", "token", "secret")):
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redacted_config[k] = "[REDACTED]"
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else:
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redacted_config[k] = v
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logging.info(f"Parsed MinerU config (sensitive fields redacted): {redacted_config}")
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MinerUParser.__init__(self, mineru_api=self.mineru_api, mineru_server_url=self.mineru_server_url)
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def check_available(self, backend: Optional[str] = None, server_url: Optional[str] = None) -> tuple[bool, str]:
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backend = backend or self.mineru_backend
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server_url = server_url or self.mineru_server_url
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return self.check_installation(backend=backend, server_url=server_url)
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def parse_pdf(self, filepath: str, binary=None, callback=None, parse_method: str = "raw", **kwargs):
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ok, reason = self.check_available()
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if not ok:
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raise RuntimeError(f"MinerU server not accessible: {reason}")
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sections, tables = MinerUParser.parse_pdf(
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self,
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filepath=filepath,
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binary=binary,
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callback=callback,
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output_dir=self.mineru_output_dir,
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backend=self.mineru_backend,
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server_url=self.mineru_server_url,
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delete_output=self.mineru_delete_output,
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parse_method=parse_method,
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**kwargs,
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)
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return sections, tables
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class PaddleOCROcrModel(Base, PaddleOCRParser):
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_FACTORY_NAME = "PaddleOCR"
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def __init__(self, key: str | dict, model_name: str, **kwargs):
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Base.__init__(self, key, model_name, **kwargs)
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raw_config = {}
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if key:
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try:
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raw_config = json.loads(key)
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except Exception:
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raw_config = {}
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# nested {"api_key": {...}} from UI
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# flat {"PADDLEOCR_*": "..."} payload auto-provisioned from env vars
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config = raw_config.get("api_key", raw_config)
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if not isinstance(config, dict):
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config = {}
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def _resolve_config(key: str, env_key: str, default=""):
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# lower-case keys (UI), upper-case PADDLEOCR_* (env auto-provision), env vars
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return config.get(key, config.get(env_key, os.environ.get(env_key, default)))
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refactor(paddleocr): migrate from sync API to async Job API (#15967)
## 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.
2026-06-16 19:34:21 +08:00
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self.paddleocr_base_url = _resolve_config("paddleocr_base_url", "PADDLEOCR_BASE_URL", "") or _resolve_config("paddleocr_api_url", "PADDLEOCR_API_URL", "")
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self.paddleocr_algorithm = _resolve_config("paddleocr_algorithm", "PADDLEOCR_ALGORITHM", "PaddleOCR-VL")
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self.paddleocr_access_token = _resolve_config("paddleocr_access_token", "PADDLEOCR_ACCESS_TOKEN", None)
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# Redact sensitive config keys before logging
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redacted_config = {}
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for k, v in config.items():
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if any(sensitive_word in k.lower() for sensitive_word in ("key", "password", "token", "secret")):
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redacted_config[k] = "[REDACTED]"
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else:
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redacted_config[k] = v
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logging.info(f"Parsed PaddleOCR config (sensitive fields redacted): {redacted_config}")
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PaddleOCRParser.__init__(
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self,
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refactor(paddleocr): migrate from sync API to async Job API (#15967)
## 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.
2026-06-16 19:34:21 +08:00
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base_url=self.paddleocr_base_url or None,
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access_token=self.paddleocr_access_token,
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algorithm=self.paddleocr_algorithm,
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)
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def check_available(self) -> tuple[bool, str]:
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return self.check_installation()
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def parse_pdf(self, filepath: str, binary=None, callback=None, parse_method: str = "raw", **kwargs):
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ok, reason = self.check_available()
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if not ok:
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raise RuntimeError(f"PaddleOCR server not accessible: {reason}")
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sections, tables = PaddleOCRParser.parse_pdf(self, filepath=filepath, binary=binary, callback=callback, parse_method=parse_method, **kwargs)
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return sections, tables
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Feat: add OpenDataLoader PDF parser backend (#14058) (#14097)
### What problem does this PR solve?
Closes #14058.
RAGFlow supports multiple PDF parsing backends (DeepDOC, MinerU,
Docling, TCADP, PaddleOCR). This PR adds **OpenDataLoader**
([opendataloader-project/opendataloader-pdf](https://github.com/opendataloader-project/opendataloader-pdf))
as a new optional backend, giving users a deterministic, local-first
alternative with competitive table extraction accuracy.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
- [x] Documentation Update
---
### Changes
#### Backend
- `deepdoc/parser/opendataloader_parser.py` — new `OpenDataLoaderParser`
class inheriting `RAGFlowPdfParser`. Implements `check_installation()`
(guards Python package + Java 11+ runtime), `parse_pdf()` with
JSON-first extraction (heading/paragraph/table/list/image/formula) and
Markdown fallback, position-tag generation compatible with the shared
`@@page\tx0\tx1\ty0\ty1##` format, and temp-dir lifecycle with cleanup.
- `rag/app/naive.py` — new `by_opendataloader()` wrapper, registered in
`PARSERS` dict, added to `chunk_token_num=0` override list.
- `rag/flow/parser/parser.py` — `"opendataloader"` branch in the
pipeline PDF handler + check validation list.
#### Infrastructure
- `docker/entrypoint.sh` — `ensure_opendataloader()` function: opt-in
via `USE_OPENDATALOADER=true`, skips gracefully if Java is not on PATH.
#### Frontend
- `web/src/components/layout-recognize-form-field.tsx` —
`OpenDataLoader` added to `ParseDocumentType` enum and parser dropdown.
Cascades automatically to the pipeline editor's Parser component.
#### Docs
- `docs/guides/dataset/select_pdf_parser.md` — added OpenDataLoader
entry and full env-var reference.
---
### Environment variables
| Variable | Default | Description |
|---|---|---|
| `USE_OPENDATALOADER` | `false` | Set `true` to install
`opendataloader-pdf` on container startup |
| `OPENDATALOADER_VERSION` | latest | Pin the PyPI release (e.g.
`==2.2.1`) |
| `OPENDATALOADER_HYBRID` | _(unset)_ | Enable hybrid AI mode (e.g.
`docling-fast`) |
| `OPENDATALOADER_IMAGE_OUTPUT` | _(unset)_ | `off` / `embedded` /
`external` |
| `OPENDATALOADER_OUTPUT_DIR` | _(tmp)_ | Persistent output dir; temp
dir used + cleaned if unset |
| `OPENDATALOADER_DELETE_OUTPUT` | `1` | `0` to retain intermediate
files for debugging |
| `OPENDATALOADER_SANITIZE` | _(unset)_ | `1` to filter prompt-injection
patterns from output |
---
### Dependencies
- **Runtime**: `opendataloader-pdf` (PyPI, Apache 2.0) — opt-in, not
added to `pyproject.toml` core deps. Installed by
`ensure_opendataloader()` at container startup when
`USE_OPENDATALOADER=true`.
- **System**: Java 11+ on PATH (JVM is the underlying engine). The
installer skips with a warning if `java` is not found.
---
### How to test
**Standalone parser:**
```bash
source .venv/bin/activate
uv pip install opendataloader-pdf
python3 -c "
import sys; sys.path.insert(0, '.')
from deepdoc.parser.opendataloader_parser import OpenDataLoaderParser
p = OpenDataLoaderParser()
print('available:', p.check_installation())
s, t = p.parse_pdf('path/to/test.pdf', parse_method='pipeline')
print(f'sections={len(s)} tables={len(t)}')
"
```
### Benchmark vs Docling
```
file parser secs sections tables
----------------------------------------------------------------------
text-heavy.pdf docling 45.29 148 10
text-heavy.pdf opendataloader 3.14 559 0
table-heavy.pdf docling 7.05 76 3
table-heavy.pdf opendataloader 3.71 90 0
complex.pdf docling 42.67 114 8
complex.pdf opendataloader 3.51 180 0
```
2026-04-24 18:33:02 +02:00
|
|
|
|
2026-06-16 19:34:38 +08:00
|
|
|
def parse_image(self, filepath: str, binary=None, callback=None, **kwargs) -> str:
|
|
|
|
|
ok, reason = self.check_available()
|
|
|
|
|
if not ok:
|
|
|
|
|
raise RuntimeError(f"PaddleOCR server not accessible: {reason}")
|
|
|
|
|
|
|
|
|
|
logging.info(f"PaddleOCR parse_image start: {filepath}")
|
|
|
|
|
result = PaddleOCRParser.parse_image(self, filepath=filepath, binary=binary, callback=callback, **kwargs)
|
|
|
|
|
logging.info(f"PaddleOCR parse_image done: {filepath}, text length: {len(result)}")
|
|
|
|
|
return result
|
|
|
|
|
|
Feat: add OpenDataLoader PDF parser backend (#14058) (#14097)
### What problem does this PR solve?
Closes #14058.
RAGFlow supports multiple PDF parsing backends (DeepDOC, MinerU,
Docling, TCADP, PaddleOCR). This PR adds **OpenDataLoader**
([opendataloader-project/opendataloader-pdf](https://github.com/opendataloader-project/opendataloader-pdf))
as a new optional backend, giving users a deterministic, local-first
alternative with competitive table extraction accuracy.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
- [x] Documentation Update
---
### Changes
#### Backend
- `deepdoc/parser/opendataloader_parser.py` — new `OpenDataLoaderParser`
class inheriting `RAGFlowPdfParser`. Implements `check_installation()`
(guards Python package + Java 11+ runtime), `parse_pdf()` with
JSON-first extraction (heading/paragraph/table/list/image/formula) and
Markdown fallback, position-tag generation compatible with the shared
`@@page\tx0\tx1\ty0\ty1##` format, and temp-dir lifecycle with cleanup.
- `rag/app/naive.py` — new `by_opendataloader()` wrapper, registered in
`PARSERS` dict, added to `chunk_token_num=0` override list.
- `rag/flow/parser/parser.py` — `"opendataloader"` branch in the
pipeline PDF handler + check validation list.
#### Infrastructure
- `docker/entrypoint.sh` — `ensure_opendataloader()` function: opt-in
via `USE_OPENDATALOADER=true`, skips gracefully if Java is not on PATH.
#### Frontend
- `web/src/components/layout-recognize-form-field.tsx` —
`OpenDataLoader` added to `ParseDocumentType` enum and parser dropdown.
Cascades automatically to the pipeline editor's Parser component.
#### Docs
- `docs/guides/dataset/select_pdf_parser.md` — added OpenDataLoader
entry and full env-var reference.
---
### Environment variables
| Variable | Default | Description |
|---|---|---|
| `USE_OPENDATALOADER` | `false` | Set `true` to install
`opendataloader-pdf` on container startup |
| `OPENDATALOADER_VERSION` | latest | Pin the PyPI release (e.g.
`==2.2.1`) |
| `OPENDATALOADER_HYBRID` | _(unset)_ | Enable hybrid AI mode (e.g.
`docling-fast`) |
| `OPENDATALOADER_IMAGE_OUTPUT` | _(unset)_ | `off` / `embedded` /
`external` |
| `OPENDATALOADER_OUTPUT_DIR` | _(tmp)_ | Persistent output dir; temp
dir used + cleaned if unset |
| `OPENDATALOADER_DELETE_OUTPUT` | `1` | `0` to retain intermediate
files for debugging |
| `OPENDATALOADER_SANITIZE` | _(unset)_ | `1` to filter prompt-injection
patterns from output |
---
### Dependencies
- **Runtime**: `opendataloader-pdf` (PyPI, Apache 2.0) — opt-in, not
added to `pyproject.toml` core deps. Installed by
`ensure_opendataloader()` at container startup when
`USE_OPENDATALOADER=true`.
- **System**: Java 11+ on PATH (JVM is the underlying engine). The
installer skips with a warning if `java` is not found.
---
### How to test
**Standalone parser:**
```bash
source .venv/bin/activate
uv pip install opendataloader-pdf
python3 -c "
import sys; sys.path.insert(0, '.')
from deepdoc.parser.opendataloader_parser import OpenDataLoaderParser
p = OpenDataLoaderParser()
print('available:', p.check_installation())
s, t = p.parse_pdf('path/to/test.pdf', parse_method='pipeline')
print(f'sections={len(s)} tables={len(t)}')
"
```
### Benchmark vs Docling
```
file parser secs sections tables
----------------------------------------------------------------------
text-heavy.pdf docling 45.29 148 10
text-heavy.pdf opendataloader 3.14 559 0
table-heavy.pdf docling 7.05 76 3
table-heavy.pdf opendataloader 3.71 90 0
complex.pdf docling 42.67 114 8
complex.pdf opendataloader 3.51 180 0
```
2026-04-24 18:33:02 +02:00
|
|
|
|
|
|
|
|
class OpenDataLoaderOcrModel(Base, OpenDataLoaderParser):
|
|
|
|
|
_FACTORY_NAME = "OpenDataLoader"
|
|
|
|
|
|
|
|
|
|
def __init__(self, key: str | dict, model_name: str, **kwargs):
|
|
|
|
|
Base.__init__(self, key, model_name, **kwargs)
|
|
|
|
|
raw_config = {}
|
|
|
|
|
if key:
|
|
|
|
|
try:
|
|
|
|
|
raw_config = json.loads(key)
|
|
|
|
|
except Exception:
|
|
|
|
|
raw_config = {}
|
|
|
|
|
|
|
|
|
|
config = raw_config.get("api_key", raw_config)
|
|
|
|
|
if not isinstance(config, dict):
|
|
|
|
|
config = {}
|
|
|
|
|
|
|
|
|
|
def _resolve_config(key: str, env_key: str, default=""):
|
|
|
|
|
return config.get(key, config.get(env_key, os.environ.get(env_key, default)))
|
|
|
|
|
|
|
|
|
|
redacted_config = {}
|
|
|
|
|
for k, v in config.items():
|
|
|
|
|
if any(s in k.lower() for s in ("key", "password", "token", "secret")):
|
|
|
|
|
redacted_config[k] = "[REDACTED]"
|
|
|
|
|
else:
|
|
|
|
|
redacted_config[k] = v
|
|
|
|
|
logging.info(f"Parsed OpenDataLoader config (sensitive fields redacted): {redacted_config}")
|
|
|
|
|
|
|
|
|
|
OpenDataLoaderParser.__init__(self)
|
|
|
|
|
self.api_url = _resolve_config("opendataloader_apiserver", "OPENDATALOADER_APISERVER", "").rstrip("/")
|
|
|
|
|
self.api_key = _resolve_config("opendataloader_api_key", "OPENDATALOADER_API_KEY", "").strip()
|
|
|
|
|
timeout_val = _resolve_config("opendataloader_timeout", "OPENDATALOADER_TIMEOUT", "600") or "600"
|
|
|
|
|
try:
|
|
|
|
|
self.timeout = int(timeout_val)
|
|
|
|
|
except (TypeError, ValueError):
|
|
|
|
|
self.timeout = 600
|
|
|
|
|
|
|
|
|
|
def check_available(self) -> tuple[bool, str]:
|
|
|
|
|
ok = self.check_installation()
|
|
|
|
|
return ok, "" if ok else "OpenDataLoader service not reachable"
|
|
|
|
|
|
|
|
|
|
def parse_pdf(self, filepath: str, binary=None, callback=None, parse_method: str = "raw", **kwargs):
|
|
|
|
|
ok, reason = self.check_available()
|
|
|
|
|
if not ok:
|
|
|
|
|
raise RuntimeError(f"OpenDataLoader service not accessible: {reason}")
|
|
|
|
|
|
|
|
|
|
sections, tables = OpenDataLoaderParser.parse_pdf(
|
|
|
|
|
self,
|
|
|
|
|
filepath=filepath,
|
|
|
|
|
binary=binary,
|
|
|
|
|
callback=callback,
|
|
|
|
|
parse_method=parse_method,
|
|
|
|
|
**kwargs,
|
|
|
|
|
)
|
|
|
|
|
return sections, tables
|
2026-07-01 13:29:28 +08:00
|
|
|
|
|
|
|
|
|
|
|
|
|
class SoMarkOcrModel(Base, SoMarkParser):
|
|
|
|
|
_FACTORY_NAME = "SoMark"
|
|
|
|
|
|
|
|
|
|
def __init__(self, key: str | dict, model_name: str, **kwargs):
|
|
|
|
|
Base.__init__(self, key, model_name, **kwargs)
|
|
|
|
|
raw_config: dict = {}
|
|
|
|
|
if isinstance(key, dict):
|
|
|
|
|
# API verify path passes the form dict directly; no JSON to parse.
|
|
|
|
|
raw_config = key
|
|
|
|
|
elif key:
|
|
|
|
|
try:
|
|
|
|
|
raw_config = json.loads(key)
|
|
|
|
|
except Exception:
|
|
|
|
|
raw_config = {}
|
|
|
|
|
|
|
|
|
|
# nested {"api_key": {...}} from UI
|
|
|
|
|
# flat {"SOMARK_*": "..."} payload auto-provisioned from env vars
|
|
|
|
|
config = raw_config.get("api_key", raw_config)
|
|
|
|
|
if not isinstance(config, dict):
|
|
|
|
|
config = {}
|
|
|
|
|
|
|
|
|
|
key_as_secret = key if isinstance(key, str) and key and not key.lstrip().startswith("{") else ""
|
|
|
|
|
|
|
|
|
|
def _resolve(ui_key: str, env_key: str, default=""):
|
|
|
|
|
return config.get(
|
|
|
|
|
ui_key,
|
|
|
|
|
config.get(
|
|
|
|
|
env_key,
|
|
|
|
|
kwargs.get(
|
|
|
|
|
ui_key,
|
|
|
|
|
kwargs.get(env_key, os.environ.get(env_key, default)),
|
|
|
|
|
),
|
|
|
|
|
),
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
def _resolve_bool(ui_key: str, env_key: str, default: bool) -> bool:
|
|
|
|
|
raw = _resolve(ui_key, env_key, int(default))
|
|
|
|
|
if isinstance(raw, bool):
|
|
|
|
|
return raw
|
|
|
|
|
if isinstance(raw, (int, float)):
|
|
|
|
|
return bool(raw)
|
|
|
|
|
return str(raw).strip().lower() in {"1", "true", "yes", "on"}
|
|
|
|
|
|
|
|
|
|
base_url = _resolve(
|
|
|
|
|
"somark_base_url",
|
|
|
|
|
"SOMARK_BASE_URL",
|
|
|
|
|
kwargs.get("base_url", "https://somark.tech/api/v1"),
|
|
|
|
|
)
|
2026-07-02 20:28:27 +08:00
|
|
|
api_key = _resolve("api_key", "SOMARK_API_KEY", key_as_secret)
|
2026-07-01 13:29:28 +08:00
|
|
|
image_format = _resolve("somark_image_format", "SOMARK_IMAGE_FORMAT", "url")
|
|
|
|
|
formula_format = _resolve("somark_formula_format", "SOMARK_FORMULA_FORMAT", "latex")
|
|
|
|
|
table_format = _resolve("somark_table_format", "SOMARK_TABLE_FORMAT", "html")
|
|
|
|
|
cs_format = _resolve("somark_cs_format", "SOMARK_CS_FORMAT", "image")
|
|
|
|
|
enable_text_cross_page = _resolve_bool("somark_enable_text_cross_page", "SOMARK_ENABLE_TEXT_CROSS_PAGE", False)
|
|
|
|
|
enable_table_cross_page = _resolve_bool("somark_enable_table_cross_page", "SOMARK_ENABLE_TABLE_CROSS_PAGE", False)
|
|
|
|
|
enable_title_level_recognition = _resolve_bool("somark_enable_title_level_recognition", "SOMARK_ENABLE_TITLE_LEVEL_RECOGNITION", False)
|
|
|
|
|
enable_inline_image = _resolve_bool("somark_enable_inline_image", "SOMARK_ENABLE_INLINE_IMAGE", True)
|
|
|
|
|
enable_table_image = _resolve_bool("somark_enable_table_image", "SOMARK_ENABLE_TABLE_IMAGE", True)
|
|
|
|
|
enable_image_understanding = _resolve_bool("somark_enable_image_understanding", "SOMARK_ENABLE_IMAGE_UNDERSTANDING", True)
|
|
|
|
|
keep_header_footer = _resolve_bool("somark_keep_header_footer", "SOMARK_KEEP_HEADER_FOOTER", False)
|
|
|
|
|
|
|
|
|
|
# Redact sensitive config keys before logging
|
|
|
|
|
redacted_config = {}
|
|
|
|
|
for k, v in config.items():
|
|
|
|
|
if any(s in k.lower() for s in ("key", "password", "token", "secret")):
|
|
|
|
|
redacted_config[k] = "[REDACTED]"
|
|
|
|
|
else:
|
|
|
|
|
redacted_config[k] = v
|
|
|
|
|
logging.info(f"Parsed SoMark config (sensitive fields redacted): {redacted_config}")
|
|
|
|
|
|
2026-07-02 20:28:27 +08:00
|
|
|
self.base_url = base_url
|
2026-07-08 09:47:29 +08:00
|
|
|
self.api_key = api_key
|
2026-07-01 13:29:28 +08:00
|
|
|
SoMarkParser.__init__(
|
|
|
|
|
self,
|
|
|
|
|
base_url=base_url,
|
|
|
|
|
api_key=api_key,
|
|
|
|
|
image_format=image_format,
|
|
|
|
|
formula_format=formula_format,
|
|
|
|
|
table_format=table_format,
|
|
|
|
|
cs_format=cs_format,
|
|
|
|
|
enable_text_cross_page=enable_text_cross_page,
|
|
|
|
|
enable_table_cross_page=enable_table_cross_page,
|
|
|
|
|
enable_title_level_recognition=enable_title_level_recognition,
|
|
|
|
|
enable_inline_image=enable_inline_image,
|
|
|
|
|
enable_table_image=enable_table_image,
|
|
|
|
|
enable_image_understanding=enable_image_understanding,
|
|
|
|
|
keep_header_footer=keep_header_footer,
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
def check_available(self) -> tuple[bool, str]:
|
|
|
|
|
return self.check_installation()
|
|
|
|
|
|
|
|
|
|
def parse_pdf(self, filepath: str, binary=None, callback=None, parse_method: str = "raw", **kwargs):
|
|
|
|
|
ok, reason = self.check_available()
|
|
|
|
|
if not ok:
|
|
|
|
|
raise RuntimeError(f"SoMark service not accessible: {reason}")
|
|
|
|
|
|
|
|
|
|
# parse_method selects the output tuple shape (see SoMarkParser._transfer_to_sections):
|
|
|
|
|
# manual/pipeline -> typed 3-tuples for the rag/flow DAG; raw/other -> 2-tuples
|
|
|
|
|
# for naive.py chunking. Thread it through like MinerU rather than dropping it.
|
|
|
|
|
sections, tables = SoMarkParser.parse_pdf(
|
|
|
|
|
self,
|
|
|
|
|
filepath=filepath,
|
|
|
|
|
binary=binary,
|
|
|
|
|
callback=callback,
|
|
|
|
|
parse_method=parse_method,
|
|
|
|
|
**kwargs,
|
|
|
|
|
)
|
|
|
|
|
return sections, tables
|