diff --git a/README.md b/README.md index 1314ad7e8..106d7b5e5 100644 --- a/README.md +++ b/README.md @@ -20,7 +20,7 @@ Static Badge - docker pull infiniflow/ragflow:v0.14.0 + docker pull infiniflow/ragflow:v0.14.1 Latest Release @@ -176,14 +176,14 @@ releases! 🌟 ``` > - To download a RAGFlow slim Docker image of a specific version, update the `RAGFLOW_IMAGE` variable in * - *docker/.env** to your desired version. For example, `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.0-slim`. After + *docker/.env** to your desired version. For example, `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.1-slim`. After making this change, rerun the command above to initiate the download. > - To download the dev version of RAGFlow Docker image *including* embedding models and Python libraries, update the `RAGFLOW_IMAGE` variable in **docker/.env** to `RAGFLOW_IMAGE=infiniflow/ragflow:dev`. After making this change, rerun the command above to initiate the download. > - To download a specific version of RAGFlow Docker image *including* embedding models and Python libraries, update the `RAGFLOW_IMAGE` variable in **docker/.env** to your desired version. For example, - `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.0`. After making this change, rerun the command above to initiate the + `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.1`. After making this change, rerun the command above to initiate the download. > **NOTE:** A RAGFlow Docker image that includes embedding models and Python libraries is approximately 9GB in size diff --git a/README_id.md b/README_id.md index a11f51a48..3fa6262c0 100644 --- a/README_id.md +++ b/README_id.md @@ -20,7 +20,7 @@ Lencana Daring - docker pull infiniflow/ragflow:v0.14.0 + docker pull infiniflow/ragflow:v0.14.1 Rilis Terbaru @@ -169,14 +169,14 @@ Coba demo kami di [https://demo.ragflow.io](https://demo.ragflow.io). ``` > - Untuk mengunduh versi tertentu dari image Docker RAGFlow slim, perbarui variabel `RAGFlow_IMAGE` di * - *docker/.env** sesuai dengan versi yang diinginkan. Misalnya, `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.0-slim`. + *docker/.env** sesuai dengan versi yang diinginkan. Misalnya, `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.1-slim`. Setelah mengubah ini, jalankan ulang perintah di atas untuk memulai unduhan. > - Untuk mengunduh versi dev dari image Docker RAGFlow *termasuk* model embedding dan library Python, perbarui variabel `RAGFlow_IMAGE` di **docker/.env** menjadi `RAGFLOW_IMAGE=infiniflow/ragflow:dev`. Setelah mengubah ini, jalankan ulang perintah di atas untuk memulai unduhan. > - Untuk mengunduh versi tertentu dari image Docker RAGFlow *termasuk* model embedding dan library Python, perbarui variabel `RAGFlow_IMAGE` di **docker/.env** sesuai dengan versi yang diinginkan. Misalnya, - `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.0`. Setelah mengubah ini, jalankan ulang perintah di atas untuk memulai unduhan. + `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.1`. Setelah mengubah ini, jalankan ulang perintah di atas untuk memulai unduhan. > **CATATAN:** Image Docker RAGFlow yang mencakup model embedding dan library Python berukuran sekitar 9GB dan mungkin memerlukan waktu lebih lama untuk dimuat. diff --git a/README_ja.md b/README_ja.md index 1d2afc233..e1854d154 100644 --- a/README_ja.md +++ b/README_ja.md @@ -20,7 +20,7 @@ Static Badge - docker pull infiniflow/ragflow:v0.14.0 + docker pull infiniflow/ragflow:v0.14.1 Latest Release @@ -148,9 +148,9 @@ $ docker compose -f docker-compose.yml up -d ``` - > - 特定のバージョンのRAGFlow slim Dockerイメージをダウンロードするには、**docker/.env**内の`RAGFlow_IMAGE`変数を希望のバージョンに更新します。例えば、`RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.0`とします。この変更を行った後、上記のコマンドを再実行してダウンロードを開始してください。 + > - 特定のバージョンのRAGFlow slim Dockerイメージをダウンロードするには、**docker/.env**内の`RAGFlow_IMAGE`変数を希望のバージョンに更新します。例えば、`RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.1`とします。この変更を行った後、上記のコマンドを再実行してダウンロードを開始してください。 > - RAGFlowの埋め込みモデルとPythonライブラリを含む開発版Dockerイメージをダウンロードするには、**docker/.env**内の`RAGFlow_IMAGE`変数を`RAGFLOW_IMAGE=infiniflow/ragflow:dev`に更新します。この変更を行った後、上記のコマンドを再実行してダウンロードを開始してください。 - > - 特定のバージョンのRAGFlow Dockerイメージ（埋め込みモデルとPythonライブラリを含む）をダウンロードするには、**docker/.env**内の`RAGFlow_IMAGE`変数を希望のバージョンに更新します。例えば、`RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.0`とします。この変更を行った後、上記のコマンドを再実行してダウンロードを開始してください。 + > - 特定のバージョンのRAGFlow Dockerイメージ（埋め込みモデルとPythonライブラリを含む）をダウンロードするには、**docker/.env**内の`RAGFlow_IMAGE`変数を希望のバージョンに更新します。例えば、`RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.1`とします。この変更を行った後、上記のコマンドを再実行してダウンロードを開始してください。 > **NOTE:** 埋め込みモデルとPythonライブラリを含むRAGFlow Dockerイメージのサイズは約9GBであり、読み込みにかなりの時間がかかる場合があります。 diff --git a/README_ko.md b/README_ko.md index 923b10362..0ad751e21 100644 --- a/README_ko.md +++ b/README_ko.md @@ -20,7 +20,7 @@ Static Badge - docker pull infiniflow/ragflow:v0.14.0 + docker pull infiniflow/ragflow:v0.14.1 Latest Release @@ -152,9 +152,9 @@ $ docker compose -f docker-compose.yml up -d ``` - > - 특정 버전의 RAGFlow slim Docker 이미지를 다운로드하려면, **docker/.env**에서 `RAGFlow_IMAGE` 변수를 원하는 버전으로 업데이트하세요. 예를 들어, `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.0-slim`으로 설정합니다. 이 변경을 완료한 후, 위의 명령을 다시 실행하여 다운로드를 시작하세요. + > - 특정 버전의 RAGFlow slim Docker 이미지를 다운로드하려면, **docker/.env**에서 `RAGFlow_IMAGE` 변수를 원하는 버전으로 업데이트하세요. 예를 들어, `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.1-slim`으로 설정합니다. 이 변경을 완료한 후, 위의 명령을 다시 실행하여 다운로드를 시작하세요. > - RAGFlow의 임베딩 모델과 Python 라이브러리를 포함한 개발 버전 Docker 이미지를 다운로드하려면, **docker/.env**에서 `RAGFlow_IMAGE` 변수를 `RAGFLOW_IMAGE=infiniflow/ragflow:dev`로 업데이트하세요. 이 변경을 완료한 후, 위의 명령을 다시 실행하여 다운로드를 시작하세요. - > - 특정 버전의 RAGFlow Docker 이미지를 임베딩 모델과 Python 라이브러리를 포함하여 다운로드하려면, **docker/.env**에서 `RAGFlow_IMAGE` 변수를 원하는 버전으로 업데이트하세요. 예를 들어, `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.0` 로 설정합니다. 이 변경을 완료한 후, 위의 명령을 다시 실행하여 다운로드를 시작하세요. + > - 특정 버전의 RAGFlow Docker 이미지를 임베딩 모델과 Python 라이브러리를 포함하여 다운로드하려면, **docker/.env**에서 `RAGFlow_IMAGE` 변수를 원하는 버전으로 업데이트하세요. 예를 들어, `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.1` 로 설정합니다. 이 변경을 완료한 후, 위의 명령을 다시 실행하여 다운로드를 시작하세요. > **NOTE:** 임베딩 모델과 Python 라이브러리를 포함한 RAGFlow Docker 이미지의 크기는 약 9GB이며, 로드하는 데 상당히 오랜 시간이 걸릴 수 있습니다. diff --git a/README_zh.md b/README_zh.md index f2710d81e..ee22fa597 100644 --- a/README_zh.md +++ b/README_zh.md @@ -20,7 +20,7 @@ Static Badge - docker pull infiniflow/ragflow:v0.14.0 + docker pull infiniflow/ragflow:v0.14.1 Latest Release @@ -149,9 +149,9 @@ $ docker compose -f docker-compose.yml up -d ``` - > - 如果你想下载并运行特定版本的 RAGFlow slim Docker 镜像，请在 **docker/.env** 文件中找到 `RAGFLOW_IMAGE` 变量，将其改为对应版本。例如 `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.0-slim`，然后再运行上述命令。 + > - 如果你想下载并运行特定版本的 RAGFlow slim Docker 镜像，请在 **docker/.env** 文件中找到 `RAGFLOW_IMAGE` 变量，将其改为对应版本。例如 `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.1-slim`，然后再运行上述命令。 > - 如果您想安装内置 embedding 模型和 Python 库的 dev 版本的 Docker 镜像，需要将 **docker/.env** 文件中的 `RAGFLOW_IMAGE` 变量修改为： `RAGFLOW_IMAGE=infiniflow/ragflow:dev`。 - > - 如果您想安装内置 embedding 模型和 Python 库的指定版本的 RAGFlow Docker 镜像，需要将 **docker/.env** 文件中的 `RAGFLOW_IMAGE` 变量修改为： `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.0`。修改后，再运行上面的命令。 + > - 如果您想安装内置 embedding 模型和 Python 库的指定版本的 RAGFlow Docker 镜像，需要将 **docker/.env** 文件中的 `RAGFLOW_IMAGE` 变量修改为： `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.1`。修改后，再运行上面的命令。 > **注意：** 安装内置 embedding 模型和 Python 库的指定版本的 RAGFlow Docker 镜像大小约 9 GB，可能需要更长时间下载，请耐心等待。 4. 服务器启动成功后再次确认服务器状态： diff --git a/docs/guides/configure_knowledge_base.md b/docs/guides/configure_knowledge_base.md index aef6ad516..457062b34 100644 --- a/docs/guides/configure_knowledge_base.md +++ b/docs/guides/configure_knowledge_base.md @@ -103,7 +103,7 @@ RAGFlow features visibility and explainability, allowing you to view the chunkin 2. Hover over each snapshot for a quick view of each chunk. -3. Double click the chunked texts to add keywords or make *manual* changes where necessary: +3. Double-click the chunked texts to add keywords or make *manual* changes where necessary: ![update chunk](https://github.com/infiniflow/ragflow/assets/93570324/1d84b408-4e9f-46fd-9413-8c1059bf9c76) @@ -111,7 +111,7 @@ RAGFlow features visibility and explainability, allowing you to view the chunkin You can add keywords to a file chunk to increase its ranking for queries containing those keywords. This action increases its keyword weight and can improve its position in search list. ::: -4. In Retrieval testing, ask a quick question in **Test text** to double check if your configurations work: +4. In Retrieval testing, ask a quick question in **Test text** to double-check if your configurations work: _As you can tell from the following, RAGFlow responds with truthful citations._ @@ -128,7 +128,7 @@ RAGFlow uses multiple recall of both full-text search and vector search in its c ## Search for knowledge base -As of RAGFlow v0.14.0, the search feature is still in a rudimentary form, supporting only knowledge base search by name. +As of RAGFlow v0.14.1, 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) diff --git a/docs/guides/deploy_local_llm.mdx b/docs/guides/deploy_local_llm.mdx index 5dcaefc5a..76c8543c6 100644 --- a/docs/guides/deploy_local_llm.mdx +++ b/docs/guides/deploy_local_llm.mdx @@ -108,7 +108,7 @@ Click on your logo **>** **Model Providers** **>** **System Model Settings** to Update your chat model accordingly in **Chat Configuration**: -> If your local model is an embedding model, update it on the configruation page of your knowledge base. +> If your local model is an embedding model, update it on the configuration page of your knowledge base. ## Deploy a local model using Xinference @@ -161,7 +161,7 @@ Click on your logo **>** **Model Providers** **>** **System Model Settings** to Update your chat model accordingly in **Chat Configuration**: -> If your local model is an embedding model, update it on the configruation page of your knowledge base. +> If your local model is an embedding model, update it on the configuration page of your knowledge base. ## Deploy a local model using IPEX-LLM diff --git a/docs/guides/develop/acquire_ragflow_api_key.md b/docs/guides/develop/acquire_ragflow_api_key.md index 82243fff8..b5d1bccc3 100644 --- a/docs/guides/develop/acquire_ragflow_api_key.md +++ b/docs/guides/develop/acquire_ragflow_api_key.md @@ -7,7 +7,7 @@ slug: /acquire_ragflow_api_key A key is required for the RAGFlow server to authenticate your requests via HTTP or a Python API. This documents provides instructions on obtaining a RAGFlow API key. -1. Click your avatar on the top right corner of the RAGFlow UI to access the configuration page. +1. Click your avatar in the top right corner of the RAGFlow UI to access the configuration page. 2. Click **API** to switch to the **API** page. 3. Obtain a RAGFlow API key: diff --git a/docs/guides/manage_files.md b/docs/guides/manage_files.md index 9756c62be..0e13998ad 100644 --- a/docs/guides/manage_files.md +++ b/docs/guides/manage_files.md @@ -81,4 +81,4 @@ 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 v0.14.0, bulk download is not supported, nor can you download an entire folder. +> As of RAGFlow v0.14.1, bulk download is not supported, nor can you download an entire folder. diff --git a/docs/guides/manage_team_members.md b/docs/guides/manage_team_members.md index f87377411..f6c958b93 100644 --- a/docs/guides/manage_team_members.md +++ b/docs/guides/manage_team_members.md @@ -17,7 +17,7 @@ By default, each RAGFlow user is assigned a single team named after their name. Team members are currently *not* allowed to invite users to your team, and only you, the team owner, is permitted to do so. ::: -To enter the **Team** page, click on your avatar on the top right corner of the page **>** Team: +To enter the **Team** page, click on your avatar in the top right corner of the page **>** Team: ![team](https://github.com/user-attachments/assets/0eac2503-26bc-4568-b3f2-bcd84069a07a) diff --git a/docs/guides/run_health_check.md b/docs/guides/run_health_check.md index fece25758..af6d8491a 100644 --- a/docs/guides/run_health_check.md +++ b/docs/guides/run_health_check.md @@ -5,7 +5,7 @@ slug: /run_health_check # Run health check on RAGFlow's dependencies -Double check the health status of RAGFlow's dependencies. +Double-check the health status of RAGFlow's dependencies. The operation of RAGFlow depends on four services: @@ -16,7 +16,7 @@ The operation of RAGFlow depends on four services: If an exception or error occurs related to any of the above services, such as `Exception: Can't connect to ES cluster`, refer to this document to check their health status. -You can also click you avatar on the top right corner of the page **>** System to view the visualized health status of RAGFlow's core services. The following screenshot shows that all services are 'green' (running healthily). The task executor displays the *cumulative* number of completed and failed document parsing tasks from the past 30 minutes: +You can also click you avatar in the top right corner of the page **>** System to view the visualized health status of RAGFlow's core services. The following screenshot shows that all services are 'green' (running healthily). The task executor displays the *cumulative* number of completed and failed document parsing tasks from the past 30 minutes: ![system_status_page](https://github.com/user-attachments/assets/b0c1a11e-93e3-4947-b17a-1bfb4cdab6e4) diff --git a/docs/guides/start_chat.md b/docs/guides/start_chat.md index 81115bd80..2120cca80 100644 --- a/docs/guides/start_chat.md +++ b/docs/guides/start_chat.md @@ -19,7 +19,7 @@ You start an AI conversation by creating an assistant. - **Assistant name** is the name of your chat assistant. Each assistant corresponds to a dialogue with a unique combination of knowledge bases, prompts, hybrid search configurations, and large model settings. - **Empty response**: - - If you wish to *confine* RAGFlow's answers to your knowledge bases, leave a response here. Then when it doesn't retrieve an answer, it *uniformly* responds with what you set here. + - If you wish to *confine* RAGFlow's answers to your knowledge bases, leave a response here. Then, when it doesn't retrieve an answer, it *uniformly* responds with what you set here. - If you wish RAGFlow to *improvise* when it doesn't retrieve an answer from your knowledge bases, leave it blank, which may give rise to hallucinations. - **Show Quote**: This is a key feature of RAGFlow and enabled by default. RAGFlow does not work like a black box. instead, it clearly shows the sources of information that its responses are based on. - Select the corresponding knowledge bases. You can select one or multiple knowledge bases, but ensure that they use the same embedding model, otherwise an error would occur. diff --git a/docs/guides/upgrade_ragflow.mdx b/docs/guides/upgrade_ragflow.mdx index 31d19d91d..e329e579a 100644 --- a/docs/guides/upgrade_ragflow.mdx +++ b/docs/guides/upgrade_ragflow.mdx @@ -62,16 +62,16 @@ 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., `v0.14.0`: +2. Switch to the latest, officially published release, e.g., `v0.14.1`: ```bash - git checkout v0.14.0 + git checkout v0.14.1 ``` 3. Update **ragflow/docker/.env** as follows: ```bash - RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.0 + RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.1 ``` 4. Update the RAGFlow image and restart RAGFlow: diff --git a/docs/quickstart.mdx b/docs/quickstart.mdx index 355747f05..b3c6721e8 100644 --- a/docs/quickstart.mdx +++ b/docs/quickstart.mdx @@ -32,9 +32,9 @@ This section provides instructions on setting up the RAGFlow server on Linux. If
1. Ensure vm.max_map_count ≥ 262144: - `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 abmornal behaviors, and the system will throw out-of-memory errors when a process reaches the limitation. + `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 v0.14.0 uses Elasticsearch for multiple recall. Setting the value of `vm.max_map_count` correctly is crucial to the proper functioning of the Elasticsearch component. + RAGFlow v0.14.1 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. - To download a RAGFlow slim Docker image of a specific version, update the `RAGFlOW_IMAGE` variable in **docker/.env** to your desired version. For example, `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.0-slim`. After making this change, rerun the command above to initiate the download. + > - To download a RAGFlow slim Docker image of a specific version, update the `RAGFlOW_IMAGE` variable in **docker/.env** to your desired version. For example, `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.1-slim`. After making this change, rerun the command above to initiate the download. > - To download the dev version of RAGFlow Docker image *including* embedding models and Python libraries, update the `RAGFlOW_IMAGE` variable in **docker/.env** to `RAGFLOW_IMAGE=infiniflow/ragflow:dev`. After making this change, rerun the command above to initiate the download. - > - To download a specific version of RAGFlow Docker image *including* embedding models and Python libraries, update the `RAGFlOW_IMAGE` variable in **docker/.env** to your desired version. For example, `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.0`. After making this change, rerun the command above to initiate the download. + > - To download a specific version of RAGFlow Docker image *including* embedding models and Python libraries, update the `RAGFlOW_IMAGE` variable in **docker/.env** to your desired version. For example, `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.1`. After making this change, rerun the command above to initiate the download. :::tip NOTE A RAGFlow Docker image that includes embedding models and Python libraries is approximately 9GB in size and may take significantly longer time to load.