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Docs: Update version references to v0.20.3 in READMEs and docs (#9581)

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### What problem does this PR solve?

- Update version tags in README files (including translations) from
v0.20.2 to v0.20.3
- Modify Docker image references and documentation to reflect new
version
- Update version badges and image descriptions
- Maintain consistency across all language variants of README files

### Type of change

- [x] Documentation Update
This commit is contained in:
Liu An
2025-08-20 10:45:44 +08:00
committed by GitHub
parent f55ff590d7
commit abb6359547
26 changed files with 63 additions and 63 deletions
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4
docs/configurations.md
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@ -99,8 +99,8 @@ RAGFlow utilizes MinIO as its object storage solution, leveraging its scalabilit
- `RAGFLOW-IMAGE`
The Docker image edition. Available editions:
- `infiniflow/ragflow:v0.20.2-slim` (default): The RAGFlow Docker image without embedding models.
- `infiniflow/ragflow:v0.20.2`: The RAGFlow Docker image with embedding models including:
- `infiniflow/ragflow:v0.20.3-slim` (default): The RAGFlow Docker image without embedding models.
- `infiniflow/ragflow:v0.20.3`: The RAGFlow Docker image with embedding models including:
- Built-in embedding models:
- `BAAI/bge-large-zh-v1.5`
- `maidalun1020/bce-embedding-base_v1`

2
docs/develop/build_docker_image.mdx
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@ -77,7 +77,7 @@ After building the infiniflow/ragflow:nightly-slim image, you are ready to launc
1. Edit Docker Compose Configuration
Open the `docker/.env` file. Find the `RAGFLOW_IMAGE` setting and change the image reference from `infiniflow/ragflow:v0.20.2-slim` to `infiniflow/ragflow:nightly-slim` to use the pre-built image.
Open the `docker/.env` file. Find the `RAGFLOW_IMAGE` setting and change the image reference from `infiniflow/ragflow:v0.20.3-slim` to `infiniflow/ragflow:nightly-slim` to use the pre-built image.
2. Launch the Service

10
docs/faq.mdx
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@ -30,17 +30,17 @@ The "garbage in garbage out" status quo remains unchanged despite the fact that
Each RAGFlow release is available in two editions:
- **Slim edition**: excludes built-in embedding models and is identified by a **-slim** suffix added to the version name. Example: `infiniflow/ragflow:v0.20.2-slim`
- **Full edition**: includes built-in embedding models and has no suffix added to the version name. Example: `infiniflow/ragflow:v0.20.2`
- **Slim edition**: excludes built-in embedding models and is identified by a **-slim** suffix added to the version name. Example: `infiniflow/ragflow:v0.20.3-slim`
- **Full edition**: includes built-in embedding models and has no suffix added to the version name. Example: `infiniflow/ragflow:v0.20.3`
---
### Which embedding models can be deployed locally?
RAGFlow offers two Docker image editions, `v0.20.2-slim` and `v0.20.2`:
RAGFlow offers two Docker image editions, `v0.20.3-slim` and `v0.20.3`:
- `infiniflow/ragflow:v0.20.2-slim` (default): The RAGFlow Docker image without embedding models.
- `infiniflow/ragflow:v0.20.2`: The RAGFlow Docker image with embedding models including:
- `infiniflow/ragflow:v0.20.3-slim` (default): The RAGFlow Docker image without embedding models.
- `infiniflow/ragflow:v0.20.3`: The RAGFlow Docker image with embedding models including:
- Built-in embedding models:
- `BAAI/bge-large-zh-v1.5`
- `maidalun1020/bce-embedding-base_v1`

2
docs/guides/agent/agent_component_reference/agent.mdx
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@ -9,7 +9,7 @@ The component equipped with reasoning, tool usage, and multi-agent collaboration
---
An **Agent** component fine-tunes the LLM and sets its prompt. From v0.20.2 onwards, an **Agent** component is able to work independently and with the following capabilities:
An **Agent** component fine-tunes the LLM and sets its prompt. From v0.20.3 onwards, an **Agent** component is able to work independently and with the following capabilities:
- Autonomous reasoning with reflection and adjustment based on environmental feedback.
- Use of tools or subagents to complete tasks.

2
docs/guides/agent/agent_component_reference/retrieval.mdx
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@ -9,7 +9,7 @@ A component that retrieves information from specified datasets.
## Scenarios
A **Retrieval** component is essential in most RAG scenarios, where information is extracted from designated knowledge bases before being sent to the LLM for content generation. As of v0.20.2, a **Retrieval** component can operate either as a workflow component or as a tool of an **Agent**, enabling the Agent to control its invocation and search queries.
A **Retrieval** component is essential in most RAG scenarios, where information is extracted from designated knowledge bases before being sent to the LLM for content generation. As of v0.20.3, a **Retrieval** component can operate either as a workflow component or as a tool of an **Agent**, enabling the Agent to control its invocation and search queries.
## Configurations

2
docs/guides/chat/start_chat.md
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@ -48,7 +48,7 @@ You start an AI conversation by creating an assistant.
- 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.
- **Variable** refers to the variables (keys) to be used in the system prompt. `{knowledge}` is a reserved variable. Click **Add** to add more variables for the system prompt.
- If you are uncertain about the logic behind **Variable**, leave it *as-is*.
- As of v0.20.2, if you add custom variables here, the only way you can pass in their values is to call:
- As of v0.20.3, if you add custom variables here, the only way you can pass in their values is to call:
- HTTP method [Converse with chat assistant](../../references/http_api_reference.md#converse-with-chat-assistant), or
- Python method [Converse with chat assistant](../../references/python_api_reference.md#converse-with-chat-assistant).

2
docs/guides/dataset/configure_knowledge_base.md
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@ -128,7 +128,7 @@ See [Run retrieval test](./run_retrieval_test.md) for details.
## Search for knowledge base
As of RAGFlow v0.20.2, the search feature is still in a rudimentary form, supporting only knowledge base search by name.
As of RAGFlow v0.20.3, the search feature is still in a rudimentary form, supporting only knowledge base search by name.
![search knowledge base](https://github.com/infiniflow/ragflow/assets/93570324/836ae94c-2438-42be-879e-c7ad2a59693e)

2
docs/guides/manage_files.md
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@ -87,4 +87,4 @@ RAGFlow's file management allows you to download an uploaded file:
![download_file](https://github.com/infiniflow/ragflow/assets/93570324/cf3b297f-7d9b-4522-bf5f-4f45743e4ed5)
> As of RAGFlow v0.20.2, bulk download is not supported, nor can you download an entire folder.
> As of RAGFlow v0.20.3, bulk download is not supported, nor can you download an entire folder.

2
docs/guides/tracing.mdx
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@ -18,7 +18,7 @@ RAGFlow ships with a built-in [Langfuse](https://langfuse.com) integration so th
Langfuse stores traces, spans and prompt payloads in a purpose-built observability backend and offers filtering and visualisations on top.
:::info NOTE
• RAGFlow **≥ 0.20.2** (contains the Langfuse connector)
• RAGFlow **≥ 0.20.3** (contains the Langfuse connector)
• A Langfuse workspace (cloud or self-hosted) with a _Project Public Key_ and _Secret Key_
:::

12
docs/guides/upgrade_ragflow.mdx
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@ -66,10 +66,10 @@ To upgrade RAGFlow, you must upgrade **both** your code **and** your Docker imag
git clone https://github.com/infiniflow/ragflow.git
```
2. Switch to the latest, officially published release, e.g., `v0.20.2`:
2. Switch to the latest, officially published release, e.g., `v0.20.3`:
```bash
git checkout -f v0.20.2
git checkout -f v0.20.3
```
3. Update **ragflow/docker/.env**:
@ -83,14 +83,14 @@ To upgrade RAGFlow, you must upgrade **both** your code **and** your Docker imag
<TabItem value="slim">
```bash
RAGFLOW_IMAGE=infiniflow/ragflow:v0.20.2-slim
RAGFLOW_IMAGE=infiniflow/ragflow:v0.20.3-slim
```
</TabItem>
<TabItem value="full">
```bash
RAGFLOW_IMAGE=infiniflow/ragflow:v0.20.2
RAGFLOW_IMAGE=infiniflow/ragflow:v0.20.3
```
</TabItem>
@ -114,10 +114,10 @@ No, you do not need to. Upgrading RAGFlow in itself will *not* remove your uploa
1. From an environment with Internet access, pull the required Docker image.
2. Save the Docker image to a **.tar** file.
```bash
docker save -o ragflow.v0.20.2.tar infiniflow/ragflow:v0.20.2
docker save -o ragflow.v0.20.3.tar infiniflow/ragflow:v0.20.3
```
3. Copy the **.tar** file to the target server.
4. Load the **.tar** file into Docker:
```bash
docker load -i ragflow.v0.20.2.tar
docker load -i ragflow.v0.20.3.tar
```

12
docs/quickstart.mdx
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@ -44,7 +44,7 @@ This section provides instructions on setting up the RAGFlow server on Linux. If
`vm.max_map_count`. This value sets the maximum number of memory map areas a process may have. Its default value is 65530. While most applications require fewer than a thousand maps, reducing this value can result in abnormal behaviors, and the system will throw out-of-memory errors when a process reaches the limitation.
RAGFlow v0.20.2 uses Elasticsearch or [Infinity](https://github.com/infiniflow/infinity) for multiple recall. Setting the value of `vm.max_map_count` correctly is crucial to the proper functioning of the Elasticsearch component.
RAGFlow v0.20.3 uses Elasticsearch or [Infinity](https://github.com/infiniflow/infinity) for multiple recall. Setting the value of `vm.max_map_count` correctly is crucial to the proper functioning of the Elasticsearch component.
<Tabs
defaultValue="linux"
@ -184,13 +184,13 @@ This section provides instructions on setting up the RAGFlow server on Linux. If
```bash
$ git clone https://github.com/infiniflow/ragflow.git
$ cd ragflow/docker
$ git checkout -f v0.20.2
$ git checkout -f v0.20.3
```
3. Use the pre-built Docker images and start up the server:
:::tip NOTE
The command below downloads the `v0.20.2-slim` edition of the RAGFlow Docker image. Refer to the following table for descriptions of different RAGFlow editions. To download a RAGFlow edition different from `v0.20.2-slim`, update the `RAGFLOW_IMAGE` variable accordingly in **docker/.env** before using `docker compose` to start the server. For example: set `RAGFLOW_IMAGE=infiniflow/ragflow:v0.20.2` for the full edition `v0.20.2`.
The command below downloads the `v0.20.3-slim` edition of the RAGFlow Docker image. Refer to the following table for descriptions of different RAGFlow editions. To download a RAGFlow edition different from `v0.20.3-slim`, update the `RAGFLOW_IMAGE` variable accordingly in **docker/.env** before using `docker compose` to start the server. For example: set `RAGFLOW_IMAGE=infiniflow/ragflow:v0.20.3` for the full edition `v0.20.3`.
:::
```bash
@ -207,8 +207,8 @@ This section provides instructions on setting up the RAGFlow server on Linux. If
| RAGFlow image tag | Image size (GB) | Has embedding models and Python packages? | Stable? |
| ------------------- | --------------- | ----------------------------------------- | ------------------------ |
| `v0.20.2` | &approx;9 | :heavy_check_mark: | Stable release |
| `v0.20.2-slim` | &approx;2 | ❌ | Stable release |
| `v0.20.3` | &approx;9 | :heavy_check_mark: | Stable release |
| `v0.20.3-slim` | &approx;2 | ❌ | Stable release |
| `nightly` | &approx;9 | :heavy_check_mark: | *Unstable* nightly build |
| `nightly-slim` | &approx;2 | ❌ | *Unstable* nightly build |
@ -217,7 +217,7 @@ This section provides instructions on setting up the RAGFlow server on Linux. If
```
:::danger IMPORTANT
The embedding models included in `v0.20.2` and `nightly` are:
The embedding models included in `v0.20.3` and `nightly` are:
- BAAI/bge-large-zh-v1.5
- maidalun1020/bce-embedding-base_v1

2
docs/references/glossary.mdx
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@ -19,7 +19,7 @@ import TOCInline from '@theme/TOCInline';
### Cross-language search
Cross-language search (also known as cross-lingual retrieval) is a feature introduced in version 0.20.2. It enables users to submit queries in one language (for example, English) and retrieve relevant documents written in other languages such as Chinese or Spanish. This feature is enabled by the system’s default chat model, which translates queries to ensure accurate matching of semantic meaning across languages.
Cross-language search (also known as cross-lingual retrieval) is a feature introduced in version 0.20.3. It enables users to submit queries in one language (for example, English) and retrieve relevant documents written in other languages such as Chinese or Spanish. This feature is enabled by the system’s default chat model, which translates queries to ensure accurate matching of semantic meaning across languages.
By enabling cross-language search, users can effortlessly access a broader range of information regardless of language barriers, significantly enhancing the system’s usability and inclusiveness.