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Fix errors (#11795)

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### What problem does this PR solve?

- typos
- IDE warnings

### Type of change

- [x] Refactoring

---------

Signed-off-by: Jin Hai <haijin.chn@gmail.com>
This commit is contained in:
Jin Hai
2025-12-08 09:42:10 +08:00
committed by GitHub
parent 8de6b97806
commit 6546f86b4e
13 changed files with 35 additions and 35 deletions
Download Patch File Download Diff File Expand all files Collapse all files

4
Dockerfile
View File

@ -78,12 +78,12 @@ RUN --mount=type=cache,id=ragflow_apt,target=/var/cache/apt,sharing=locked \
# A modern version of cargo is needed for the latest version of the Rust compiler.
RUN apt update && apt install -y curl build-essential \
&& if [ "$NEED_MIRROR" == "1" ]; then \
# Use TUNA mirrors for rustup/rust dist files
# Use TUNA mirrors for rustup/rust dist files \
export RUSTUP_DIST_SERVER="https://mirrors.tuna.tsinghua.edu.cn/rustup"; \
export RUSTUP_UPDATE_ROOT="https://mirrors.tuna.tsinghua.edu.cn/rustup/rustup"; \
echo "Using TUNA mirrors for Rustup."; \
fi; \
# Force curl to use HTTP/1.1
# Force curl to use HTTP/1.1 \
curl --proto '=https' --tlsv1.2 --http1.1 -sSf https://sh.rustup.rs | bash -s -- -y --profile minimal \
&& echo 'export PATH="/root/.cargo/bin:${PATH}"' >> /root/.bashrc

4
common/data_source/discord_connector.py
View File

@ -33,7 +33,7 @@ def _convert_message_to_document(
metadata: dict[str, str | list[str]] = {}
semantic_substring = ""
# Only messages from TextChannels will make it here but we have to check for it anyways
# Only messages from TextChannels will make it here, but we have to check for it anyway
if isinstance(message.channel, TextChannel) and (channel_name := message.channel.name):
metadata["Channel"] = channel_name
semantic_substring += f" in Channel: #{channel_name}"
@ -176,7 +176,7 @@ def _manage_async_retrieval(
# parse requested_start_date_string to datetime
pull_date: datetime | None = datetime.strptime(requested_start_date_string, "%Y-%m-%d").replace(tzinfo=timezone.utc) if requested_start_date_string else None
# Set start_time to the later of start and pull_date, or whichever is provided
# Set start_time to the most recent of start and pull_date, or whichever is provided
start_time = max(filter(None, [start, pull_date])) if start or pull_date else None
end_time: datetime | None = end

8
deepdoc/parser/html_parser.py
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@ -151,7 +151,7 @@ class RAGFlowHtmlParser:
block_content = []
current_content = ""
table_info_list = []
lask_block_id = None
last_block_id = None
for item in parser_result:
content = item.get("content")
tag_name = item.get("tag_name")
@ -160,11 +160,11 @@ class RAGFlowHtmlParser:
if block_id:
if title_flag:
content = f"{TITLE_TAGS[tag_name]} {content}"
if lask_block_id != block_id:
if lask_block_id is not None:
if last_block_id != block_id:
if last_block_id is not None:
block_content.append(current_content)
current_content = content
lask_block_id = block_id
last_block_id = block_id
else:
current_content += (" " if current_content else "") + content
else:

4
deepdoc/vision/ocr.py
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@ -582,7 +582,7 @@ class OCR:
self.crop_image_res_index = 0
def get_rotate_crop_image(self, img, points):
'''
"""
img_height, img_width = img.shape[0:2]
left = int(np.min(points[:, 0]))
right = int(np.max(points[:, 0]))
@ -591,7 +591,7 @@ class OCR:
img_crop = img[top:bottom, left:right, :].copy()
points[:, 0] = points[:, 0] - left
points[:, 1] = points[:, 1] - top
'''
"""
assert len(points) == 4, "shape of points must be 4*2"
img_crop_width = int(
max(

18
deepdoc/vision/postprocess.py
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@ -67,10 +67,10 @@ class DBPostProcess:
[[1, 1], [1, 1]])
def polygons_from_bitmap(self, pred, _bitmap, dest_width, dest_height):
'''
"""
_bitmap: single map with shape (1, H, W),
whose values are binarized as {0, 1}
'''
"""
bitmap = _bitmap
height, width = bitmap.shape
@ -114,10 +114,10 @@ class DBPostProcess:
return boxes, scores
def boxes_from_bitmap(self, pred, _bitmap, dest_width, dest_height):
'''
"""
_bitmap: single map with shape (1, H, W),
whose values are binarized as {0, 1}
'''
"""
bitmap = _bitmap
height, width = bitmap.shape
@ -192,9 +192,9 @@ class DBPostProcess:
return box, min(bounding_box[1])
def box_score_fast(self, bitmap, _box):
'''
"""
box_score_fast: use bbox mean score as the mean score
'''
"""
h, w = bitmap.shape[:2]
box = _box.copy()
xmin = np.clip(np.floor(box[:, 0].min()).astype("int32"), 0, w - 1)
@ -209,9 +209,9 @@ class DBPostProcess:
return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0]
def box_score_slow(self, bitmap, contour):
'''
box_score_slow: use polyon mean score as the mean score
'''
"""
box_score_slow: use polygon mean score as the mean score
"""
h, w = bitmap.shape[:2]
contour = contour.copy()
contour = np.reshape(contour, (-1, 2))

4
docker/docker-compose.yml
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@ -25,7 +25,7 @@ services:
# - --no-transport-streamable-http-enabled # Disable Streamable HTTP transport (/mcp endpoint)
# - --no-json-response # Disable JSON response mode in Streamable HTTP transport (instead of SSE over HTTP)
# Example configration to start Admin server:
# Example configuration to start Admin server:
# command:
# - --enable-adminserver
ports:
@ -74,7 +74,7 @@ services:
# - --no-transport-streamable-http-enabled # Disable Streamable HTTP transport (/mcp endpoint)
# - --no-json-response # Disable JSON response mode in Streamable HTTP transport (instead of SSE over HTTP)
# Example configration to start Admin server:
# Example configuration to start Admin server:
# command:
# - --enable-adminserver
ports:

2
docs/faq.mdx
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@ -151,7 +151,7 @@ See [Build a RAGFlow Docker image](./develop/build_docker_image.mdx).
### Cannot access https://huggingface.co
A locally deployed RAGflow downloads OCR models from [Huggingface website](https://huggingface.co) by default. If your machine is unable to access this site, the following error occurs and PDF parsing fails:
A locally deployed RAGFlow downloads OCR models from [Huggingface website](https://huggingface.co) by default. If your machine is unable to access this site, the following error occurs and PDF parsing fails:
```
FileNotFoundError: [Errno 2] No such file or directory: '/root/.cache/huggingface/hub/models--InfiniFlow--deepdoc/snapshots/be0c1e50eef6047b412d1800aa89aba4d275f997/ocr.res'

4
docs/guides/agent/best_practices/accelerate_agent_question_answering.md
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@ -45,13 +45,13 @@ Click the light bulb icon above the *current* dialogue and scroll down the popup
| Item name | Description |
| ----------------- | --------------------------------------------------------------------------------------------- |
| ----------------- |-----------------------------------------------------------------------------------------------|
| Total | Total time spent on this conversation round, including chunk retrieval and answer generation. |
| Check LLM | Time to validate the specified LLM. |
| Create retriever | Time to create a chunk retriever. |
| Bind embedding | Time to initialize an embedding model instance. |
| Bind LLM | Time to initialize an LLM instance. |
| Tune question | Time to optimize the user query using the context of the mult-turn conversation. |
| Tune question | Time to optimize the user query using the context of the multi-turn conversation. |
| Bind reranker | Time to initialize an reranker model instance for chunk retrieval. |
| Generate keywords | Time to extract keywords from the user query. |
| Retrieval | Time to retrieve the chunks. |

2
docs/guides/chat/best_practices/accelerate_question_answering.mdx
View File

@ -37,7 +37,7 @@ Please note that rerank models are essential in certain scenarios. There is alwa
| Create retriever | Time to create a chunk retriever. |
| Bind embedding | Time to initialize an embedding model instance. |
| Bind LLM | Time to initialize an LLM instance. |
| Tune question | Time to optimize the user query using the context of the mult-turn conversation. |
| Tune question | Time to optimize the user query using the context of the multi-turn conversation. |
| Bind reranker | Time to initialize an reranker model instance for chunk retrieval. |
| Generate keywords | Time to extract keywords from the user query. |
| Retrieval | Time to retrieve the chunks. |

2
docs/guides/manage_users_and_services.md
View File

@ -8,7 +8,7 @@ slug: /manage_users_and_services
The Admin CLI and Admin Service form a client-server architectural suite for RAGflow system administration. The Admin CLI serves as an interactive command-line interface that receives instructions and displays execution results from the Admin Service in real-time. This duo enables real-time monitoring of system operational status, supporting visibility into RAGflow Server services and dependent components including MySQL, Elasticsearch, Redis, and MinIO. In administrator mode, they provide user management capabilities that allow viewing users and performing critical operations—such as user creation, password updates, activation status changes, and comprehensive user data deletion—even when corresponding web interface functionalities are disabled.
The Admin CLI and Admin Service form a client-server architectural suite for RAGFlow system administration. The Admin CLI serves as an interactive command-line interface that receives instructions and displays execution results from the Admin Service in real-time. This duo enables real-time monitoring of system operational status, supporting visibility into RAGFlow Server services and dependent components including MySQL, Elasticsearch, Redis, and MinIO. In administrator mode, they provide user management capabilities that allow viewing users and performing critical operations—such as user creation, password updates, activation status changes, and comprehensive user data deletion—even when corresponding web interface functionalities are disabled.

2
docs/guides/models/deploy_local_llm.mdx
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@ -305,7 +305,7 @@ With the Ollama service running, open a new terminal and run `./ollama pull <mod
</TabItem>
</Tabs>
### 4. Configure RAGflow
### 4. Configure RAGFlow
To enable IPEX-LLM accelerated Ollama in RAGFlow, you must also complete the configurations in RAGFlow. The steps are identical to those outlined in the *Deploy a local model using Ollama* section:

4
example/sdk/dataset_example.py
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@ -14,9 +14,9 @@
# limitations under the License.
#
'''
"""
The example is about CRUD operations (Create, Read, Update, Delete) on a dataset.
'''
"""
from ragflow_sdk import RAGFlow
import sys

12
sandbox/executor_manager/core/container.py
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@ -122,15 +122,15 @@ async def create_container(name: str, language: SupportLanguage) -> bool:
logger.info(f"Sandbox config:\n\t {create_args}")
try:
returncode, _, stderr = await async_run_command(*create_args, timeout=10)
if returncode != 0:
return_code, _, stderr = await async_run_command(*create_args, timeout=10)
if return_code != 0:
logger.error(f"❌ Container creation failed {name}: {stderr}")
return False
if language == SupportLanguage.NODEJS:
copy_cmd = ["docker", "exec", name, "bash", "-c", "cp -a /app/node_modules /workspace/"]
returncode, _, stderr = await async_run_command(*copy_cmd, timeout=10)
if returncode != 0:
return_code, _, stderr = await async_run_command(*copy_cmd, timeout=10)
if return_code != 0:
logger.error(f"❌ Failed to prepare dependencies for {name}: {stderr}")
return False
@ -185,7 +185,7 @@ async def allocate_container_blocking(language: SupportLanguage, timeout=10) ->
async def container_is_running(name: str) -> bool:
"""Asynchronously check the container status"""
try:
returncode, stdout, _ = await async_run_command("docker", "inspect", "-f", "{{.State.Running}}", name, timeout=2)
return returncode == 0 and stdout.strip() == "true"
return_code, stdout, _ = await async_run_command("docker", "inspect", "-f", "{{.State.Running}}", name, timeout=2)
return return_code == 0 and stdout.strip() == "true"
except Exception:
return False