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Docs: Miscellaneous editorial updates (#7865)

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### Type of change


- [x] Documentation Update
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2
docs/guides/agent/agent_component_reference/code.mdx
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@ -23,6 +23,8 @@ After defining an input variable, you are required to select from the dropdown m
## Coding field
This field allows you to enter and edit your source code.
### A Python code example
```Python

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docs/guides/chat/start_chat.md
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@ -42,7 +42,7 @@ You start an AI conversation by creating an assistant.
- **Rerank model** sets the reranker model to use. It is left empty by default.
- If **Rerank model** is left empty, the hybrid score system uses keyword similarity and vector similarity, and the default weight assigned to the vector similarity component is 1-0.7=0.3.
- If **Rerank model** is selected, the hybrid score system uses keyword similarity and reranker score, and the default weight assigned to the reranker score is 1-0.7=0.3.
- **Cross-language search**: Optional
- [Cross-language search](../../references/glossary.mdx#cross-language-search): Optional
Select one or more target languages from the dropdown menu. The system’s default chat model will then translate your query into the selected target language(s). This translation ensures accurate semantic matching across languages, allowing you to retrieve relevant results regardless of language differences.
- When selecting target languages, please ensure that these languages are present in the knowledge base to guarantee an effective search.
- If no target language is selected, the system will search only in the language of your query, which may cause relevant information in other languages to be missed.

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docs/guides/dataset/configure_knowledge_base.md
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@ -68,7 +68,7 @@ The following embedding models can be deployed locally:
- maidalun1020/bce-embedding-base_v1
:::danger IMPORTANT
Please note these two embedding models support both English and Chinese. If your knowledge base contains other languages, the performance may be COMPROMISED.
These two embedding models are optimized specifically for English and Chinese, so performance may be compromised if you use them to embed documents in other languages.
:::
### Upload file

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docs/guides/dataset/run_retrieval_test.md
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@ -62,7 +62,7 @@ Using a knowledge graph in a retrieval test will significantly increase the time
### Cross-language search
To perform a cross-language search, select one or more target languages from the dropdown menu. The system’s default chat model will then translate your query entered in the Test text field into the selected target language(s). This translation ensures accurate semantic matching across languages, allowing you to retrieve relevant results regardless of language differences.
To perform a [cross-language search](../../references/glossary.mdx#cross-language-search), select one or more target languages from the dropdown menu. The system’s default chat model will then translate your query entered in the Test text field into the selected target language(s). This translation ensures accurate semantic matching across languages, allowing you to retrieve relevant results regardless of language differences.
:::tip NOTE
- When selecting target languages, please ensure that these languages are present in the knowledge base to guarantee an effective search.

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docs/guides/dataset/select_pdf_parser.md
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@ -33,7 +33,7 @@ RAGFlow isn't one-size-fits-all. It is built for flexibility and supports deeper
2. Select the option that works best with your scenario:
- DeepDoc: (Default) The default visual model for OCR, TSR, and DLR tasks, which is time-consuming.
- DeepDoc: (Default) The default visual model performing OCR, TSR, and DLR tasks on PDFs, which can be time-consuming.
- Naive: Skip OCR, TSR, and DLR tasks if *all* your PDFs are plain text.
- A third-party visual model provided by a specific model provider.

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docs/guides/dataset/set_metadata.md
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@ -25,4 +25,4 @@ Ensure that your metadata is in JSON format; otherwise, your updates will not be
### Can I set metadata for multiple documents at once?
No, RAGFlow does not support batch metadata setting. If you still consider this feature essential, please [raise an issue](https://github.com/infiniflow/ragflow/issues) explaining your use case and its importance.
No, you must set metadata *individually* for each document, as RAGFlow does not support batch setting of metadata. If you still consider this feature essential, please [raise an issue](https://github.com/infiniflow/ragflow/issues) explaining your use case and its importance.

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docs/quickstart.mdx
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@ -205,7 +205,7 @@ This section provides instructions on setting up the RAGFlow server on Linux. If
<APITable>
```
| RAGFlow image tag | Image size (GB) | Has embedding models and Python packages?:collision: | Stable? |
| RAGFlow image tag | Image size (GB) | Has embedding models and Python packages? | Stable? |
| ------------------- | --------------- | ----------------------------------------- | ------------------------ |
| `v0.19.0` | &approx;9 | :heavy_check_mark: | Stable release |
| `v0.19.0-slim` | &approx;2 | ❌ | Stable release |
@ -217,12 +217,12 @@ This section provides instructions on setting up the RAGFlow server on Linux. If
```
:::danger IMPORTANT
:collision: The embedding models included in `v0.19.0` and `nightly` are:
The embedding models included in `v0.19.0` and `nightly` are:
- BAAI/bge-large-zh-v1.5
- maidalun1020/bce-embedding-base_v1
Please note these two embedding models support both English and Chinese. If your knowledge base contains other languages, the performance may be COMPROMISED.
These two embedding models are optimized specifically for English and Chinese, so performance may be compromised if you use them to embed documents in other languages.
:::
4. Check the server status after having the server up and running:

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docs/release_notes.md
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@ -14,12 +14,12 @@ Each RAGFlow release is available in two editions:
:::
:::danger IMPORTANT
:collision: The embedding models included in a full edition are:
The embedding models included in a full edition are:
- BAAI/bge-large-zh-v1.5
- maidalun1020/bce-embedding-base_v1
Please note these two embedding models support both English and Chinese. If your knowledge base contains other languages, the performance may be COMPROMISED.
These two embedding models are optimized specifically for English and Chinese, so performance may be compromised if you use them to embed documents in other languages.
:::
## v0.19.0
@ -28,7 +28,7 @@ Released on May 26, 2025.
### New features
- Cross-language search is supported in the Knowledge and Chat modules, enhancing search accuracy and user experience in multilingual environments, such as in Chinese-English knowledge bases.
- [Cross-language search](./references/glossary.mdx#cross-language-search) is supported in the Knowledge and Chat modules, enhancing search accuracy and user experience in multilingual environments, such as in Chinese-English knowledge bases.
- Agent component: A new Code component supports Python and JavaScript scripts, enabling developers to handle more complex tasks like dynamic data processing.
- Enhanced image display: Images in Chat and Search now render directly within responses, rather than as external references. Knowledge retrieval testing can retrieve images directly, instead of texts extracted from images.
- Claude 4 and ChatGPT o3: Developers can now use the newly released, most advanced Claude model alongside OpenAI’s latest ChatGPT o3 inference model.
@ -39,6 +39,14 @@ Released on May 26, 2025.
- Markdown rendering: Image references in a markdown file can be displayed after chunking. Thanks to [Woody-Hu](https://github.com/Woody-Hu).
- Document engine support: OpenSearch can now be used as RAGFlow's document engine. Thanks to [pyyuhao](https://github.com/pyyuhao).
### Documentation
#### Added documents
- [Select PDF parser](./guides/dataset/select_pdf_parser.md)
- [Enable Excel2HTML](./guides/dataset/enable_excel2html.md)
- [Code component](./guides/agent/agent_component_reference/code.mdx)
## v0.18.0
Released on April 23, 2025.