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Docs: Update version references to v0.22.0 in READMEs and docs (#11211)
### 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
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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
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1. Ensure the Admin Service is running.
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2. Install ragflow-cli.
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```bash
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pip install ragflow-cli==0.21.1
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pip install ragflow-cli==0.22.0
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```
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3. Launch the CLI client:
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```bash

admin/client/admin_client.py

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self.session.headers.update({
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'Content-Type': 'application/json',
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'Authorization': response.headers['Authorization'],
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'User-Agent': 'RAGFlow-CLI/0.21.1'
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'User-Agent': 'RAGFlow-CLI/0.22.0'
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})
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print("Authentication successful.")
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return True

docker/.env

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

docs/configurations.md

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- `SVR_HTTP_PORT`
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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`.
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- `RAGFLOW-IMAGE`
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The Docker image edition. Defaults to `infiniflow/ragflow:v0.21.1` (the RAGFlow Docker image without embedding models).
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The Docker image edition. Defaults to `infiniflow/ragflow:v0.22.0` (the RAGFlow Docker image without embedding models).
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:::tip NOTE
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If you cannot download the RAGFlow Docker image, try the following mirrors.

docs/develop/build_docker_image.mdx

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1. Edit Docker Compose Configuration
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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.
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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.
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2. Launch the Service

docs/guides/chat/start_chat.md

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- 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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- **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.
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- If you are uncertain about the logic behind **Variable**, leave it *as-is*.
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- As of v0.21.1, if you add custom variables here, the only way you can pass in their values is to call:
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- As of v0.17.2, if you add custom variables here, the only way you can pass in their values is to call:
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- HTTP method [Converse with chat assistant](../../references/http_api_reference.md#converse-with-chat-assistant), or
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- Python method [Converse with chat assistant](../../references/python_api_reference.md#converse-with-chat-assistant).
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docs/guides/dataset/configure_knowledge_base.md

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![change chunking method](https://raw.githubusercontent.com/infiniflow/ragflow-docs/main/images/change_chunking_method.jpg)
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<details>
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<summary>From v0.21.1 onward, RAGFlow supports ingestion pipeline for customized data ingestion and cleansing workflows.</summary>
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<summary>From v0.21.0 onward, RAGFlow supports ingestion pipeline for customized data ingestion and cleansing workflows.</summary>
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To use a customized data pipeline:
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## Search for dataset
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As of RAGFlow v0.21.1, the search feature is still in a rudimentary form, supporting only dataset search by name.
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As of RAGFlow v0.22.0, the search feature is still in a rudimentary form, supporting only dataset search by name.
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![search dataset](https://raw.githubusercontent.com/infiniflow/ragflow-docs/main/images/search_datasets.jpg)
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docs/guides/manage_files.md

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![download_file](https://github.com/infiniflow/ragflow/assets/93570324/cf3b297f-7d9b-4522-bf5f-4f45743e4ed5)
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> As of RAGFlow v0.21.1, bulk download is not supported, nor can you download an entire folder.
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> As of RAGFlow v0.22.0, bulk download is not supported, nor can you download an entire folder.

docs/guides/manage_users_and_services.md

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2. Install ragflow-cli.
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```bash
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pip install ragflow-cli==0.21.1
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pip install ragflow-cli==0.22.0
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```
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3. Launch the CLI client:

docs/guides/tracing.mdx

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Langfuse stores traces, spans and prompt payloads in a purpose-built observability backend and offers filtering and visualisations on top.
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:::info NOTE
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• RAGFlow **≥ 0.21.1** (contains the Langfuse connector)
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• RAGFlow **≥ 0.18.0** (contains the Langfuse connector)
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• A Langfuse workspace (cloud or self-hosted) with a _Project Public Key_ and _Secret Key_
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:::
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