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Update Octoverse to README (#10859)

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### Type of change

- [x] Documentation Update
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Yingfeng
2025-10-29 00:34:39 +08:00
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41
README.md
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@ -43,7 +43,9 @@
<a href="https://demo.ragflow.io">Demo</a>
</h4>
#
<div align="center" style="margin-top:20px;margin-bottom:20px;">
<img src="https://raw.githubusercontent.com/infiniflow/ragflow-docs/refs/heads/image/image/ragflow-octoverse.png" width="1200"/>
</div>
<div align="center">
<a href="https://trendshift.io/repositories/9064" target="_blank"><img src="https://trendshift.io/api/badge/repositories/9064" alt="infiniflow%2Fragflow | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
@ -175,22 +177,21 @@ releases! 🌟
> ```bash
> vm.max_map_count=262144
> ```
>
2. Clone the repo:
```bash
$ git clone https://github.com/infiniflow/ragflow.git
```
3. Start up the server using the pre-built Docker images:
> [!CAUTION]
> All Docker images are built for x86 platforms. We don't currently offer Docker images for ARM64.
> If you are on an ARM64 platform, follow [this guide](https://ragflow.io/docs/dev/build_docker_image) to build a Docker image compatible with your system.
> The command below downloads the `v0.21.1-slim` edition of the RAGFlow Docker image. See the following table for descriptions of different RAGFlow editions. To download a RAGFlow edition different from `v0.21.1-slim`, update the `RAGFLOW_IMAGE` variable accordingly in **docker/.env** before using `docker compose` to start the server.
> The command below downloads the `v0.21.1-slim` edition of the RAGFlow Docker image. See the following table for descriptions of different RAGFlow editions. To download a RAGFlow edition different from `v0.21.1-slim`, update the `RAGFLOW_IMAGE` variable accordingly in **docker/.env** before using `docker compose` to start the server.
```bash
```bash
$ cd ragflow/docker
# Use CPU for embedding and DeepDoc tasks:
$ docker compose -f docker-compose.yml up -d
@ -198,16 +199,15 @@ releases! 🌟
# To use GPU to accelerate embedding and DeepDoc tasks:
# sed -i '1i DEVICE=gpu' .env
# docker compose -f docker-compose.yml up -d
```
```
| RAGFlow image tag | Image size (GB) | Has embedding models? | Stable? |
|-------------------|-----------------|-----------------------|--------------------------|
| v0.21.1 | &approx;9 | ✔️ | Stable release |
| v0.21.1-slim | &approx;2 | ❌ | Stable release |
| nightly | &approx;2 | ❌ | _Unstable_ nightly build |
| RAGFlow image tag | Image size (GB) | Has embedding models? | Stable? |
| ----------------- | --------------- | --------------------- | -------------------------- |
| v0.21.1 | &approx;9 | ✔️ | Stable release |
| v0.21.1-slim | &approx;2 | ❌ | Stable release |
| nightly | &approx;2 | ❌ | _Unstable_ nightly build |
> Note: Starting with `v0.22.0`, we ship only the slim edition and no longer append the **-slim** suffix to the image tag.
> Note: Starting with `v0.22.0`, we ship only the slim edition and no longer append the **-slim** suffix to the image tag.
4. Check the server status after having the server up and running:
@ -230,14 +230,17 @@ releases! 🌟
> If you skip this confirmation step and directly log in to RAGFlow, your browser may prompt a `network anormal`
> error because, at that moment, your RAGFlow may not be fully initialized.
>
5. In your web browser, enter the IP address of your server and log in to RAGFlow.
> With the default settings, you only need to enter `http://IP_OF_YOUR_MACHINE` (**sans** port number) as the default
> HTTP serving port `80` can be omitted when using the default configurations.
>
6. In [service_conf.yaml.template](./docker/service_conf.yaml.template), select the desired LLM factory in `user_default_llm` and update
the `API_KEY` field with the corresponding API key.
> See [llm_api_key_setup](https://ragflow.io/docs/dev/llm_api_key_setup) for more information.
>
_The show is on!_
@ -276,7 +279,6 @@ RAGFlow uses Elasticsearch by default for storing full text and vectors. To swit
> `-v` will delete the docker container volumes, and the existing data will be cleared.
2. Set `DOC_ENGINE` in **docker/.env** to `infinity`.
3. Start the containers:
```bash
@ -303,7 +305,6 @@ docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly
```bash
pipx install uv pre-commit
```
2. Clone the source code and install Python dependencies:
```bash
@ -313,7 +314,6 @@ docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly
uv run download_deps.py
pre-commit install
```
3. Launch the dependent services (MinIO, Elasticsearch, Redis, and MySQL) using Docker Compose:
```bash
@ -325,13 +325,11 @@ docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly
```
127.0.0.1 es01 infinity mysql minio redis sandbox-executor-manager
```
4. If you cannot access HuggingFace, set the `HF_ENDPOINT` environment variable to use a mirror site:
```bash
export HF_ENDPOINT=https://hf-mirror.com
```
5. If your operating system does not have jemalloc, please install it as follows:
```bash
@ -344,7 +342,6 @@ docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly
# macOS
sudo brew install jemalloc
```
6. Launch backend service:
```bash
@ -352,14 +349,12 @@ docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly
export PYTHONPATH=$(pwd)
bash docker/launch_backend_service.sh
```
7. Install frontend dependencies:
```bash
cd web
npm install
```
8. Launch frontend service:
```bash
@ -369,14 +364,12 @@ docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly
_The following output confirms a successful launch of the system:_
![](https://github.com/user-attachments/assets/0daf462c-a24d-4496-a66f-92533534e187)
9. Stop RAGFlow front-end and back-end service after development is complete:
```bash
pkill -f "ragflow_server.py|task_executor.py"
```
## 📚 Documentation
- [Quickstart](https://ragflow.io/docs/dev/)

38
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@ -43,7 +43,13 @@
<a href="https://demo.ragflow.io">Demo</a>
</h4>
#
<div align="center" style="margin-top:20px;margin-bottom:20px;">
<img src="https://raw.githubusercontent.com/infiniflow/ragflow-docs/refs/heads/image/image/ragflow-octoverse.png" width="1200"/>
</div>
<div align="center">
<a href="https://trendshift.io/repositories/9064" target="_blank"><img src="https://trendshift.io/api/badge/repositories/9064" alt="infiniflow%2Fragflow | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
</div>
<details open>
<summary><b>📕 Daftar Isi </b> </summary>
@ -169,13 +175,12 @@ Coba demo kami di [https://demo.ragflow.io](https://demo.ragflow.io).
> ```bash
> vm.max_map_count=262144
> ```
>
2. Clone repositori:
```bash
$ git clone https://github.com/infiniflow/ragflow.git
```
3. Bangun image Docker pre-built dan jalankan server:
> [!CAUTION]
@ -184,7 +189,7 @@ Coba demo kami di [https://demo.ragflow.io](https://demo.ragflow.io).
> Perintah di bawah ini mengunduh edisi v0.21.1 dari gambar Docker RAGFlow. Silakan merujuk ke tabel berikut untuk deskripsi berbagai edisi RAGFlow. Untuk mengunduh edisi RAGFlow yang berbeda dari v0.21.1, perbarui variabel RAGFLOW_IMAGE di docker/.env sebelum menggunakan docker compose untuk memulai server.
```bash
```bash
$ cd ragflow/docker
# Use CPU for embedding and DeepDoc tasks:
$ docker compose -f docker-compose.yml up -d
@ -192,12 +197,12 @@ Coba demo kami di [https://demo.ragflow.io](https://demo.ragflow.io).
# To use GPU to accelerate embedding and DeepDoc tasks:
# sed -i '1i DEVICE=gpu' .env
# docker compose -f docker-compose.yml up -d
```
```
| RAGFlow image tag | Image size (GB) | Has embedding models? | Stable? |
| ----------------- | --------------- | --------------------- | ------------------------ |
| v0.21.1 | &approx;9 | ✔️ | Stable release |
| v0.21.1-slim | &approx;2 | ❌ | Stable release |
| RAGFlow image tag | Image size (GB) | Has embedding models? | Stable? |
| ----------------- | --------------- | --------------------- | -------------------------- |
| v0.21.1 | &approx;9 | ✔️ | Stable release |
| v0.21.1-slim | &approx;2 | ❌ | Stable release |
| nightly | &approx;2 | ❌ | _Unstable_ nightly build |
> Catatan: Mulai dari `v0.22.0`, kami hanya menyediakan edisi slim dan tidak lagi menambahkan akhiran **-slim** pada tag image.
@ -223,14 +228,17 @@ Coba demo kami di [https://demo.ragflow.io](https://demo.ragflow.io).
> Jika Anda melewatkan langkah ini dan langsung login ke RAGFlow, browser Anda mungkin menampilkan error `network anormal`
> karena RAGFlow mungkin belum sepenuhnya siap.
>
2. Buka browser web Anda, masukkan alamat IP server Anda, dan login ke RAGFlow.
> Dengan pengaturan default, Anda hanya perlu memasukkan `http://IP_DEVICE_ANDA` (**tanpa** nomor port) karena
> port HTTP default `80` bisa dihilangkan saat menggunakan konfigurasi default.
>
3. Dalam [service_conf.yaml.template](./docker/service_conf.yaml.template), pilih LLM factory yang diinginkan di `user_default_llm` dan perbarui
bidang `API_KEY` dengan kunci API yang sesuai.
> Lihat [llm_api_key_setup](https://ragflow.io/docs/dev/llm_api_key_setup) untuk informasi lebih lanjut.
>
_Sistem telah siap digunakan!_
@ -269,7 +277,6 @@ docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly
```bash
pipx install uv pre-commit
```
2. Clone kode sumber dan instal dependensi Python:
```bash
@ -279,7 +286,6 @@ docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly
uv run download_deps.py
pre-commit install
```
3. Jalankan aplikasi yang diperlukan (MinIO, Elasticsearch, Redis, dan MySQL) menggunakan Docker Compose:
```bash
@ -291,13 +297,11 @@ docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly
```
127.0.0.1 es01 infinity mysql minio redis sandbox-executor-manager
```
4. Jika Anda tidak dapat mengakses HuggingFace, atur variabel lingkungan `HF_ENDPOINT` untuk menggunakan situs mirror:
```bash
export HF_ENDPOINT=https://hf-mirror.com
```
5. Jika sistem operasi Anda tidak memiliki jemalloc, instal sebagai berikut:
```bash
@ -308,7 +312,6 @@ docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly
# mac
sudo brew install jemalloc
```
6. Jalankan aplikasi backend:
```bash
@ -316,14 +319,12 @@ docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly
export PYTHONPATH=$(pwd)
bash docker/launch_backend_service.sh
```
7. Instal dependensi frontend:
```bash
cd web
npm install
```
8. Jalankan aplikasi frontend:
```bash
@ -333,15 +334,12 @@ docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly
_Output berikut menandakan bahwa sistem berhasil diluncurkan:_
![](https://github.com/user-attachments/assets/0daf462c-a24d-4496-a66f-92533534e187)
9. Hentikan layanan front-end dan back-end RAGFlow setelah pengembangan selesai:
```bash
pkill -f "ragflow_server.py|task_executor.py"
```
## 📚 Dokumentasi
- [Quickstart](https://ragflow.io/docs/dev/)

49
README_ja.md
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@ -43,7 +43,13 @@
<a href="https://demo.ragflow.io">Demo</a>
</h4>
#
<div align="center" style="margin-top:20px;margin-bottom:20px;">
<img src="https://raw.githubusercontent.com/infiniflow/ragflow-docs/refs/heads/image/image/ragflow-octoverse.png" width="1200"/>
</div>
<div align="center">
<a href="https://trendshift.io/repositories/9064" target="_blank"><img src="https://trendshift.io/api/badge/repositories/9064" alt="infiniflow%2Fragflow | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
</div>
## 💡 RAGFlow とは？
@ -148,22 +154,21 @@
> ```bash
> vm.max_map_count=262144
> ```
>
2. リポジトリをクローンする:
```bash
$ git clone https://github.com/infiniflow/ragflow.git
```
3. ビルド済みの Docker イメージをビルドし、サーバーを起動する:
> [!CAUTION]
> 現在、公式に提供されているすべての Docker イメージは x86 アーキテクチャ向けにビルドされており、ARM64 用の Docker イメージは提供されていません。
> ARM64 アーキテクチャのオペレーティングシステムを使用している場合は、[このドキュメント](https://ragflow.io/docs/dev/build_docker_image)を参照して Docker イメージを自分でビルドしてください。
> 以下のコマンドは、RAGFlow Docker イメージの v0.21.1 エディションをダウンロードします。異なる RAGFlow エディションの説明については、以下の表を参照してください。v0.21.1 とは異なるエディションをダウンロードするには、docker/.env ファイルの RAGFLOW_IMAGE 変数を適宜更新し、docker compose を使用してサーバーを起動してください。
> 以下のコマンドは、RAGFlow Docker イメージの v0.21.1 エディションをダウンロードします。異なる RAGFlow エディションの説明については、以下の表を参照してください。v0.21.1 とは異なるエディションをダウンロードするには、docker/.env ファイルの RAGFLOW_IMAGE 変数を適宜更新し、docker compose を使用してサーバーを起動してください。
```bash
```bash
$ cd ragflow/docker
# Use CPU for embedding and DeepDoc tasks:
$ docker compose -f docker-compose.yml up -d
@ -171,15 +176,15 @@
# To use GPU to accelerate embedding and DeepDoc tasks:
# sed -i '1i DEVICE=gpu' .env
# docker compose -f docker-compose.yml up -d
```
```
| RAGFlow image tag | Image size (GB) | Has embedding models? | Stable? |
| ----------------- | --------------- | --------------------- | ------------------------ |
| v0.21.1 | &approx;9 | ✔️ | Stable release |
| v0.21.1-slim | &approx;2 | ❌ | Stable release |
| nightly | &approx;2 | ❌ | _Unstable_ nightly build |
| RAGFlow image tag | Image size (GB) | Has embedding models? | Stable? |
| ----------------- | --------------- | --------------------- | -------------------------- |
| v0.21.1 | &approx;9 | ✔️ | Stable release |
| v0.21.1-slim | &approx;2 | ❌ | Stable release |
| nightly | &approx;2 | ❌ | _Unstable_ nightly build |
> 注意：`v0.22.0` 以降、当プロジェクトでは slim エディションのみを提供し、イメージタグに **-slim** サフィックスを付けなくなりました。
> 注意：`v0.22.0` 以降、当プロジェクトでは slim エディションのみを提供し、イメージタグに **-slim** サフィックスを付けなくなりました。
1. サーバーを立ち上げた後、サーバーの状態を確認する:
@ -200,12 +205,15 @@
```
> もし確認ステップをスキップして直接 RAGFlow にログインした場合、その時点で RAGFlow が完全に初期化されていない可能性があるため、ブラウザーがネットワーク異常エラーを表示するかもしれません。
>
2. ウェブブラウザで、プロンプトに従ってサーバーの IP アドレスを入力し、RAGFlow にログインします。
> デフォルトの設定を使用する場合、デフォルトの HTTP サービングポート `80` は省略できるので、与えられたシナリオでは、`http://IP_OF_YOUR_MACHINE`（ポート番号は省略）だけを入力すればよい。
>
3. [service_conf.yaml.template](./docker/service_conf.yaml.template) で、`user_default_llm` で希望の LLM ファクトリを選択し、`API_KEY` フィールドを対応する API キーで更新する。
> 詳しくは [llm_api_key_setup](https://ragflow.io/docs/dev/llm_api_key_setup) を参照してください。
>
_これで初期設定完了！ショーの開幕です！_
@ -234,18 +242,22 @@
RAGFlow はデフォルトで Elasticsearch を使用して全文とベクトルを保存します。［Infinity］に切り替え（https://github.com/infiniflow/infinity/)、次の手順に従います。
1. 実行中のすべてのコンテナを停止するには：
```bash
$ docker compose -f docker/docker-compose.yml down -v
```
Note: `-v` は docker コンテナのボリュームを削除し、既存のデータをクリアします。
2. **docker/.env** の「DOC \_ ENGINE」を「infinity」に設定します。
3. 起動コンテナ：
```bash
$ docker compose -f docker-compose.yml up -d
```
> [!WARNING]
> Linux/arm64 マシンでの Infinity への切り替えは正式にサポートされていません。
>
## 🔧 ソースコードで Docker イメージを作成（埋め込みモデルなし）
@ -264,7 +276,6 @@ docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly
```bash
pipx install uv pre-commit
```
2. ソースコードをクローンし、Python の依存関係をインストールする:
```bash
@ -274,7 +285,6 @@ docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly
uv run download_deps.py
pre-commit install
```
3. Docker Compose を使用して依存サービス（MinIO、Elasticsearch、Redis、MySQL）を起動する:
```bash
@ -286,13 +296,11 @@ docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly
```
127.0.0.1 es01 infinity mysql minio redis sandbox-executor-manager
```
4. HuggingFace にアクセスできない場合は、`HF_ENDPOINT` 環境変数を設定してミラーサイトを使用してください:
```bash
export HF_ENDPOINT=https://hf-mirror.com
```
5. オペレーティングシステムにjemallocがない場合は、次のようにインストールします:
```bash
@ -303,7 +311,6 @@ docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly
# mac
sudo brew install jemalloc
```
6. バックエンドサービスを起動する:
```bash
@ -311,14 +318,12 @@ docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly
export PYTHONPATH=$(pwd)
bash docker/launch_backend_service.sh
```
7. フロントエンドの依存関係をインストールする:
```bash
cd web
npm install
```
8. フロントエンドサービスを起動する:
```bash
@ -328,14 +333,12 @@ docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly
_以下の画面で、システムが正常に起動したことを示します:_
![](https://github.com/user-attachments/assets/0daf462c-a24d-4496-a66f-92533534e187)
9. 開発が完了したら、RAGFlow のフロントエンド サービスとバックエンド サービスを停止します:
```bash
pkill -f "ragflow_server.py|task_executor.py"
```
## 📚 ドキュメンテーション
- [Quickstart](https://ragflow.io/docs/dev/)

9
README_ko.md
View File

@ -43,7 +43,14 @@
<a href="https://demo.ragflow.io">Demo</a>
</h4>
#
<div align="center" style="margin-top:20px;margin-bottom:20px;">
<img src="https://raw.githubusercontent.com/infiniflow/ragflow-docs/refs/heads/image/image/ragflow-octoverse.png" width="1200"/>
</div>
<div align="center">
<a href="https://trendshift.io/repositories/9064" target="_blank"><img src="https://trendshift.io/api/badge/repositories/9064" alt="infiniflow%2Fragflow | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
</div>
## 💡 RAGFlow란?

157
README_pt_br.md
View File

@ -43,7 +43,13 @@
<a href="https://demo.ragflow.io">Demo</a>
</h4>
#
<div align="center" style="margin-top:20px;margin-bottom:20px;">
<img src="https://raw.githubusercontent.com/infiniflow/ragflow-docs/refs/heads/image/image/ragflow-octoverse.png" width="1200"/>
</div>
<div align="center">
<a href="https://trendshift.io/repositories/9064" target="_blank"><img src="https://trendshift.io/api/badge/repositories/9064" alt="infiniflow%2Fragflow | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
</div>
<details open>
<summary><b>📕 Índice</b></summary>
@ -148,42 +154,41 @@ Experimente nossa demo em [https://demo.ragflow.io](https://demo.ragflow.io).
### 🚀 Iniciar o servidor
1. Certifique-se de que `vm.max_map_count` >= 262144:
1. Certifique-se de que `vm.max_map_count` >= 262144:
> Para verificar o valor de `vm.max_map_count`:
>
> ```bash
> $ sysctl vm.max_map_count
> ```
>
> Se necessário, redefina `vm.max_map_count` para um valor de pelo menos 262144:
>
> ```bash
> # Neste caso, defina para 262144:
> $ sudo sysctl -w vm.max_map_count=262144
> ```
>
> Essa mudança será resetada após a reinicialização do sistema. Para garantir que a alteração permaneça permanente, adicione ou atualize o valor de `vm.max_map_count` em **/etc/sysctl.conf**:
>
> ```bash
> vm.max_map_count=262144
> ```
> Para verificar o valor de `vm.max_map_count`:
>
> ```bash
> $ sysctl vm.max_map_count
> ```
>
> Se necessário, redefina `vm.max_map_count` para um valor de pelo menos 262144:
>
> ```bash
> # Neste caso, defina para 262144:
> $ sudo sysctl -w vm.max_map_count=262144
> ```
>
> Essa mudança será resetada após a reinicialização do sistema. Para garantir que a alteração permaneça permanente, adicione ou atualize o valor de `vm.max_map_count` em **/etc/sysctl.conf**:
>
> ```bash
> vm.max_map_count=262144
> ```
>
2. Clone o repositório:
2. Clone o repositório:
```bash
$ git clone https://github.com/infiniflow/ragflow.git
```
3. Inicie o servidor usando as imagens Docker pré-compiladas:
```bash
$ git clone https://github.com/infiniflow/ragflow.git
```
3. Inicie o servidor usando as imagens Docker pré-compiladas:
> [!CAUTION]
> Todas as imagens Docker são construídas para plataformas x86. Atualmente, não oferecemos imagens Docker para ARM64.
> Se você estiver usando uma plataforma ARM64, por favor, utilize [este guia](https://ragflow.io/docs/dev/build_docker_image) para construir uma imagem Docker compatível com o seu sistema.
> O comando abaixo baixa a edição `v0.21.1` da imagem Docker do RAGFlow. Consulte a tabela a seguir para descrições de diferentes edições do RAGFlow. Para baixar uma edição do RAGFlow diferente da `v0.21.1`, atualize a variável `RAGFLOW_IMAGE` conforme necessário no **docker/.env** antes de usar `docker compose` para iniciar o servidor.
> O comando abaixo baixa a edição`v0.21.1` da imagem Docker do RAGFlow. Consulte a tabela a seguir para descrições de diferentes edições do RAGFlow. Para baixar uma edição do RAGFlow diferente da `v0.21.1`, atualize a variável `RAGFLOW_IMAGE` conforme necessário no **docker/.env** antes de usar `docker compose` para iniciar o servidor.
```bash
```bash
$ cd ragflow/docker
# Use CPU for embedding and DeepDoc tasks:
$ docker compose -f docker-compose.yml up -d
@ -191,43 +196,44 @@ Experimente nossa demo em [https://demo.ragflow.io](https://demo.ragflow.io).
# To use GPU to accelerate embedding and DeepDoc tasks:
# sed -i '1i DEVICE=gpu' .env
# docker compose -f docker-compose.yml up -d
```
| Tag da imagem RAGFlow | Tamanho da imagem (GB) | Possui modelos de incorporação? | Estável? |
| --------------------- | ---------------------- | --------------------------------- | ------------------------------ |
| v0.21.1 | &approx;9 | ✔️ | Lançamento estável |
| v0.21.1-slim | &approx;2 | ❌ | Lançamento estável |
| nightly | &approx;2 | ❌ | Construção noturna instável |
> Observação: A partir da`v0.22.0`, distribuímos apenas a edição slim e não adicionamos mais o sufixo **-slim** às tags das imagens.
4. Verifique o status do servidor após tê-lo iniciado:
```bash
$ docker logs -f docker-ragflow-cpu-1
```
| Tag da imagem RAGFlow | Tamanho da imagem (GB) | Possui modelos de incorporação? | Estável? |
| --------------------- | ---------------------- | ------------------------------- | --------------------------- |
| v0.21.1 | &approx;9 | ✔️ | Lançamento estável |
| v0.21.1-slim | &approx;2 | ❌ | Lançamento estável |
| nightly | &approx;2 | ❌ | Construção noturna instável |
_O seguinte resultado confirma o lançamento bem-sucedido do sistema:_
> Observação: A partir da `v0.22.0`, distribuímos apenas a edição slim e não adicionamos mais o sufixo **-slim** às tags das imagens.
```bash
____ ___ ______ ______ __
/ __ \ / | / ____// ____// /____ _ __
/ /_/ // /| | / / __ / /_ / // __ \| | /| / /
/ _, _// ___ |/ /_/ // __/ / // /_/ /| |/ |/ /
/_/ |_|/_/ |_|\____//_/ /_/ \____/ |__/|__/
4. Verifique o status do servidor após tê-lo iniciado:
* Rodando em todos os endereços (0.0.0.0)
```
```bash
$ docker logs -f docker-ragflow-cpu-1
```
> Se você pular essa etapa de confirmação e acessar diretamente o RAGFlow, seu navegador pode exibir um erro `network anormal`, pois, nesse momento, seu RAGFlow pode não estar totalmente inicializado.
>
5. No seu navegador, insira o endereço IP do seu servidor e faça login no RAGFlow.
_O seguinte resultado confirma o lançamento bem-sucedido do sistema:_
> Com as configurações padrão, você só precisa digitar `http://IP_DO_SEU_MÁQUINA` (**sem** o número da porta), pois a porta HTTP padrão `80` pode ser omitida ao usar as configurações padrão.
>
6. Em [service_conf.yaml.template](./docker/service_conf.yaml.template), selecione a fábrica LLM desejada em `user_default_llm` e atualize o campo `API_KEY` com a chave de API correspondente.
```bash
____ ___ ______ ______ __
/ __ \ / | / ____// ____// /____ _ __
/ /_/ // /| | / / __ / /_ / // __ \| | /| / /
/ _, _// ___ |/ /_/ // __/ / // /_/ /| |/ |/ /
/_/ |_|/_/ |_|\____//_/ /_/ \____/ |__/|__/
* Rodando em todos os endereços (0.0.0.0)
```
> Se você pular essa etapa de confirmação e acessar diretamente o RAGFlow, seu navegador pode exibir um erro `network anormal`, pois, nesse momento, seu RAGFlow pode não estar totalmente inicializado.
5. No seu navegador, insira o endereço IP do seu servidor e faça login no RAGFlow.
> Com as configurações padrão, você só precisa digitar `http://IP_DO_SEU_MÁQUINA` (**sem** o número da porta), pois a porta HTTP padrão `80` pode ser omitida ao usar as configurações padrão.
6. Em [service_conf.yaml.template](./docker/service_conf.yaml.template), selecione a fábrica LLM desejada em `user_default_llm` e atualize o campo `API_KEY` com a chave de API correspondente.
> Consulte [llm_api_key_setup](https://ragflow.io/docs/dev/llm_api_key_setup) para mais informações.
> Consulte [llm_api_key_setup](https://ragflow.io/docs/dev/llm_api_key_setup) para mais informações.
>
_O show está no ar!_
@ -258,9 +264,9 @@ O RAGFlow usa o Elasticsearch por padrão para armazenar texto completo e vetore
```bash
$ docker compose -f docker/docker-compose.yml down -v
```
Note: `-v` irá deletar os volumes do contêiner, e os dados existentes serão apagados.
2. Defina `DOC_ENGINE` no **docker/.env** para `infinity`.
3. Inicie os contêineres:
```bash
@ -287,7 +293,6 @@ docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly
```bash
pipx install uv pre-commit
```
2. Clone o código-fonte e instale as dependências Python:
```bash
@ -297,7 +302,6 @@ docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly
uv run download_deps.py
pre-commit install
```
3. Inicie os serviços dependentes (MinIO, Elasticsearch, Redis e MySQL) usando Docker Compose:
```bash
@ -309,24 +313,21 @@ docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly
```
127.0.0.1 es01 infinity mysql minio redis sandbox-executor-manager
```
4. Se não conseguir acessar o HuggingFace, defina a variável de ambiente `HF_ENDPOINT` para usar um site espelho:
```bash
export HF_ENDPOINT=https://hf-mirror.com
```
5. Se o seu sistema operacional não tiver jemalloc, instale-o da seguinte maneira:
```bash
# ubuntu
sudo apt-get install libjemalloc-dev
# centos
sudo yum instalar jemalloc
# mac
sudo brew install jemalloc
```
```bash
# ubuntu
sudo apt-get install libjemalloc-dev
# centos
sudo yum instalar jemalloc
# mac
sudo brew install jemalloc
```
6. Lance o serviço de back-end:
```bash
@ -334,14 +335,12 @@ docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly
export PYTHONPATH=$(pwd)
bash docker/launch_backend_service.sh
```
7. Instale as dependências do front-end:
```bash
cd web
npm install
```
8. Lance o serviço de front-end:
```bash
@ -351,13 +350,11 @@ docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly
_O seguinte resultado confirma o lançamento bem-sucedido do sistema:_
![](https://github.com/user-attachments/assets/0daf462c-a24d-4496-a66f-92533534e187)
9. Pare os serviços de front-end e back-end do RAGFlow após a conclusão do desenvolvimento:
```bash
pkill -f "ragflow_server.py|task_executor.py"
```
```bash
pkill -f "ragflow_server.py|task_executor.py"
```
## 📚 Documentação

55
README_tzh.md
View File

@ -43,7 +43,9 @@
<a href="https://demo.ragflow.io">Demo</a>
</h4>
#
<div align="center" style="margin-top:20px;margin-bottom:20px;">
<img src="https://raw.githubusercontent.com/infiniflow/ragflow-docs/refs/heads/image/image/ragflow-octoverse.png" width="1200"/>
</div>
<div align="center">
<a href="https://trendshift.io/repositories/9064" target="_blank"><img src="https://trendshift.io/api/badge/repositories/9064" alt="infiniflow%2Fragflow | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
@ -171,22 +173,21 @@
> ```bash
> vm.max_map_count=262144
> ```
>
2. 克隆倉庫：
```bash
$ git clone https://github.com/infiniflow/ragflow.git
```
3. 進入 **docker** 資料夾，利用事先編譯好的 Docker 映像啟動伺服器：
> [!CAUTION]
> 所有 Docker 映像檔都是為 x86 平台建置的。目前，我們不提供 ARM64 平台的 Docker 映像檔。
> 如果您使用的是 ARM64 平台，請使用 [這份指南](https://ragflow.io/docs/dev/build_docker_image) 來建置適合您系統的 Docker 映像檔。
> 執行以下指令會自動下載 RAGFlow slim Docker 映像 `v0.21.1`。請參考下表查看不同 Docker 發行版的說明。如需下載不同於 `v0.21.1` 的 Docker 映像，請在執行 `docker compose` 啟動服務之前先更新 **docker/.env** 檔案內的 `RAGFLOW_IMAGE` 變數。
> 執行以下指令會自動下載 RAGFlow slim Docker 映像 `v0.21.1`。請參考下表查看不同 Docker 發行版的說明。如需下載不同於 `v0.21.1` 的 Docker 映像，請在執行 `docker compose` 啟動服務之前先更新 **docker/.env** 檔案內的 `RAGFLOW_IMAGE` 變數。
```bash
```bash
$ cd ragflow/docker
# Use CPU for embedding and DeepDoc tasks:
$ docker compose -f docker-compose.yml up -d
@ -194,21 +195,21 @@
# To use GPU to accelerate embedding and DeepDoc tasks:
# sed -i '1i DEVICE=gpu' .env
# docker compose -f docker-compose.yml up -d
```
```
| RAGFlow image tag | Image size (GB) | Has embedding models? | Stable? |
| ----------------- | --------------- | --------------------- | ------------------------ |
| v0.21.1 | &approx;9 | ✔️ | Stable release |
| v0.21.1-slim | &approx;2 | ❌ | Stable release |
| nightly | &approx;2 | ❌ | _Unstable_ nightly build |
| RAGFlow image tag | Image size (GB) | Has embedding models? | Stable? |
| ----------------- | --------------- | --------------------- | -------------------------- |
| v0.21.1 | &approx;9 | ✔️ | Stable release |
| v0.21.1-slim | &approx;2 | ❌ | Stable release |
| nightly | &approx;2 | ❌ | _Unstable_ nightly build |
> 注意：自 `v0.22.0` 起，我們僅發佈 slim 版本，並且不再在映像標籤後附加 **-slim** 後綴。
> 注意：自 `v0.22.0` 起，我們僅發佈 slim 版本，並且不再在映像標籤後附加 **-slim** 後綴。
> [!TIP]
> 如果你遇到 Docker 映像檔拉不下來的問題，可以在 **docker/.env** 檔案內根據變數 `RAGFLOW_IMAGE` 的註解提示選擇華為雲或阿里雲的對應映像。
>
> - 華為雲鏡像名：`swr.cn-north-4.myhuaweicloud.com/infiniflow/ragflow`
> - 阿里雲鏡像名：`registry.cn-hangzhou.aliyuncs.com/infiniflow/ragflow`
> [!TIP]
> 如果你遇到 Docker 映像檔拉不下來的問題，可以在 **docker/.env** 檔案內根據變數 `RAGFLOW_IMAGE` 的註解提示選擇華為雲或阿里雲的對應映像。
>
> - 華為雲鏡像名：`swr.cn-north-4.myhuaweicloud.com/infiniflow/ragflow`
> - 阿里雲鏡像名：`registry.cn-hangzhou.aliyuncs.com/infiniflow/ragflow`
4. 伺服器啟動成功後再次確認伺服器狀態：
@ -229,12 +230,15 @@
```
> 如果您跳過這一步驟系統確認步驟就登入 RAGFlow，你的瀏覽器有可能會提示 `network anormal` 或 `網路異常`，因為 RAGFlow 可能並未完全啟動成功。
>
5. 在你的瀏覽器中輸入你的伺服器對應的 IP 位址並登入 RAGFlow。
> 上面這個範例中，您只需輸入 http://IP_OF_YOUR_MACHINE 即可：未改動過設定則無需輸入連接埠（預設的 HTTP 服務連接埠 80）。
>
6. 在 [service_conf.yaml.template](./docker/service_conf.yaml.template) 檔案的 `user_default_llm` 欄位設定 LLM factory，並在 `API_KEY` 欄填入和你選擇的大模型相對應的 API key。
> 詳見 [llm_api_key_setup](https://ragflow.io/docs/dev/llm_api_key_setup)。
>
_好戲開始，接著奏樂接著舞！ _
@ -252,7 +256,7 @@
> [./docker/README](./docker/README.md) 解釋了 [service_conf.yaml.template](./docker/service_conf.yaml.template) 用到的環境變數設定和服務配置。
如需更新預設的 HTTP 服務連接埠(80), 可以在[docker-compose.yml](./docker/docker-compose.yml) 檔案中將配置`80:80` 改為`<YOUR_SERVING_PORT>:80` 。
如需更新預設的 HTTP 服務連接埠(80), 可以在[docker-compose.yml](./docker/docker-compose.yml) 檔案中將配置 `80:80` 改為 `<YOUR_SERVING_PORT>:80` 。
> 所有系統配置都需要透過系統重新啟動生效：
>
@ -269,10 +273,9 @@ RAGFlow 預設使用 Elasticsearch 儲存文字和向量資料. 如果要切換
```bash
$ docker compose -f docker/docker-compose.yml down -v
```
Note: `-v` 將會刪除 docker 容器的 volumes，已有的資料會被清空。
2. 設定 **docker/.env** 目錄中的 `DOC_ENGINE` 為 `infinity`.
3. 啟動容器:
```bash
@ -300,7 +303,6 @@ docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly
pipx install uv pre-commit
export UV_INDEX=https://mirrors.aliyun.com/pypi/simple
```
2. 下載原始碼並安裝 Python 依賴：
```bash
@ -310,7 +312,6 @@ docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly
uv run download_deps.py
pre-commit install
```
3. 透過 Docker Compose 啟動依賴的服務（MinIO, Elasticsearch, Redis, and MySQL）：
```bash
@ -322,13 +323,11 @@ docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly
```
127.0.0.1 es01 infinity mysql minio redis sandbox-executor-manager
```
4. 如果無法存取 HuggingFace，可以把環境變數 `HF_ENDPOINT` 設為對應的鏡像網站：
```bash
export HF_ENDPOINT=https://hf-mirror.com
```
5. 如果你的操作系统没有 jemalloc，请按照如下方式安装：
```bash
@ -339,7 +338,6 @@ docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly
# mac
sudo brew install jemalloc
```
6. 啟動後端服務：
```bash
@ -347,14 +345,12 @@ docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly
export PYTHONPATH=$(pwd)
bash docker/launch_backend_service.sh
```
7. 安裝前端依賴：
```bash
cd web
npm install
```
8. 啟動前端服務：
```bash
@ -364,15 +360,16 @@ docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly
以下界面說明系統已成功啟動：_
![](https://github.com/user-attachments/assets/0daf462c-a24d-4496-a66f-92533534e187)
```
```
9. 開發完成後停止 RAGFlow 前端和後端服務：
```bash
pkill -f "ragflow_server.py|task_executor.py"
```
## 📚 技術文檔
- [Quickstart](https://ragflow.io/docs/dev/)

4
README_zh.md
View File

@ -43,7 +43,9 @@
<a href="https://demo.ragflow.io">Demo</a>
</h4>
#
<div align="center" style="margin-top:20px;margin-bottom:20px;">
<img src="https://raw.githubusercontent.com/infiniflow/ragflow-docs/refs/heads/image/image/ragflow-octoverse.png" width="1200"/>
</div>
<div align="center">
<a href="https://trendshift.io/repositories/9064" target="_blank"><img src="https://trendshift.io/api/badge/repositories/9064" alt="infiniflow%2Fragflow | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>