mirror of
https://git.mirrors.martin98.com/https://github.com/infiniflow/ragflow.git
synced 2025-04-23 22:50:17 +08:00
Added 0.17.0 release notes (#5608)
### What problem does this PR solve? ### Type of change - [x] Documentation Update
This commit is contained in:
writinwaters
GitHub
parent
f256e1a59a
commit
fb4b5b0a06
@ -173,7 +173,11 @@ releases! 🌟
|
||||
|
||||
3. Start up the server using the pre-built Docker images:
|
||||
|
||||
> The command below downloads the `v0.17.0-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 `v0.17.0-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:v0.17.0` for the full edition `v0.17.0`.
|
||||
> [!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 `v0.17.0-slim` edition of the RAGFlow Docker image. See the following table for descriptions of different RAGFlow editions. To download a RAGFlow edition different from `v0.17.0-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:v0.17.0` for the full edition `v0.17.0`.
|
||||
|
||||
```bash
|
||||
$ cd ragflow/docker
|
||||
|
||||
10
README_id.md
10
README_id.md
@ -166,6 +166,10 @@ Coba demo kami di [https://demo.ragflow.io](https://demo.ragflow.io).
|
||||
|
||||
3. Bangun image Docker pre-built dan jalankan server:
|
||||
|
||||
> [!CAUTION]
|
||||
> Semua gambar Docker dibangun untuk platform x86. Saat ini, kami tidak menawarkan gambar Docker untuk ARM64.
|
||||
> Jika Anda menggunakan platform ARM64, [silakan gunakan panduan ini untuk membangun gambar Docker yang kompatibel dengan sistem Anda](https://ragflow.io/docs/dev/build_docker_image).
|
||||
|
||||
> Perintah di bawah ini mengunduh edisi v0.17.0-slim dari gambar Docker RAGFlow. Silakan merujuk ke tabel berikut untuk deskripsi berbagai edisi RAGFlow. Untuk mengunduh edisi RAGFlow yang berbeda dari v0.17.0-slim, perbarui variabel RAGFLOW_IMAGE di docker/.env sebelum menggunakan docker compose untuk memulai server. Misalnya, atur RAGFLOW_IMAGE=infiniflow/ragflow:v0.17.0 untuk edisi lengkap v0.17.0.
|
||||
|
||||
```bash
|
||||
@ -180,7 +184,7 @@ Coba demo kami di [https://demo.ragflow.io](https://demo.ragflow.io).
|
||||
| nightly | ≈9 | :heavy_check_mark: | _Unstable_ nightly build |
|
||||
| nightly-slim | ≈2 | ❌ | _Unstable_ nightly build |
|
||||
|
||||
4. Periksa status server setelah server aktif dan berjalan:
|
||||
1. Periksa status server setelah server aktif dan berjalan:
|
||||
|
||||
```bash
|
||||
$ docker logs -f ragflow-server
|
||||
@ -202,10 +206,10 @@ 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.
|
||||
|
||||
5. Buka browser web Anda, masukkan alamat IP server Anda, dan login ke RAGFlow.
|
||||
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.
|
||||
6. Dalam [service_conf.yaml.template](./docker/service_conf.yaml.template), pilih LLM factory yang diinginkan di `user_default_llm` dan perbarui
|
||||
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.
|
||||
|
||||
10
README_ja.md
10
README_ja.md
@ -146,6 +146,10 @@
|
||||
|
||||
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 v0.17.0-slim 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。v0.17.0-slim 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。例えば、完全版 v0.17.0 をダウンロードするには、RAGFLOW_IMAGE=infiniflow/ragflow:v0.17.0 と設定します。
|
||||
|
||||
```bash
|
||||
@ -160,7 +164,7 @@
|
||||
| nightly | ≈9 | :heavy_check_mark: | _Unstable_ nightly build |
|
||||
| nightly-slim | ≈2 | ❌ | _Unstable_ nightly build |
|
||||
|
||||
4. サーバーを立ち上げた後、サーバーの状態を確認する:
|
||||
1. サーバーを立ち上げた後、サーバーの状態を確認する:
|
||||
|
||||
```bash
|
||||
$ docker logs -f ragflow-server
|
||||
@ -180,9 +184,9 @@
|
||||
|
||||
> 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。
|
||||
|
||||
5. 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。
|
||||
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。
|
||||
6. [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。
|
||||
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。
|
||||
|
||||
|
||||
10
README_ko.md
10
README_ko.md
@ -147,6 +147,10 @@
|
||||
|
||||
3. 미리 빌드된 Docker 이미지를 생성하고 서버를 시작하세요:
|
||||
|
||||
> [!CAUTION]
|
||||
> 모든 Docker 이미지는 x86 플랫폼을 위해 빌드되었습니다. 우리는 현재 ARM64 플랫폼을 위한 Docker 이미지를 제공하지 않습니다.
|
||||
> ARM64 플랫폼을 사용 중이라면, [시스템과 호환되는 Docker 이미지를 빌드하려면 이 가이드를 사용해 주세요](https://ragflow.io/docs/dev/build_docker_image).
|
||||
|
||||
> 아래 명령어는 RAGFlow Docker 이미지의 v0.17.0-slim 버전을 다운로드합니다. 다양한 RAGFlow 버전에 대한 설명은 다음 표를 참조하십시오. v0.17.0-slim과 다른 RAGFlow 버전을 다운로드하려면, docker/.env 파일에서 RAGFLOW_IMAGE 변수를 적절히 업데이트한 후 docker compose를 사용하여 서버를 시작하십시오. 예를 들어, 전체 버전인 v0.17.0을 다운로드하려면 RAGFLOW_IMAGE=infiniflow/ragflow:v0.17.0로 설정합니다.
|
||||
|
||||
```bash
|
||||
@ -161,7 +165,7 @@
|
||||
| nightly | ≈9 | :heavy_check_mark: | _Unstable_ nightly build |
|
||||
| nightly-slim | ≈2 | ❌ | _Unstable_ nightly build |
|
||||
|
||||
4. 서버가 시작된 후 서버 상태를 확인하세요:
|
||||
1. 서버가 시작된 후 서버 상태를 확인하세요:
|
||||
|
||||
```bash
|
||||
$ docker logs -f ragflow-server
|
||||
@ -181,9 +185,9 @@
|
||||
|
||||
> 만약 확인 단계를 건너뛰고 바로 RAGFlow에 로그인하면, RAGFlow가 완전히 초기화되지 않았기 때문에 브라우저에서 `network anormal` 오류가 발생할 수 있습니다.
|
||||
|
||||
5. 웹 브라우저에 서버의 IP 주소를 입력하고 RAGFlow에 로그인하세요.
|
||||
2. 웹 브라우저에 서버의 IP 주소를 입력하고 RAGFlow에 로그인하세요.
|
||||
> 기본 설정을 사용할 경우, `http://IP_OF_YOUR_MACHINE`만 입력하면 됩니다 (포트 번호는 제외). 기본 HTTP 서비스 포트 `80`은 기본 구성으로 사용할 때 생략할 수 있습니다.
|
||||
6. [service_conf.yaml.template](./docker/service_conf.yaml.template) 파일에서 원하는 LLM 팩토리를 `user_default_llm`에 선택하고, `API_KEY` 필드를 해당 API 키로 업데이트하세요.
|
||||
3. [service_conf.yaml.template](./docker/service_conf.yaml.template) 파일에서 원하는 LLM 팩토리를 `user_default_llm`에 선택하고, `API_KEY` 필드를 해당 API 키로 업데이트하세요.
|
||||
|
||||
> 자세한 내용은 [llm_api_key_setup](https://ragflow.io/docs/dev/llm_api_key_setup)를 참조하세요.
|
||||
|
||||
|
||||
@ -166,6 +166,10 @@ Experimente nossa demo em [https://demo.ragflow.io](https://demo.ragflow.io).
|
||||
|
||||
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 `v0.17.0-slim` 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 `v0.17.0-slim`, atualize a variável `RAGFLOW_IMAGE` conforme necessário no **docker/.env** antes de usar `docker compose` para iniciar o servidor. Por exemplo: defina `RAGFLOW_IMAGE=infiniflow/ragflow:v0.17.0` para a edição completa `v0.17.0`.
|
||||
|
||||
```bash
|
||||
|
||||
@ -145,6 +145,10 @@
|
||||
|
||||
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 `v0.17.0-slim`。請參考下表查看不同 Docker 發行版的說明。如需下載不同於 `v0.17.0-slim` 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。例如，你可以透過設定 `RAGFLOW_IMAGE=infiniflow/ragflow:v0.17.0` 來下載 RAGFlow 鏡像的 `v0.17.0` 完整發行版。
|
||||
|
||||
```bash
|
||||
|
||||
@ -146,6 +146,10 @@
|
||||
|
||||
3. 进入 **docker** 文件夹，利用提前编译好的 Docker 镜像启动服务器：
|
||||
|
||||
> [!CAUTION]
|
||||
> 请注意，目前官方提供的所有 Docker 镜像均基于 x86 架构构建，并不提供基于 ARM64 的 Docker 镜像。
|
||||
> 如果你的操作系统是 ARM64 架构，请参考[这篇文档](https://ragflow.io/docs/dev/build_docker_image)自行构建 Docker 镜像。
|
||||
|
||||
> 运行以下命令会自动下载 RAGFlow slim Docker 镜像 `v0.17.0-slim`。请参考下表查看不同 Docker 发行版的描述。如需下载不同于 `v0.17.0-slim` 的 Docker 镜像，请在运行 `docker compose` 启动服务之前先更新 **docker/.env** 文件内的 `RAGFLOW_IMAGE` 变量。比如，你可以通过设置 `RAGFLOW_IMAGE=infiniflow/ragflow:v0.17.0` 来下载 RAGFlow 镜像的 `v0.17.0` 完整发行版。
|
||||
|
||||
```bash
|
||||
|
||||
@ -1,8 +1,8 @@
|
||||
{
|
||||
"label": "Developer guides",
|
||||
"label": "Developers",
|
||||
"position": 4,
|
||||
"link": {
|
||||
"type": "generated-index",
|
||||
"description": "Guides for Hardcore Developers"
|
||||
"description": "Guides for hardcore developers"
|
||||
}
|
||||
}
|
||||
|
||||
@ -3,7 +3,7 @@ sidebar_position: 3
|
||||
slug: /acquire_ragflow_api_key
|
||||
---
|
||||
|
||||
# Acquire a RAGFlow API key
|
||||
# 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.
|
||||
|
||||
|
||||
@ -3,7 +3,7 @@ sidebar_position: 1
|
||||
slug: /build_docker_image
|
||||
---
|
||||
|
||||
# Build a RAGFlow Docker Image
|
||||
# Build RAGFlow Docker image
|
||||
import Tabs from '@theme/Tabs';
|
||||
import TabItem from '@theme/TabItem';
|
||||
|
||||
|
||||
@ -3,11 +3,11 @@ sidebar_position: 2
|
||||
slug: /launch_ragflow_from_source
|
||||
---
|
||||
|
||||
# Launch a RAGFlow Service from Source
|
||||
# Launch RAGFlow service from source
|
||||
|
||||
A guide explaining how to set up a RAGFlow service from its source code. By following this guide, you'll be able to debug using the source code.
|
||||
|
||||
## Target Audience
|
||||
## Target audience
|
||||
|
||||
Developers who have added new features or modified existing code and wish to debug using the source code, *provided that* their machine has the target deployment environment set up.
|
||||
|
||||
@ -22,11 +22,11 @@ Developers who have added new features or modified existing code and wish to deb
|
||||
If you have not installed Docker on your local machine (Windows, Mac, or Linux), see the [Install Docker Engine](https://docs.docker.com/engine/install/) guide.
|
||||
:::
|
||||
|
||||
## Launch the Service from Source
|
||||
## Launch a service from source
|
||||
|
||||
To launch the RAGFlow service from source code:
|
||||
To launch a RAGFlow service from source code:
|
||||
|
||||
### Clone the RAGFlow Repository
|
||||
### Clone the RAGFlow repository
|
||||
|
||||
```bash
|
||||
git clone https://github.com/infiniflow/ragflow.git
|
||||
@ -52,7 +52,7 @@ cd ragflow/
|
||||
```
|
||||
*A virtual environment named `.venv` is created, and all Python dependencies are installed into the new environment.*
|
||||
|
||||
### Launch Third-party Services
|
||||
### Launch third-party services
|
||||
|
||||
The following command launches the 'base' services (MinIO, Elasticsearch, Redis, and MySQL) using Docker Compose:
|
||||
|
||||
@ -70,7 +70,7 @@ docker compose -f docker/docker-compose-base.yml up -d
|
||||
|
||||
2. In **docker/service_conf.yaml.template**, update mysql port to `5455` and es port to `1200`, as specified in **docker/.env**.
|
||||
|
||||
### Launch the RAGFlow Backend Service
|
||||
### Launch the RAGFlow backend service
|
||||
|
||||
1. Comment out the `nginx` line in **docker/entrypoint.sh**.
|
||||
|
||||
|
||||
@ -3,9 +3,9 @@ sidebar_position: 10
|
||||
slug: /faq
|
||||
---
|
||||
|
||||
# FAQ
|
||||
# FAQs
|
||||
|
||||
Queries regarding general features, troubleshooting, usage, and more.
|
||||
Answers to questions about general features, troubleshooting, usage, and more.
|
||||
|
||||
---
|
||||
|
||||
|
||||
@ -3,7 +3,7 @@ sidebar_position: 3
|
||||
slug: /embed_agent_into_webpage
|
||||
---
|
||||
|
||||
# Embed agent into a webpage
|
||||
# Embed agent into webpage
|
||||
|
||||
You can use iframe to embed an agent into a third-party webpage.
|
||||
|
||||
|
||||
@ -3,7 +3,11 @@ sidebar_position: 2
|
||||
slug: /general_purpose_chatbot
|
||||
---
|
||||
|
||||
# Create a general-purpose chatbot
|
||||
# Create chatbot
|
||||
|
||||
Create a general-purpose chatbot.
|
||||
|
||||
---
|
||||
|
||||
Chatbot is one of the most common AI scenarios. However, effectively understanding user queries and responding appropriately remains a challenge. RAGFlow's general-purpose chatbot agent is our attempt to tackle this longstanding issue.
|
||||
|
||||
|
||||
@ -3,10 +3,10 @@ sidebar_position: 2
|
||||
slug: /accelerate_question_answering
|
||||
---
|
||||
|
||||
# Accelerate question answering
|
||||
# Accelerate answering
|
||||
import APITable from '@site/src/components/APITable';
|
||||
|
||||
A checklist to speed up document parsing and question answering.
|
||||
A checklist to speed up question answering.
|
||||
|
||||
---
|
||||
|
||||
@ -24,7 +24,7 @@ Please note that some of your settings may consume a significant amount of time.
|
||||
```
|
||||
|
||||
| Item name | Description |
|
||||
| ----------------- | ------------------------------------------------------------ |
|
||||
| ----------------- | --------------------------------------------------------------------------------------------- |
|
||||
| Total | Total time spent on this conversation round, including chunk retrieval and answer generation. |
|
||||
| Check LLM | Time to validate the specified LLM. |
|
||||
| Create retriever | Time to create a chunk retriever. |
|
||||
|
||||
@ -3,7 +3,7 @@ sidebar_position: 1
|
||||
slug: /start_chat
|
||||
---
|
||||
|
||||
# Chat
|
||||
# Start AI chat
|
||||
|
||||
Initiate an AI-powered chat with a configured chat assistant.
|
||||
|
||||
|
||||
@ -3,10 +3,10 @@ sidebar_position: 9
|
||||
slug: /accelerate_doc_indexing
|
||||
---
|
||||
|
||||
# Accelerate document indexing
|
||||
# Accelerate indexing
|
||||
import APITable from '@site/src/components/APITable';
|
||||
|
||||
A checklist to speed up document parsing.
|
||||
A checklist to speed up document parsing and indexing.
|
||||
|
||||
---
|
||||
|
||||
|
||||
@ -7,6 +7,10 @@ slug: /manage_files
|
||||
|
||||
Knowledge base, hallucination-free chat, and file management are the three pillars of RAGFlow. RAGFlow's file management allows you to upload files individually or in bulk. You can then link an uploaded file to multiple target knowledge bases. This guide showcases some basic usages of the file management feature.
|
||||
|
||||
:::danger IMPORTANT
|
||||
Compared to uploading files directly to various knowledge bases, uploading them to RAGFlow's file management and then linking them to different knowledge bases is *not* an unnecessary step, particularly when you want to delete some parsed files or an entire knowledge base but retain the original files.
|
||||
:::
|
||||
|
||||
## Create folder
|
||||
|
||||
RAGFlow's file management allows you to establish your file system with nested folder structures. To create a folder in the root directory of RAGFlow:
|
||||
|
||||
@ -3,7 +3,7 @@ sidebar_position: 2
|
||||
slug: /deploy_local_llm
|
||||
---
|
||||
|
||||
# Deploy a local LLM
|
||||
# Deploy LLM locally
|
||||
import Tabs from '@theme/Tabs';
|
||||
import TabItem from '@theme/TabItem';
|
||||
|
||||
|
||||
@ -42,7 +42,6 @@ A complete list of models supported by RAGFlow, which will continue to expand.
|
||||
| Ollama | :heavy_check_mark: | :heavy_check_mark: | | :heavy_check_mark: | | |
|
||||
| OpenAI | :heavy_check_mark: | :heavy_check_mark: | | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
|
||||
| OpenAI-API-Compatible | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | | |
|
||||
| VLLM | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | | |
|
||||
| OpenRouter | :heavy_check_mark: | | | :heavy_check_mark: | | |
|
||||
| PerfXCloud | :heavy_check_mark: | :heavy_check_mark: | | | | |
|
||||
| Replicate | :heavy_check_mark: | :heavy_check_mark: | | | | |
|
||||
@ -54,6 +53,7 @@ A complete list of models supported by RAGFlow, which will continue to expand.
|
||||
| TogetherAI | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | | |
|
||||
| Tongyi-Qianwen | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
|
||||
| Upstage | :heavy_check_mark: | :heavy_check_mark: | | | | |
|
||||
| VLLM | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | | |
|
||||
| VolcEngine | :heavy_check_mark: | | | | | |
|
||||
| Voyage AI | | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | | |
|
||||
| Xinference | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
|
||||
|
||||
@ -7,6 +7,46 @@ slug: /release_notes
|
||||
|
||||
Key features, improvements and bug fixes in the latest releases.
|
||||
|
||||
## v0.17.0
|
||||
|
||||
Released on March 3, 2025.
|
||||
|
||||
### New features
|
||||
|
||||
1. AI chat: Implements Deep Research for agentic reasoning. To activate this, enable the **Reasoning** toggle under the **Prompt Engine** tab of your chat assistant dialogue.
|
||||
2. AI chat: Leverages Tavily-based web search to enhance contexts in agentic reasoning. To activate this, enter the correct Tavily API key under the **Assistant Setting** tab of your chat assistant dialogue.
|
||||
3. AI chat: Supports initiating a chat without specifying knowledge bases.
|
||||
4. AI chat: HTML files can also be previewed and referenced, in addition to PDF files.
|
||||
5. Dataset: Adds a **Layout recognition & OCR** dropdown menu to dataset configurations. This includes a DeepDoc model option, which is time-consuming, a much faster **naive** option (plain text), which skips DLR (Document Layout Recognition), OCR (Optimal Character Recognition), and TSR (Table Structure Recognition) tasks, and several currently *experimental* large model options.
|
||||
6. Agent component: **(x)** or a forward slash `/` can be used to insert available keys (variables) in the system prompt field of the **Generate** or **Template** component.
|
||||
7. Object storage: Supports using Aliyun OSS (Object Storage Service) as a file storage option.
|
||||
8. Models: Updates the supported model list for Tongyi-Qianwen, adding DeepSeek-specific models; adds ModelScope as a model provider.
|
||||
9. APIs: Document metadata can be updated through an API.
|
||||
|
||||
The following diagram illustrates the workflow of RAGFlow's Deep Research:
|
||||
|
||||
|
||||
|
||||
The following is a screenshot of a conversation that integrates Deep Research:
|
||||
|
||||
|
||||
|
||||
### Related APIs
|
||||
|
||||
#### HTTP APIs
|
||||
|
||||
Adds a body parameter `"meta_fields"` to the [Update document](./references/http_api_reference.md#update-document) method.
|
||||
|
||||
#### Python APIs
|
||||
|
||||
Adds a key option `"meta_fields"` to the [Update document](./references/python_api_reference.md#update-document) method.
|
||||
|
||||
### Documentation
|
||||
|
||||
#### Added documents
|
||||
|
||||
[Run retrieval test](./guides/dataset/run_retrieval_test.md)
|
||||
|
||||
## v0.16.0
|
||||
|
||||
Released on February 6, 2025.
|
||||
|
||||
@ -534,8 +534,8 @@ This auto-tag feature enhances retrieval by adding another layer of domain-speci
|
||||
reasoningTip:
|
||||
'It will trigger reasoning process like Deepseek-R1/OpenAI o1. Integrates an agentic search process into the reasoning workflow, allowing models itself to dynamically retrieve external knowledge whenever they encounter uncertain information.',
|
||||
tavilyApiKeyTip:
|
||||
'If API key is set correctly, it will utilize Tavily to do web search as a supplement to knowledge bases.',
|
||||
tavilyApiKeyMessage: 'Please enter your Tavily Api Key',
|
||||
'If an API key is correctly set here, Tavily-based web searches will be used to supplement knowledge base retrieval.',
|
||||
tavilyApiKeyMessage: 'Please enter your Tavily API Key',
|
||||
tavilyApiKeyHelp: 'How to get it?',
|
||||
},
|
||||
setting: {
|
||||
|
||||
@ -106,7 +106,7 @@ export default {
|
||||
processBeginAt: '流程開始於',
|
||||
processDuration: '過程持續時間',
|
||||
progressMsg: '進度消息',
|
||||
testingDescription: '最後一步！成功後，剩下的就交給 RAGFlow 吧。',
|
||||
testingDescription: '完成召回測試：確保你的設定可以從資料庫正確地召回文字區塊。',
|
||||
similarityThreshold: '相似度閾值',
|
||||
similarityThresholdTip:
|
||||
'我們使用混合相似度得分來評估兩行文本之間的距離。它是加權關鍵詞相似度和向量餘弦相似度。如果查詢和塊之間的相似度小於此閾值，則該塊將被過濾掉。',
|
||||
@ -514,7 +514,7 @@ export default {
|
||||
'它將觸發類似Deepseek-R1/OpenAI o1的推理過程。將代理搜尋過程整合到推理工作流程中，使得模型本身能夠在遇到不確定資訊時動態地檢索外部知識。',
|
||||
tavilyApiKeyTip:
|
||||
'如果 API 金鑰設定正確，它將利用 Tavily 進行網路搜尋作為知識庫的補充。',
|
||||
tavilyApiKeyMessage: '請輸入你的 Tavily Api Key',
|
||||
tavilyApiKeyMessage: '請輸入你的 Tavily API Key',
|
||||
tavilyApiKeyHelp: '如何獲取？',
|
||||
},
|
||||
setting: {
|
||||
|
||||
@ -106,7 +106,7 @@ export default {
|
||||
processBeginAt: '开始于',
|
||||
processDuration: '持续时间',
|
||||
progressMsg: '进度',
|
||||
testingDescription: '最后一步！ 成功后，剩下的就交给 RAGFlow 吧。',
|
||||
testingDescription: '请完成召回测试：确保你的配置可以从数据库召回正确的文本块。',
|
||||
similarityThreshold: '相似度阈值',
|
||||
similarityThresholdTip:
|
||||
'我们使用混合相似度得分来评估两行文本之间的距离。 它是加权关键词相似度和向量余弦相似度。 如果查询和块之间的相似度小于此阈值，则该块将被过滤掉。',
|
||||
@ -530,7 +530,7 @@ General：实体和关系提取提示来自 GitHub - microsoft/graphrag：基于
|
||||
'它将像Deepseek-R1 / OpenAI o1一样触发推理过程。将代理搜索过程集成到推理工作流中，允许模型本身在遇到不确定信息时动态地检索外部知识。',
|
||||
tavilyApiKeyTip:
|
||||
'如果 API 密钥设置正确，它将利用 Tavily 进行网络搜索作为知识库的补充。',
|
||||
tavilyApiKeyMessage: '请输入你的 Tavily Api Key',
|
||||
tavilyApiKeyMessage: '请输入你的 Tavily API Key',
|
||||
tavilyApiKeyHelp: '如何获取？',
|
||||
},
|
||||
setting: {
|
||||
|
||||
@ -147,7 +147,7 @@ const AssistantSetting = ({
|
||||
>
|
||||
<Switch onChange={handleTtsChange} />
|
||||
</Form.Item>
|
||||
<Form.Item label={'Tavily Api Key'} tooltip={t('tavilyApiKeyTip')}>
|
||||
<Form.Item label={'Tavily API Key'} tooltip={t('tavilyApiKeyTip')}>
|
||||
<div className="flex flex-col gap-1">
|
||||
<Form.Item name={['prompt_config', 'tavily_api_key']} noStyle>
|
||||
<Input.Password placeholder={t('tavilyApiKeyMessage')} />
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user