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Docs: Update version references to v0.22.0 in READMEs and docs (#11211)

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### What problem does this PR solve?

- Update version tags in README files (including translations) from
v0.21.1 to v0.22.0
- 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
2025-11-12 14:54:28 +08:00
committed by GitHub
parent a81f6d1b24
commit a36a0fe71c
18 changed files with 26 additions and 26 deletions
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2
admin/client/README.md
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@ -48,7 +48,7 @@ It consists of a server-side Service and a command-line client (CLI), both imple
1. Ensure the Admin Service is running.
2. Install ragflow-cli.
```bash
pip install ragflow-cli==0.21.1
pip install ragflow-cli==0.22.0
```
3. Launch the CLI client:
```bash

2
admin/client/admin_client.py
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@ -378,7 +378,7 @@ class AdminCLI(Cmd):
self.session.headers.update({
'Content-Type': 'application/json',
'Authorization': response.headers['Authorization'],
'User-Agent': 'RAGFlow-CLI/0.21.1'
'User-Agent': 'RAGFlow-CLI/0.22.0'
})
print("Authentication successful.")
return True

4
docker/.env
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@ -109,8 +109,8 @@ SVR_MCP_PORT=9382
RAGFLOW_IMAGE=infiniflow/ragflow:v0.22.0
# If you cannot download the RAGFlow Docker image:
# RAGFLOW_IMAGE=swr.cn-north-4.myhuaweicloud.com/infiniflow/ragflow:v0.21.1
# RAGFLOW_IMAGE=registry.cn-hangzhou.aliyuncs.com/infiniflow/ragflow:v0.21.1
# RAGFLOW_IMAGE=swr.cn-north-4.myhuaweicloud.com/infiniflow/ragflow:v0.22.0
# RAGFLOW_IMAGE=registry.cn-hangzhou.aliyuncs.com/infiniflow/ragflow:v0.22.0
#
# - For the `nightly` edition, uncomment either of the following:
# RAGFLOW_IMAGE=swr.cn-north-4.myhuaweicloud.com/infiniflow/ragflow:nightly

2
docs/configurations.md
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@ -97,7 +97,7 @@ RAGFlow utilizes MinIO as its object storage solution, leveraging its scalabilit
- `SVR_HTTP_PORT`
The port used to expose RAGFlow's HTTP API service to the host machine, allowing **external** access to the service running inside the Docker container. Defaults to `9380`.
- `RAGFLOW-IMAGE`
The Docker image edition. Defaults to `infiniflow/ragflow:v0.21.1` (the RAGFlow Docker image without embedding models).
The Docker image edition. Defaults to `infiniflow/ragflow:v0.22.0` (the RAGFlow Docker image without embedding models).
:::tip NOTE
If you cannot download the RAGFlow Docker image, try the following mirrors.

2
docs/develop/build_docker_image.mdx
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@ -47,7 +47,7 @@ After building the infiniflow/ragflow:nightly image, you are ready to launch a f
1. Edit Docker Compose Configuration
Open the `docker/.env` file. Find the `RAGFLOW_IMAGE` setting and change the image reference from `infiniflow/ragflow:v0.21.1` to `infiniflow/ragflow:nightly` to use the pre-built image.
Open the `docker/.env` file. Find the `RAGFLOW_IMAGE` setting and change the image reference from `infiniflow/ragflow:v0.22.0` to `infiniflow/ragflow:nightly` to use the pre-built image.
2. Launch the Service

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 v0.21.1, if you add custom variables here, the only way you can pass in their values is to call:
- As of v0.17.2, 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).

4
docs/guides/dataset/configure_knowledge_base.md
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@ -59,7 +59,7 @@ You can also change a file's chunking method on the **Files** page.
![change chunking method](https://raw.githubusercontent.com/infiniflow/ragflow-docs/main/images/change_chunking_method.jpg)
<details>
<summary>From v0.21.1 onward, RAGFlow supports ingestion pipeline for customized data ingestion and cleansing workflows.</summary>
<summary>From v0.21.0 onward, RAGFlow supports ingestion pipeline for customized data ingestion and cleansing workflows.</summary>
To use a customized data pipeline:
@ -133,7 +133,7 @@ See [Run retrieval test](./run_retrieval_test.md) for details.
## Search for dataset
As of RAGFlow v0.21.1, the search feature is still in a rudimentary form, supporting only dataset search by name.
As of RAGFlow v0.22.0, the search feature is still in a rudimentary form, supporting only dataset search by name.
![search dataset](https://raw.githubusercontent.com/infiniflow/ragflow-docs/main/images/search_datasets.jpg)

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 v0.21.1, bulk download is not supported, nor can you download an entire folder.
> As of RAGFlow v0.22.0, bulk download is not supported, nor can you download an entire folder.

2
docs/guides/manage_users_and_services.md
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@ -46,7 +46,7 @@ The Admin CLI and Admin Service form a client-server architectural suite for RAG
2. Install ragflow-cli.
```bash
pip install ragflow-cli==0.21.1
pip install ragflow-cli==0.22.0
```
3. Launch the CLI client:

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 **≥ 0.21.1** (contains the Langfuse connector)
• RAGFlow **≥ 0.18.0** (contains the Langfuse connector)
• A Langfuse workspace (cloud or self-hosted) with a _Project Public Key_ and _Secret Key_
:::

10
docs/guides/upgrade_ragflow.mdx
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@ -48,16 +48,16 @@ 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., `v0.21.1`:
2. Switch to the latest, officially published release, e.g., `v0.22.0`:
```bash
git checkout -f v0.21.1
git checkout -f v0.22.0
```
3. Update **ragflow/docker/.env**:
```bash
RAGFLOW_IMAGE=infiniflow/ragflow:v0.21.1
RAGFLOW_IMAGE=infiniflow/ragflow:v0.22.0
```
4. Update the RAGFlow image and restart RAGFlow:
@ -78,10 +78,10 @@ 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.v0.21.1.tar infiniflow/ragflow:v0.21.1
docker save -o ragflow.v0.22.0.tar infiniflow/ragflow:v0.22.0
```
3. Copy the **.tar** file to the target server.
4. Load the **.tar** file into Docker:
```bash
docker load -i ragflow.v0.21.1.tar
docker load -i ragflow.v0.22.0.tar
```

6
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 v0.21.1 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 v0.22.0 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"
@ -184,7 +184,7 @@ 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 v0.21.1
$ git checkout -f v0.22.0
```
3. Use the pre-built Docker images and start up the server:
@ -200,7 +200,7 @@ This section provides instructions on setting up the RAGFlow server on Linux. If
| RAGFlow image tag | Image size (GB) | Stable? |
| ------------------- | --------------- | ------------------------ |
| v0.21.1 | &approx;2 | Stable release |
| v0.22.0 | &approx;2 | Stable release |
| nightly | &approx;2 | _Unstable_ nightly build |
```mdx-code-block

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 0.21.1. 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 0.19.0. 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.

2
helm/values.yaml
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@ -56,7 +56,7 @@ env:
ragflow:
image:
repository: infiniflow/ragflow
tag: v0.21.1-slim
tag: v0.22.0
pullPolicy: IfNotPresent
pullSecrets: []
# Optional service configuration overrides

2
pyproject.toml
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@ -1,6 +1,6 @@
[project]
name = "ragflow"
version = "0.21.1"
version = "0.22.0"
description = "[RAGFlow](https://ragflow.io/) is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding. It offers a streamlined RAG workflow for businesses of any scale, combining LLM (Large Language Models) to provide truthful question-answering capabilities, backed by well-founded citations from various complex formatted data."
authors = [{ name = "Zhichang Yu", email = "yuzhichang@gmail.com" }]
license-files = ["LICENSE"]

2
sdk/python/pyproject.toml
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@ -1,6 +1,6 @@
[project]
name = "ragflow-sdk"
version = "0.21.1"
version = "0.22.0"
description = "Python client sdk of [RAGFlow](https://github.com/infiniflow/ragflow). RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding."
authors = [{ name = "Zhichang Yu", email = "yuzhichang@gmail.com" }]
license = { text = "Apache License, Version 2.0" }

2
sdk/python/uv.lock generated
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@ -353,7 +353,7 @@ wheels = [
[[package]]
name = "ragflow-sdk"
version = "0.21.1"
version = "0.22.0"
source = { virtual = "." }
dependencies = [
{ name = "beartype" },

2
uv.lock generated
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@ -5196,7 +5196,7 @@ wheels = [
[[package]]
name = "ragflow"
version = "0.21.1"
version = "0.22.0"
source = { virtual = "." }
dependencies = [
{ name = "akshare" },