docs: 创建官网，更新文档 (#67)
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# 贡献指南
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本文档提供了向 RAGFlow-Plus 提交贡献的指导原则和主要注意事项。
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- 如需报告错误，请通过[GitHub issue](https://github.com/zstar1003/ragflow-plus/issues/new/choose)提交问题。
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- 其他疑问可先在[Discussions](https://github.com/zstar1003/ragflow-plus/discussions)中查阅现有讨论或发起新话题。
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## 可贡献的内容
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以下列举部分可贡献的方向（非完整列表）：
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- 提议或实现新功能
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- 修复错误
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- 添加测试用例或演示案例
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- 发布博客或教程
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- 更新现有文档、代码或注释
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- 建议更友好的错误提示
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## 提交拉取请求（PR）
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### 通用流程
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1. Fork本GitHub仓库
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2. 将fork克隆到本地：
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`git clone git@github.com:<你的用户名>/ragflow-plus.git`
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3. 创建本地分支：
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`git checkout -b my-branch`
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4. 提交信息需包含充分说明：
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`git commit -m '提交信息需包含充分说明'`
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5. 推送更改到GitHub（含必要提交信息）：
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`git push origin my-branch`
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6. 提交PR等待审核
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### PR提交前注意事项
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- 大型PR建议拆分为多个独立小PR，便于追踪开发历史
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- 单个PR应专注解决一个问题，无关改动需保持最小化
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- 新增功能需包含测试用例，既验证代码正确性也防止未来变更引发问题
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### PR描述规范
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- 标题需简洁清晰且信息完整
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- 如关联GitHub issue，请在描述中引用对应编号
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- 对**重大变更**或**API修改**需在描述中包含详细设计说明
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15
README.md
15
README.md
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@ -89,11 +89,16 @@ pnpm dev
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## 🛠️ 如何贡献
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1. Fork 本仓库
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2. 创建您的特性分支 (`git checkout -b feature/AmazingFeature`)
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3. 提交您的更改 (`git commit -m 'Add some AmazingFeature'`)
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4. 推送到分支 (`git push origin feature/AmazingFeature`)
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5. 打开一个 Pull Request
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1. Fork本GitHub仓库
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2. 将fork克隆到本地：
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`git clone git@github.com:<你的用户名>/ragflow-plus.git`
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3. 创建本地分支：
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`git checkout -b my-branch`
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4. 提交信息需包含充分说明：
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`git commit -m '提交信息需包含充分说明'`
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5. 推送更改到GitHub（含必要提交信息）：
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`git push origin my-branch`
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6. 提交PR等待审核
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## 📄 交流群
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{
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"label": "Get Started",
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"position": 1,
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"link": {
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"type": "generated-index",
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"description": "RAGFlow Quick Start"
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}
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}
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---
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sidebar_position: 1
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slug: /configurations
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---
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# Configuration
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Configurations for deploying RAGFlow via Docker.
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## Guidelines
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When it comes to system configurations, you will need to manage the following files:
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- [.env](https://github.com/infiniflow/ragflow/blob/main/docker/.env): Contains important environment variables for Docker.
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- [service_conf.yaml.template](https://github.com/infiniflow/ragflow/blob/main/docker/service_conf.yaml.template): Configures the back-end services. It specifies the system-level configuration for RAGFlow and is used by its API server and task executor. Upon container startup, the `service_conf.yaml` file will be generated based on this template file. This process replaces any environment variables within the template, allowing for dynamic configuration tailored to the container's environment.
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- [docker-compose.yml](https://github.com/infiniflow/ragflow/blob/main/docker/docker-compose.yml): The Docker Compose file for starting up the RAGFlow service.
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To update the default HTTP serving port (80), go to [docker-compose.yml](https://github.com/infiniflow/ragflow/blob/main/docker/docker-compose.yml) and change `80:80`
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to `<YOUR_SERVING_PORT>:80`.
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:::tip NOTE
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Updates to the above configurations require a reboot of all containers to take effect:
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```bash
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docker compose -f docker/docker-compose.yml up -d
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```
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:::
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## Docker Compose
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- **docker-compose.yml**
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Sets up environment for RAGFlow and its dependencies.
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- **docker-compose-base.yml**
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Sets up environment for RAGFlow's dependencies: Elasticsearch/[Infinity](https://github.com/infiniflow/infinity), MySQL, MinIO, and Redis.
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:::danger IMPORTANT
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We do not actively maintain **docker-compose-CN-oc9.yml**, **docker-compose-gpu-CN-oc9.yml**, or **docker-compose-gpu.yml**, so use them at your own risk. However, you are welcome to file a pull request to improve any of them.
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:::
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## Docker environment variables
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The [.env](https://github.com/infiniflow/ragflow/blob/main/docker/.env) file contains important environment variables for Docker.
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### Elasticsearch
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- `STACK_VERSION`
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The version of Elasticsearch. Defaults to `8.11.3`
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- `ES_PORT`
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The port used to expose the Elasticsearch service to the host machine, allowing **external** access to the service running inside the Docker container. Defaults to `1200`.
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- `ELASTIC_PASSWORD`
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The password for Elasticsearch.
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### Kibana
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- `KIBANA_PORT`
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The port used to expose the Kibana service to the host machine, allowing **external** access to the service running inside the Docker container. Defaults to `6601`.
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- `KIBANA_USER`
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The username for Kibana. Defaults to `rag_flow`.
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- `KIBANA_PASSWORD`
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The password for Kibana. Defaults to `infini_rag_flow`.
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### Resource management
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- `MEM_LIMIT`
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The maximum amount of the memory, in bytes, that *a specific* Docker container can use while running. Defaults to `8073741824`.
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### MySQL
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- `MYSQL_PASSWORD`
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The password for MySQL.
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- `MYSQL_PORT`
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The port used to expose the MySQL service to the host machine, allowing **external** access to the MySQL database running inside the Docker container. Defaults to `5455`.
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### MinIO
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- `MINIO_CONSOLE_PORT`
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The port used to expose the MinIO console interface to the host machine, allowing **external** access to the web-based console running inside the Docker container. Defaults to `9001`
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- `MINIO_PORT`
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The port used to expose the MinIO API service to the host machine, allowing **external** access to the MinIO object storage service running inside the Docker container. Defaults to `9000`.
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- `MINIO_USER`
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The username for MinIO.
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- `MINIO_PASSWORD`
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The password for MinIO.
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### Redis
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- `REDIS_PORT`
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The port used to expose the Redis service to the host machine, allowing **external** access to the Redis service running inside the Docker container. Defaults to `6379`.
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- `REDIS_PASSWORD`
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The password for Redis.
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### RAGFlow
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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. Available editions:
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- `infiniflow/ragflow:v0.17.2-slim` (default): The RAGFlow Docker image without embedding models.
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- `infiniflow/ragflow:v0.17.2`: The RAGFlow Docker image with embedding models including:
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- Built-in embedding models:
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- `BAAI/bge-large-zh-v1.5`
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- `BAAI/bge-reranker-v2-m3`
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- `maidalun1020/bce-embedding-base_v1`
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- `maidalun1020/bce-reranker-base_v1`
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- Embedding models that will be downloaded once you select them in the RAGFlow UI:
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- `BAAI/bge-base-en-v1.5`
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- `BAAI/bge-large-en-v1.5`
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- `BAAI/bge-small-en-v1.5`
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- `BAAI/bge-small-zh-v1.5`
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- `jinaai/jina-embeddings-v2-base-en`
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- `jinaai/jina-embeddings-v2-small-en`
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- `nomic-ai/nomic-embed-text-v1.5`
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- `sentence-transformers/all-MiniLM-L6-v2`
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:::tip NOTE
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If you cannot download the RAGFlow Docker image, try the following mirrors.
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- For the `nightly-slim` edition:
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- `RAGFLOW_IMAGE=swr.cn-north-4.myhuaweicloud.com/infiniflow/ragflow:nightly-slim` or,
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- `RAGFLOW_IMAGE=registry.cn-hangzhou.aliyuncs.com/infiniflow/ragflow:nightly-slim`.
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- For the `nightly` edition:
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- `RAGFLOW_IMAGE=swr.cn-north-4.myhuaweicloud.com/infiniflow/ragflow:nightly` or,
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- `RAGFLOW_IMAGE=registry.cn-hangzhou.aliyuncs.com/infiniflow/ragflow:nightly`.
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:::
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### Timezone
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- `TIMEZONE`
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The local time zone. Defaults to `'Asia/Shanghai'`.
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### Hugging Face mirror site
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- `HF_ENDPOINT`
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The mirror site for huggingface.co. It is disabled by default. You can uncomment this line if you have limited access to the primary Hugging Face domain.
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### MacOS
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- `MACOS`
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Optimizations for macOS. It is disabled by default. You can uncomment this line if your OS is macOS.
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## Service configuration
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[service_conf.yaml.template](https://github.com/infiniflow/ragflow/blob/main/docker/service_conf.yaml.template) specifies the system-level configuration for RAGFlow and is used by its API server and task executor.
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### `ragflow`
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- `host`: The API server's IP address inside the Docker container. Defaults to `0.0.0.0`.
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- `port`: The API server's serving port inside the Docker container. Defaults to `9380`.
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### `mysql`
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- `name`: The MySQL database name. Defaults to `rag_flow`.
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- `user`: The username for MySQL.
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- `password`: The password for MySQL.
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- `port`: The MySQL serving port inside the Docker container. Defaults to `3306`.
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- `max_connections`: The maximum number of concurrent connections to the MySQL database. Defaults to `100`.
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- `stale_timeout`: Timeout in seconds.
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### `minio`
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- `user`: The username for MinIO.
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- `password`: The password for MinIO.
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- `host`: The MinIO serving IP *and* port inside the Docker container. Defaults to `minio:9000`.
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### `oauth`
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The OAuth configuration for signing up or signing in to RAGFlow using a third-party account. It is disabled by default. To enable this feature, uncomment the corresponding lines in **service_conf.yaml.template**.
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- `github`: The GitHub authentication settings for your application. Visit the [GitHub Developer Settings](https://github.com/settings/developers) page to obtain your client_id and secret_key.
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### `user_default_llm`
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The default LLM to use for a new RAGFlow user. It is disabled by default. To enable this feature, uncomment the corresponding lines in **service_conf.yaml.template**.
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- `factory`: The LLM supplier. Available options:
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- `"OpenAI"`
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- `"DeepSeek"`
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- `"Moonshot"`
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- `"Tongyi-Qianwen"`
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- `"VolcEngine"`
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- `"ZHIPU-AI"`
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- `api_key`: The API key for the specified LLM. You will need to apply for your model API key online.
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:::tip NOTE
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If you do not set the default LLM here, configure the default LLM on the **Settings** page in the RAGFlow UI.
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:::
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{
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"label": "Developers",
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"position": 4,
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"link": {
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"type": "generated-index",
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"description": "Guides for hardcore developers"
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}
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}
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@ -1,18 +0,0 @@
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---
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sidebar_position: 3
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slug: /acquire_ragflow_api_key
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---
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# Acquire RAGFlow API key
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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.
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1. Click your avatar in the top right corner of the RAGFlow UI to access the configuration page.
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2. Click **API** to switch to the **API** page.
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3. Obtain a RAGFlow API key:
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:::tip NOTE
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See the [RAGFlow HTTP API reference](../references/http_api_reference.md) or the [RAGFlow Python API reference](../references/python_api_reference.md) for a complete reference of RAGFlow's HTTP or Python APIs.
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:::
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---
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sidebar_position: 1
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slug: /build_docker_image
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---
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# Build RAGFlow Docker image
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import Tabs from '@theme/Tabs';
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import TabItem from '@theme/TabItem';
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A guide explaining how to build a RAGFlow Docker image from its source code. By following this guide, you'll be able to create a local Docker image that can be used for development, debugging, or testing purposes.
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## Target Audience
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- Developers who have added new features or modified the existing code and require a Docker image to view and debug their changes.
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- Developers seeking to build a RAGFlow Docker image for an ARM64 platform.
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- Testers aiming to explore the latest features of RAGFlow in a Docker image.
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## Prerequisites
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- CPU ≥ 4 cores
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- RAM ≥ 16 GB
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- Disk ≥ 50 GB
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- Docker ≥ 24.0.0 & Docker Compose ≥ v2.26.1
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## Build a Docker image
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<Tabs
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defaultValue="without"
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values={[
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{label: 'Build a Docker image without embedding models', value: 'without'},
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{label: 'Build a Docker image including embedding models', value: 'including'}
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]}>
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<TabItem value="without">
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This image is approximately 2 GB in size and relies on external LLM and embedding services.
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:::danger IMPORTANT
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- While we also test RAGFlow on ARM64 platforms, we do not maintain RAGFlow Docker images for ARM. However, you can build an image yourself on a `linux/arm64` or `darwin/arm64` host machine as well.
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- For ARM64 platforms, please upgrade the `xgboost` version in **pyproject.toml** to `1.6.0` and ensure **unixODBC** is properly installed.
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:::
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```bash
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git clone https://github.com/infiniflow/ragflow.git
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cd ragflow/
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uv run download_deps.py
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docker build -f Dockerfile.deps -t infiniflow/ragflow_deps .
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docker build --build-arg LIGHTEN=1 -f Dockerfile -t infiniflow/ragflow:nightly-slim .
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```
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</TabItem>
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<TabItem value="including">
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|
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This image is approximately 9 GB in size. As it includes embedding models, it relies on external LLM services only.
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|
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:::danger IMPORTANT
|
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- While we also test RAGFlow on ARM64 platforms, we do not maintain RAGFlow Docker images for ARM. However, you can build an image yourself on a `linux/arm64` or `darwin/arm64` host machine as well.
|
||||
- For ARM64 platforms, please upgrade the `xgboost` version in **pyproject.toml** to `1.6.0` and ensure **unixODBC** is properly installed.
|
||||
:::
|
||||
|
||||
```bash
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git clone https://github.com/infiniflow/ragflow.git
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cd ragflow/
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uv run download_deps.py
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docker build -f Dockerfile.deps -t infiniflow/ragflow_deps .
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docker build -f Dockerfile -t infiniflow/ragflow:nightly .
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```
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</TabItem>
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</Tabs>
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## Launch a RAGFlow Service from Docker for MacOS
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After building the infiniflow/ragflow:nightly-slim image, you are ready to launch a fully-functional RAGFlow service with all the required components, such as Elasticsearch, MySQL, MinIO, Redis, and more.
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## Example: Apple M2 Pro (Sequoia)
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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:v0.17.2-slim` to `infiniflow/ragflow:nightly-slim` to use the pre-built image.
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2. Launch the Service
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```bash
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cd docker
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$ docker compose -f docker-compose-macos.yml up -d
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```
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3. Access the RAGFlow Service
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Once the setup is complete, open your web browser and navigate to http://127.0.0.1 or your server's \<IP_ADDRESS\>; (the default port is \<PORT\> = 80). You will be directed to the RAGFlow welcome page. Enjoy!🍻
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|
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@ -1,139 +0,0 @@
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---
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sidebar_position: 2
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slug: /launch_ragflow_from_source
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---
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# Launch service from source
|
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|
||||
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.
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## 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.
|
||||
|
||||
## Prerequisites
|
||||
|
||||
- CPU ≥ 4 cores
|
||||
- RAM ≥ 16 GB
|
||||
- Disk ≥ 50 GB
|
||||
- Docker ≥ 24.0.0 & Docker Compose ≥ v2.26.1
|
||||
|
||||
:::tip NOTE
|
||||
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 a service from source
|
||||
|
||||
To launch a RAGFlow service from source code:
|
||||
|
||||
### Clone the RAGFlow repository
|
||||
|
||||
```bash
|
||||
git clone https://github.com/infiniflow/ragflow.git
|
||||
cd ragflow/
|
||||
```
|
||||
|
||||
### Install Python dependencies
|
||||
|
||||
1. Install uv:
|
||||
|
||||
```bash
|
||||
pipx install uv
|
||||
```
|
||||
|
||||
2. Install Python dependencies:
|
||||
- slim:
|
||||
```bash
|
||||
uv sync --python 3.10 # install RAGFlow dependent python modules
|
||||
```
|
||||
- full:
|
||||
```bash
|
||||
uv sync --python 3.10 --all-extras # install RAGFlow dependent python modules
|
||||
```
|
||||
*A virtual environment named `.venv` is created, and all Python dependencies are installed into the new environment.*
|
||||
|
||||
### Launch third-party services
|
||||
|
||||
The following command launches the 'base' services (MinIO, Elasticsearch, Redis, and MySQL) using Docker Compose:
|
||||
|
||||
```bash
|
||||
docker compose -f docker/docker-compose-base.yml up -d
|
||||
```
|
||||
|
||||
### Update `host` and `port` Settings for Third-party Services
|
||||
|
||||
1. Add the following line to `/etc/hosts` to resolve all hosts specified in **docker/service_conf.yaml.template** to `127.0.0.1`:
|
||||
|
||||
```
|
||||
127.0.0.1 es01 infinity mysql minio redis
|
||||
```
|
||||
|
||||
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
|
||||
|
||||
1. Comment out the `nginx` line in **docker/entrypoint.sh**.
|
||||
|
||||
```
|
||||
# /usr/sbin/nginx
|
||||
```
|
||||
|
||||
2. Activate the Python virtual environment:
|
||||
|
||||
```bash
|
||||
source .venv/bin/activate
|
||||
export PYTHONPATH=$(pwd)
|
||||
```
|
||||
|
||||
3. **Optional:** If you cannot access HuggingFace, set the HF_ENDPOINT environment variable to use a mirror site:
|
||||
|
||||
```bash
|
||||
export HF_ENDPOINT=https://hf-mirror.com
|
||||
```
|
||||
|
||||
4. Run the **entrypoint.sh** script to launch the backend service:
|
||||
|
||||
```
|
||||
bash docker/entrypoint.sh
|
||||
```
|
||||
|
||||
### Launch the RAGFlow frontend service
|
||||
|
||||
1. Navigate to the `web` directory and install the frontend dependencies:
|
||||
|
||||
```bash
|
||||
cd web
|
||||
npm install
|
||||
```
|
||||
|
||||
2. Update `proxy.target` in **.umirc.ts** to `http://127.0.0.1:9380`:
|
||||
|
||||
```bash
|
||||
vim .umirc.ts
|
||||
```
|
||||
|
||||
3. Start up the RAGFlow frontend service:
|
||||
|
||||
```bash
|
||||
npm run dev
|
||||
```
|
||||
|
||||
*The following message appears, showing the IP address and port number of your frontend service:*
|
||||
|
||||
|
||||
|
||||
### Access the RAGFlow service
|
||||
|
||||
In your web browser, enter `http://127.0.0.1:<PORT>/`, ensuring the port number matches that shown in the screenshot above.
|
||||
|
||||
### Stop the RAGFlow service when the development is done
|
||||
|
||||
1. Stop the RAGFlow frontend service:
|
||||
```bash
|
||||
pkill npm
|
||||
```
|
||||
|
||||
2. Stop the RAGFlow backend service:
|
||||
```bash
|
||||
pkill -f "docker/entrypoint.sh"
|
||||
```
|
||||
526
docs/faq.md
526
docs/faq.md
|
|
@ -1,464 +1,72 @@
|
|||
---
|
||||
sidebar_position: 10
|
||||
slug: /faq
|
||||
---
|
||||
# 常见问题 (FAQ)
|
||||
|
||||
# FAQs
|
||||
## 问题 1：如何部署 RagflowPlus？
|
||||
|
||||
Answers to questions about general features, troubleshooting, usage, and more.
|
||||
**回答：** 您可以通过 Docker Compose 或源码进行部署。
|
||||
|
||||
- **Docker Compose (推荐):**
|
||||
- GPU 版本: `docker compose -f docker/docker-compose_gpu.yml up -d`
|
||||
- CPU 版本: `docker compose -f docker/docker-compose.yml up -d`
|
||||
- **源码运行:** 请参考“快速开始”部分。
|
||||
|
||||
注：对于后台MinerU解析，默认仍采用CPU，如需更换为GPU版本，需保证预留显存在8GB以上，并在 `docker/magic-pdf.json` 文件中修改 `device-mode` 为 `cuda`。
|
||||
|
||||
## 问题 2：RagflowPlus 能和 Ragflow 同时使用吗？
|
||||
|
||||
**回答：** RagflowPlus 采用了独立的前后台系统，数据和 Ragflow 互通，但不建议和 Ragflow 同时使用。如需同时使用，可通过修改端口/切换启动方式来实现，但需承担部分接口不一致导致的风险。
|
||||
|
||||
## 问题 3：后台解析时报错：存储桶不存在:f62a03f61fdd11f0b1301a12a4193bf3。
|
||||
|
||||
**回答：** 此问题是由于解析的文件是由 Ragflow 原本的文件系统上传的，RagflowPlus 重构了文件上传的相关接口，因此解析新文件时，建议通过 RagflowPlus 的后台管理系统进行上传。
|
||||
|
||||
## 问题 4：支持解析的文档类型？
|
||||
|
||||
**回答：** 目前支持的文档类型包括：PDF、PPT、Word三种文件类型，后续会逐步支持更多类型。
|
||||
|
||||
## 问题 5：embedding模型的向量维度非1024，导致后台解析出错。
|
||||
|
||||
**回答：** 建议使用`bge-m3`模型进行解析，该模型的向量维度为1024。解析模型不建议频繁更换，否则容易影响检索匹配。
|
||||
|
||||
## 问题 6：docker镜像支持arm平台吗？
|
||||
|
||||
**回答：** 鉴于 Ragflow 也不维护arm平台的镜像，RagflowPlus 也无计划推出和维护arm平台的镜像。
|
||||
|
||||
## 问题 7：不想用vllm，只想用ollama快速部署，如何设置？
|
||||
|
||||
**回答：** 注释掉`docker-compose`相关文件的`vllm-bge`、和`vllm-deepseek`即可，再进行启动。
|
||||
|
||||
---
|
||||
|
||||
# 以下是v0.2.1版本已知存在的问题：
|
||||
|
||||
## 问题 1：后台团队管理菜单中，成员>10人时，添加成员时显示不完全。
|
||||
|
||||
**回答：** 已排查到问题原因，下版本会修复。
|
||||
|
||||
## 问题 2：后台成员添加时，创建时间显示异常。
|
||||
|
||||
**回答：** 已排查出该时区问题，下版本会修复。
|
||||
|
||||
## 问题 3：embedding模型使用在线API，在后台解析模型测试中，无法正常连接。
|
||||
|
||||
**回答：** 当前仅适配本地添加的embedding模型，在线API需要额外进行适配，后续计划先适配使用较多的硅基流动平台。
|
||||
|
||||
## 问题 4：前台保存知识库配置时，提示出错。
|
||||
|
||||
**回答：** 下版本将进一步优化前台的交互。
|
||||
|
||||
## 问题 5：聊天界面上传文件时，文件直接被插入到知识库，模型没有读取。
|
||||
|
||||
**回答：** 下版本将重构上传文件的交互逻辑。
|
||||
|
||||
## 问题 6：前台知识库界面进行检索时，出现异常。
|
||||
|
||||
**回答：** 下版本将修复该问题。
|
||||
|
||||
## 问题 7：内网环境下，服务器docker部署，远程访问时，偶尔出现连接问题。
|
||||
|
||||
**回答：** 该问题有待进一步排查。
|
||||
|
||||
---
|
||||
|
||||
## General features
|
||||
|
||||
---
|
||||
|
||||
### What sets RAGFlow apart from other RAG products?
|
||||
|
||||
The "garbage in garbage out" status quo remains unchanged despite the fact that LLMs have advanced Natural Language Processing (NLP) significantly. In response, RAGFlow introduces two unique features compared to other Retrieval-Augmented Generation (RAG) products.
|
||||
|
||||
- Fine-grained document parsing: Document parsing involves images and tables, with the flexibility for you to intervene as needed.
|
||||
- Traceable answers with reduced hallucinations: You can trust RAGFlow's responses as you can view the citations and references supporting them.
|
||||
|
||||
---
|
||||
|
||||
### Where to find the version of RAGFlow? How to interpret it?
|
||||
|
||||
You can find the RAGFlow version number on the **System** page of the UI:
|
||||
|
||||
|
||||
|
||||
If you build RAGFlow from source, the version number is also in the system log:
|
||||
|
||||
```
|
||||
____ ___ ______ ______ __
|
||||
/ __ \ / | / ____// ____// /____ _ __
|
||||
/ /_/ // /| | / / __ / /_ / // __ \| | /| / /
|
||||
/ _, _// ___ |/ /_/ // __/ / // /_/ /| |/ |/ /
|
||||
/_/ |_|/_/ |_|\____//_/ /_/ \____/ |__/|__/
|
||||
|
||||
2025-02-18 10:10:43,835 INFO 1445658 RAGFlow version: v0.15.0-50-g6daae7f2 full
|
||||
```
|
||||
|
||||
Where:
|
||||
|
||||
- `v0.15.0`: The officially published release.
|
||||
- `50`: The number of git commits since the official release.
|
||||
- `g6daae7f2`: `g` is the prefix, and `6daae7f2` is the first seven characters of the current commit ID.
|
||||
- `full`/`slim`: The RAGFlow edition.
|
||||
- `full`: The full RAGFlow edition.
|
||||
- `slim`: The RAGFlow edition without embedding models and Python packages.
|
||||
|
||||
---
|
||||
|
||||
### Why does it take longer for RAGFlow to parse a document than LangChain?
|
||||
|
||||
We put painstaking effort into document pre-processing tasks like layout analysis, table structure recognition, and OCR (Optical Character Recognition) using our vision models. This contributes to the additional time required.
|
||||
|
||||
---
|
||||
|
||||
### Why does RAGFlow require more resources than other projects?
|
||||
|
||||
RAGFlow has a number of built-in models for document structure parsing, which account for the additional computational resources.
|
||||
|
||||
---
|
||||
|
||||
### Which architectures or devices does RAGFlow support?
|
||||
|
||||
We officially support x86 CPU and nvidia GPU. While we also test RAGFlow on ARM64 platforms, we do not maintain RAGFlow Docker images for ARM. If you are on an ARM platform, follow [this guide](./develop/build_docker_image.mdx) to build a RAGFlow Docker image.
|
||||
|
||||
---
|
||||
|
||||
### Which embedding models can be deployed locally?
|
||||
|
||||
RAGFlow offers two Docker image editions, `v0.17.2-slim` and `v0.17.2`:
|
||||
|
||||
- `infiniflow/ragflow:v0.17.2-slim` (default): The RAGFlow Docker image without embedding models.
|
||||
- `infiniflow/ragflow:v0.17.2`: The RAGFlow Docker image with embedding models including:
|
||||
- Built-in embedding models:
|
||||
- `BAAI/bge-large-zh-v1.5`
|
||||
- `BAAI/bge-reranker-v2-m3`
|
||||
- `maidalun1020/bce-embedding-base_v1`
|
||||
- `maidalun1020/bce-reranker-base_v1`
|
||||
- Embedding models that will be downloaded once you select them in the RAGFlow UI:
|
||||
- `BAAI/bge-base-en-v1.5`
|
||||
- `BAAI/bge-large-en-v1.5`
|
||||
- `BAAI/bge-small-en-v1.5`
|
||||
- `BAAI/bge-small-zh-v1.5`
|
||||
- `jinaai/jina-embeddings-v2-base-en`
|
||||
- `jinaai/jina-embeddings-v2-small-en`
|
||||
- `nomic-ai/nomic-embed-text-v1.5`
|
||||
- `sentence-transformers/all-MiniLM-L6-v2`
|
||||
|
||||
---
|
||||
|
||||
### Do you offer an API for integration with third-party applications?
|
||||
|
||||
The corresponding APIs are now available. See the [RAGFlow HTTP API Reference](./references/http_api_reference.md) or the [RAGFlow Python API Reference](./references/python_api_reference.md) for more information.
|
||||
|
||||
---
|
||||
|
||||
### Do you support stream output?
|
||||
|
||||
Yes, we do.
|
||||
|
||||
---
|
||||
|
||||
### Is it possible to share dialogue through URL?
|
||||
|
||||
No, this feature is not supported.
|
||||
|
||||
---
|
||||
|
||||
### Do you support multiple rounds of dialogues, referencing previous dialogues as context for the current query?
|
||||
|
||||
Yes, we support enhancing user queries based on existing context of an ongoing conversation:
|
||||
|
||||
1. On the **Chat** page, hover over the desired assistant and select **Edit**.
|
||||
2. In the **Chat Configuration** popup, click the **Prompt Engine** tab.
|
||||
3. Switch on **Multi-turn optimization** to enable this feature.
|
||||
|
||||
---
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
---
|
||||
|
||||
### Issues with Docker images
|
||||
|
||||
---
|
||||
|
||||
#### How to build the RAGFlow image from scratch?
|
||||
|
||||
See [Build a RAGFlow Docker image](./develop/build_docker_image.mdx).
|
||||
|
||||
---
|
||||
|
||||
### Issues with huggingface models
|
||||
|
||||
---
|
||||
|
||||
#### Cannot access https://huggingface.co
|
||||
|
||||
A locally deployed RAGflow downloads OCR and embedding modules from [Huggingface website](https://huggingface.co) by default. If your machine is unable to access this site, the following error occurs and PDF parsing fails:
|
||||
|
||||
```
|
||||
FileNotFoundError: [Errno 2] No such file or directory: '/root/.cache/huggingface/hub/models--InfiniFlow--deepdoc/snapshots/be0c1e50eef6047b412d1800aa89aba4d275f997/ocr.res'
|
||||
```
|
||||
|
||||
To fix this issue, use https://hf-mirror.com instead:
|
||||
|
||||
1. Stop all containers and remove all related resources:
|
||||
|
||||
```bash
|
||||
cd ragflow/docker/
|
||||
docker compose down
|
||||
```
|
||||
|
||||
2. Uncomment the following line in **ragflow/docker/.env**:
|
||||
|
||||
```
|
||||
# HF_ENDPOINT=https://hf-mirror.com
|
||||
```
|
||||
|
||||
3. Start up the server:
|
||||
|
||||
```bash
|
||||
docker compose up -d
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
#### `MaxRetryError: HTTPSConnectionPool(host='hf-mirror.com', port=443)`
|
||||
|
||||
This error suggests that you do not have Internet access or are unable to connect to hf-mirror.com. Try the following:
|
||||
|
||||
1. Manually download the resource files from [huggingface.co/InfiniFlow/deepdoc](https://huggingface.co/InfiniFlow/deepdoc) to your local folder **~/deepdoc**.
|
||||
2. Add a volumes to **docker-compose.yml**, for example:
|
||||
|
||||
```
|
||||
- ~/deepdoc:/ragflow/rag/res/deepdoc
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Issues with RAGFlow servers
|
||||
|
||||
---
|
||||
|
||||
#### `WARNING: can't find /raglof/rag/res/borker.tm`
|
||||
|
||||
Ignore this warning and continue. All system warnings can be ignored.
|
||||
|
||||
---
|
||||
|
||||
#### `network anomaly There is an abnormality in your network and you cannot connect to the server.`
|
||||
|
||||
|
||||
|
||||
You will not log in to RAGFlow unless the server is fully initialized. Run `docker logs -f ragflow-server`.
|
||||
|
||||
*The server is successfully initialized, if your system displays the following:*
|
||||
|
||||
```
|
||||
____ ___ ______ ______ __
|
||||
/ __ \ / | / ____// ____// /____ _ __
|
||||
/ /_/ // /| | / / __ / /_ / // __ \| | /| / /
|
||||
/ _, _// ___ |/ /_/ // __/ / // /_/ /| |/ |/ /
|
||||
/_/ |_|/_/ |_|\____//_/ /_/ \____/ |__/|__/
|
||||
|
||||
* Running on all addresses (0.0.0.0)
|
||||
* Running on http://127.0.0.1:9380
|
||||
* Running on http://x.x.x.x:9380
|
||||
INFO:werkzeug:Press CTRL+C to quit
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Issues with RAGFlow backend services
|
||||
|
||||
---
|
||||
|
||||
#### `Realtime synonym is disabled, since no redis connection`
|
||||
|
||||
Ignore this warning and continue. All system warnings can be ignored.
|
||||
|
||||
|
||||
|
||||
---
|
||||
|
||||
#### Why does my document parsing stall at under one percent?
|
||||
|
||||
|
||||
|
||||
Click the red cross beside the 'parsing status' bar, then restart the parsing process to see if the issue remains. If the issue persists and your RAGFlow is deployed locally, try the following:
|
||||
|
||||
1. Check the log of your RAGFlow server to see if it is running properly:
|
||||
|
||||
```bash
|
||||
docker logs -f ragflow-server
|
||||
```
|
||||
|
||||
2. Check if the **task_executor.py** process exists.
|
||||
3. Check if your RAGFlow server can access hf-mirror.com or huggingface.com.
|
||||
|
||||
---
|
||||
|
||||
#### Why does my pdf parsing stall near completion, while the log does not show any error?
|
||||
|
||||
Click the red cross beside the 'parsing status' bar, then restart the parsing process to see if the issue remains. If the issue persists and your RAGFlow is deployed locally, the parsing process is likely killed due to insufficient RAM. Try increasing your memory allocation by increasing the `MEM_LIMIT` value in **docker/.env**.
|
||||
|
||||
:::note
|
||||
Ensure that you restart up your RAGFlow server for your changes to take effect!
|
||||
|
||||
```bash
|
||||
docker compose stop
|
||||
```
|
||||
|
||||
```bash
|
||||
docker compose up -d
|
||||
```
|
||||
|
||||
:::
|
||||
|
||||
|
||||
|
||||
---
|
||||
|
||||
#### `Index failure`
|
||||
|
||||
An index failure usually indicates an unavailable Elasticsearch service.
|
||||
|
||||
---
|
||||
|
||||
#### How to check the log of RAGFlow?
|
||||
|
||||
```bash
|
||||
tail -f ragflow/docker/ragflow-logs/*.log
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
#### How to check the status of each component in RAGFlow?
|
||||
|
||||
1. Check the status of the Elasticsearch Docker container:
|
||||
|
||||
```bash
|
||||
$ docker ps
|
||||
```
|
||||
|
||||
*The following is an example result:*
|
||||
|
||||
```bash
|
||||
5bc45806b680 infiniflow/ragflow:latest "./entrypoint.sh" 11 hours ago Up 11 hours 0.0.0.0:80->80/tcp, :::80->80/tcp, 0.0.0.0:443->443/tcp, :::443->443/tcp, 0.0.0.0:9380->9380/tcp, :::9380->9380/tcp ragflow-server
|
||||
91220e3285dd docker.elastic.co/elasticsearch/elasticsearch:8.11.3 "/bin/tini -- /usr/l…" 11 hours ago Up 11 hours (healthy) 9300/tcp, 0.0.0.0:9200->9200/tcp, :::9200->9200/tcp ragflow-es-01
|
||||
d8c86f06c56b mysql:5.7.18 "docker-entrypoint.s…" 7 days ago Up 16 seconds (healthy) 0.0.0.0:3306->3306/tcp, :::3306->3306/tcp ragflow-mysql
|
||||
cd29bcb254bc quay.io/minio/minio:RELEASE.2023-12-20T01-00-02Z "/usr/bin/docker-ent…" 2 weeks ago Up 11 hours 0.0.0.0:9001->9001/tcp, :::9001->9001/tcp, 0.0.0.0:9000->9000/tcp, :::9000->9000/tcp ragflow-minio
|
||||
```
|
||||
|
||||
2. Follow [this document](./guides/run_health_check.md) to check the health status of the Elasticsearch service.
|
||||
|
||||
:::danger IMPORTANT
|
||||
The status of a Docker container status does not necessarily reflect the status of the service. You may find that your services are unhealthy even when the corresponding Docker containers are up running. Possible reasons for this include network failures, incorrect port numbers, or DNS issues.
|
||||
:::
|
||||
|
||||
---
|
||||
|
||||
#### `Exception: Can't connect to ES cluster`
|
||||
|
||||
1. Check the status of the Elasticsearch Docker container:
|
||||
|
||||
```bash
|
||||
$ docker ps
|
||||
```
|
||||
|
||||
*The status of a healthy Elasticsearch component should look as follows:*
|
||||
|
||||
```
|
||||
91220e3285dd docker.elastic.co/elasticsearch/elasticsearch:8.11.3 "/bin/tini -- /usr/l…" 11 hours ago Up 11 hours (healthy) 9300/tcp, 0.0.0.0:9200->9200/tcp, :::9200->9200/tcp ragflow-es-01
|
||||
```
|
||||
|
||||
2. Follow [this document](./guides/run_health_check.md) to check the health status of the Elasticsearch service.
|
||||
|
||||
:::danger IMPORTANT
|
||||
The status of a Docker container status does not necessarily reflect the status of the service. You may find that your services are unhealthy even when the corresponding Docker containers are up running. Possible reasons for this include network failures, incorrect port numbers, or DNS issues.
|
||||
:::
|
||||
|
||||
3. If your container keeps restarting, ensure `vm.max_map_count` >= 262144 as per [this README](https://github.com/infiniflow/ragflow?tab=readme-ov-file#-start-up-the-server). Updating the `vm.max_map_count` value in **/etc/sysctl.conf** is required, if you wish to keep your change permanent. Note that this configuration works only for Linux.
|
||||
|
||||
---
|
||||
|
||||
#### Can't start ES container and get `Elasticsearch did not exit normally`
|
||||
|
||||
This is because you forgot to update the `vm.max_map_count` value in **/etc/sysctl.conf** and your change to this value was reset after a system reboot.
|
||||
|
||||
---
|
||||
|
||||
#### `{"data":null,"code":100,"message":"<NotFound '404: Not Found'>"}`
|
||||
|
||||
Your IP address or port number may be incorrect. If you are using the default configurations, enter `http://<IP_OF_YOUR_MACHINE>` (**NOT 9380, AND NO PORT NUMBER REQUIRED!**) in your browser. This should work.
|
||||
|
||||
---
|
||||
|
||||
#### `Ollama - Mistral instance running at 127.0.0.1:11434 but cannot add Ollama as model in RagFlow`
|
||||
|
||||
A correct Ollama IP address and port is crucial to adding models to Ollama:
|
||||
|
||||
- If you are on demo.ragflow.io, ensure that the server hosting Ollama has a publicly accessible IP address. Note that 127.0.0.1 is not a publicly accessible IP address.
|
||||
- If you deploy RAGFlow locally, ensure that Ollama and RAGFlow are in the same LAN and can communicate with each other.
|
||||
|
||||
See [Deploy a local LLM](./guides/models/deploy_local_llm.mdx) for more information.
|
||||
|
||||
---
|
||||
|
||||
#### Do you offer examples of using DeepDoc to parse PDF or other files?
|
||||
|
||||
Yes, we do. See the Python files under the **rag/app** folder.
|
||||
|
||||
---
|
||||
|
||||
#### Why did I fail to upload a 128MB+ file to my locally deployed RAGFlow?
|
||||
|
||||
Ensure that you update the **MAX_CONTENT_LENGTH** environment variable:
|
||||
|
||||
1. In **ragflow/docker/.env**, uncomment environment variable `MAX_CONTENT_LENGTH`:
|
||||
|
||||
```
|
||||
MAX_CONTENT_LENGTH=176160768 # 168MB
|
||||
```
|
||||
|
||||
2. Update **ragflow/docker/nginx/nginx.conf**:
|
||||
|
||||
```
|
||||
client_max_body_size 168M
|
||||
```
|
||||
|
||||
3. Restart the RAGFlow server:
|
||||
|
||||
```
|
||||
docker compose up ragflow -d
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
#### `FileNotFoundError: [Errno 2] No such file or directory`
|
||||
|
||||
1. Check the status of the MinIO Docker container:
|
||||
|
||||
```bash
|
||||
$ docker ps
|
||||
```
|
||||
|
||||
*The status of a healthy Elasticsearch component should look as follows:*
|
||||
|
||||
```bash
|
||||
cd29bcb254bc quay.io/minio/minio:RELEASE.2023-12-20T01-00-02Z "/usr/bin/docker-ent…" 2 weeks ago Up 11 hours 0.0.0.0:9001->9001/tcp, :::9001->9001/tcp, 0.0.0.0:9000->9000/tcp, :::9000->9000/tcp ragflow-minio
|
||||
```
|
||||
|
||||
2. Follow [this document](./guides/run_health_check.md) to check the health status of the Elasticsearch service.
|
||||
|
||||
:::danger IMPORTANT
|
||||
The status of a Docker container status does not necessarily reflect the status of the service. You may find that your services are unhealthy even when the corresponding Docker containers are up running. Possible reasons for this include network failures, incorrect port numbers, or DNS issues.
|
||||
:::
|
||||
|
||||
---
|
||||
|
||||
## Usage
|
||||
|
||||
---
|
||||
|
||||
### How to increase the length of RAGFlow responses?
|
||||
|
||||
1. Right-click the desired dialog to display the **Chat Configuration** window.
|
||||
2. Switch to the **Model Setting** tab and adjust the **Max Tokens** slider to get the desired length.
|
||||
3. Click **OK** to confirm your change.
|
||||
|
||||
---
|
||||
|
||||
### How to run RAGFlow with a locally deployed LLM?
|
||||
|
||||
You can use Ollama or Xinference to deploy local LLM. See [here](./guides/models/deploy_local_llm.mdx) for more information.
|
||||
|
||||
---
|
||||
|
||||
### Is it possible to add an LLM that is not supported?
|
||||
|
||||
If your model is not currently supported but has APIs compatible with those of OpenAI, click **OpenAI-API-Compatible** on the **Model providers** page to configure your model:
|
||||
|
||||
|
||||
|
||||
---
|
||||
|
||||
### How to interconnect RAGFlow with Ollama?
|
||||
|
||||
- If RAGFlow is locally deployed, ensure that your RAGFlow and Ollama are in the same LAN.
|
||||
- If you are using our online demo, ensure that the IP address of your Ollama server is public and accessible.
|
||||
|
||||
See [here](./guides/models/deploy_local_llm.mdx) for more information.
|
||||
|
||||
---
|
||||
|
||||
### `Error: Range of input length should be [1, 30000]`
|
||||
|
||||
This error occurs because there are too many chunks matching your search criteria. Try reducing the **TopN** and increasing **Similarity threshold** to fix this issue:
|
||||
|
||||
1. Click **Chat** in the middle top of the page.
|
||||
2. Right-click the desired conversation > **Edit** > **Prompt Engine**
|
||||
3. Reduce the **TopN** and/or raise **Similarity threshold**.
|
||||
4. Click **OK** to confirm your changes.
|
||||
|
||||
|
||||
|
||||
---
|
||||
|
||||
### How to get an API key for integration with third-party applications?
|
||||
|
||||
See [Acquire a RAGFlow API key](./develop/acquire_ragflow_api_key.md).
|
||||
|
||||
---
|
||||
|
||||
### How to upgrade RAGFlow?
|
||||
|
||||
See [Upgrade RAGFlow](./guides/upgrade_ragflow.mdx) for more information.
|
||||
|
||||
---
|
||||
*如有更多问题，欢迎在 GitHub 上提交 Issue。*
|
||||
|
|
@ -1,8 +0,0 @@
|
|||
{
|
||||
"label": "Guides",
|
||||
"position": 3,
|
||||
"link": {
|
||||
"type": "generated-index",
|
||||
"description": "Guides for RAGFlow users and developers."
|
||||
}
|
||||
}
|
||||
|
|
@ -1,8 +0,0 @@
|
|||
{
|
||||
"label": "Agents",
|
||||
"position": 3,
|
||||
"link": {
|
||||
"type": "generated-index",
|
||||
"description": "RAGFlow v0.8.0 introduces an agent mechanism, featuring a no-code workflow editor on the front end and a comprehensive graph-based task orchestration framework on the backend."
|
||||
}
|
||||
}
|
||||
|
|
@ -1,8 +0,0 @@
|
|||
{
|
||||
"label": "Agent Components",
|
||||
"position": 20,
|
||||
"link": {
|
||||
"type": "generated-index",
|
||||
"description": "A complete reference for RAGFlow's agent components."
|
||||
}
|
||||
}
|
||||
|
|
@ -1,74 +0,0 @@
|
|||
---
|
||||
sidebar_position: 1
|
||||
slug: /begin_component
|
||||
---
|
||||
|
||||
# Begin component
|
||||
|
||||
The starting component in a workflow.
|
||||
|
||||
---
|
||||
|
||||
The **Begin** component sets an opening greeting or accepts inputs from the user. It is automatically populated onto the canvas when you create an agent, whether from a template or from scratch (from a blank template). There should be only one **Begin** component in the workflow.
|
||||
|
||||
## Scenarios
|
||||
|
||||
A **Begin** component is essential in all cases. Every agent includes a **Begin** component, which cannot be deleted.
|
||||
|
||||
## Configurations
|
||||
|
||||
Click the component to display its **Configuration** window. Here, you can set an opening greeting and the input parameters (global variables) for the agent.
|
||||
|
||||
### ID
|
||||
|
||||
The ID is the unique identifier for the component within the workflow. Unlike the IDs of other components, the ID of the **Begin** component *cannot* be changed.
|
||||
|
||||
### Opening greeting
|
||||
|
||||
An opening greeting is the agent's first message to the user. It can be a welcoming remark or an instruction to guide the user forward.
|
||||
|
||||
### Global variables
|
||||
|
||||
You can set global variables within the **Begin** component, which can be either required or optional. Once established, users will need to provide values for these variables when interacting or chatting with the agent. Click **+ Add variable** to add a global variable, each with the following attributes:
|
||||
|
||||
- **Key**: *Required*
|
||||
The unique variable name.
|
||||
- **Name**: *Required*
|
||||
A descriptive name providing additional details about the variable.
|
||||
For example, if **Key** is set to `lang`, you can set its **Name** to `Target language`.
|
||||
- **Type**: *Required*
|
||||
The type of the variable:
|
||||
- **line**: Accepts a single line of text without line breaks.
|
||||
- **paragraph**: Accepts multiple lines of text, including line breaks.
|
||||
- **options**: Requires the user to select a value for this variable from a dropdown menu. And you are required to set *at least* one option for the dropdown menu.
|
||||
- **file**: Requires the user to upload one or multiple files.
|
||||
- **integer**: Accepts an integer as input.
|
||||
- **boolean**: Requires the user to toggle between on and off.
|
||||
- **Optional**: A toggle indicating whether the variable is optional.
|
||||
|
||||
## Examples
|
||||
|
||||
As mentioned earlier, the **Begin** component is indispensable for an agent. Still, you can take a look at our three-step interpreter agent template, where the **Begin** component takes two global variables:
|
||||
|
||||
1. Click the **Agent** tab at the top center of the page to access the **Agent** page.
|
||||
2. Click **+ Create agent** on the top right of the page to open the **agent template** page.
|
||||
3. On the **agent template** page, hover over the **Interpreter** card and click **Use this template**.
|
||||
4. Name your new agent and click **OK** to enter the workflow editor.
|
||||
5. Click on the **Begin** component to display its **Configuration** window.
|
||||
|
||||
## Frequently asked questions
|
||||
|
||||
### Is the uploaded file in a knowledge base?
|
||||
|
||||
No. Files uploaded to an agent as input are not stored in a knowledge base and will not be chunked using RAGFlow's built-in chunk methods. However, RAGFlow's built-in OSR, DLR, and TSR models will still be applied to process the document.
|
||||
|
||||
### How to upload a webpage or file from a URL?
|
||||
|
||||
If you set the type of a variable as **file**, your users will be able to upload a file either from their local device or from an accessible URL. For example:
|
||||
|
||||
|
||||
|
||||
### File size limit for an uploaded file
|
||||
|
||||
The maximum file size for each uploaded file is determined by the variable `MAX_CONTENT_LENGTH` in `/docker/.env`. It defaults to 128 MB. If you change the default file size, ensure you also update the value of `client_max_body_size` in `/docker/nginx/nginx.conf` accordingly.
|
||||
|
||||
|
|
@ -1,111 +0,0 @@
|
|||
---
|
||||
sidebar_position: 5
|
||||
slug: /categorize_component
|
||||
---
|
||||
|
||||
# Categorize component
|
||||
|
||||
A component that classifies user inputs and applies strategies accordingly.
|
||||
|
||||
---
|
||||
|
||||
A **Categorize** component is usually the downstream of the **Interact** component.
|
||||
|
||||
## Scenarios
|
||||
|
||||
A **Categorize** component is essential when you need the LLM to help you identify user intentions and apply appropriate processing strategies.
|
||||
|
||||
## Configurations
|
||||
|
||||
### Input
|
||||
|
||||
The **Categorize** component relies on input variables to specify its data inputs (queries). Click **+ Add variable** in the **Input** section to add the desired input variables. There are two types of input variables: **Reference** and **Text**.
|
||||
|
||||
- **Reference**: Uses a component's output or a user input as the data source. You are required to select from the dropdown menu:
|
||||
- A component ID under **Component Output**, or
|
||||
- A global variable under **Begin input**, which is defined in the **Begin** component.
|
||||
- **Text**: Uses fixed text as the query. You are required to enter static text.
|
||||
|
||||
### Model
|
||||
|
||||
Click the dropdown menu of **Model** to show the model configuration window.
|
||||
|
||||
- **Model**: The chat model to use.
|
||||
- Ensure you set the chat model correctly on the **Model providers** page.
|
||||
- You can use different models for different components to increase flexibility or improve overall performance.
|
||||
- **Preset configurations**: A shortcut to **Temperature**, **Top P**, **Presence penalty**, and **Frequency penalty** settings, indicating the freedom level of the model. From **Improvise**, **Precise**, to **Balance**, each preset configuration corresponds to a unique combination of **Temperature**, **Top P**, **Presence penalty**, and **Frequency penalty**.
|
||||
This parameter has three options:
|
||||
- **Improvise**: Produces more creative responses.
|
||||
- **Precise**: (Default) Produces more conservative responses.
|
||||
- **Balance**: A middle ground between **Improvise** and **Precise**.
|
||||
- **Temperature**: The randomness level of the model's output.
|
||||
Defaults to 0.1.
|
||||
- Lower values lead to more deterministic and predictable outputs.
|
||||
- Higher values lead to more creative and varied outputs.
|
||||
- A temperature of zero results in the same output for the same prompt.
|
||||
- **Top P**: Nucleus sampling.
|
||||
- Reduces the likelihood of generating repetitive or unnatural text by setting a threshold *P* and restricting the sampling to tokens with a cumulative probability exceeding *P*.
|
||||
- Defaults to 0.3.
|
||||
- **Presence penalty**: Encourages the model to include a more diverse range of tokens in the response.
|
||||
- A higher **presence penalty** value results in the model being more likely to generate tokens not yet been included in the generated text.
|
||||
- Defaults to 0.4.
|
||||
- **Frequency penalty**: Discourages the model from repeating the same words or phrases too frequently in the generated text.
|
||||
- A higher **frequency penalty** value results in the model being more conservative in its use of repeated tokens.
|
||||
- Defaults to 0.7.
|
||||
- **Max tokens**: Sets the maximum length of the model's output, measured in the number of tokens.
|
||||
- Defaults to 512.
|
||||
- If disabled, you lift the maximum token limit, allowing the model to determine the number of tokens in its responses.
|
||||
|
||||
:::tip NOTE
|
||||
- It is not necessary to stick with the same model for all components. If a specific model is not performing well for a particular task, consider using a different one.
|
||||
- If you are uncertain about the mechanism behind **Temperature**, **Top P**, **Presence penalty**, and **Frequency penalty**, simply choose one of the three options of **Preset configurations**.
|
||||
:::
|
||||
|
||||
### Message window size
|
||||
|
||||
An integer specifying the number of previous dialogue rounds to input into the LLM. For example, if it is set to 12, the tokens from the last 12 dialogue rounds will be fed to the LLM. This feature consumes additional tokens.
|
||||
|
||||
Defaults to 1.
|
||||
|
||||
:::tip IMPORTANT
|
||||
This feature is used for multi-turn dialogue *only*. If your **Categorize** component is not part of a multi-turn dialogue (i.e., it is not in a loop), leave this field as-is.
|
||||
:::
|
||||
|
||||
### Category name
|
||||
|
||||
A **Categorize** component must have at least two categories. This field sets the name of the category. Click **+ Add Item** to include the intended categories.
|
||||
|
||||
:::tip NOTE
|
||||
You will notice that the category name is auto-populated. No worries. Each category is assigned a random name upon creation. Feel free to change it to a name that is understandable to the LLM.
|
||||
:::
|
||||
|
||||
#### Description
|
||||
|
||||
Description of this category.
|
||||
|
||||
You can input criteria, situation, or information that may help the LLM determine which inputs belong in this category.
|
||||
|
||||
#### Examples
|
||||
|
||||
Additional examples that may help the LLM determine which inputs belong in this category.
|
||||
|
||||
:::danger IMPORTANT
|
||||
Examples are more helpful than the description if you want the LLM to classify particular cases into this category.
|
||||
:::
|
||||
|
||||
#### Next step
|
||||
|
||||
Specifies the downstream component of this category.
|
||||
|
||||
- Once you specify the ID of the downstream component, a link is established between this category and the corresponding component.
|
||||
- If you manually link this category to a downstream component on the canvas, the ID of that component is auto-populated.
|
||||
|
||||
## Examples
|
||||
|
||||
You can explore our customer service agent template, where a **Categorize** component (component ID: **Question Categorize**) has four defined categories and takes data inputs from an **Interact** component (component ID: **Interface**):
|
||||
|
||||
1. Click the **Agent** tab at the top center of the page to access the **Agent** page.
|
||||
2. Click **+ Create agent** on the top right of the page to open the **agent template** page.
|
||||
3. On the **agent template** page, hover over the **Interpreter** card and click **Use this template**.
|
||||
4. Name your new agent and click **OK** to enter the workflow editor.
|
||||
|
||||
|
|
@ -1,26 +0,0 @@
|
|||
---
|
||||
sidebar_position: 10
|
||||
slug: /concentrator_component
|
||||
---
|
||||
|
||||
# Concentrator component
|
||||
|
||||
A component that directs execution flow to multiple downstream components.
|
||||
|
||||
---
|
||||
|
||||
The **Concentrator** component acts as a "repeater" of execution flow, transmitting a flow to multiple downstream components.
|
||||
|
||||
|
||||
## Scenarios
|
||||
|
||||
A **Concentrator** component enhances the current UX design. For a component originally designed to support only one downstream component, you can append a **Concentrator**, enabling it to have multiple downstream components.
|
||||
|
||||
## Examples
|
||||
|
||||
Explore our general-purpose chatbot agent template, featuring a **Concentrator** component (component ID: **medical**) that relays an execution flow from category 2 of the **Categorize** component to two translator components:
|
||||
|
||||
1. Click the **Agent** tab at the top center of the page to access the **Agent** page.
|
||||
2. Click **+ Create agent** on the top right of the page to open the **agent template** page.
|
||||
3. On the **agent template** page, hover over the **General-purpose chatbot** card and click **Use this template**.
|
||||
4. Name your new agent and click **OK** to enter the workflow editor.
|
||||
|
|
@ -1,113 +0,0 @@
|
|||
---
|
||||
sidebar_position: 2
|
||||
slug: /generate_component
|
||||
---
|
||||
|
||||
# Generate component
|
||||
|
||||
The component that prompts the LLM to respond appropriately.
|
||||
|
||||
---
|
||||
|
||||
A **Generate** component fine-tunes the LLM and sets its prompt.
|
||||
|
||||
## Scenarios
|
||||
|
||||
A **Generate** component is essential when you need the LLM to assist with summarizing, translating, or controlling various tasks.
|
||||
|
||||
## Configurations
|
||||
|
||||
### Model
|
||||
|
||||
Click the dropdown menu of **Model** to show the model configuration window.
|
||||
|
||||
- **Model**: The chat model to use.
|
||||
- Ensure you set the chat model correctly on the **Model providers** page.
|
||||
- You can use different models for different components to increase flexibility or improve overall performance.
|
||||
- **Preset configurations**: A shortcut to **Temperature**, **Top P**, **Presence penalty**, and **Frequency penalty** settings, indicating the freedom level of the model. From **Improvise**, **Precise**, to **Balance**, each preset configuration corresponds to a unique combination of **Temperature**, **Top P**, **Presence penalty**, and **Frequency penalty**.
|
||||
This parameter has three options:
|
||||
- **Improvise**: Produces more creative responses.
|
||||
- **Precise**: (Default) Produces more conservative responses.
|
||||
- **Balance**: A middle ground between **Improvise** and **Precise**.
|
||||
- **Temperature**: The randomness level of the model's output.
|
||||
Defaults to 0.1.
|
||||
- Lower values lead to more deterministic and predictable outputs.
|
||||
- Higher values lead to more creative and varied outputs.
|
||||
- A temperature of zero results in the same output for the same prompt.
|
||||
- **Top P**: Nucleus sampling.
|
||||
- Reduces the likelihood of generating repetitive or unnatural text by setting a threshold *P* and restricting the sampling to tokens with a cumulative probability exceeding *P*.
|
||||
- Defaults to 0.3.
|
||||
- **Presence penalty**: Encourages the model to include a more diverse range of tokens in the response.
|
||||
- A higher **presence penalty** value results in the model being more likely to generate tokens not yet been included in the generated text.
|
||||
- Defaults to 0.4.
|
||||
- **Frequency penalty**: Discourages the model from repeating the same words or phrases too frequently in the generated text.
|
||||
- A higher **frequency penalty** value results in the model being more conservative in its use of repeated tokens.
|
||||
- Defaults to 0.7.
|
||||
- **Max tokens**: Sets the maximum length of the model's output, measured in the number of tokens.
|
||||
- Defaults to 512.
|
||||
- If disabled, you lift the maximum token limit, allowing the model to determine the number of tokens in its responses.
|
||||
|
||||
:::tip NOTE
|
||||
- It is not necessary to stick with the same model for all components. If a specific model is not performing well for a particular task, consider using a different one.
|
||||
- If you are uncertain about the mechanism behind **Temperature**, **Top P**, **Presence penalty**, and **Frequency penalty**, simply choose one of the three options of **Preset configurations**.
|
||||
:::
|
||||
|
||||
### System prompt
|
||||
|
||||
Typically, you use the system prompt to describe the task for the LLM, specify how it should respond, and outline other miscellaneous requirements. We do not plan to elaborate on this topic, as it can be as extensive as prompt engineering. However, please be aware that the system prompt is often used in conjunction with keys (variables), which serve as various data inputs for the LLM.
|
||||
|
||||
Keys in a system prompt should be enclosed in curly braces. Below is a prompt excerpt of a **Generate** component from the **Interpreter** template (component ID: **Reflect**):
|
||||
|
||||
```text
|
||||
Your task is to read a source text and a translation to {target_lang}, and give constructive suggestions to improve the translation. The source text and initial translation, delimited by XML tags <SOURCE_TEXT></SOURCE_TEXT> and <TRANSLATION></TRANSLATION>, are as follows:
|
||||
|
||||
<SOURCE_TEXT>
|
||||
{source_text}
|
||||
</SOURCE_TEXT>
|
||||
|
||||
<TRANSLATION>
|
||||
{translation_1}
|
||||
</TRANSLATION>
|
||||
|
||||
When writing suggestions, pay attention to whether there are ways to improve the translation's fluency, by applying {target_lang} grammar, spelling and punctuation rules, and ensuring there are no unnecessary repetitions.
|
||||
- Each suggestion should address one specific part of the translation.
|
||||
- Output the suggestions only.
|
||||
```
|
||||
|
||||
Where `{source_text}` and `{target_lang}` are global variables defined by the **Begin** component, while `{translation_1}` is the output of another **Generate** component with the component ID **Translate directly**.
|
||||
|
||||
|
||||
:::danger IMPORTANT
|
||||
A **Generate** component relies on keys (variables) to specify its data inputs. Its immediate upstream component is *not* necessarily its data input, and the arrows in the workflow indicate *only* the processing sequence. Keys in a **Generate** component are used in conjunction with the system prompt to specify data inputs for the LLM. Use a forward slash `/` to show the keys to use.
|
||||
:::
|
||||
|
||||
### Cite
|
||||
|
||||
This toggle sets whether to cite the original text as reference.
|
||||
|
||||
|
||||
:::tip NOTE
|
||||
This feature is used for multi-turn dialogue *only* and is applicable *only* when the original documents are uploaded to a knowledge base and have finished file parsing.
|
||||
:::
|
||||
|
||||
|
||||
### Message window size
|
||||
|
||||
An integer specifying the number of previous dialogue rounds to input into the LLM. For example, if it is set to 12, the tokens from the last 12 dialogue rounds will be fed to the LLM. This feature consumes additional tokens.
|
||||
|
||||
:::tip IMPORTANT
|
||||
This feature is used for multi-turn dialogue *only*.
|
||||
:::
|
||||
|
||||
|
||||
## Examples
|
||||
|
||||
You can explore our three-step interpreter agent template, where a **Generate** component (component ID: **Reflect**) takes three global variables:
|
||||
|
||||
1. Click the **Agent** tab at the top center of the page to access the **Agent** page.
|
||||
2. Click **+ Create agent** on the top right of the page to open the **agent template** page.
|
||||
3. On the **agent template** page, hover over the **Interpreter** card and click **Use this template**.
|
||||
4. Name your new agent and click **OK** to enter the workflow editor.
|
||||
5. Click on component **Reflect**, to display its **Configuration** window, where:
|
||||
- `{target_lang}` and `{source_text}` are defined in the **Begin** component and require user input.
|
||||
- `{translation_1}` is the output from the upstream component **Translate directly**.
|
||||
|
|
@ -1,21 +0,0 @@
|
|||
---
|
||||
sidebar_position: 3
|
||||
slug: /interact_component
|
||||
---
|
||||
|
||||
# Interact component
|
||||
|
||||
A component that accepts user inputs and displays responses.
|
||||
|
||||
---
|
||||
|
||||
An **Interact** component serves as the interface between human and bot, receiving user inputs and displaying the agent's responses.
|
||||
|
||||
|
||||
## Scenarios
|
||||
|
||||
An **Interact** component is essential where you need to display the agent's responses or require user-computer interaction.
|
||||
|
||||
## Examples
|
||||
|
||||
You can explore our three-step interpreter agent template, where the **Interact** component is used to display the final translation, or our customer service agent template, where the **Interact** component is the immediate downstream of **Begin** and is used to display multi-turn dialogue between the user and the agent.
|
||||
|
|
@ -1,77 +0,0 @@
|
|||
---
|
||||
sidebar_position: 12
|
||||
slug: /iteration_component
|
||||
---
|
||||
|
||||
# Iteration component
|
||||
|
||||
A component that splits text input into text segments and iterates a predefined workflow for each one.
|
||||
|
||||
---
|
||||
|
||||
An **Interaction** component can divide text input into text segments and apply its built-in component workflow to each segment.
|
||||
|
||||
|
||||
## Scenario
|
||||
|
||||
An **Iteration** component is essential when a workflow loop is required and the loop count is *not* fixed but depends on number of segments created from the output of specific agent components.
|
||||
|
||||
- If, for instance, you plan to feed several paragraphs into an LLM for content generation, each with its own focus, and feeding them to the LLM all at once could create confusion or contradictions, then you can use an **Iteration** component, which encapsulates a **Generate** component, to repeat the content generation process for each paragraph.
|
||||
- Another example: If you wish to use the LLM to translate a lengthy paper into a target language without exceeding its token limit, consider using an **Iteration** component, which encapsulates a **Generate** component, to break the paper into smaller pieces and repeat the translation process for each one.
|
||||
|
||||
## Internal components
|
||||
|
||||
### IterationItem
|
||||
|
||||
Each **Iteration** component includes an internal **IterationItem** component. The **IterationItem** component serves as both the starting point and input node of the workflow within the **Iteration** component. It manages the loop of the workflow for all text segments created from the input.
|
||||
|
||||
:::tip NOTE
|
||||
The **IterationItem** component is visible *only* to the components encapsulated by the current **Iteration** components.
|
||||
:::
|
||||
|
||||
|
||||
|
||||
### Build an internal workflow
|
||||
|
||||
You are allowed to pull other components into the **Iteration** component to build an internal workflow, and these "added internal components" are no longer visible to components outside of the current **Iteration** component.
|
||||
|
||||
:::danger IMPORTANT
|
||||
To reference the created text segments from an added internal component, simply add a **Reference** variable that equals **IterationItem** within the **Input** section of that internal component. There is no need to reference the corresponding external component, as the **IterationItem** component manages the loop of the workflow for all created text segments.
|
||||
:::
|
||||
|
||||
:::tip NOTE
|
||||
An added internal component can reference an external component when necessary.
|
||||
:::
|
||||
|
||||
## Configurations
|
||||
|
||||
### Input
|
||||
|
||||
The **Iteration** component uses input variables to specify its data inputs, namely the texts to be segmented. You are allowed to specify multiple input sources for the **Iteration** component. Click **+ Add variable** in the **Input** section to include the desired input variables. There are two types of input variables: **Reference** and **Text**.
|
||||
|
||||
- **Reference**: Uses a component's output or a user input as the data source. You are required to select from the dropdown menu:
|
||||
- A component ID under **Component Output**, or
|
||||
- A global variable under **Begin input**, which is defined in the **Begin** component.
|
||||
- **Text**: Uses fixed text as the query. You are required to enter static text.
|
||||
|
||||
### Delimiter
|
||||
|
||||
The delimiter to use to split the text input into segments:
|
||||
|
||||
- Comma (Default)
|
||||
- Line break
|
||||
- Tab
|
||||
- Underline
|
||||
- Forward slash
|
||||
- Dash
|
||||
- Semicolon
|
||||
|
||||
## Examples
|
||||
|
||||
Explore our research report generator agent template, where the **Iteration** component (component ID: **Sections**) takes subtitles from the **Subtitles** component and generates sections for them:
|
||||
|
||||
1. Click the **Agent** tab at the top center of the page to access the **Agent** page.
|
||||
2. Click **+ Create agent** on the top right of the page to open the **agent template** page.
|
||||
3. On the **agent template** page, hover over the **Customer service** card and click **Use this template**.
|
||||
4. Name your new agent and click **OK** to enter the workflow editor.
|
||||
5. Click on the **Iteration** component to display its **Configuration** window.
|
||||
|
|
@ -1,79 +0,0 @@
|
|||
---
|
||||
sidebar_position: 6
|
||||
slug: /keyword_component
|
||||
---
|
||||
|
||||
# Keyword component
|
||||
|
||||
A component that extracts keywords from a user query.
|
||||
|
||||
---
|
||||
|
||||
A **Keyword** component uses the specified LLM to extract keywords from a user query.
|
||||
|
||||
## Scenarios
|
||||
|
||||
A **Keyword** component is essential where you need to prepare keywords for a potential keyword search.
|
||||
|
||||
## Configurations
|
||||
|
||||
### Input
|
||||
|
||||
The **Keyword** component relies on input variables to specify its data inputs (queries). Click **+ Add variable** in the **Input** section to add the desired input variables. There are two types of input variables: **Reference** and **Text**.
|
||||
|
||||
- **Reference**: Uses a component's output or a user input as the data source. You are required to select from the dropdown menu:
|
||||
- A component ID under **Component Output**, or
|
||||
- A global variable under **Begin input**, which is defined in the **Begin** component.
|
||||
- **Text**: Uses fixed text as the query. You are required to enter static text.
|
||||
|
||||
|
||||
### Model
|
||||
|
||||
Click the dropdown menu of **Model** to show the model configuration window.
|
||||
|
||||
- **Model**: The chat model to use.
|
||||
- Ensure you set the chat model correctly on the **Model providers** page.
|
||||
- You can use different models for different components to increase flexibility or improve overall performance.
|
||||
- **Preset configurations**: A shortcut to **Temperature**, **Top P**, **Presence penalty**, and **Frequency penalty** settings, indicating the freedom level of the model. From **Improvise**, **Precise**, to **Balance**, each preset configuration corresponds to a unique combination of **Temperature**, **Top P**, **Presence penalty**, and **Frequency penalty**.
|
||||
This parameter has three options:
|
||||
- **Improvise**: Produces more creative responses.
|
||||
- **Precise**: (Default) Produces more conservative responses.
|
||||
- **Balance**: A middle ground between **Improvise** and **Precise**.
|
||||
- **Temperature**: The randomness level of the model's output.
|
||||
Defaults to 0.1.
|
||||
- Lower values lead to more deterministic and predictable outputs.
|
||||
- Higher values lead to more creative and varied outputs.
|
||||
- A temperature of zero results in the same output for the same prompt.
|
||||
- **Top P**: Nucleus sampling.
|
||||
- Reduces the likelihood of generating repetitive or unnatural text by setting a threshold *P* and restricting the sampling to tokens with a cumulative probability exceeding *P*.
|
||||
- Defaults to 0.3.
|
||||
- **Presence penalty**: Encourages the model to include a more diverse range of tokens in the response.
|
||||
- A higher **presence penalty** value results in the model being more likely to generate tokens not yet been included in the generated text.
|
||||
- Defaults to 0.4.
|
||||
- **Frequency penalty**: Discourages the model from repeating the same words or phrases too frequently in the generated text.
|
||||
- A higher **frequency penalty** value results in the model being more conservative in its use of repeated tokens.
|
||||
- Defaults to 0.7.
|
||||
- **Max tokens**: Sets the maximum length of the model's output, measured in the number of tokens.
|
||||
- Defaults to 512.
|
||||
- If disabled, you lift the maximum token limit, allowing the model to determine the number of tokens in its responses.
|
||||
|
||||
:::tip NOTE
|
||||
- It is not necessary to stick with the same model for all components. If a specific model is not performing well for a particular task, consider using a different one.
|
||||
- If you are uncertain about the mechanism behind **Temperature**, **Top P**, **Presence penalty**, and **Frequency penalty**, simply choose one of the three options of **Preset**.
|
||||
:::
|
||||
|
||||
|
||||
### Number of keywords
|
||||
|
||||
An integer specifying the number of keywords to extract from the user query. Defaults to 3. Please note that the number of extracted keywords depends on the LLM's capabilities and the token count in the user query, and may *not* match the integer you set.
|
||||
|
||||
|
||||
## Examples
|
||||
|
||||
Explore our general-purpose chatbot agent template, where the **Keyword** component (component ID: **keywords**) is used to extract keywords from financial inputs for a potential stock search in the **akshare** component:
|
||||
|
||||
1. Click the **Agent** tab at the top center of the page to access the **Agent** page.
|
||||
2. Click **+ Create agent** on the top right of the page to open the **agent template** page.
|
||||
3. On the **agent template** page, hover over the **General-purpose chatbot** card and click **Use this template**.
|
||||
4. Name your new agent and click **OK** to enter the workflow editor.
|
||||
5. Click on the **Keyword** component to display its **Configuration** window.
|
||||
|
|
@ -1,30 +0,0 @@
|
|||
---
|
||||
sidebar_position: 7
|
||||
slug: /message_component
|
||||
---
|
||||
|
||||
# Message component
|
||||
|
||||
A component that sends out a static message.
|
||||
|
||||
---
|
||||
|
||||
A **Message** component sends out a static message. If multiple messages are supplied, it randomly selects one to send.
|
||||
|
||||
## Configurations
|
||||
|
||||
### Messages
|
||||
|
||||
The message to send out.
|
||||
|
||||
Click **+ Add message** to add message options. When multiple messages are supplied, the **Message** component randomly selects one to send.
|
||||
|
||||
## Examples
|
||||
|
||||
Explore our customer service agent template, where the **Message** component (component ID: **What else?**) randomly sends out a message to the user interface if the user inputs is related to personal contact information:
|
||||
|
||||
1. Click the **Agent** tab at the top center of the page to access the **Agent** page.
|
||||
2. Click **+ Create agent** on the top right of the page to open the **agent template** page.
|
||||
3. On the **agent template** page, hover over the **Customer service** card and click **Use this template**.
|
||||
4. Name your new agent and click **OK** to enter the workflow editor.
|
||||
5. Click on the **Message** component to display its **Configuration** window.
|
||||
|
|
@ -1,22 +0,0 @@
|
|||
---
|
||||
sidebar_position: 13
|
||||
slug: /note_component
|
||||
---
|
||||
|
||||
# Note component
|
||||
|
||||
The component that keeps design notes.
|
||||
|
||||
---
|
||||
|
||||
A **note** component allows you to keep design notes, including details about an agent, the output of specific components, the rationale of a particular design, or any information that may assist you, your users, or your fellow developers understand the agent.
|
||||
|
||||
## Examples
|
||||
|
||||
Explore our customer service agent template, which has five **Note** components:
|
||||
|
||||
1. Click the **Agent** tab at the top center of the page to access the **Agent** page.
|
||||
2. Click **+ Create agent** on the top right of the page to open the **agent template** page.
|
||||
3. On the **agent template** page, hover over the **Customer service** card and click **Use this template**.
|
||||
4. Name your new agent and click **OK** to enter the workflow editor.
|
||||
5. Click on the **note** component to add or update notes.
|
||||
|
|
@ -1,75 +0,0 @@
|
|||
---
|
||||
sidebar_position: 4
|
||||
slug: /retrieval_component
|
||||
---
|
||||
|
||||
# Retrieval component
|
||||
|
||||
A component that retrieves information from specified datasets.
|
||||
|
||||
## Scenarios
|
||||
|
||||
A **Retrieval** component is essential in most RAG scenarios, where information is extracted from designated knowledge bases before being sent to the LLM for content generation.
|
||||
|
||||
## Configurations
|
||||
|
||||
Click on a **Retrieval** component to open its configuration window.
|
||||
|
||||
### Input
|
||||
|
||||
The **Retrieval** component relies on input variables to specify its data inputs (queries). Click **+ Add variable** in the **Input** section to add the desired input variables. There are two types of input variables: **Reference** and **Text**.
|
||||
|
||||
- **Reference**: Uses a component's output or a user input as the data source. You are required to select from the dropdown menu:
|
||||
- A component ID under **Component Output**, or
|
||||
- A global variable under **Begin input**, which is defined in the **Begin** component.
|
||||
- **Text**: Uses fixed text as the query. You are required to enter static text.
|
||||
|
||||
### Similarity threshold
|
||||
|
||||
RAGFlow employs a combination of weighted keyword similarity and weighted vector cosine similarity during retrieval. This parameter sets the threshold for similarities between the user query and chunks stored in the datasets. Any chunk with a similarity score below this threshold will be excluded from the results.
|
||||
|
||||
Defaults to 0.2.
|
||||
|
||||
### Keyword similarity weight
|
||||
|
||||
This parameter sets the weight of keyword similarity in the combined similarity score. The total of the two weights must equal 1.0. Its default value is 0.7, which means the weight of vector similarity in the combined search is 1 - 0.7 = 0.3.
|
||||
|
||||
### Top N
|
||||
|
||||
This parameter selects the "Top N" chunks from retrieved ones and feed them to the LLM.
|
||||
|
||||
Defaults to 8.
|
||||
|
||||
|
||||
### Rerank model
|
||||
|
||||
*Optional*
|
||||
|
||||
If a rerank model is selected, a combination of weighted keyword similarity and weighted reranking score will be used for retrieval.
|
||||
|
||||
:::caution WARNING
|
||||
Using a rerank model will *significantly* increase the system's response time.
|
||||
:::
|
||||
|
||||
|
||||
### Knowledge bases
|
||||
|
||||
*Required*
|
||||
|
||||
You are required to select the knowledge base(s) to retrieve data from.
|
||||
|
||||
:::danger IMPORTANT
|
||||
If you select multiple knowledge bases, you must ensure that the knowledge bases (datasets) you select use the same embedding model; otherwise, an error message would occur.
|
||||
:::
|
||||
|
||||
|
||||
|
||||
## Examples
|
||||
|
||||
Explore our customer service agent template, where the **Retrieval** component (component ID: **Search product info**) is used to search the dataset and send the Top N results to the LLM:
|
||||
|
||||
1. Click the **Agent** tab at the top center of the page to access the **Agent** page.
|
||||
2. Click **+ Create agent** on the top right of the page to open the **agent template** page.
|
||||
3. On the **agent template** page, hover over the **Customer service** card and click **Use this template**.
|
||||
4. Name your new agent and click **OK** to enter the workflow editor.
|
||||
5. Click on the **Retrieval** component to display its **Configuration** window.
|
||||
|
|
@ -1,82 +0,0 @@
|
|||
---
|
||||
sidebar_position: 8
|
||||
slug: /rewrite_component
|
||||
---
|
||||
|
||||
# Rewrite component
|
||||
|
||||
A component that rewrites a user query.
|
||||
|
||||
---
|
||||
|
||||
A **Rewrite** component uses a specified LLM to rewrite a user query from the **Interact** component, based on the context of previous dialogues.
|
||||
|
||||
## Scenarios
|
||||
|
||||
A **Rewrite** component is essential when you need to optimize a user query based on the context of previous conversations. It is usually the upstream component of a **Retrieval** component.
|
||||
|
||||
:::tip NOTE
|
||||
See also the [Keyword](./keyword.mdx) component, a similar component used for multi-turn optimization.
|
||||
:::
|
||||
|
||||
## Configurations
|
||||
|
||||
:::tip NOTE
|
||||
The **Rewrite** component uses the user-agent interaction from the **Interact** component as its data input. Therefore, there is no need to specify its data inputs in the Configurations.
|
||||
:::
|
||||
|
||||
### Model
|
||||
|
||||
Click the dropdown menu of **Model** to show the model configuration window.
|
||||
|
||||
- **Model**: The chat model to use.
|
||||
- Ensure you set the chat model correctly on the **Model providers** page.
|
||||
- You can use different models for different components to increase flexibility or improve overall performance.
|
||||
- **Preset configurations**: A shortcut to **Temperature**, **Top P**, **Presence penalty**, and **Frequency penalty** settings, indicating the freedom level of the model. From **Improvise**, **Precise**, to **Balance**, each preset configuration corresponds to a unique combination of **Temperature**, **Top P**, **Presence penalty**, and **Frequency penalty**.
|
||||
This parameter has three options:
|
||||
- **Improvise**: Produces more creative responses.
|
||||
- **Precise**: (Default) Produces more conservative responses.
|
||||
- **Balance**: A middle ground between **Improvise** and **Precise**.
|
||||
- **Temperature**: The randomness level of the model's output.
|
||||
Defaults to 0.1.
|
||||
- Lower values lead to more deterministic and predictable outputs.
|
||||
- Higher values lead to more creative and varied outputs.
|
||||
- A temperature of zero results in the same output for the same prompt.
|
||||
- **Top P**: Nucleus sampling.
|
||||
- Reduces the likelihood of generating repetitive or unnatural text by setting a threshold *P* and restricting the sampling to tokens with a cumulative probability exceeding *P*.
|
||||
- Defaults to 0.3.
|
||||
- **Presence penalty**: Encourages the model to include a more diverse range of tokens in the response.
|
||||
- A higher **presence penalty** value results in the model being more likely to generate tokens not yet been included in the generated text.
|
||||
- Defaults to 0.4.
|
||||
- **Frequency penalty**: Discourages the model from repeating the same words or phrases too frequently in the generated text.
|
||||
- A higher **frequency penalty** value results in the model being more conservative in its use of repeated tokens.
|
||||
- Defaults to 0.7.
|
||||
- **Max tokens**: Sets the maximum length of the model's output, measured in the number of tokens.
|
||||
- Defaults to 512.
|
||||
- If disabled, you lift the maximum token limit, allowing the model to determine the number of tokens in its responses.
|
||||
|
||||
:::tip NOTE
|
||||
- It is not necessary to stick with the same model for all components. If a specific model is not performing well for a particular task, consider using a different one.
|
||||
- If you are uncertain about the mechanism behind **Temperature**, **Top P**, **Presence penalty**, and **Frequency penalty**, simply choose one of the three options of **Preset configurations**.
|
||||
:::
|
||||
|
||||
|
||||
### Message window size
|
||||
|
||||
An integer specifying the number of previous dialogue rounds to input into the LLM. For example, if it is set to 12, the tokens from the last 12 dialogue rounds will be fed to the LLM. This feature consumes additional tokens.
|
||||
|
||||
Defaults to 1.
|
||||
|
||||
:::tip IMPORTANT
|
||||
This feature is used for multi-turn dialogue *only*. If your **Categorize** component is not part of a multi-turn dialogue (i.e., it is not in a loop), leave this field as-is.
|
||||
:::
|
||||
|
||||
## Examples
|
||||
|
||||
Explore our customer service agent template, where the **Rewrite** component (component ID: **Refine Question**) is used to optimize a product-specific user query based on context of previous dialogues before passing it on to the **Retrieval** component.
|
||||
|
||||
1. Click the **Agent** tab at the top center of the page to access the **Agent** page.
|
||||
2. Click **+ Create agent** on the top right of the page to open the **agent template** page.
|
||||
3. On the **agent template** page, hover over the **Customer service** card and click **Use this template**.
|
||||
4. Name your new agent and click **OK** to enter the workflow editor.
|
||||
5. Click on the **Rewrite** component to display its **Configuration** window.
|
||||
|
|
@ -1,62 +0,0 @@
|
|||
---
|
||||
sidebar_position: 9
|
||||
slug: /switch_component
|
||||
---
|
||||
|
||||
# Switch component
|
||||
|
||||
A component that evaluates whether specified conditions are met and directs the follow of execution accordingly.
|
||||
|
||||
---
|
||||
|
||||
A **Switch** component evaluates conditions based on the output of specific components, directing the flow of execution accordingly to enable complex branching logic.
|
||||
|
||||
## Scenarios
|
||||
|
||||
A **Switch** component is essential for condition-based direction of execution flow. While it shares similarities with the [Categorize](./categorize.mdx) component, which is also used in multi-pronged strategies, the key distinction lies in their approach: the evaluation of the **Switch** component is rule-based, whereas the **Categorize** component involves AI and uses an LLM for decision-making.
|
||||
|
||||
## Configurations
|
||||
|
||||
### Case n
|
||||
|
||||
A **Switch** component must have at least one case, each with multiple specified conditions and *only one* downstream component. When multiple conditions are specified for a case, you must set the logical relationship between them to either AND or OR.
|
||||
|
||||
#### Next step
|
||||
|
||||
Specifies the downstream component of this case.
|
||||
|
||||
- *Once you specify the ID of the downstream component, a link is established between this case and the corresponding component.*
|
||||
- *If you manually link this case to a downstream component on the canvas, the ID of that component is auto-populated.*
|
||||
|
||||
#### Condition
|
||||
|
||||
Evaluates whether the output of specific components meets certain conditions, with **Component ID**, **Operator**, and **Value** together forming a conditional expression.
|
||||
|
||||
:::danger IMPORTANT
|
||||
When you have added multiple conditions for a specific case, a **Logical operator** field appears, requiring you to set the logical relationship between these conditions as either AND or OR.
|
||||
|
||||
:::
|
||||
|
||||
- **Component ID**: The ID of the corresponding component.
|
||||
- **Operator**: The operator required to form a conditional expression.
|
||||
- Equals
|
||||
- Not equal
|
||||
- Greater than
|
||||
- Greater equal
|
||||
- Less than
|
||||
- Less equal
|
||||
- Contains
|
||||
- Not contains
|
||||
- Starts with
|
||||
- Ends with
|
||||
- Is empty
|
||||
- Not empty
|
||||
- **Value**: A single value, which can be an integer, float, or string.
|
||||
- Delimiters, multiple values, or expressions are *not* supported.
|
||||
- Strings need not be wrapped in `""` or `''`.
|
||||
|
||||
### ELSE
|
||||
|
||||
**Required**. Specifies the downstream component if none of the conditions defined above are met.
|
||||
|
||||
*Once you specify the ID of the downstream component, a link is established between ELSE and the corresponding component.*
|
||||
|
|
@ -1,49 +0,0 @@
|
|||
---
|
||||
sidebar_position: 11
|
||||
slug: /template_component
|
||||
---
|
||||
|
||||
# Template component
|
||||
|
||||
A component that formats user inputs or the outputs of other components.
|
||||
|
||||
---
|
||||
|
||||
A **Template** component acts as a content formatter. It is usually the upstream component of an **Interact** component.
|
||||
|
||||
|
||||
## Scenarios
|
||||
|
||||
A **Template** component is useful for organizing various sources of data or information into specific formats.
|
||||
|
||||
## Configurations
|
||||
|
||||
### Content
|
||||
|
||||
Used together with Keys to organize various data or information sources into desired formats. Example:
|
||||
|
||||
```text
|
||||
<h2>{subtitle}</h2>
|
||||
<div>{content}</div>
|
||||
```
|
||||
|
||||
Where `{subtitle}` and `{content}` are defined keys.
|
||||
|
||||
### Key
|
||||
|
||||
A **Template** component relies on keys (variables) to specify its data or information sources. Its immediate upstream component is *not* necessarily its input, and the arrows in the workflow indicate *only* the processing sequence.
|
||||
|
||||
Values of keys are categorized into two groups:
|
||||
|
||||
- **Component Output**: The value of the key should be a component ID.
|
||||
- **Begin Input**: The value of the key should be the name of a global variable defined in the **Begin** component.
|
||||
|
||||
## Examples
|
||||
|
||||
Explore our research report generator agent template, where the **Template** component (component ID: **Article**) organizes user input and the outputs of the **Sections** component into HTML format:
|
||||
|
||||
1. Click the **Agent** tab at the top center of the page to access the **Agent** page.
|
||||
2. Click **+ Create agent** on the top right of the page to open the **agent template** page.
|
||||
3. On the **agent template** page, hover over the **Research report generator** card and click **Use this template**.
|
||||
4. Name your new agent and click **OK** to enter the workflow editor.
|
||||
5. Click on the **Template** component to display its **Configuration** window
|
||||
|
|
@ -1,83 +0,0 @@
|
|||
---
|
||||
sidebar_position: 1
|
||||
slug: /agent_introduction
|
||||
---
|
||||
|
||||
# Introduction to agents
|
||||
|
||||
Key concepts, basic operations, a quick view of the agent editor.
|
||||
|
||||
---
|
||||
|
||||
## Key concepts
|
||||
|
||||
Agents and RAG are complementary techniques, each enhancing the other’s capabilities in business applications. RAGFlow v0.8.0 introduces an agent mechanism, featuring a no-code workflow editor on the front end and a comprehensive graph-based task orchestration framework on the back end. This mechanism is built on top of RAGFlow's existing RAG solutions and aims to orchestrate search technologies such as query intent classification, conversation leading, and query rewriting to:
|
||||
|
||||
- Provide higher retrievals and,
|
||||
- Accommodate more complex scenarios.
|
||||
|
||||
## Create an agent
|
||||
|
||||
:::tip NOTE
|
||||
|
||||
Before proceeding, ensure that:
|
||||
|
||||
1. You have properly set the LLM to use. See the guides on [Configure your API key](../models/llm_api_key_setup.md) or [Deploy a local LLM](../models/deploy_local_llm.mdx) for more information.
|
||||
2. You have a knowledge base configured and the corresponding files properly parsed. See the guide on [Configure a knowledge base](../dataset/configure_knowledge_base.md) for more information.
|
||||
|
||||
:::
|
||||
|
||||
Click the **Agent** tab in the middle top of the page to show the **Agent** page. As shown in the screenshot below, the cards on this page represent the created agents, which you can continue to edit.
|
||||
|
||||
|
||||
|
||||
We also provide templates catered to different business scenarios. You can either generate your agent from one of our agent templates or create one from scratch:
|
||||
|
||||
1. Click **+ Create agent** to show the **agent template** page:
|
||||
|
||||
|
||||
|
||||
2. To create an agent from scratch, click the **Blank** card. Alternatively, to create an agent from one of our templates, hover over the desired card, such as **General-purpose chatbot**, click **Use this template**, name your agent in the pop-up dialogue, and click **OK** to confirm.
|
||||
|
||||
*You are now taken to the **no-code workflow editor** page. The left panel lists the components (operators): Above the dividing line are the RAG-specific components; below the line are tools. We are still working to expand the component list.*
|
||||
|
||||
|
||||
|
||||
3. General speaking, now you can do the following:
|
||||
- Drag and drop a desired component to your workflow,
|
||||
- Select the knowledge base to use,
|
||||
- Update settings of specific components,
|
||||
- Update LLM settings
|
||||
- Sets the input and output for a specific component, and more.
|
||||
4. Click **Save** to apply changes to your agent and **Run** to test it.
|
||||
|
||||
## Components
|
||||
|
||||
Please review the flowing description of the RAG-specific components before you proceed:
|
||||
|
||||
| Component | Description |
|
||||
|----------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|
||||
| **Retrieval** | A component that retrieves information from specified knowledge bases and returns 'Empty response' if no information is found. Ensure the correct knowledge bases are selected. |
|
||||
| **Generate** | A component that prompts the LLM to generate responses. You must ensure the prompt is set correctly. |
|
||||
| **Interact** | A component that serves as the interface between human and the bot, receiving user inputs and displaying the agent's responses. |
|
||||
| **Categorize** | A component that uses the LLM to classify user inputs into predefined categories. Ensure you specify the name, description, and examples for each category, along with the corresponding next component. |
|
||||
| **Message** | A component that sends out a static message. If multiple messages are supplied, it randomly selects one to send. Ensure its downstream is **Interact**, the interface component. |
|
||||
| **Rewrite** | A component that rewrites a user query from the **Interact** component, based on the context of previous dialogues. |
|
||||
| **Keyword** | A component that extracts keywords from a user query, with TopN specifying the number of keywords to extract. |
|
||||
|
||||
:::caution NOTE
|
||||
|
||||
- Ensure **Rewrite**'s upstream component is **Relevant** and downstream component is **Retrieval**.
|
||||
- Ensure the downstream component of **Message** is **Interact**.
|
||||
- The downstream component of **Begin** is always **Interact**.
|
||||
|
||||
:::
|
||||
|
||||
## Basic operations
|
||||
|
||||
| Operation | Description |
|
||||
|---------------------------|------------------------------------------------------------------------------------------------------------------------------------------|
|
||||
| Add a component | Drag and drop the desired component from the left panel onto the canvas. |
|
||||
| Delete a component | On the canvas, hover over the three dots (...) of the component to display the delete option, then select it to remove the component. |
|
||||
| Copy a component | On the canvas, hover over the three dots (...) of the component to display the copy option, then select it to make a copy the component. |
|
||||
| Update component settings | On the canvas, click the desired component to display the component settings. |
|
||||
|
|
@ -1,20 +0,0 @@
|
|||
---
|
||||
sidebar_position: 3
|
||||
slug: /embed_agent_into_webpage
|
||||
---
|
||||
|
||||
# Embed agent into webpage
|
||||
|
||||
You can use iframe to embed an agent into a third-party webpage.
|
||||
|
||||
:::caution WARNING
|
||||
If your agent's **Begin** component takes a key of **file** type (a **file** type variable), you *cannot* embed it into a webpage.
|
||||
:::
|
||||
|
||||
1. Before proceeding, you must [acquire an API key](../models/llm_api_key_setup.md); otherwise, an error message would appear.
|
||||
2. On the **Agent** page, click an intended agent **>** **Edit** to access its editing page.
|
||||
3. Click **Embed into webpage** on the top right corner of the canvas to show the **iframe** window:
|
||||
|
||||
|
||||
|
||||
4. Copy the iframe and embed it into a specific location on your webpage.
|
||||
|
|
@ -1,105 +0,0 @@
|
|||
---
|
||||
sidebar_position: 2
|
||||
slug: /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.
|
||||
|
||||
This chatbot closely resembles the chatbot introduced in [Start an AI chat](../chat/start_chat.md), but with a key difference - it introduces a reflective mechanism that allows it to improve the retrieval from the target knowledge bases by rewriting the user's query.
|
||||
|
||||
This document provides guides on creating such a chatbot using our chatbot template.
|
||||
|
||||
## Prerequisites
|
||||
|
||||
1. Ensure you have properly set the LLM to use. See the guides on [Configure your API key](../models/llm_api_key_setup.md) or [Deploy a local LLM](../models/deploy_local_llm.mdx) for more information.
|
||||
2. Ensure you have a knowledge base configured and the corresponding files properly parsed. See the guide on [Configure a knowledge base](../dataset/configure_knowledge_base.md) for more information.
|
||||
3. Make sure you have read the [Introduction to Agentic RAG](./agent_introduction.md).
|
||||
|
||||
## Create a chatbot agent from template
|
||||
|
||||
To create a general-purpose chatbot agent using our template:
|
||||
|
||||
1. Click the **Agent** tab in the middle top of the page to show the **Agent** page.
|
||||
2. Click **+ Create agent** on the top right of the page to show the **agent template** page.
|
||||
3. On the **agent template** page, hover over the card on **General-purpose chatbot** and click **Use this template**.
|
||||
*You are now directed to the **no-code workflow editor** page.*
|
||||
|
||||
|
||||
|
||||
:::tip NOTE
|
||||
RAGFlow's no-code editor spares you the trouble of coding, making agent development effortless.
|
||||
:::
|
||||
|
||||
## Understand each component in the template
|
||||
|
||||
Here’s a breakdown of each component and its role and requirements in the chatbot template:
|
||||
|
||||
- **Begin**
|
||||
- Function: Sets an opening greeting for users.
|
||||
- Purpose: Establishes a welcoming atmosphere and prepares the user for interaction.
|
||||
|
||||
- **Interact**
|
||||
- Function: Serves as the interface between human and the bot.
|
||||
- Role: Acts as the downstream component of **Begin**.
|
||||
|
||||
- **Retrieval**
|
||||
- Function: Retrieves information from specified knowledge base(s).
|
||||
- Requirement: Must have `knowledgebases` set up to function.
|
||||
|
||||
- **Relevant**
|
||||
- Function: Assesses the relevance of the retrieved information from the **Retrieval** component to the user query.
|
||||
- Process:
|
||||
- If relevant, it directs the data to the **Generate** component for final response generation.
|
||||
- Otherwise, it triggers the **Rewrite** component to refine the user query and redo the retrival process.
|
||||
|
||||
- **Generate**
|
||||
- Function: Prompts the LLM to generate responses based on the retrieved information.
|
||||
- Note: The prompt settings allow you to control the way in which the LLM generates responses. Be sure to review the prompts and make necessary changes.
|
||||
|
||||
- **Rewrite**:
|
||||
- Function: Refines a user query when no relevant information from the knowledge base is retrieved.
|
||||
- Usage: Often used in conjunction with **Relevant** and **Retrieval** to create a reflective/feedback loop.
|
||||
|
||||
## Configure your chatbot agent
|
||||
|
||||
1. Click **Begin** to set an opening greeting:
|
||||
|
||||
|
||||
2. Click **Retrieval** to select the right knowledge base(s) and make any necessary adjustments:
|
||||
|
||||
|
||||
3. Click **Generate** to configure the LLM's summarization behavior:
|
||||
3.1. Confirm the model.
|
||||
3.2. Review the prompt settings. If there are variables, ensure they match the correct component IDs:
|
||||
|
||||
|
||||
4. Click **Relevant** to review or change its settings:
|
||||
*You may retain the current settings, but feel free to experiment with changes to understand how the agent operates.*
|
||||
|
||||
|
||||
5. Click **Rewrite** to select a different model for query rewriting or update the maximum loop times for query rewriting:
|
||||
|
||||
|
||||
|
||||
:::danger NOTE
|
||||
Increasing the maximum loop times may significantly extend the time required to receive the final response.
|
||||
:::
|
||||
|
||||
1. Update your workflow where you see necessary.
|
||||
|
||||
2. Click to **Save** to apply your changes.
|
||||
*Your agent appears as one of the agent cards on the **Agent** page.*
|
||||
|
||||
## Test your chatbot agent
|
||||
|
||||
1. Find your chatbot agent on the **Agent** page:
|
||||
|
||||
|
||||
2. Experiment with your questions to verify if this chatbot functions as intended:
|
||||
|
||||
|
|
@ -1,416 +0,0 @@
|
|||
---
|
||||
sidebar_position: 10
|
||||
slug: /text2sql_agent
|
||||
---
|
||||
|
||||
# Create a Text2SQL agent
|
||||
|
||||
Build a Text2SQL agent leveraging RAGFlow's RAG capabilities. Contributed by @TeslaZY.
|
||||
|
||||
## Scenario
|
||||
|
||||
The Text2SQL agent bridges the gap between Natural Language Processing (NLP) and Structured Query Language (SQL). Its key advantages are as follows:
|
||||
|
||||
- **Assisting non-technical users with SQL**: Not all users have a background in SQL or understand the structure of the tables involved in queries. With a Text2SQL agent, users can pose questions or request data in natural language without needing an in-depth knowledge of the database structure or SQL syntax.
|
||||
|
||||
- **Enhancing SQL development efficiency**: For those familiar with SQL, the Text2SQL agent streamlines the process by enabling users to construct complex queries quickly, without the need to code each part manually.
|
||||
|
||||
- **Minimizing errors**: Manually writing SQL queries can be error-prone, particularly for complex queries or for users not well-versed in the database structure. The Text2SQL agent can interpret natural language instructions and generate accurate SQL queries, thereby reducing potential syntax and logic errors.
|
||||
|
||||
- **Boosting data analysis capabilities**: In business intelligence and data analysis, swiftly gaining insights from data is critical. The Text2SQL agent facilitates extracting valuable information from databases more directly and conveniently, thus aiding in accelerating decision-making.
|
||||
|
||||
- **Automation and integration**: The Text2SQL agent can be integrated into larger systems to support automated workflows, such as automatic report generation and data monitoring. It can also integrate seamlessly with other services and technologies, offering richer application possibilities.
|
||||
|
||||
- **Support for multiple languages and varied expressions**: People can express the same idea in numerous ways. An effective Text2SQL system should be capable of understanding diverse expressions and accurately converting them into SQL queries.
|
||||
|
||||
In summary, the Text2SQL agent seeks to make database queries more intuitive and user-friendly while ensuring efficiency and accuracy. It caters to a broad spectrum of users, from completely non-technical individuals to seasoned data analysts and developers.
|
||||
|
||||
However, traditional Text2SQL solutions often require model fine-tuning, which can substantially escalate deployment and maintenance costs when implemented in enterprise environments alongside RAG or Agent components. RAGFlow’s RAG-based Text2SQL utilizes an existing (connected) large language model (LLM), allowing for seamless integration with other RAG/Agent components without the necessity for additional fine-tuned models.
|
||||
|
||||
## Recipe
|
||||
|
||||
A list of components required:
|
||||
|
||||
- [Begin](./agent_component_reference/begin.mdx)
|
||||
- [Interact](./agent_component_reference/interact.mdx)
|
||||
- [Retrieval](./agent_component_reference/retrieval.mdx)
|
||||
- [Generate](./agent_component_reference/generate.mdx)
|
||||
- ExeSQL
|
||||
|
||||
## Procedure
|
||||
|
||||
### Preparation of Data
|
||||
|
||||
#### Database Environment
|
||||
|
||||
Mysql-8.0.39
|
||||
|
||||
#### Database Table Creation Statements
|
||||
|
||||
```sql
|
||||
SET NAMES utf8mb4;
|
||||
|
||||
-- ----------------------------
|
||||
-- Table structure for Customers
|
||||
-- ----------------------------
|
||||
DROP TABLE IF EXISTS `Customers`;
|
||||
CREATE TABLE `Customers` (
|
||||
`CustomerID` int NOT NULL AUTO_INCREMENT,
|
||||
`UserName` varchar(50) COLLATE utf8mb4_unicode_ci DEFAULT NULL,
|
||||
`Email` varchar(100) COLLATE utf8mb4_unicode_ci DEFAULT NULL,
|
||||
`PhoneNumber` varchar(20) COLLATE utf8mb4_unicode_ci DEFAULT NULL,
|
||||
PRIMARY KEY (`CustomerID`)
|
||||
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_unicode_ci;
|
||||
|
||||
-- ----------------------------
|
||||
-- Table structure for Products
|
||||
-- ----------------------------
|
||||
DROP TABLE IF EXISTS `Products`;
|
||||
CREATE TABLE `Products` (
|
||||
`ProductID` int NOT NULL AUTO_INCREMENT,
|
||||
`ProductName` varchar(100) COLLATE utf8mb4_unicode_ci DEFAULT NULL,
|
||||
`Description` text COLLATE utf8mb4_unicode_ci,
|
||||
`Price` decimal(10,2) DEFAULT NULL,
|
||||
`StockQuantity` int DEFAULT NULL,
|
||||
PRIMARY KEY (`ProductID`)
|
||||
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_unicode_ci;
|
||||
|
||||
-- ----------------------------
|
||||
-- Table structure for Orders
|
||||
-- ----------------------------
|
||||
DROP TABLE IF EXISTS `Orders`;
|
||||
CREATE TABLE `Orders` (
|
||||
`OrderID` int NOT NULL AUTO_INCREMENT,
|
||||
`CustomerID` int DEFAULT NULL,
|
||||
`OrderDate` date DEFAULT NULL,
|
||||
`TotalPrice` decimal(10,2) DEFAULT NULL,
|
||||
PRIMARY KEY (`OrderID`),
|
||||
KEY `CustomerID` (`CustomerID`)
|
||||
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_unicode_ci;
|
||||
|
||||
-- ----------------------------
|
||||
-- Table structure for OrderDetails
|
||||
-- ----------------------------
|
||||
DROP TABLE IF EXISTS `OrderDetails`;
|
||||
CREATE TABLE `OrderDetails` (
|
||||
`OrderDetailID` int NOT NULL AUTO_INCREMENT,
|
||||
`OrderID` int DEFAULT NULL,
|
||||
`ProductID` int DEFAULT NULL,
|
||||
`UnitPrice` decimal(10,2) DEFAULT NULL,
|
||||
`Quantity` int DEFAULT NULL,
|
||||
`TotalPrice` decimal(10,2) DEFAULT NULL,
|
||||
PRIMARY KEY (`OrderDetailID`),
|
||||
KEY `OrderID` (`OrderID`),
|
||||
KEY `ProductID` (`ProductID`)
|
||||
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_unicode_ci;
|
||||
```
|
||||
|
||||
#### Generate Test Data
|
||||
|
||||
```sql
|
||||
START TRANSACTION;
|
||||
INSERT INTO Customers (UserName, Email, PhoneNumber) VALUES
|
||||
('Alice', 'alice@example.com', '123456789'),
|
||||
('Bob', 'bob@example.com', '987654321'),
|
||||
('Charlie', 'charlie@example.com', '112233445'),
|
||||
('Diana', 'diana@example.com', '555666777'),
|
||||
('Eve', 'eve@example.com', '999888777'),
|
||||
('Frank', 'frank@example.com', '123123123'),
|
||||
('Grace', 'grace@example.com', '456456456'),
|
||||
('Hugo', 'hugo@example.com', '789789789'),
|
||||
('Ivy', 'ivy@example.com', '321321321'),
|
||||
('Jack', 'jack@example.com', '654654654');
|
||||
|
||||
INSERT INTO Products (ProductName, Description, Price, StockQuantity) VALUES
|
||||
('Laptop', 'High performance laptop', 1200.00, 50),
|
||||
('Smartphone', 'Latest model smartphone', 800.00, 100),
|
||||
('Tablet', 'Portable tablet device', 300.00, 75),
|
||||
('Headphones', 'Noise-cancelling headphones', 150.00, 200),
|
||||
('Camera', 'Professional camera', 600.00, 30),
|
||||
('Monitor', '24-inch Full HD monitor', 200.00, 45),
|
||||
('Keyboard', 'Mechanical keyboard', 100.00, 150),
|
||||
('Mouse', 'Ergonomic gaming mouse', 50.00, 250),
|
||||
('Speaker', 'Wireless Bluetooth speaker', 80.00, 120),
|
||||
('Router', 'Wi-Fi router with high speed', 120.00, 90);
|
||||
|
||||
INSERT INTO Orders (CustomerID, OrderDate, TotalPrice) VALUES
|
||||
(1, '2024-01-15', 0),
|
||||
(2, '2024-02-01', 0),
|
||||
(3, '2024-03-05', 0),
|
||||
(4, '2024-04-10', 0),
|
||||
(5, '2024-05-15', 0),
|
||||
(6, '2024-06-20', 0),
|
||||
(7, '2024-07-25', 0),
|
||||
(8, '2024-08-30', 0),
|
||||
(9, '2024-09-05', 0),
|
||||
(10, '2024-10-10', 0),
|
||||
(1, '2024-11-15', 0),
|
||||
(2, '2024-12-01', 0),
|
||||
(3, '2024-01-05', 0),
|
||||
(4, '2024-02-10', 0),
|
||||
(5, '2024-03-15', 0),
|
||||
(6, '2024-04-20', 0),
|
||||
(7, '2024-05-25', 0),
|
||||
(8, '2024-06-30', 0),
|
||||
(9, '2024-07-05', 0),
|
||||
(10, '2024-08-10', 0);
|
||||
|
||||
INSERT INTO OrderDetails (OrderID, ProductID, UnitPrice, Quantity, TotalPrice) VALUES
|
||||
(1, 1, (SELECT Price FROM Products WHERE ProductID = 1), 2, (SELECT Price * 2 FROM Products WHERE ProductID = 1)),
|
||||
(1, 2, (SELECT Price FROM Products WHERE ProductID = 2), 1, (SELECT Price FROM Products WHERE ProductID = 2)),
|
||||
(2, 3, (SELECT Price FROM Products WHERE ProductID = 3), 3, (SELECT Price * 3 FROM Products WHERE ProductID = 3)),
|
||||
(2, 4, (SELECT Price FROM Products WHERE ProductID = 4), 1, (SELECT Price FROM Products WHERE ProductID = 4)),
|
||||
(3, 5, (SELECT Price FROM Products WHERE ProductID = 5), 1, (SELECT Price FROM Products WHERE ProductID = 5)),
|
||||
(3, 6, (SELECT Price FROM Products WHERE ProductID = 6), 2, (SELECT Price * 2 FROM Products WHERE ProductID = 6)),
|
||||
(4, 7, (SELECT Price FROM Products WHERE ProductID = 7), 5, (SELECT Price * 5 FROM Products WHERE ProductID = 7)),
|
||||
(5, 8, (SELECT Price FROM Products WHERE ProductID = 8), 3, (SELECT Price * 3 FROM Products WHERE ProductID = 8)),
|
||||
(5, 9, (SELECT Price FROM Products WHERE ProductID = 9), 2, (SELECT Price * 2 FROM Products WHERE ProductID = 9)),
|
||||
(6, 10, (SELECT Price FROM Products WHERE ProductID = 10), 4, (SELECT Price * 4 FROM Products WHERE ProductID = 10)),
|
||||
(7, 2, (SELECT Price FROM Products WHERE ProductID = 2), 4, (SELECT Price * 4 FROM Products WHERE ProductID = 2)),
|
||||
(7, 8, (SELECT Price FROM Products WHERE ProductID = 8), 3, (SELECT Price * 3 FROM Products WHERE ProductID = 8)),
|
||||
(8, 1, (SELECT Price FROM Products WHERE ProductID = 1), 1, (SELECT Price FROM Products WHERE ProductID = 1)),
|
||||
(8, 9, (SELECT Price FROM Products WHERE ProductID = 9), 2, (SELECT Price * 2 FROM Products WHERE ProductID = 9)),
|
||||
(8, 10, (SELECT Price FROM Products WHERE ProductID = 10), 5, (SELECT Price * 5 FROM Products WHERE ProductID = 10)),
|
||||
(9, 3, (SELECT Price FROM Products WHERE ProductID = 3), 5, (SELECT Price * 5 FROM Products WHERE ProductID = 3)),
|
||||
(9, 6, (SELECT Price FROM Products WHERE ProductID = 6), 1, (SELECT Price FROM Products WHERE ProductID = 6)),
|
||||
(10, 4, (SELECT Price FROM Products WHERE ProductID = 4), 2, (SELECT Price * 2 FROM Products WHERE ProductID = 4)),
|
||||
(10, 7, (SELECT Price FROM Products WHERE ProductID = 7), 3, (SELECT Price * 3 FROM Products WHERE ProductID = 7)),
|
||||
(11, 5, (SELECT Price FROM Products WHERE ProductID = 5), 1, (SELECT Price FROM Products WHERE ProductID = 5)),
|
||||
(11, 10, (SELECT Price FROM Products WHERE ProductID = 10), 4, (SELECT Price * 4 FROM Products WHERE ProductID = 10)),
|
||||
(12, 1, (SELECT Price FROM Products WHERE ProductID = 1), 3, (SELECT Price * 3 FROM Products WHERE ProductID = 1)),
|
||||
(12, 8, (SELECT Price FROM Products WHERE ProductID = 8), 2, (SELECT Price * 2 FROM Products WHERE ProductID = 8)),
|
||||
(13, 2, (SELECT Price FROM Products WHERE ProductID = 2), 1, (SELECT Price FROM Products WHERE ProductID = 2)),
|
||||
(13, 9, (SELECT Price FROM Products WHERE ProductID = 9), 3, (SELECT Price * 3 FROM Products WHERE ProductID = 9)),
|
||||
(14, 3, (SELECT Price FROM Products WHERE ProductID = 3), 4, (SELECT Price * 4 FROM Products WHERE ProductID = 3)),
|
||||
(14, 6, (SELECT Price FROM Products WHERE ProductID = 6), 2, (SELECT Price * 2 FROM Products WHERE ProductID = 6)),
|
||||
(15, 4, (SELECT Price FROM Products WHERE ProductID = 4), 5, (SELECT Price * 5 FROM Products WHERE ProductID = 4)),
|
||||
(15, 7, (SELECT Price FROM Products WHERE ProductID = 7), 1, (SELECT Price FROM Products WHERE ProductID = 7)),
|
||||
(16, 5, (SELECT Price FROM Products WHERE ProductID = 5), 2, (SELECT Price * 2 FROM Products WHERE ProductID = 5)),
|
||||
(16, 10, (SELECT Price FROM Products WHERE ProductID = 10), 3, (SELECT Price * 3 FROM Products WHERE ProductID = 10)),
|
||||
(17, 1, (SELECT Price FROM Products WHERE ProductID = 1), 4, (SELECT Price * 4 FROM Products WHERE ProductID = 1)),
|
||||
(17, 8, (SELECT Price FROM Products WHERE ProductID = 8), 1, (SELECT Price FROM Products WHERE ProductID = 8)),
|
||||
(18, 2, (SELECT Price FROM Products WHERE ProductID = 2), 5, (SELECT Price * 5 FROM Products WHERE ProductID = 2)),
|
||||
(18, 9, (SELECT Price FROM Products WHERE ProductID = 9), 2, (SELECT Price * 2 FROM Products WHERE ProductID = 9)),
|
||||
(19, 3, (SELECT Price FROM Products WHERE ProductID = 3), 3, (SELECT Price * 3 FROM Products WHERE ProductID = 3)),
|
||||
(19, 6, (SELECT Price FROM Products WHERE ProductID = 6), 4, (SELECT Price * 4 FROM Products WHERE ProductID = 6)),
|
||||
(20, 4, (SELECT Price FROM Products WHERE ProductID = 4), 1, (SELECT Price FROM Products WHERE ProductID = 4)),
|
||||
(20, 7, (SELECT Price FROM Products WHERE ProductID = 7), 5, (SELECT Price * 5 FROM Products WHERE ProductID = 7));
|
||||
|
||||
-- Update Orders Table's TotalPrice
|
||||
UPDATE Orders o
|
||||
JOIN (
|
||||
SELECT OrderID, SUM(TotalPrice) as order_total
|
||||
FROM OrderDetails
|
||||
GROUP BY OrderID
|
||||
) od ON o.OrderID = od.OrderID
|
||||
SET o.TotalPrice = od.order_total;
|
||||
|
||||
COMMIT;
|
||||
|
||||
```
|
||||
### Configure Knowledge Base
|
||||
|
||||
For RAGFlow’s RAG-based Text2SQL, the following knowledge bases are typically required:
|
||||
|
||||
- **DDL**: Database table creation statements.
|
||||
- **DB_Description**: Detailed descriptions of tables and columns.
|
||||
- **Q->SQL**: Natural language query descriptions along with corresponding SQL query examples (Question-Answer pairs).
|
||||
|
||||
However, in specialized query scenarios, user queries might include abbreviations or synonyms for domain-specific terms. If a user references a synonym for a domain-specific term, the system may fail to generate the correct SQL query. Therefore, it is advisable to incorporate a thesaurus for synonyms to assist the Agent in generating more accurate SQL queries.
|
||||
|
||||
- **TextSQL_Thesaurus**: A thesaurus covering domain-specific terms and their synonyms.
|
||||
|
||||
#### Configure DDL Knowledge Base
|
||||
|
||||
1. The content of the DDL text is as follows:
|
||||
```sql
|
||||
CREATE TABLE Customers (
|
||||
CustomerID int NOT NULL AUTO_INCREMENT,
|
||||
UserName varchar(50) COLLATE utf8mb4_unicode_ci DEFAULT NULL,
|
||||
Email varchar(100) COLLATE utf8mb4_unicode_ci DEFAULT NULL,
|
||||
PhoneNumber varchar(20) COLLATE utf8mb4_unicode_ci DEFAULT NULL,
|
||||
PRIMARY KEY (CustomerID)
|
||||
) ENGINE=InnoDB AUTO_INCREMENT=11 DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_unicode_ci;
|
||||
|
||||
CREATE TABLE Products (
|
||||
ProductID int NOT NULL AUTO_INCREMENT,
|
||||
ProductName varchar(100) COLLATE utf8mb4_unicode_ci DEFAULT NULL,
|
||||
Description text COLLATE utf8mb4_unicode_ci,
|
||||
Price decimal(10,2) DEFAULT NULL,
|
||||
StockQuantity int DEFAULT NULL,
|
||||
PRIMARY KEY (ProductID)
|
||||
) ENGINE=InnoDB AUTO_INCREMENT=11 DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_unicode_ci;
|
||||
|
||||
CREATE TABLE Orders (
|
||||
OrderID int NOT NULL AUTO_INCREMENT,
|
||||
CustomerID int DEFAULT NULL,
|
||||
OrderDate date DEFAULT NULL,
|
||||
TotalPrice decimal(10,2) DEFAULT NULL,
|
||||
PRIMARY KEY (OrderID),
|
||||
KEY CustomerID (CustomerID)
|
||||
) ENGINE=InnoDB AUTO_INCREMENT=21 DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_unicode_ci;
|
||||
|
||||
CREATE TABLE OrderDetails (
|
||||
OrderDetailID int NOT NULL AUTO_INCREMENT,
|
||||
OrderID int DEFAULT NULL,
|
||||
ProductID int DEFAULT NULL,
|
||||
UnitPrice decimal(10,2) DEFAULT NULL,
|
||||
Quantity int DEFAULT NULL,
|
||||
TotalPrice decimal(10,2) DEFAULT NULL,
|
||||
PRIMARY KEY (OrderDetailID),
|
||||
KEY OrderID (OrderID),
|
||||
KEY ProductID (ProductID)
|
||||
) ENGINE=InnoDB AUTO_INCREMENT=40 DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_unicode_ci;
|
||||
```
|
||||
2. Set the chunk data for the DLL knowledge base
|
||||
|
||||
|
||||
#### Configure DB_Description Knowledge Base
|
||||
|
||||
1. the content of the DB_Description text is as follows:
|
||||
2.
|
||||
```markdown
|
||||
### Customers (Customer Information Table)
|
||||
The Customers table records detailed information about different customers in the online store. Here is the meaning of each field within this table:
|
||||
- CustomerID: A unique identifier for a customer, auto-incremented.
|
||||
- UserName: The name used by the customer for logging into the online store or displayed on the site.
|
||||
- Email: The email address of the customer, which can be used for account verification, password recovery, and order updates.
|
||||
- PhoneNumber: The phone number of the customer, useful for contact purposes such as delivery notifications or customer service.
|
||||
|
||||
### Products (Product Information Table)
|
||||
The Products table contains information about the products offered by the online store. Each field within this table represents:
|
||||
- ProductID: A unique identifier for a product, auto-incremented.
|
||||
- ProductName: The name of the product, such as laptop, smartphone, nounch, etc.
|
||||
- Description: Detailed information about the product.
|
||||
- Price: The selling price of the product, stored as a decimal value to accommodate currency formatting.
|
||||
- StockQuantity: The quantity of the product available in stock.
|
||||
|
||||
### Orders (Order Information Table)
|
||||
The Orders table tracks orders placed by customers. This table includes fields that denote:
|
||||
- OrderID: A unique identifier for an order, auto-incremented.
|
||||
- CustomerID: A foreign key that references the CustomerID in the Customers table, indicating which customer placed the order.
|
||||
- OrderDate: The date when the order was placed.
|
||||
- TotalPrice: The total price of all items in the order, calculated at the time of purchase.
|
||||
|
||||
### OrderDetails (Order Details Table)
|
||||
The OrderDetails table provides detailed information about each item in an order. Fields within this table include:
|
||||
- OrderDetailID: A unique identifier for each line item in an order, auto-incremented.
|
||||
- OrderID: A foreign key that references the OrderID in the Orders table, linking the detail to a specific order.
|
||||
- ProductID: A foreign key that references the ProductID in the Products table, specifying which product was ordered.
|
||||
- UnitPrice: The price per unit of the product at the time of order.
|
||||
- Quantity: The number of units of the product ordered.
|
||||
- TotalPrice: The total price for this particular item in the order, calculated as UnitPrice * Quantity.
|
||||
```
|
||||
|
||||
2. set the chunk data for the DB_Description knowledge base
|
||||
|
||||
#### Configure Q->SQL Knowledge Base
|
||||
1. Q->SQL Excel Document
|
||||
[QA.xlsx](https://github.com/user-attachments/files/18258416/QA.xlsx)
|
||||
2. Upload the Q->SQL Excel document to the Q->SQL knowledge base and set the chunk data as follows via parsing:
|
||||
|
||||
#### Configure TextSQL_Thesaurus Knowledge Base
|
||||
1. the content of the TextSQL_Thesaurus text is as follows:
|
||||
```txt
|
||||
###
|
||||
Standard noun: StockQuantity
|
||||
Synonyms: stock,stockpile,inventory
|
||||
###
|
||||
Standard noun: UserName
|
||||
Synonyms: user name, user's name
|
||||
###
|
||||
Standard noun: Quantity
|
||||
Synonyms: amount,number
|
||||
###
|
||||
Standard noun: Smartphone
|
||||
Synonyms: phone, mobile phone, smart phone, mobilephone
|
||||
###
|
||||
Standard noun: ProductName
|
||||
Synonyms: product name, product's name
|
||||
###
|
||||
Standard noun: tablet
|
||||
Synonyms: pad,Pad
|
||||
###
|
||||
Standard noun: laptop
|
||||
Synonyms: laptop computer,laptop pc
|
||||
```
|
||||
2. set the chunk data for the TextSQL_Thesaurus knowledge base
|
||||
|
||||
|
||||
### Build the Agent
|
||||
1. Create an Agent using the Text2SQL Agent template.
|
||||
2. Enter the configuration page of the Agent to start the setup process.
|
||||
3. Create a Retrieval node and name it Thesaurus; create an ExeSQL node.
|
||||
4. Configure the Q->SQL, DDL, DB_Description, and TextSQL_Thesaurus knowledge bases. Please refer to the following:
|
||||
|
||||
5. Configure the Generate node, named LLM's prompt:
|
||||
- Add this content to the prompt provided by the template to provide the thesaurus content to the LLM:
|
||||
```plaintext
|
||||
## You may use the following Thesaurus statements. For example, what I ask is from Synonyms, you must use Standard noun to generate SQL. Use responses to past questions also to guide you: {sql_thesaurus}.
|
||||
```
|
||||
- Ensure the mapping between keys and component IDs is configured correctly.
|
||||
- The configuration result should look like this:
|
||||
|
||||
6. Configure the ExecSQL node, filling in the configuration information for the MySQL database.
|
||||
|
||||
7. Set an opener in the Begin component like:
|
||||
```plaintext
|
||||
Hi! I'm your electronic products online store business data analysis assistant. What can I do for you?
|
||||
|
||||
### Run and Test the Agent
|
||||
1. click the Run button to start the agent.
|
||||
2. input the question:
|
||||
```
|
||||
Help me summarize stock quantities for each product
|
||||
```
|
||||
3. click the send button to send the question to the agent.
|
||||
4. The agent will respond with the following:
|
||||
|
||||
|
||||
|
||||
### Debug the Agent
|
||||
|
||||
Since version 0.15.0, ragflow has introduced step-by-step execution for Agent components/tools, providing a robust mechanism for debugging and testing. Let's explore how to perform a step run.
|
||||
|
||||
1. To enter Test Run mode, you can either click the triangle icon located above the component or access the component's detail page by clicking on the component itself. Once there, select the Test Run button in the upper right corner of the component details.
|
||||
|
||||
|
||||
|
||||
2. Enter a question that does not exist in the Q->SQL knowledge base but is similar in nature.
|
||||
Click the Run button to receive the component's output.
|
||||
|
||||
```
|
||||
Find all customers who has bought a mobile phone
|
||||
```
|
||||
|
||||
3. As the image shows, no matching information was retrieved from the Q->SQL knowledge base, yet a similar question exists within the database. Adjust the Rerank model, "Similarity threshold," or "Keyword similarity weight" accordingly to return relevant content.
|
||||
|
||||
|
||||
|
||||
4. Observe the inputs and outputs of the LLM node and ExeSQL node.
|
||||
|
||||
|
||||
5. The agent now produces the correct SQL query result.
|
||||
|
||||
6. For a query about "mobile phone," the agent successfully generates the appropriate SQL query using "Smartphone." This showcases how the thesaurus guides the LLM in generating accurate SQL queries.
|
||||
|
||||
With this, you maybe appreciate the capabilities of Step Run. It undoubtedly assists in constructing more effective agents.
|
||||
|
||||
## Troubleshooting
|
||||
### Total: 0 No record in the database!
|
||||
1. Confirm if the sql is correct. If so, check the connection information of the database.
|
||||
2. If the connection information is correct, maybe there is actually no data matching your query in the database.
|
||||
|
||||
## Considerations
|
||||
|
||||
In real production scenarios within vertical domains, several considerations are essential for effective Text2SQL implementation:
|
||||
|
||||
1. **Handling DDL and DB_Description**: Dealing with Data Definition Language (DDL) statements and database descriptions requires substantial debugging experience. It is crucial to discern which information is vital and which may be redundant, depending on the true business context. This includes determining the relevance of table attributes such as primary keys, foreign keys, indexes, and so forth.
|
||||
|
||||
2. **Maintaining Quality QA Data**: Ensuring a high standard for question-and-answer data significantly aids the LLM in generating more accurate SQL queries.
|
||||
|
||||
3. **Managing Domain-Specific Synonyms**: Professional domain synonyms can greatly impact the generation of SQL query conditions. Therefore, maintaining an extensive and up-to-date synonym library is critical to mitigate this challenge.
|
||||
|
||||
4. **Facilitating User Feedback**: Implementing a feedback mechanism within the Agent allows users to provide correct SQL queries. Administrators can then use this feedback to automatically generate corresponding QA data, reducing the need for manual maintenance.
|
||||
|
||||
In summary, achieving high-quality output from Text2SQL remains contingent upon high-quality input. Constructing robust question-and-answer datasets is at the core of optimizing RAGFlow's Text2SQL capabilities.
|
||||
|
|
@ -1,8 +0,0 @@
|
|||
{
|
||||
"label": "Chat",
|
||||
"position": 1,
|
||||
"link": {
|
||||
"type": "generated-index",
|
||||
"description": "Chat-specific guides."
|
||||
}
|
||||
}
|
||||
|
|
@ -1,41 +0,0 @@
|
|||
---
|
||||
sidebar_position: 2
|
||||
slug: /accelerate_question_answering
|
||||
---
|
||||
|
||||
# Accelerate answering
|
||||
import APITable from '@site/src/components/APITable';
|
||||
|
||||
A checklist to speed up question answering.
|
||||
|
||||
---
|
||||
|
||||
Please note that some of your settings may consume a significant amount of time. If you often find that your question answering is time-consuming, here is a checklist to consider:
|
||||
|
||||
- In the **Prompt Engine** tab of your **Chat Configuration** dialogue, disabling **Multi-turn optimization** will reduce the time required to get an answer from the LLM.
|
||||
- In the **Prompt Engine** tab of your **Chat Configuration** dialogue, leaving the **Rerank model** field empty will significantly decrease retrieval time.
|
||||
- In the **Assistant Setting** tab of your **Chat Configuration** dialogue, disabling **Keyword analysis** will reduce the time to receive an answer from the LLM.
|
||||
- When chatting with your chat assistant, click the light bulb icon above the *current* dialogue and scroll down the popup window to view the time taken for each task:
|
||||
|
||||
|
||||
|
||||
```mdx-code-block
|
||||
<APITable>
|
||||
```
|
||||
|
||||
| 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. |
|
||||
| Bind embedding | Time to initialize an embedding model instance. |
|
||||
| Bind LLM | Time to initialize an LLM instance. |
|
||||
| Tune question | Time to optimize the user query using the context of the mult-turn conversation. |
|
||||
| Bind reranker | Time to initialize an reranker model instance for chunk retrieval. |
|
||||
| Generate keywords | Time to extract keywords from the user query. |
|
||||
| Retrieval | Time to retrieve the chunks. |
|
||||
| Generate answer | Time to generate the answer. |
|
||||
|
||||
```mdx-code-block
|
||||
</APITable>
|
||||
```
|
||||
|
|
@ -1,28 +0,0 @@
|
|||
---
|
||||
sidebar_position: 3
|
||||
slug: /implement_deep_research
|
||||
---
|
||||
|
||||
# Implement deep research
|
||||
|
||||
Implements deep research for agentic reasoning.
|
||||
|
||||
---
|
||||
|
||||
From v0.17.0 onward, RAGFlow supports integrating agentic reasoning in an AI chat. The following diagram illustrates the workflow of RAGFlow's deep research:
|
||||
|
||||
|
||||
|
||||
To activate this feature:
|
||||
|
||||
1. Enable the **Reasoning** toggle under the **Prompt Engine** tab of your chat assistant dialogue.
|
||||
|
||||
|
||||
|
||||
2. Enter the correct Tavily API key under the **Assistant Setting** tab of your chat assistant dialogue to leverage Tavily-based web search
|
||||
|
||||
|
||||
|
||||
*The following is a screenshot of a conversation that integrates Deep Research:*
|
||||
|
||||
|
||||
|
|
@ -1,94 +0,0 @@
|
|||
---
|
||||
sidebar_position: 1
|
||||
slug: /start_chat
|
||||
---
|
||||
|
||||
# Start AI chat
|
||||
|
||||
Initiate an AI-powered chat with a configured chat assistant.
|
||||
|
||||
---
|
||||
|
||||
Knowledge base, hallucination-free chat, and file management are the three pillars of RAGFlow. Chats in RAGFlow are based on a particular knowledge base or multiple knowledge bases. Once you have created your knowledge base, finished file parsing, and [run a retrieval test](../dataset/run_retrieval_test.md), you can go ahead and start an AI conversation.
|
||||
|
||||
## Start an AI chat
|
||||
|
||||
You start an AI conversation by creating an assistant.
|
||||
|
||||
1. Click the **Chat** tab in the middle top of the page **>** **Create an assistant** to show the **Chat Configuration** dialogue *of your next dialogue*.
|
||||
|
||||
> RAGFlow offers you the flexibility of choosing a different chat model for each dialogue, while allowing you to set the default models in **System Model Settings**.
|
||||
|
||||
2. Update **Assistant Setting**:
|
||||
|
||||
- **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 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.
|
||||
|
||||
3. Update **Prompt Engine**:
|
||||
|
||||
- In **System**, you fill in the prompts for your LLM, you can also leave the default prompt as-is for the beginning.
|
||||
- **Similarity threshold** sets the similarity "bar" for each chunk of text. The default is 0.2. Text chunks with lower similarity scores are filtered out of the final response.
|
||||
- **Keyword similarity weight** is set to 0.7 by default. RAGFlow uses a hybrid score system to evaluate the relevance of different text chunks. This value sets the weight assigned to the keyword similarity component in the hybrid score.
|
||||
- If **Rerank model** is left empty, the hybrid score system uses keyword similarity and vector similarity, and the default weight assigned to the vector similarity component is 1-0.7=0.3.
|
||||
- If **Rerank model** is selected, the hybrid score system uses keyword similarity and reranker score, and the default weight assigned to the reranker score is 1-0.7=0.3.
|
||||
- **Top N** determines the *maximum* number of chunks to feed to the LLM. In other words, even if more chunks are retrieved, only the top N chunks are provided as input.
|
||||
- **Multi-turn optimization** enhances user queries using existing context in a multi-round conversation. It is enabled by default. When enabled, it will consume additional LLM tokens and significantly increase the time to generate answers.
|
||||
- **Rerank model** sets the reranker model to use. It is left empty by default.
|
||||
- If **Rerank model** is left empty, the hybrid score system uses keyword similarity and vector similarity, and the default weight assigned to the vector similarity component is 1-0.7=0.3.
|
||||
- If **Rerank model** is selected, the hybrid score system uses keyword similarity and reranker score, and the default weight assigned to the reranker score is 1-0.7=0.3.
|
||||
- **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*.
|
||||
|
||||
4. Update **Model Setting**:
|
||||
|
||||
- In **Model**: you select the chat model. Though you have selected the default chat model in **System Model Settings**, RAGFlow allows you to choose an alternative chat model for your dialogue.
|
||||
- **Preset configurations** refers to the level that the LLM improvises. From **Improvise**, **Precise**, to **Balance**, each preset configuration corresponds to a unique combination of **Temperature**, **Top P**, **Presence penalty**, and **Frequency penalty**.
|
||||
- **Temperature**: Level of the prediction randomness of the LLM. The higher the value, the more creative the LLM is.
|
||||
- **Top P** is also known as "nucleus sampling". See [here](https://en.wikipedia.org/wiki/Top-p_sampling) for more information.
|
||||
- **Max Tokens**: The maximum length of the LLM's responses. Note that the responses may be curtailed if this value is set too low.
|
||||
|
||||
5. Now, let's start the show:
|
||||
|
||||
|
||||
|
||||
:::tip NOTE
|
||||
|
||||
1. Click the light bulb icon above the answer to view the expanded system prompt:
|
||||
|
||||
|
||||
|
||||
*The light bulb icon is available only for the current dialogue.*
|
||||
|
||||
2. Scroll down the expanded prompt to view the time consumed for each task:
|
||||
|
||||
|
||||
:::
|
||||
|
||||
## Update settings of an existing chat assistant
|
||||
|
||||
Hover over an intended chat assistant **>** **Edit** to show the chat configuration dialogue:
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
## Integrate chat capabilities into your application or webpage
|
||||
|
||||
RAGFlow offers HTTP and Python APIs for you to integrate RAGFlow's capabilities into your applications. Read the following documents for more information:
|
||||
|
||||
- [Acquire a RAGFlow API key](../../develop/acquire_ragflow_api_key.md)
|
||||
- [HTTP API reference](../../references/http_api_reference.md)
|
||||
- [Python API reference](../../references/python_api_reference.md)
|
||||
|
||||
You can use iframe to embed the created chat assistant into a third-party webpage:
|
||||
|
||||
1. Before proceeding, you must [acquire an API key](../models/llm_api_key_setup.md); otherwise, an error message would appear.
|
||||
2. Hover over an intended chat assistant **>** **Edit** to show the **iframe** window:
|
||||
|
||||
|
||||
|
||||
3. Copy the iframe and embed it into a specific location on your webpage.
|
||||
|
|
@ -1,8 +0,0 @@
|
|||
{
|
||||
"label": "Datasets",
|
||||
"position": 0,
|
||||
"link": {
|
||||
"type": "generated-index",
|
||||
"description": "Guides on configuring a knowledge base."
|
||||
}
|
||||
}
|
||||
|
|
@ -1,19 +0,0 @@
|
|||
---
|
||||
sidebar_position: 9
|
||||
slug: /accelerate_doc_indexing
|
||||
---
|
||||
|
||||
# Accelerate indexing
|
||||
import APITable from '@site/src/components/APITable';
|
||||
|
||||
A checklist to speed up document parsing and indexing.
|
||||
|
||||
---
|
||||
|
||||
Please note that some of your settings may consume a significant amount of time. If you often find that document parsing is time-consuming, here is a checklist to consider:
|
||||
|
||||
- Use GPU to reduce embedding time.
|
||||
- On the configuration page of your knowledge base, switch off **Use RAPTOR to enhance retrieval**.
|
||||
- Extracting knowledge graph (GraphRAG) is time-consuming.
|
||||
- Disable **Auto-keyword** and **Auto-question** on the configuration page of yor knowledge base, as both depend on the LLM.
|
||||
- **v0.17.0:** If your document is plain text PDF and does not require GPU-intensive processes like OCR (Optical Character Recognition), TSR (Table Structure Recognition), or DLA (Document Layout Analysis), you can choose **Naive** over **DeepDoc** or other time-consuming large model options in the **Document parser** dropdown. This will substantially reduce document parsing time.
|
||||
|
|
@ -1,144 +0,0 @@
|
|||
---
|
||||
sidebar_position: 0
|
||||
slug: /configure_knowledge_base
|
||||
---
|
||||
|
||||
# Configure knowledge base
|
||||
|
||||
Knowledge base, hallucination-free chat, and file management are the three pillars of RAGFlow. RAGFlow's AI chats are based on knowledge bases. Each of RAGFlow's knowledge bases serves as a knowledge source, *parsing* files uploaded from your local machine and file references generated in **File Management** into the real 'knowledge' for future AI chats. This guide demonstrates some basic usages of the knowledge base feature, covering the following topics:
|
||||
|
||||
- Create a knowledge base
|
||||
- Configure a knowledge base
|
||||
- Search for a knowledge base
|
||||
- Delete a knowledge base
|
||||
|
||||
## Create knowledge base
|
||||
|
||||
With multiple knowledge bases, you can build more flexible, diversified question answering. To create your first knowledge base:
|
||||
|
||||
|
||||
|
||||
_Each time a knowledge base is created, a folder with the same name is generated in the **root/.knowledgebase** directory._
|
||||
|
||||
## Configure knowledge base
|
||||
|
||||
The following screenshot shows the configuration page of a knowledge base. A proper configuration of your knowledge base is crucial for future AI chats. For example, choosing the wrong embedding model or chunk method would cause unexpected semantic loss or mismatched answers in chats.
|
||||
|
||||
|
||||
|
||||
This section covers the following topics:
|
||||
|
||||
- Select chunk method
|
||||
- Select embedding model
|
||||
- Upload file
|
||||
- Parse file
|
||||
- Intervene with file parsing results
|
||||
- Run retrieval testing
|
||||
|
||||
### Select chunk method
|
||||
|
||||
RAGFlow offers multiple chunking template to facilitate chunking files of different layouts and ensure semantic integrity. In **Chunk method**, you can choose the default template that suits the layouts and formats of your files. The following table shows the descriptions and the compatible file formats of each supported chunk template:
|
||||
|
||||
| **Template** | Description | File format |
|
||||
|--------------|-----------------------------------------------------------------------|------------------------------------------------------------------------------|
|
||||
| General | Files are consecutively chunked based on a preset chunk token number. | DOCX, XLSX, XLS (Excel97~2003), PPT, PDF, TXT, JPEG, JPG, PNG, TIF, GIF, CSV |
|
||||
| Q&A | | XLSX, XLS (Excel97~2003), CSV/TXT |
|
||||
| Manual | | PDF |
|
||||
| Table | | XLSX, XLS (Excel97~2003), CSV/TXT |
|
||||
| Paper | | PDF |
|
||||
| Book | | DOCX, PDF, TXT |
|
||||
| Laws | | DOCX, PDF, TXT |
|
||||
| Presentation | | PDF, PPTX |
|
||||
| Picture | | JPEG, JPG, PNG, TIF, GIF |
|
||||
| One | The entire document is chunked as one. | DOCX, XLSX, XLS (Excel97~2003), PDF, TXT |
|
||||
|
||||
You can also change a file's chunk method on the **Datasets** page.
|
||||
|
||||
|
||||
|
||||
### Select embedding model
|
||||
|
||||
An embedding model converts chunks into embeddings. It cannot be changed once the knowledge base has chunks. To switch to a different embedding model, you must delete all existing chunks in the knowledge base. The obvious reason is that we *must* ensure that files in a specific knowledge base are converted to embeddings using the *same* embedding model (ensure that they are compared in the same embedding space).
|
||||
|
||||
The following embedding models can be deployed locally:
|
||||
|
||||
- BAAI/bge-large-zh-v1.5
|
||||
- BAAI/bge-base-en-v1.5
|
||||
- BAAI/bge-large-en-v1.5
|
||||
- BAAI/bge-small-en-v1.5
|
||||
- BAAI/bge-small-zh-v1.5
|
||||
- jinaai/jina-embeddings-v2-base-en
|
||||
- jinaai/jina-embeddings-v2-small-en
|
||||
- nomic-ai/nomic-embed-text-v1.5
|
||||
- sentence-transformers/all-MiniLM-L6-v2
|
||||
- maidalun1020/bce-embedding-base_v1
|
||||
|
||||
### Upload file
|
||||
|
||||
- RAGFlow's **File Management** allows you to link a file to multiple knowledge bases, in which case each target knowledge base holds a reference to the file.
|
||||
- In **Knowledge Base**, you are also given the option of uploading a single file or a folder of files (bulk upload) from your local machine to a knowledge base, in which case the knowledge base holds file copies.
|
||||
|
||||
While uploading files directly to a knowledge base seems more convenient, we *highly* recommend uploading files to **File Management** and then linking them to the target knowledge bases. This way, you can avoid permanently deleting files uploaded to the knowledge base.
|
||||
|
||||
### Parse file
|
||||
|
||||
File parsing is a crucial topic in knowledge base configuration. The meaning of file parsing in RAGFlow is twofold: chunking files based on file layout and building embedding and full-text (keyword) indexes on these chunks. After having selected the chunk method and embedding model, you can start parsing a file:
|
||||
|
||||
|
||||
|
||||
- Click the play button next to **UNSTART** to start file parsing.
|
||||
- Click the red-cross icon and then refresh, if your file parsing stalls for a long time.
|
||||
- As shown above, RAGFlow allows you to use a different chunk method for a particular file, offering flexibility beyond the default method.
|
||||
- As shown above, RAGFlow allows you to enable or disable individual files, offering finer control over knowledge base-based AI chats.
|
||||
|
||||
### Intervene with file parsing results
|
||||
|
||||
RAGFlow features visibility and explainability, allowing you to view the chunking results and intervene where necessary. To do so:
|
||||
|
||||
1. Click on the file that completes file parsing to view the chunking results:
|
||||
|
||||
_You are taken to the **Chunk** page:_
|
||||
|
||||
|
||||
|
||||
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:
|
||||
|
||||
|
||||
|
||||
:::caution NOTE
|
||||
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:
|
||||
|
||||
_As you can tell from the following, RAGFlow responds with truthful citations._
|
||||
|
||||
|
||||
|
||||
### Run retrieval testing
|
||||
|
||||
RAGFlow uses multiple recall of both full-text search and vector search in its chats. Prior to setting up an AI chat, consider adjusting the following parameters to ensure that the intended information always turns up in answers:
|
||||
|
||||
- Similarity threshold: Chunks with similarities below the threshold will be filtered. By default, it is set to 0.2.
|
||||
- Vector similarity weight: The percentage by which vector similarity contributes to the overall score. By default, it is set to 0.3.
|
||||
|
||||
See [Run retrieval test](./run_retrieval_test.md) for details.
|
||||
|
||||
|
||||
|
||||
## Search for knowledge base
|
||||
|
||||
As of RAGFlow v0.17.2, the search feature is still in a rudimentary form, supporting only knowledge base search by name.
|
||||
|
||||
|
||||
|
||||
## Delete knowledge base
|
||||
|
||||
You are allowed to delete a knowledge base. Hover your mouse over the three dot of the intended knowledge base card and the **Delete** option appears. Once you delete a knowledge base, the associated folder under **root/.knowledge** directory is AUTOMATICALLY REMOVED. The consequence is:
|
||||
|
||||
- The files uploaded directly to the knowledge base are gone;
|
||||
- The file references, which you created from within **File Management**, are gone, but the associated files still exist in **File Management**.
|
||||
|
||||
|
||||
|
|
@ -1,84 +0,0 @@
|
|||
---
|
||||
sidebar_position: 2
|
||||
slug: /construct_knowledge_graph
|
||||
---
|
||||
|
||||
# Construct knowledge graph
|
||||
|
||||
Generate a knowledge graph for your knowledge base.
|
||||
|
||||
---
|
||||
|
||||
To enhance multi-hop question-answering, RAGFlow adds a knowledge graph construction step between data extraction and indexing, as illustrated below. This step creates additional chunks from existing ones generated by your specified chunk method.
|
||||
|
||||
|
||||
|
||||
From v0.16.0 onward, RAGFlow supports constructing a knowledge graph on a knowledge base, allowing you to construct a *unified* graph across multiple files within your knowledge base. When a newly uploaded file starts parsing, the generated graph will automatically update.
|
||||
|
||||
:::danger WARNING
|
||||
Constructing a knowledge graph requires significant memory, computational resources, and tokens.
|
||||
:::
|
||||
|
||||
## Scenarios
|
||||
|
||||
Knowledge graphs are especially useful for multi-hop question-answering involving *nested* logic. They outperform traditional extraction approaches when you are performing question answering on books or works with complex entities and relationships.
|
||||
|
||||
## Prerequisites
|
||||
|
||||
The system's default chat model is used to generate knowledge graph. Before proceeding, ensure that you have a chat model properly configured:
|
||||
|
||||
|
||||
|
||||
## Configurations
|
||||
|
||||
### Entity types (*Required*)
|
||||
|
||||
The types of the entities to extract from your knowledge base. The default types are: **organization**, **person**, **event**, and **category**. Add or remove types to suit your specific knowledge base.
|
||||
|
||||
### Method
|
||||
|
||||
The method to use to construct knowledge graph:
|
||||
|
||||
- **General**: Use prompts provided by [GraphRAG](https://github.com/microsoft/graphrag) to extract entities and relationships.
|
||||
- **Light**: (Default) Use prompts provided by [LightRAG](https://github.com/HKUDS/LightRAG) to extract entities and relationships. This option consumes fewer tokens, less memory, and fewer computational resources.
|
||||
|
||||
### Entity resolution
|
||||
|
||||
Whether to enable entity resolution. You can think of this as an entity deduplication switch. When enabled, the LLM will combine similar entities - e.g., '2025' and 'the year of 2025', or 'IT' and 'Information Technology' - to construct a more effective graph.
|
||||
|
||||
- (Default) Disable entity resolution.
|
||||
- Enable entity resolution. This option consumes more tokens.
|
||||
|
||||
### Community report generation
|
||||
|
||||
In a knowledge graph, a community is a cluster of entities linked by relationships. You can have the LLM generate an abstract for each community, known as a community report. See [here](https://www.microsoft.com/en-us/research/blog/graphrag-improving-global-search-via-dynamic-community-selection/) for more information. This indicates whether to generate community reports:
|
||||
|
||||
- Generate community reports. This option consumes more tokens.
|
||||
- (Default) Do not generate community reports.
|
||||
|
||||
## Procedure
|
||||
|
||||
1. On the **Configuration** page of your knowledge base, switch on **Extract knowledge graph** or adjust its settings as needed, and click **Save** to confirm your changes.
|
||||
|
||||
- *The default knowledge graph configurations for your knowledge base are now set and files uploaded from this point onward will automatically use these settings during parsing.*
|
||||
- *Files parsed before this update will retain their original knowledge graph settings.*
|
||||
|
||||
2. The knowledge graph of your knowledge base does *not* automatically update *until* a newly uploaded file is parsed.
|
||||
|
||||
_A **Knowledge graph** entry appears under **Configuration** once a knowledge graph is created._
|
||||
|
||||
3. Click **Knowledge graph** to view the details of the generated graph.
|
||||
|
||||
## Frequently asked questions
|
||||
|
||||
### Can I have different knowledge graph settings for different files in my knowledge base?
|
||||
|
||||
Yes, you can. Just one graph is generated per knowledge base. The smaller graphs of your files will be *combined* into one big, unified graph at the end of the graph extraction process.
|
||||
|
||||
### Does the knowledge graph automatically update when I remove a related file?
|
||||
|
||||
Nope. The knowledge graph does *not* automatically update *until* a newly uploaded graph is parsed.
|
||||
|
||||
### How to remove a generated knowledge graph?
|
||||
|
||||
To remove the generated knowledge graph, delete all related files in your knowledge base. Although the **Knowledge graph** entry will still be visible, the graph has actually been deleted.
|
||||
|
|
@ -1,86 +0,0 @@
|
|||
---
|
||||
sidebar_position: 10
|
||||
slug: /run_retrieval_test
|
||||
---
|
||||
|
||||
# Run retrieval test
|
||||
|
||||
Conduct a retrieval test on your knowledge base to check whether the intended chunks can be retrieved.
|
||||
|
||||
---
|
||||
|
||||
After your files are uploaded and parsed, it is recommended that you run a retrieval test before proceeding with the chat assistant configuration. Running a retrieval test is *not* an unnecessary or superfluous step at all! Just like fine-tuning a precision instrument, RAGFlow requires careful tuning to deliver optimal question answering performance. Your knowledge base settings, chat assistant configurations, and the specified large and small models can all significantly impact the final results. Running a retrieval test verifies whether the intended chunks can be recovered, allowing you to quickly identify areas for improvement or pinpoint any issue that needs addressing. For instance, when debugging your question answering system, if you know that the correct chunks can be retrieved, you can focus your efforts elsewhere. For example, in issue [#5627](https://github.com/infiniflow/ragflow/issues/5627), the problem was found to be due to the LLM's limitations.
|
||||
|
||||
During a retrieval test, chunks created from your specified chunk method are retrieved using a hybrid search. This search combines weighted keyword similarity with either weighted vector cosine similarity or a weighted reranking score, depending on your settings:
|
||||
|
||||
- If no rerank model is selected, weighted keyword similarity will be combined with weighted vector cosine similarity.
|
||||
- If a rerank model is selected, weighted keyword similarity will be combined with weighted vector reranking score.
|
||||
|
||||
In contrast, chunks created from [knowledge graph construction](./construct_knowledge_graph.md) are retrieved solely using vector cosine similarity.
|
||||
|
||||
## Prerequisites
|
||||
|
||||
- Your files are uploaded and successfully parsed before running a retrieval test.
|
||||
- A knowledge graph must be successfully built before enabling **Use knowledge graph**.
|
||||
|
||||
## Configurations
|
||||
|
||||
### Similarity threshold
|
||||
|
||||
This sets the bar for retrieving chunks: chunks with similarities below the threshold will be filtered out. By default, the threshold is set to 0.2.
|
||||
|
||||
### Keyword similarity weight
|
||||
|
||||
This sets the weight of keyword similarity in the combined similarity score, whether used with vector cosine similarity or a reranking score. By default, it is set to 0.7, making the weight of the other component 0.3 (1 - 0.7).
|
||||
|
||||
### Rerank model
|
||||
|
||||
- If left empty, RAGFlow will use a combination of weighted keyword similarity and weighted vector cosine similarity.
|
||||
- If a rerank model is selected, weighted keyword similarity will be combined with weighted vector reranking score.
|
||||
|
||||
:::danger IMPORTANT
|
||||
Using a rerank model will significantly increase the time to receive a response.
|
||||
:::
|
||||
|
||||
### Use knowledge graph
|
||||
|
||||
In a knowledge graph, an entity description, a relationship description, or a community report each exists as an independent chunk. This switch indicates whether to add these chunks to the retrieval.
|
||||
|
||||
The switch is disabled by default. When enabled, RAGFlow performs the following during a retrieval test:
|
||||
|
||||
1. Extract entities and entity types from your query using the LLM.
|
||||
2. Retrieve top N entities from the graph based on their PageRank values, using the extracted entity types.
|
||||
3. Find similar entities and their N-hop relationships from the graph using the embeddings of the extracted query entities.
|
||||
4. Retrieve similar relationships from the graph using the query embedding.
|
||||
5. Rank these retrieved entities and relationships by multiplying each one's PageRank value with its similarity score to the query, returning the top n as the final retrieval.
|
||||
6. Retrieve the report for the community involving the most entities in the final retrieval.
|
||||
*The retrieved entity descriptions, relationship descriptions, and the top 1 community report are sent to the LLM for content generation.*
|
||||
|
||||
:::danger IMPORTANT
|
||||
Using a knowledge graph in a retrieval test will significantly increase the time to receive a response.
|
||||
:::
|
||||
|
||||
### Test text
|
||||
|
||||
This field is where you put in your testing query.
|
||||
|
||||
## Procedure
|
||||
|
||||
1. Navigate to the **Retrieval testing** page of your knowledge base, enter your query in **Test text**, and click **Testing** to run the test.
|
||||
2. If the results are unsatisfactory, tune the options listed in the Configuration section and rerun the test.
|
||||
|
||||
*The following is a screenshot of a retrieval test conducted without using knowledge graph. It demonstrates a hybrid search combining weighted keyword similarity and weighted vector cosine similarity. The overall hybrid similarity score is 28.56, calculated as 25.17 (term similarity score) x 0.7 + 36.49 (vector similarity score) x 0.3:*
|
||||
|
||||
|
||||
*The following is a screenshot of a retrieval test conducted using a knowledge graph. It shows that only vector similarity is used for knowledge graph-generated chunks:*
|
||||
|
||||
|
||||
:::caution WARNING
|
||||
If you have adjusted the default settings, such as keyword similarity weight or similarity threshold, to achieve the optimal results, be aware that these changes will not be automatically saved. You must apply them to your chat assistant settings or the **Retrieval** agent component settings.
|
||||
:::
|
||||
|
||||
## Frequently asked questions
|
||||
|
||||
### Is an LLM used when the Use Knowledge Graph switch is enabled?
|
||||
|
||||
Yes, your LLM will be involved to analyze your query and extract the related entities and relationship from the knowledge graph. This also explains why additional tokens and time will be consumed.
|
||||
|
|
@ -1,22 +0,0 @@
|
|||
---
|
||||
sidebar_position: 1
|
||||
slug: /set_metada
|
||||
---
|
||||
|
||||
# Set metadata
|
||||
|
||||
Add metadata to an uploaded file
|
||||
|
||||
---
|
||||
|
||||
On the **Dataset** page of your knowledge base, you can add metadata to any uploaded file. This approach enables you to 'tag' additional information like URL, author, date, and more to an existing file or dataset. In an AI-powered chat, such information will be sent to the LLM with the retrieved chunks for content generation.
|
||||
|
||||
For example, if you have a dataset of HTML files and want the LLM to cite the source URL when responding to your query, add a `"url"` parameter to each file's metadata.
|
||||
|
||||
|
||||
|
||||
:::tip NOTE
|
||||
Ensure that your metadata is in JSON format; otherwise, your updates will not be applied.
|
||||
:::
|
||||
|
||||
|
||||
|
|
@ -1,103 +0,0 @@
|
|||
---
|
||||
sidebar_position: 6
|
||||
slug: /use_tag_sets
|
||||
---
|
||||
|
||||
# Use tag set
|
||||
|
||||
Use a tag set to tag chunks in your datasets.
|
||||
|
||||
---
|
||||
|
||||
Retrieval accuracy is the touchstone for a production-ready RAG framework. In addition to retrieval-enhancing approaches like auto-keyword, auto-question, and knowledge graph, RAGFlow introduces an auto-tagging feature to address semantic gaps. The auto-tagging feature automatically maps tags in the user-defined tag sets to relevant chunks within your knowledge base based on similarity with each chunk. This automation mechanism allows you to apply an additional "layer" of domain-specific knowledge to existing datasets, which is particularly useful when dealing with a large number of chunks.
|
||||
|
||||
To use this feature, ensure you have at least one properly configured tag set, specify the tag set(s) on the **Configuration** page of your knowledge base (dataset), and then re-parse your documents to initiate the auto-tag process. During this process, each chunk in your dataset is compared with every entry in the specified tag set(s), and tags are automatically applied based on similarity.
|
||||
|
||||
## Scenarios
|
||||
|
||||
Auto-tagging applies in situations where chunks are so similar to each other that the intended chunks cannot be distinguished from the rest. For example, when you have a few chunks about iPhone and a majority about iPhone case or iPhone accessaries, it becomes difficult to retrieve the iPhone-specific chunks without additional information.
|
||||
|
||||
## Create tag set
|
||||
|
||||
You can consider a tag set as a closed set, and the tags to attach to the chunks in your dataset (knowledge base) are *exclusively* from the specified tag set. You use a tag set to "inform" RAGFlow which chunks to tag and which tags to apply.
|
||||
|
||||
### Prepare a tag table file
|
||||
|
||||
A tag set can comprise one or multiple table files in XLSX, CSV, or TXT formats. Each table file in the tag set contains two columns, **Description** and **Tag**:
|
||||
|
||||
- The first column provides descriptions of the tags listed in the second column. These descriptions can be example chunks or example queries. Similarity will be calculated between each entry in this column and every chunk in your dataset.
|
||||
- The **Tag** column includes tags to pair with the description entries. Multiple tags should be separated by a comma (,).
|
||||
|
||||
:::tip NOTE
|
||||
As a rule of thumb, consider including the following entries in your tag table:
|
||||
|
||||
- Descriptions of intended chunks, along with their corresponding tags.
|
||||
- User queries that fail to retrieve the correct responses using other methods, ensuring their tags match the intended chunks in your dataset.
|
||||
:::
|
||||
|
||||
### Create a tag set
|
||||
|
||||
1. Click **+ Create knowledge base** to create a knowledge base.
|
||||
2. Navigate to the **Configuration** page of the created knowledge base and choose **Tag** as the default chunk method.
|
||||
3. Navigate to the **Dataset** page and upload and parse your table file in XLSX, CSV, or TXT formats.
|
||||
_A tag cloud appears under the **Tag view** section, indicating the tag set is created:_
|
||||
|
||||
4. Click the **Table** tab to view the tag frequency table:
|
||||
|
||||
|
||||
:::danger IMPORTANT
|
||||
A tag set is *not* involved in document indexing or retrieval. Do not specify a tag set when configuring your chat assistant or agent.
|
||||
:::
|
||||
|
||||
## Tag chunks
|
||||
|
||||
Once a tag set is created, you can apply it to your dataset:
|
||||
|
||||
1. Navigate to the **Configuration** page of your knowledge base (dataset).
|
||||
2. Select the tag set from the **Tag sets** dropdown and click **Save** to confirm.
|
||||
|
||||
:::tip NOTE
|
||||
If the tag set is missing from the dropdown, check that it has been created or configured correctly.
|
||||
:::
|
||||
|
||||
3. Re-parse your documents to start the auto-tagging process.
|
||||
_In an AI chat scenario using auto-tagged datasets, each query will be tagged using the corresponding tag set(s) and chunks with these tags will have a higher chance to be retrieved._
|
||||
|
||||
## Update tag set
|
||||
|
||||
Creating a tag set is *not* for once and for all. Oftentimes, you may find it necessary to update or delete existing tags or add new entries.
|
||||
|
||||
- You can update the existing tag set in the tag frequency table.
|
||||
- To add new entries, you can add and parse new table files in XLSX, CSV, or TXT formats.
|
||||
|
||||
### Update tag set in tag frequency table
|
||||
|
||||
1. Navigate to the **Configuration** page in your tag set.
|
||||
2. Click the **Table** tab under **Tag view** to view the tag frequncy table, where you can update tag names or delete tags.
|
||||
|
||||
:::danger IMPORTANT
|
||||
When a tag set is updated, you must re-parse the documents in your dataset so that their tags can be updated accordingly.
|
||||
:::
|
||||
|
||||
### Add new table files
|
||||
|
||||
1. Navigate to the **Configuration** page in your tag set.
|
||||
2. Navigate to the **Dataset** page and upload and parse your table file in XLSX, CSV, or TXT formats.
|
||||
|
||||
:::danger IMPORTANT
|
||||
If you add new table files to your tag set, it is at your own discretion whether to re-parse your documents in your datasets.
|
||||
:::
|
||||
|
||||
## Frequently asked questions
|
||||
|
||||
### Can I reference more than one tag set?
|
||||
|
||||
Yes, you can. Usually one tag set suffices. When using multiple tag sets, ensure they are independent of each other; otherwise, consider merging your tag sets.
|
||||
|
||||
### Difference between a tag set and a standard knowledge base?
|
||||
|
||||
A standard knowledge base is a dataset. It will be searched by RAGFlow's document engine and the retrieved chunks will be fed to the LLM. In contrast, a tag set is used solely to attach tags to chunks within your dataset. It does not directly participate in the retrieval process, and you should not choose a tag set when selecting datasets for your chat assistant or agent.
|
||||
|
||||
### Difference between auto-tag and auto-keyword?
|
||||
|
||||
Both features enhance retrieval in RAGFlow. The auto-keyword feature relies on the LLM and consumes a significant number of tokens, whereas the auto-tag feature is based on vector similarity and predefined tag set(s). You can view the keywords applied in the auto-keyword feature as an open set, as they are generated by the LLM. In contrast, a tag set can be considered a user-defined close set, requiring upload tag set(s) in specified formats before use.
|
||||
|
|
@ -1,88 +0,0 @@
|
|||
---
|
||||
sidebar_position: 5
|
||||
slug: /manage_files
|
||||
---
|
||||
|
||||
# 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:
|
||||
|
||||
|
||||
|
||||
> Each knowledge base in RAGFlow has a corresponding folder under the **root/.knowledgebase** directory. You are not allowed to create a subfolder within it.
|
||||
|
||||
## Upload file
|
||||
|
||||
RAGFlow's file management supports file uploads from your local machine, allowing both individual and bulk uploads:
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
## Preview file
|
||||
|
||||
RAGFlow's file management supports previewing files in the following formats:
|
||||
|
||||
- Documents (PDF, DOCS)
|
||||
- Tables (XLSX)
|
||||
- Pictures (JPEG, JPG, PNG, TIF, GIF)
|
||||
|
||||
|
||||
|
||||
## Link file to knowledge bases
|
||||
|
||||
RAGFlow's file management allows you to *link* an uploaded file to multiple knowledge bases, creating a file reference in each target knowledge base. Therefore, deleting a file in your file management will AUTOMATICALLY REMOVE all related file references across the knowledge bases.
|
||||
|
||||
|
||||
|
||||
You can link your file to one knowledge base or multiple knowledge bases at one time:
|
||||
|
||||
|
||||
|
||||
## Move file to a specific folder
|
||||
|
||||
|
||||
|
||||
## Search files or folders
|
||||
|
||||
**File Management** only supports file name and folder name filtering in the current directory (files or folders in the child directory will not be retrieved).
|
||||
|
||||
|
||||
|
||||
## Rename file or folder
|
||||
|
||||
RAGFlow's file management allows you to rename a file or folder:
|
||||
|
||||
|
||||
|
||||
|
||||
## Delete files or folders
|
||||
|
||||
RAGFlow's file management allows you to delete files or folders individually or in bulk.
|
||||
|
||||
To delete a file or folder:
|
||||
|
||||
|
||||
|
||||
To bulk delete files or folders:
|
||||
|
||||
|
||||
|
||||
> - You are not allowed to delete the **root/.knowledgebase** folder.
|
||||
> - Deleting files that have been linked to knowledge bases will **AUTOMATICALLY REMOVE** all associated file references across the knowledge bases.
|
||||
|
||||
## Download uploaded file
|
||||
|
||||
RAGFlow's file management allows you to download an uploaded file:
|
||||
|
||||
|
||||
|
||||
> As of RAGFlow v0.17.2, bulk download is not supported, nor can you download an entire folder.
|
||||
|
|
@ -1,41 +0,0 @@
|
|||
---
|
||||
sidebar_position: 4
|
||||
slug: /manage_team_members
|
||||
---
|
||||
|
||||
# Team
|
||||
|
||||
Invite or remove team members, join or leave a team.
|
||||
|
||||
---
|
||||
|
||||
By default, each RAGFlow user is assigned a single team named after their name. RAGFlow allows you to invite RAGFlow users to your team. Your team members can help you:
|
||||
|
||||
- Upload documents to your datasets (knowledge bases).
|
||||
- Update document configurations in your datasets.
|
||||
- Update the default configurations for your datasets.
|
||||
- Parse documents in your datasets.
|
||||
|
||||
:::tip NOTE
|
||||
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 in the top right corner of the page **>** Team:
|
||||
|
||||
|
||||
|
||||
_On the **Team** page, you can view the information about members of your team and the teams you have joined._
|
||||
|
||||
## Invite team members
|
||||
|
||||
You are, by default, the owner of your own team and the only person permitted to invite users to join your team or remove team members.
|
||||
|
||||
|
||||
|
||||
## Remove team members
|
||||
|
||||
|
||||
|
||||
## Accept or decline team invite
|
||||
|
||||
|
||||
|
|
@ -1,8 +0,0 @@
|
|||
{
|
||||
"label": "Models",
|
||||
"position": -1,
|
||||
"link": {
|
||||
"type": "generated-index",
|
||||
"description": "Guides on model settings."
|
||||
}
|
||||
}
|
||||
|
|
@ -1,354 +0,0 @@
|
|||
---
|
||||
sidebar_position: 2
|
||||
slug: /deploy_local_llm
|
||||
---
|
||||
|
||||
# Deploy LLM locally
|
||||
import Tabs from '@theme/Tabs';
|
||||
import TabItem from '@theme/TabItem';
|
||||
|
||||
Run models locally using Ollama, Xinference, or other frameworks.
|
||||
|
||||
---
|
||||
|
||||
RAGFlow supports deploying models locally using Ollama, Xinference, IPEX-LLM, or jina. If you have locally deployed models to leverage or wish to enable GPU or CUDA for inference acceleration, you can bind Ollama or Xinference into RAGFlow and use either of them as a local "server" for interacting with your local models.
|
||||
|
||||
RAGFlow seamlessly integrates with Ollama and Xinference, without the need for further environment configurations. You can use them to deploy two types of local models in RAGFlow: chat models and embedding models.
|
||||
|
||||
:::tip NOTE
|
||||
This user guide does not intend to cover much of the installation or configuration details of Ollama or Xinference; its focus is on configurations inside RAGFlow. For the most current information, you may need to check out the official site of Ollama or Xinference.
|
||||
:::
|
||||
|
||||
## Deploy local models using Ollama
|
||||
|
||||
[Ollama](https://github.com/ollama/ollama) enables you to run open-source large language models that you deployed locally. It bundles model weights, configurations, and data into a single package, defined by a Modelfile, and optimizes setup and configurations, including GPU usage.
|
||||
|
||||
:::note
|
||||
- For information about downloading Ollama, see [here](https://github.com/ollama/ollama?tab=readme-ov-file#ollama).
|
||||
- For a complete list of supported models and variants, see the [Ollama model library](https://ollama.com/library).
|
||||
:::
|
||||
|
||||
### 1. Deploy ollama using docker
|
||||
|
||||
```bash
|
||||
sudo docker run --name ollama -p 11434:11434 ollama/ollama
|
||||
time=2024-12-02T02:20:21.360Z level=INFO source=routes.go:1248 msg="Listening on [::]:11434 (version 0.4.6)"
|
||||
time=2024-12-02T02:20:21.360Z level=INFO source=common.go:49 msg="Dynamic LLM libraries" runners="[cpu cpu_avx cpu_avx2 cuda_v11 cuda_v12]"
|
||||
```
|
||||
|
||||
Ensure ollama is listening on all IP address:
|
||||
```bash
|
||||
sudo ss -tunlp | grep 11434
|
||||
tcp LISTEN 0 4096 0.0.0.0:11434 0.0.0.0:* users:(("docker-proxy",pid=794507,fd=4))
|
||||
tcp LISTEN 0 4096 [::]:11434 [::]:* users:(("docker-proxy",pid=794513,fd=4))
|
||||
```
|
||||
|
||||
Pull models as you need. It's recommended to start with `llama3.2` (a 3B chat model) and `bge-m3` (a 567M embedding model):
|
||||
```bash
|
||||
sudo docker exec ollama ollama pull llama3.2
|
||||
pulling dde5aa3fc5ff... 100% ▕████████████████▏ 2.0 GB
|
||||
success
|
||||
```
|
||||
|
||||
```bash
|
||||
sudo docker exec ollama ollama pull bge-m3
|
||||
pulling daec91ffb5dd... 100% ▕████████████████▏ 1.2 GB
|
||||
success
|
||||
```
|
||||
|
||||
### 2. Ensure Ollama is accessible
|
||||
|
||||
If RAGFlow runs in Docker and Ollama runs on the same host machine, check if ollama is accessible from inside the RAGFlow container:
|
||||
```bash
|
||||
sudo docker exec -it ragflow-server bash
|
||||
root@8136b8c3e914:/ragflow# curl http://host.docker.internal:11434/
|
||||
Ollama is running
|
||||
```
|
||||
|
||||
If RAGFlow runs from source code and Ollama runs on the same host machine, check if ollama is accessible from RAGFlow host machine:
|
||||
```bash
|
||||
curl http://localhost:11434/
|
||||
Ollama is running
|
||||
```
|
||||
|
||||
If RAGFlow and Ollama run on different machines, check if ollama is accessible from RAGFlow host machine:
|
||||
```bash
|
||||
curl http://${IP_OF_OLLAMA_MACHINE}:11434/
|
||||
Ollama is running
|
||||
```
|
||||
|
||||
### 4. Add Ollama
|
||||
|
||||
In RAGFlow, click on your logo on the top right of the page **>** **Model providers** and add Ollama to RAGFlow:
|
||||
|
||||
|
||||
|
||||
|
||||
### 5. Complete basic Ollama settings
|
||||
|
||||
In the popup window, complete basic settings for Ollama:
|
||||
|
||||
1. Ensure model name and type match those been pulled at step 1, For example, (`llama3.2`, `chat`), (`bge-m3`, `embedding`).
|
||||
2. Ensure that the base URL match which been determined at step 2.
|
||||
3. OPTIONAL: Switch on the toggle under **Does it support Vision?** if your model includes an image-to-text model.
|
||||
|
||||
|
||||
:::caution WARNING
|
||||
Improper base URL settings will trigger the following error:
|
||||
```bash
|
||||
Max retries exceeded with url: /api/chat (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0xffff98b81ff0>: Failed to establish a new connection: [Errno 111] Connection refused'))
|
||||
```
|
||||
:::
|
||||
|
||||
### 6. Update System Model Settings
|
||||
|
||||
Click on your logo **>** **Model providers** **>** **System Model Settings** to update your model:
|
||||
|
||||
*You should now be able to find **llama3.2** from the dropdown list under **Chat model**, and **bge-m3** from the dropdown list under **Embedding model**.*
|
||||
|
||||
> If your local model is an embedding model, you should find your local model under **Embedding model**.
|
||||
|
||||
### 7. Update Chat Configuration
|
||||
|
||||
Update your chat model accordingly in **Chat Configuration**:
|
||||
|
||||
> If your local model is an embedding model, update it on the configuration page of your knowledge base.
|
||||
|
||||
## Deploy a local model using Xinference
|
||||
|
||||
Xorbits Inference ([Xinference](https://github.com/xorbitsai/inference)) enables you to unleash the full potential of cutting-edge AI models.
|
||||
|
||||
:::note
|
||||
- For information about installing Xinference Ollama, see [here](https://inference.readthedocs.io/en/latest/getting_started/).
|
||||
- For a complete list of supported models, see the [Builtin Models](https://inference.readthedocs.io/en/latest/models/builtin/).
|
||||
:::
|
||||
|
||||
To deploy a local model, e.g., **Mistral**, using Xinference:
|
||||
|
||||
### 1. Check firewall settings
|
||||
|
||||
Ensure that your host machine's firewall allows inbound connections on port 9997.
|
||||
|
||||
### 2. Start an Xinference instance
|
||||
|
||||
```bash
|
||||
$ xinference-local --host 0.0.0.0 --port 9997
|
||||
```
|
||||
|
||||
### 3. Launch your local model
|
||||
|
||||
Launch your local model (**Mistral**), ensuring that you replace `${quantization}` with your chosen quantization method:
|
||||
|
||||
```bash
|
||||
$ xinference launch -u mistral --model-name mistral-v0.1 --size-in-billions 7 --model-format pytorch --quantization ${quantization}
|
||||
```
|
||||
### 4. Add Xinference
|
||||
|
||||
In RAGFlow, click on your logo on the top right of the page **>** **Model providers** and add Xinference to RAGFlow:
|
||||
|
||||
|
||||
|
||||
### 5. Complete basic Xinference settings
|
||||
|
||||
Enter an accessible base URL, such as `http://<your-xinference-endpoint-domain>:9997/v1`.
|
||||
> For rerank model, please use the `http://<your-xinference-endpoint-domain>:9997/v1/rerank` as the base URL.
|
||||
|
||||
### 6. Update System Model Settings
|
||||
|
||||
Click on your logo **>** **Model providers** **>** **System Model Settings** to update your model.
|
||||
|
||||
*You should now be able to find **mistral** from the dropdown list under **Chat model**.*
|
||||
|
||||
> If your local model is an embedding model, you should find your local model under **Embedding model**.
|
||||
|
||||
### 7. Update Chat Configuration
|
||||
|
||||
Update your chat model accordingly in **Chat Configuration**:
|
||||
|
||||
> 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
|
||||
|
||||
[IPEX-LLM](https://github.com/intel-analytics/ipex-llm) is a PyTorch library for running LLMs on local Intel CPUs or GPUs (including iGPU or discrete GPUs like Arc, Flex, and Max) with low latency. It supports Ollama on Linux and Windows systems.
|
||||
|
||||
To deploy a local model, e.g., **Qwen2**, using IPEX-LLM-accelerated Ollama:
|
||||
|
||||
### 1. Check firewall settings
|
||||
|
||||
Ensure that your host machine's firewall allows inbound connections on port 11434. For example:
|
||||
|
||||
```bash
|
||||
sudo ufw allow 11434/tcp
|
||||
```
|
||||
|
||||
### 2. Launch Ollama service using IPEX-LLM
|
||||
|
||||
#### 2.1 Install IPEX-LLM for Ollama
|
||||
|
||||
:::tip NOTE
|
||||
IPEX-LLM's supports Ollama on Linux and Windows systems.
|
||||
:::
|
||||
|
||||
For detailed information about installing IPEX-LLM for Ollama, see [Run llama.cpp with IPEX-LLM on Intel GPU Guide](https://github.com/intel-analytics/ipex-llm/blob/main/docs/mddocs/Quickstart/llama_cpp_quickstart.md):
|
||||
- [Prerequisites](https://github.com/intel-analytics/ipex-llm/blob/main/docs/mddocs/Quickstart/llama_cpp_quickstart.md#0-prerequisites)
|
||||
- [Install IPEX-LLM cpp with Ollama binaries](https://github.com/intel-analytics/ipex-llm/blob/main/docs/mddocs/Quickstart/llama_cpp_quickstart.md#1-install-ipex-llm-for-llamacpp)
|
||||
|
||||
*After the installation, you should have created a Conda environment, e.g., `llm-cpp`, for running Ollama commands with IPEX-LLM.*
|
||||
|
||||
#### 2.2 Initialize Ollama
|
||||
|
||||
1. Activate the `llm-cpp` Conda environment and initialize Ollama:
|
||||
|
||||
<Tabs
|
||||
defaultValue="linux"
|
||||
values={[
|
||||
{label: 'Linux', value: 'linux'},
|
||||
{label: 'Windows', value: 'windows'},
|
||||
]}>
|
||||
<TabItem value="linux">
|
||||
|
||||
```bash
|
||||
conda activate llm-cpp
|
||||
init-ollama
|
||||
```
|
||||
</TabItem>
|
||||
<TabItem value="windows">
|
||||
|
||||
Run these commands with *administrator privileges in Miniforge Prompt*:
|
||||
|
||||
```cmd
|
||||
conda activate llm-cpp
|
||||
init-ollama.bat
|
||||
```
|
||||
</TabItem>
|
||||
</Tabs>
|
||||
|
||||
2. If the installed `ipex-llm[cpp]` requires an upgrade to the Ollama binary files, remove the old binary files and reinitialize Ollama using `init-ollama` (Linux) or `init-ollama.bat` (Windows).
|
||||
|
||||
*A symbolic link to Ollama appears in your current directory, and you can use this executable file following standard Ollama commands.*
|
||||
|
||||
#### 2.3 Launch Ollama service
|
||||
|
||||
1. Set the environment variable `OLLAMA_NUM_GPU` to `999` to ensure that all layers of your model run on the Intel GPU; otherwise, some layers may default to CPU.
|
||||
2. For optimal performance on Intel Arc™ A-Series Graphics with Linux OS (Kernel 6.2), set the following environment variable before launching the Ollama service:
|
||||
|
||||
```bash
|
||||
export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
|
||||
```
|
||||
3. Launch the Ollama service:
|
||||
|
||||
<Tabs
|
||||
defaultValue="linux"
|
||||
values={[
|
||||
{label: 'Linux', value: 'linux'},
|
||||
{label: 'Windows', value: 'windows'},
|
||||
]}>
|
||||
<TabItem value="linux">
|
||||
|
||||
```bash
|
||||
export OLLAMA_NUM_GPU=999
|
||||
export no_proxy=localhost,127.0.0.1
|
||||
export ZES_ENABLE_SYSMAN=1
|
||||
source /opt/intel/oneapi/setvars.sh
|
||||
export SYCL_CACHE_PERSISTENT=1
|
||||
|
||||
./ollama serve
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="windows">
|
||||
|
||||
Run the following command *in Miniforge Prompt*:
|
||||
|
||||
```cmd
|
||||
set OLLAMA_NUM_GPU=999
|
||||
set no_proxy=localhost,127.0.0.1
|
||||
set ZES_ENABLE_SYSMAN=1
|
||||
set SYCL_CACHE_PERSISTENT=1
|
||||
|
||||
ollama serve
|
||||
```
|
||||
</TabItem>
|
||||
</Tabs>
|
||||
|
||||
|
||||
:::tip NOTE
|
||||
To enable the Ollama service to accept connections from all IP addresses, use `OLLAMA_HOST=0.0.0.0 ./ollama serve` rather than simply `./ollama serve`.
|
||||
:::
|
||||
|
||||
*The console displays messages similar to the following:*
|
||||
|
||||
|
||||
|
||||
### 3. Pull and Run Ollama model
|
||||
|
||||
#### 3.1 Pull Ollama model
|
||||
|
||||
With the Ollama service running, open a new terminal and run `./ollama pull <model_name>` (Linux) or `ollama.exe pull <model_name>` (Windows) to pull the desired model. e.g., `qwen2:latest`:
|
||||
|
||||
|
||||
|
||||
#### 3.2 Run Ollama model
|
||||
|
||||
<Tabs
|
||||
defaultValue="linux"
|
||||
values={[
|
||||
{label: 'Linux', value: 'linux'},
|
||||
{label: 'Windows', value: 'windows'},
|
||||
]}>
|
||||
<TabItem value="linux">
|
||||
|
||||
```bash
|
||||
./ollama run qwen2:latest
|
||||
```
|
||||
</TabItem>
|
||||
<TabItem value="windows">
|
||||
|
||||
```cmd
|
||||
ollama run qwen2:latest
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
</Tabs>
|
||||
|
||||
### 4. Configure RAGflow
|
||||
|
||||
To enable IPEX-LLM accelerated Ollama in RAGFlow, you must also complete the configurations in RAGFlow. The steps are identical to those outlined in the *Deploy a local model using Ollama* section:
|
||||
|
||||
1. [Add Ollama](#4-add-ollama)
|
||||
2. [Complete basic Ollama settings](#5-complete-basic-ollama-settings)
|
||||
3. [Update System Model Settings](#6-update-system-model-settings)
|
||||
4. [Update Chat Configuration](#7-update-chat-configuration)
|
||||
|
||||
## Deploy a local model using jina
|
||||
|
||||
To deploy a local model, e.g., **gpt2**, using jina:
|
||||
|
||||
### 1. Check firewall settings
|
||||
|
||||
Ensure that your host machine's firewall allows inbound connections on port 12345.
|
||||
|
||||
```bash
|
||||
sudo ufw allow 12345/tcp
|
||||
```
|
||||
|
||||
### 2. Install jina package
|
||||
|
||||
```bash
|
||||
pip install jina
|
||||
```
|
||||
|
||||
### 3. Deploy a local model
|
||||
|
||||
Step 1: Navigate to the **rag/svr** directory.
|
||||
|
||||
```bash
|
||||
cd rag/svr
|
||||
```
|
||||
|
||||
Step 2: Run **jina_server.py**, specifying either the model's name or its local directory:
|
||||
|
||||
```bash
|
||||
python jina_server.py --model_name gpt2
|
||||
```
|
||||
> The script only supports models downloaded from Hugging Face.
|
||||
|
|
@ -1,54 +0,0 @@
|
|||
---
|
||||
sidebar_position: 1
|
||||
slug: /llm_api_key_setup
|
||||
---
|
||||
|
||||
# Configure model API key
|
||||
|
||||
An API key is required for RAGFlow to interact with an online AI model. This guide provides information about setting your model API key in RAGFlow.
|
||||
|
||||
## Get model API key
|
||||
|
||||
RAGFlow supports most mainstream LLMs. Please refer to [Supported Models](../../references/supported_models.mdx) for a complete list of supported models. You will need to apply for your model API key online. Note that most LLM providers grant newly-created accounts trial credit, which will expire in a couple of months, or a promotional amount of free quota.
|
||||
|
||||
:::note
|
||||
If you find your online LLM is not on the list, don't feel disheartened. The list is expanding, and you can [file a feature request](https://github.com/infiniflow/ragflow/issues/new?assignees=&labels=feature+request&projects=&template=feature_request.yml&title=%5BFeature+Request%5D%3A+) with us! Alternatively, if you have customized or locally-deployed models, you can [bind them to RAGFlow using Ollama, Xinference, or LocalAI](./deploy_local_llm.mdx).
|
||||
:::
|
||||
|
||||
## Configure model API key
|
||||
|
||||
You have two options for configuring your model API key:
|
||||
|
||||
- Configure it in **service_conf.yaml.template** before starting RAGFlow.
|
||||
- Configure it on the **Model providers** page after logging into RAGFlow.
|
||||
|
||||
### Configure model API key before starting up RAGFlow
|
||||
|
||||
1. Navigate to **./docker/ragflow**.
|
||||
2. Find entry **user_default_llm**:
|
||||
- Update `factory` with your chosen LLM.
|
||||
- Update `api_key` with yours.
|
||||
- Update `base_url` if you use a proxy to connect to the remote service.
|
||||
3. Reboot your system for your changes to take effect.
|
||||
4. Log into RAGFlow.
|
||||
_After logging into RAGFlow, you will find your chosen model appears under **Added models** on the **Model providers** page._
|
||||
|
||||
### Configure model API key after logging into RAGFlow
|
||||
|
||||
:::caution WARNING
|
||||
After logging into RAGFlow, configuring your model API key through the **service_conf.yaml.template** file will no longer take effect.
|
||||
:::
|
||||
|
||||
After logging into RAGFlow, you can *only* configure API Key on the **Model providers** page:
|
||||
|
||||
1. Click on your logo on the top right of the page **>** **Model providers**.
|
||||
2. Find your model card under **Models to be added** and click **Add the model**:
|
||||
|
||||
3. Paste your model API key.
|
||||
4. Fill in your base URL if you use a proxy to connect to the remote service.
|
||||
5. Click **OK** to confirm your changes.
|
||||
|
||||
:::note
|
||||
To update an existing model API key at a later point:
|
||||
|
||||
:::
|
||||
|
|
@ -1,33 +0,0 @@
|
|||
---
|
||||
sidebar_position: 7
|
||||
slug: /run_health_check
|
||||
---
|
||||
|
||||
# Monitoring
|
||||
|
||||
Double-check the health status of RAGFlow's dependencies.
|
||||
|
||||
---
|
||||
|
||||
The operation of RAGFlow depends on four services:
|
||||
|
||||
- **Elasticsearch** (default) or [Infinity](https://github.com/infiniflow/infinity) as the document engine
|
||||
- **MySQL**
|
||||
- **Redis**
|
||||
- **MinIO** for object storage
|
||||
|
||||
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 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:
|
||||
|
||||
|
||||
|
||||
Services with a yellow or red light are not running properly. The following is a screenshot of the system page after running `docker stop ragflow-es-10`:
|
||||
|
||||
|
||||
|
||||
You can click on a specific 30-second time interval to view the details of completed and failed tasks:
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
@ -1,82 +0,0 @@
|
|||
---
|
||||
sidebar_position: 11
|
||||
slug: /upgrade_ragflow
|
||||
---
|
||||
|
||||
# Upgrade
|
||||
import Tabs from '@theme/Tabs';
|
||||
import TabItem from '@theme/TabItem';
|
||||
|
||||
Upgrade RAGFlow to `nightly-slim`/`nightly` or the latest, published release.
|
||||
|
||||
## Upgrade RAGFlow to `nightly-slim`/`nightly`, the most recent, tested Docker image
|
||||
|
||||
`nightly-slim` refers to the RAGFlow Docker image *without* embedding models, while `nightly` refers to the RAGFlow Docker image with embedding models. For details on their differences, see [ragflow/docker/.env](https://github.com/infiniflow/ragflow/blob/main/docker/.env).
|
||||
|
||||
To upgrade RAGFlow, you must upgrade **both** your code **and** your Docker image:
|
||||
|
||||
1. Clone the repo
|
||||
|
||||
```bash
|
||||
git clone https://github.com/infiniflow/ragflow.git
|
||||
```
|
||||
|
||||
2. Update **ragflow/docker/.env**:
|
||||
|
||||
<Tabs
|
||||
defaultValue="nightly-slim"
|
||||
values={[
|
||||
{label: 'nightly-slim', value: 'nightly-slim'},
|
||||
{label: 'nightly', value: 'nightly'},
|
||||
]}>
|
||||
<TabItem value="nightly-slim">
|
||||
|
||||
```bash
|
||||
RAGFLOW_IMAGE=infiniflow/ragflow:nightly-slim
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="nightly">
|
||||
|
||||
```bash
|
||||
RAGFLOW_IMAGE=infiniflow/ragflow:nightly
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
</Tabs>
|
||||
|
||||
3. Update RAGFlow image and restart RAGFlow:
|
||||
|
||||
```bash
|
||||
docker compose -f docker/docker-compose.yml pull
|
||||
docker compose -f docker/docker-compose.yml up -d
|
||||
```
|
||||
|
||||
## Upgrade RAGFlow to the most recent, officially published release
|
||||
|
||||
To upgrade RAGFlow, you must upgrade **both** your code **and** your Docker image:
|
||||
|
||||
1. Clone the repo
|
||||
|
||||
```bash
|
||||
git clone https://github.com/infiniflow/ragflow.git
|
||||
```
|
||||
|
||||
2. Switch to the latest, officially published release, e.g., `v0.17.2`:
|
||||
|
||||
```bash
|
||||
git checkout -f v0.17.2
|
||||
```
|
||||
|
||||
3. Update **ragflow/docker/.env** as follows:
|
||||
|
||||
```bash
|
||||
RAGFLOW_IMAGE=infiniflow/ragflow:v0.17.2
|
||||
```
|
||||
|
||||
4. Update the RAGFlow image and restart RAGFlow:
|
||||
|
||||
```bash
|
||||
docker compose -f docker/docker-compose.yml pull
|
||||
docker compose -f docker/docker-compose.yml up -d
|
||||
```
|
||||
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|
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|
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|
|
@ -0,0 +1,228 @@
|
|||
<!DOCTYPE html>
|
||||
<html lang="zh-CN">
|
||||
<head>
|
||||
<meta charset="UTF-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
<title>RagflowPlus</title>
|
||||
<link rel="icon" href="images/favicon.ico" type="image/x-icon">
|
||||
<script src="https://cdn.jsdelivr.net/npm/marked/marked.min.js"></script>
|
||||
<link href="https://unpkg.com/aos@2.3.1/dist/aos.css" rel="stylesheet">
|
||||
<link rel="stylesheet" href="style.css">
|
||||
</head>
|
||||
<body>
|
||||
<!-- Header -->
|
||||
<header>
|
||||
<div class="container">
|
||||
<nav class="navbar">
|
||||
<div class="logo">
|
||||
<img src="images/ragflow-plus.png" alt="RagflowPlus Logo">
|
||||
</div>
|
||||
<ul class="nav-links">
|
||||
<li><a href="#features">功能</a></li>
|
||||
<li><a href="#demo">演示</a></li>
|
||||
<li><a href="#quick-start">快速开始</a></li>
|
||||
<li><a href="#community">社群</a></li>
|
||||
<li><a href="#faq">常见问题</a></li>
|
||||
<li><a href="#dev-plan">开发计划</a></li>
|
||||
<li><a href="https://github.com/zstar1003/ragflow-plus" target="_blank" title="GitHub Repository">
|
||||
GitHub
|
||||
</a></li>
|
||||
</ul>
|
||||
</nav>
|
||||
</div>
|
||||
</header>
|
||||
|
||||
<!-- Hero Section -->
|
||||
<section class="hero">
|
||||
<div class="container">
|
||||
<div class="hero-content" data-aos="fade-right">
|
||||
<h1>RagflowPlus - 为实际应用而生的增强型 RAG 框架</h1>
|
||||
<p>基于Ragflow的二次开发项目，提供增强的文件解析、图文输出和全新文档撰写模式</p>
|
||||
<div class="cta-buttons">
|
||||
<a href="#quick-start" class="btn btn-primary">立即开始</a>
|
||||
<a href="https://www.bilibili.com/video/BV1UJLezaEEE" class="btn btn-secondary">观看演示</a>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</section>
|
||||
|
||||
<!-- Features Section -->
|
||||
<section id="features" class="features">
|
||||
<div class="container" data-aos="fade-up">
|
||||
<div class="section-title">
|
||||
<h2>主要特性</h2>
|
||||
<p>RagflowPlus基于Ragflow进行二次开发，解决了实际应用中的多个问题</p>
|
||||
</div>
|
||||
<div class="features-grid">
|
||||
<div class="feature-card" data-aos="zoom-in">
|
||||
<div class="icon">📊</div>
|
||||
<h3>管理模式</h3>
|
||||
<p>完整后台管理系统，支持用户、团队、配置、文件和知识库管理</p>
|
||||
</div>
|
||||
|
||||
<div class="feature-card" data-aos="zoom-in" data-aos-delay="100">
|
||||
<div class="icon">🔒</div>
|
||||
<h3>权限回收</h3>
|
||||
<p>简化前台界面，优化用户体验</p>
|
||||
</div>
|
||||
|
||||
<div class="feature-card" data-aos="zoom-in" data-aos-delay="200">
|
||||
<div class="icon">🔍</div>
|
||||
<h3>解析增强</h3>
|
||||
<p>使用MinerU替代DeepDoc算法，支持图片解析，效果更佳</p>
|
||||
</div>
|
||||
|
||||
<div class="feature-card" data-aos="zoom-in">
|
||||
<div class="icon">🖼️</div>
|
||||
<h3>图文输出</h3>
|
||||
<p>回答时输出引用文本块关联的相关图片</p>
|
||||
</div>
|
||||
|
||||
<div class="feature-card" data-aos="zoom-in" data-aos-delay="100">
|
||||
<div class="icon">✍️</div>
|
||||
<h3>文档撰写模式</h3>
|
||||
<p>全新的文档模式交互体验</p>
|
||||
</div>
|
||||
|
||||
<div class="feature-card" data-aos="zoom-in" data-aos-delay="200">
|
||||
<div class="icon">🚀</div>
|
||||
<h3>更多功能</h3>
|
||||
<p>更多功能正在开发中，敬请期待</p>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</section>
|
||||
|
||||
<!-- Demo Section -->
|
||||
<section id="demo" class="demo" data-aos="fade-up">
|
||||
<div class="container">
|
||||
<div class="section-title">
|
||||
<h2>视频演示</h2>
|
||||
<p>观看RagflowPlus的操作演示和教程</p>
|
||||
</div>
|
||||
<div class="video-container">
|
||||
<a href="https://www.bilibili.com/video/BV1UJLezaEEE">
|
||||
<img src="images/video-thumbnail.jpg" alt="Ragflow-Plus演示视频缩略图">
|
||||
<div class="play-button">▶</div>
|
||||
</a>
|
||||
</div>
|
||||
</div>
|
||||
</section>
|
||||
|
||||
<!-- Quick Start Section -->
|
||||
<section id="quick-start" class="quick-start" data-aos="fade-up">
|
||||
<div class="container">
|
||||
<div class="section-title">
|
||||
<h2>快速开始</h2>
|
||||
<p>快速部署和使用RagflowPlus的详细指南</p>
|
||||
</div>
|
||||
|
||||
<div class="installation-option">
|
||||
<h3>使用Docker Compose运行</h3>
|
||||
<p>在项目根目录下执行</p>
|
||||
|
||||
<div class="code-tabs">
|
||||
<div class="tab-header">
|
||||
<button class="tab-button active" onclick="openTab(event, 'gpu')">GPU版本</button>
|
||||
<button class="tab-button" onclick="openTab(event, 'cpu')">CPU版本</button>
|
||||
</div>
|
||||
|
||||
<div id="gpu" class="tab-content active">
|
||||
<pre><code>docker compose -f docker/docker-compose_gpu.yml up -d</code></pre>
|
||||
</div>
|
||||
|
||||
<div id="cpu" class="tab-content">
|
||||
<pre><code>docker compose -f docker/docker-compose.yml up -d</code></pre>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<p>访问地址：<code>服务器ip:80</code>，进入到前台界面</p>
|
||||
<p>访问地址：<code>服务器ip:8888</code>，进入到后台管理界面</p>
|
||||
<p>图文教程：<a href="https://blog.csdn.net/qq1198768105/article/details/147475488">CSDN教程</a></p>
|
||||
</div>
|
||||
|
||||
<div class="installation-option">
|
||||
<h3>源码运行</h3>
|
||||
<h4>0. 启动必要组件</h4>
|
||||
<p>docker启动ragflow-mysql、ragflow-es-01、ragflow-minio三个容器</p>
|
||||
|
||||
<h4>1. 启动后台管理系统：</h4>
|
||||
<p>启动后端：进入到<code>management/server</code>，执行：</p>
|
||||
<pre><code>python app.py</code></pre>
|
||||
|
||||
<p>启动前端：进入到<code>management\web</code>，执行：</p>
|
||||
<pre><code>pnpm dev</code></pre>
|
||||
|
||||
<h4>2. 启动前台交互系统：</h4>
|
||||
<p>启动后端：项目根目录下执行：</p>
|
||||
<pre><code>python -m api.ragflow_server</code></pre>
|
||||
|
||||
<p>启动前端：进入到<code>web</code>，执行：</p>
|
||||
<pre><code>pnpm dev</code></pre>
|
||||
</div>
|
||||
</div>
|
||||
</section>
|
||||
|
||||
<!-- Community Section -->
|
||||
<section id="community" class="community" data-aos="fade-up">
|
||||
<div class="container">
|
||||
<h2>加入社群</h2>
|
||||
<p>如果有需求或问题反馈，可加入交流群进行讨论</p>
|
||||
<div class="qrcodes-wrapper">
|
||||
<div class="qrcode-container">
|
||||
<img src="images/group.jpg" alt="Ragflow-Plus交流群二维码">
|
||||
<p>扫码加入交流群2群</p>
|
||||
</div>
|
||||
<div class="qrcode-container">
|
||||
<img src="images/qrcode.jpg" alt="[我有一计]微信公众号二维码">
|
||||
<p>扫码关注公众号</p>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</section>
|
||||
|
||||
<section id="faq" class="faq-section" data-aos="fade-up">
|
||||
<div class="container">
|
||||
<div class="section-title">
|
||||
<h2>常见问题</h2>
|
||||
<p>您可能会关心的一些常见问题及其解答</p>
|
||||
</div>
|
||||
<!-- Markdown 内容将加载到这里 -->
|
||||
<div id="faq-container">
|
||||
<p>正在加载常见问题...</p>
|
||||
</div>
|
||||
</div>
|
||||
</section>
|
||||
|
||||
<!-- Development Plan Section -->
|
||||
<section id="dev-plan" class="dev-plan-section" data-aos="fade-up">
|
||||
<div class="container">
|
||||
<div class="section-title">
|
||||
<h2>开发计划</h2>
|
||||
<p>了解 RagflowPlus 的未来开发方向和计划。</p>
|
||||
</div>
|
||||
<div id="dev-plan-container">
|
||||
<!-- Markdown content will be loaded here -->
|
||||
<p>正在加载开发计划...</p>
|
||||
</div>
|
||||
</div>
|
||||
</section>
|
||||
|
||||
<!-- Footer -->
|
||||
<footer>
|
||||
<div class="container">
|
||||
<div class="footer-content">
|
||||
<div class="links">
|
||||
<a href="../LICENSE">许可证</a>
|
||||
<a href="https://github.com/zstar1003/ragflow-plus">GitHub</a>
|
||||
</div>
|
||||
<div class="copyright">
|
||||
© 2025 RagflowPlus. All Rights Reserved.
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</footer>
|
||||
<script src="https://unpkg.com/aos@2.3.1/dist/aos.js"></script>
|
||||
<script src="script.js"></script>
|
||||
</body>
|
||||
</html>
|
||||
|
|
@ -0,0 +1,18 @@
|
|||
# RagflowPlus 开发计划
|
||||
|
||||
## 下版本计划
|
||||
- [ ] 修复v0.2.1版本中已知问题
|
||||
- [ ] 前台知识库界面，增加文本块关联的图片预览显示，并增加图像获取接口
|
||||
- [ ] 前台聊天界面，增加字体样式动态配置
|
||||
- [ ] 后台管理系统中，调整知识库创建逻辑，支持为指定用户创建私人知识库
|
||||
- [ ] 后台管理系统中，优化用户配置管理交互
|
||||
- [ ] 后台管理系统中，整体优化表格的时间排序，方便查看显示最新添加的记录
|
||||
- [ ] 后台管理系统中，增加用户会话管理功能
|
||||
- [ ] 拓展文档解析的支持类型
|
||||
- [ ] 同步部分 Ragflow v0.18.0 版本的新功能
|
||||
|
||||
欢迎提交PR，共同推进项目的发展。
|
||||
|
||||
---
|
||||
|
||||
*请注意：开发计划可能会根据社区反馈和实际情况进行调整。*
|
||||
|
|
@ -1,362 +0,0 @@
|
|||
---
|
||||
sidebar_position: 0
|
||||
slug: /
|
||||
---
|
||||
|
||||
# Get started
|
||||
import Tabs from '@theme/Tabs';
|
||||
import TabItem from '@theme/TabItem';
|
||||
import APITable from '@site/src/components/APITable';
|
||||
|
||||
RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding. When integrated with LLMs, it is capable of providing truthful question-answering capabilities, backed by well-founded citations from various complex formatted data.
|
||||
|
||||
This quick start guide describes a general process from:
|
||||
|
||||
- Starting up a local RAGFlow server,
|
||||
- Creating a knowledge base,
|
||||
- Intervening with file parsing, to
|
||||
- Establishing an AI chat based on your datasets.
|
||||
|
||||
:::danger IMPORTANT
|
||||
We officially support x86 CPU and Nvidia GPU, and this document offers instructions on deploying RAGFlow using Docker on x86 platforms. While we also test RAGFlow on ARM64 platforms, we do not maintain RAGFlow Docker images for ARM.
|
||||
|
||||
If you are on an ARM platform, follow [this guide](./develop/build_docker_image.mdx) to build a RAGFlow Docker image.
|
||||
:::
|
||||
|
||||
## Prerequisites
|
||||
|
||||
- CPU ≥ 4 cores (x86);
|
||||
- RAM ≥ 16 GB;
|
||||
- Disk ≥ 50 GB;
|
||||
- Docker ≥ 24.0.0 & Docker Compose ≥ v2.26.1.
|
||||
|
||||
## Start up the server
|
||||
|
||||
This section provides instructions on setting up the RAGFlow server on Linux. If you are on a different operating system, no worries. Most steps are alike.
|
||||
|
||||
<details>
|
||||
<summary>1. Ensure <code>vm.max_map_count</code> ≥ 262144:</summary>
|
||||
|
||||
`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 v0.17.2 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"
|
||||
values={[
|
||||
{label: 'Linux', value: 'linux'},
|
||||
{label: 'macOS', value: 'macos'},
|
||||
{label: 'Windows', value: 'windows'},
|
||||
]}>
|
||||
<TabItem value="linux">
|
||||
1.1. Check the value of `vm.max_map_count`:
|
||||
|
||||
```bash
|
||||
$ sysctl vm.max_map_count
|
||||
```
|
||||
|
||||
1.2. Reset `vm.max_map_count` to a value at least 262144 if it is not.
|
||||
|
||||
```bash
|
||||
$ sudo sysctl -w vm.max_map_count=262144
|
||||
```
|
||||
|
||||
:::caution WARNING
|
||||
This change will be reset after a system reboot. If you forget to update the value the next time you start up the server, you may get a `Can't connect to ES cluster` exception.
|
||||
:::
|
||||
|
||||
1.3. To ensure your change remains permanent, add or update the `vm.max_map_count` value in **/etc/sysctl.conf** accordingly:
|
||||
|
||||
```bash
|
||||
vm.max_map_count=262144
|
||||
```
|
||||
</TabItem>
|
||||
<TabItem value="macos">
|
||||
If you are on macOS with Docker Desktop, run the following command to update `vm.max_map_count`:
|
||||
|
||||
```bash
|
||||
docker run --rm --privileged --pid=host alpine sysctl -w vm.max_map_count=262144
|
||||
```
|
||||
|
||||
:::caution WARNING
|
||||
This change will be reset after a system reboot. If you forget to update the value the next time you start up the server, you may get a `Can't connect to ES cluster` exception.
|
||||
:::
|
||||
|
||||
To make your change persistent, create a file with proper settings:
|
||||
|
||||
1.1. Create a file:
|
||||
|
||||
```shell
|
||||
sudo nano /Library/LaunchDaemons/com.user.vmmaxmap.plist
|
||||
```
|
||||
|
||||
1.2. Open the file:
|
||||
|
||||
```shell
|
||||
sudo launchctl load /Library/LaunchDaemons/com.user.vmmaxmap.plist
|
||||
```
|
||||
|
||||
1.3. Add settings:
|
||||
|
||||
```xml
|
||||
<?xml version="1.0" encoding="UTF-8"?>
|
||||
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
|
||||
<plist version="1.0">
|
||||
<dict>
|
||||
<key>Label</key>
|
||||
<string>com.user.vmmaxmap</string>
|
||||
<key>ProgramArguments</key>
|
||||
<array>
|
||||
<string>/usr/sbin/sysctl</string>
|
||||
<string>-w</string>
|
||||
<string>vm.max_map_count=262144</string>
|
||||
</array>
|
||||
<key>RunAtLoad</key>
|
||||
<true/>
|
||||
</dict>
|
||||
</plist>
|
||||
```
|
||||
|
||||
1.4. After saving the file, load the new daemon:
|
||||
|
||||
```shell
|
||||
sudo launchctl load /Library/LaunchDaemons/com.user.vmmaxmap.plist
|
||||
```
|
||||
|
||||
:::note
|
||||
If the above steps do not work, consider using [this workaround](https://github.com/docker/for-mac/issues/7047#issuecomment-1791912053), which employs a container and does not require manual editing of the macOS settings.
|
||||
:::
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="windows">
|
||||
|
||||
#### If you are on Windows with Docker Desktop, then you *must* use docker-machine to set `vm.max_map_count`:
|
||||
|
||||
```bash
|
||||
$ docker-machine ssh
|
||||
$ sudo sysctl -w vm.max_map_count=262144
|
||||
```
|
||||
#### If you are on Windows with Docker Desktop WSL 2 backend, then use docker-desktop to set `vm.max_map_count`:
|
||||
|
||||
1.1. Run the following in WSL:
|
||||
```bash
|
||||
$ wsl -d docker-desktop -u root
|
||||
$ sysctl -w vm.max_map_count=262144
|
||||
```
|
||||
|
||||
:::caution WARNING
|
||||
This change will be reset after you restart Docker. If you forget to update the value the next time you start up the server, you may get a `Can't connect to ES cluster` exception.
|
||||
:::
|
||||
|
||||
1.2. If you do not wish to have to run those commands each time you restart Docker, you can update your `%USERPROFILE%.wslconfig` as follows to keep your change permanent and globally for all WSL distributions:
|
||||
|
||||
```bash
|
||||
[wsl2]
|
||||
kernelCommandLine = "sysctl.vm.max_map_count=262144"
|
||||
```
|
||||
*This causes all WSL2 virtual machines to have that setting assigned when they start.*
|
||||
|
||||
:::note
|
||||
If you are on Windows 11 or Windows 10 version 22H2, and have installed the Microsoft Store version of WSL, you can also update the **/etc/sysctl.conf** within the docker-desktop WSL distribution to keep your change permanent:
|
||||
|
||||
```bash
|
||||
$ wsl -d docker-desktop -u root
|
||||
$ vi /etc/sysctl.conf
|
||||
```
|
||||
|
||||
```bash
|
||||
# Append a line, which reads:
|
||||
vm.max_map_count = 262144
|
||||
```
|
||||
:::
|
||||
</TabItem>
|
||||
</Tabs>
|
||||
|
||||
</details>
|
||||
|
||||
2. Clone the repo:
|
||||
|
||||
```bash
|
||||
$ git clone https://github.com/infiniflow/ragflow.git
|
||||
$ cd ragflow/docker
|
||||
$ git checkout -f v0.17.2
|
||||
```
|
||||
|
||||
3. Use the pre-built Docker images and start up the server:
|
||||
|
||||
:::tip NOTE
|
||||
The command below downloads the `v0.17.2-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 `v0.17.2-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:v0.17.2` for the full edition `v0.17.2`.
|
||||
:::
|
||||
|
||||
```bash
|
||||
$ docker compose -f docker-compose.yml up -d
|
||||
```
|
||||
|
||||
```mdx-code-block
|
||||
<APITable>
|
||||
```
|
||||
|
||||
| RAGFlow image tag | Image size (GB) | Has embedding models and Python packages? | Stable? |
|
||||
| ------------------- | --------------- | ----------------------------------------- | ------------------------ |
|
||||
| `v0.17.2` | ≈9 | :heavy_check_mark: | Stable release |
|
||||
| `v0.17.2-slim` | ≈2 | ❌ | Stable release |
|
||||
| `nightly` | ≈9 | :heavy_check_mark: | *Unstable* nightly build |
|
||||
| `nightly-slim` | ≈2 | ❌ | *Unstable* nightly build |
|
||||
|
||||
```mdx-code-block
|
||||
</APITable>
|
||||
```
|
||||
|
||||
4. Check the server status after having the server up and running:
|
||||
|
||||
```bash
|
||||
$ docker logs -f ragflow-server
|
||||
```
|
||||
|
||||
_The following output confirms a successful launch of the system:_
|
||||
|
||||
```bash
|
||||
____ ___ ______ ______ __
|
||||
/ __ \ / | / ____// ____// /____ _ __
|
||||
/ /_/ // /| | / / __ / /_ / // __ \| | /| / /
|
||||
/ _, _// ___ |/ /_/ // __/ / // /_/ /| |/ |/ /
|
||||
/_/ |_|/_/ |_|\____//_/ /_/ \____/ |__/|__/
|
||||
|
||||
* Running on all addresses (0.0.0.0)
|
||||
```
|
||||
|
||||
> If you skip this confirmation step and directly log in to RAGFlow, your browser may prompt a `network anomaly` 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.
|
||||
|
||||
:::caution WARNING
|
||||
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.
|
||||
:::
|
||||
|
||||
## Configure LLMs
|
||||
|
||||
RAGFlow is a RAG engine and needs to work with an LLM to offer grounded, hallucination-free question-answering capabilities. RAGFlow supports most mainstream LLMs. For a complete list of supported models, please refer to [Supported Models](./references/supported_models.mdx).
|
||||
|
||||
:::note
|
||||
RAGFlow also supports deploying LLMs locally using Ollama, Xinference, or LocalAI, but this part is not covered in this quick start guide.
|
||||
:::
|
||||
|
||||
To add and configure an LLM:
|
||||
|
||||
1. Click on your logo on the top right of the page **>** **Model providers**:
|
||||
|
||||
|
||||
|
||||
> Each RAGFlow account is able to use **text-embedding-v2** for free, an embedding model of Tongyi-Qianwen. This is why you can see Tongyi-Qianwen in the **Added models** list. And you may need to update your Tongyi-Qianwen API key at a later point.
|
||||
|
||||
2. Click on the desired LLM and update the API key accordingly (DeepSeek-V2 in this case):
|
||||
|
||||
|
||||
|
||||
*Your added models appear as follows:*
|
||||
|
||||
|
||||
|
||||
3. Click **System Model Settings** to select the default models:
|
||||
|
||||
- Chat model,
|
||||
- Embedding model,
|
||||
- Image-to-text model.
|
||||
|
||||
|
||||
|
||||
> Some models, such as the image-to-text model **qwen-vl-max**, are subsidiary to a specific LLM. And you may need to update your API key to access these models.
|
||||
|
||||
## Create your first knowledge base
|
||||
|
||||
You are allowed to upload files to a knowledge base in RAGFlow and parse them into datasets. A knowledge base is virtually a collection of datasets. Question answering in RAGFlow can be based on a particular knowledge base or multiple knowledge bases. File formats that RAGFlow supports include documents (PDF, DOC, DOCX, TXT, MD), tables (CSV, XLSX, XLS), pictures (JPEG, JPG, PNG, TIF, GIF), and slides (PPT, PPTX).
|
||||
|
||||
To create your first knowledge base:
|
||||
|
||||
1. Click the **Knowledge Base** tab in the top middle of the page **>** **Create knowledge base**.
|
||||
|
||||
2. Input the name of your knowledge base and click **OK** to confirm your changes.
|
||||
|
||||
_You are taken to the **Configuration** page of your knowledge base._
|
||||
|
||||
|
||||
|
||||
3. RAGFlow offers multiple chunk templates that cater to different document layouts and file formats. Select the embedding model and chunk method (template) for your knowledge base.
|
||||
|
||||
:::danger IMPORTANT
|
||||
Once you have selected an embedding model and used it to parse a file, you are no longer allowed to change it. The obvious reason is that we must ensure that all files in a specific knowledge base are parsed using the *same* embedding model (ensure that they are being compared in the same embedding space).
|
||||
:::
|
||||
|
||||
_You are taken to the **Dataset** page of your knowledge base._
|
||||
|
||||
4. Click **+ Add file** **>** **Local files** to start uploading a particular file to the knowledge base.
|
||||
|
||||
5. In the uploaded file entry, click the play button to start file parsing:
|
||||
|
||||
|
||||
|
||||
_When the file parsing completes, its parsing status changes to **SUCCESS**._
|
||||
|
||||
:::caution NOTE
|
||||
- If your file parsing gets stuck at below 1%, see [this FAQ](https://ragflow.io/docs/dev/faq#why-does-my-document-parsing-stall-at-under-one-percent).
|
||||
- If your file parsing gets stuck at near completion, see [this FAQ](https://ragflow.io/docs/dev/faq#why-does-my-pdf-parsing-stall-near-completion-while-the-log-does-not-show-any-error)
|
||||
:::
|
||||
|
||||
## Intervene with file parsing
|
||||
|
||||
RAGFlow features visibility and explainability, allowing you to view the chunking results and intervene where necessary. To do so:
|
||||
|
||||
1. Click on the file that completes file parsing to view the chunking results:
|
||||
|
||||
_You are taken to the **Chunk** page:_
|
||||
|
||||
|
||||
|
||||
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:
|
||||
|
||||
|
||||
|
||||
:::caution NOTE
|
||||
You can add keywords to a file chunk to improve 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:
|
||||
|
||||
_As you can tell from the following, RAGFlow responds with truthful citations._
|
||||
|
||||
|
||||
|
||||
## Set up an AI chat
|
||||
|
||||
Conversations in RAGFlow are based on a particular knowledge base or multiple knowledge bases. Once you have created your knowledge base and finished file parsing, you can go ahead and start an AI conversation.
|
||||
|
||||
1. Click the **Chat** tab in the middle top of the mage **>** **Create an assistant** to show the **Chat Configuration** dialogue *of your next dialogue*.
|
||||
> RAGFlow offer the flexibility of choosing a different chat model for each dialogue, while allowing you to set the default models in **System Model Settings**.
|
||||
|
||||
2. Update **Assistant Setting**:
|
||||
|
||||
- Name your assistant and specify your knowledge bases.
|
||||
- **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 RAGFlow to *improvise* when it doesn't retrieve an answer from your knowledge bases, leave it blank, which may give rise to hallucinations.
|
||||
|
||||
3. Update **Prompt Engine** or leave it as is for the beginning.
|
||||
|
||||
4. Update **Model Setting**.
|
||||
|
||||
5. Now, let's start the show:
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
:::tip NOTE
|
||||
RAGFlow also offers HTTP and Python APIs for you to integrate RAGFlow's capabilities into your applications. Read the following documents for more information:
|
||||
|
||||
- [Acquire a RAGFlow API key](./develop/acquire_ragflow_api_key.md)
|
||||
- [HTTP API reference](./references/http_api_reference.md)
|
||||
- [Python API reference](./references/python_api_reference.md)
|
||||
:::
|
||||
|
|
@ -1,8 +0,0 @@
|
|||
{
|
||||
"label": "References",
|
||||
"position": 6,
|
||||
"link": {
|
||||
"type": "generated-index",
|
||||
"description": "Miscellaneous References"
|
||||
}
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
|
|
@ -1,75 +0,0 @@
|
|||
---
|
||||
sidebar_position: 0
|
||||
slug: /supported_models
|
||||
---
|
||||
|
||||
# Supported models
|
||||
import APITable from '@site/src/components/APITable';
|
||||
|
||||
A complete list of models supported by RAGFlow, which will continue to expand.
|
||||
|
||||
```mdx-code-block
|
||||
<APITable>
|
||||
```
|
||||
|
||||
| Provider | Chat | Embedding | Rerank | Img2txt | Speech2txt | TTS |
|
||||
| --------------------- | ------------------ | ------------------ | ------------------ | ------------------ | ------------------ | ------------------ |
|
||||
| Anthropic | :heavy_check_mark: | | | | | |
|
||||
| Azure-OpenAI | :heavy_check_mark: | :heavy_check_mark: | | :heavy_check_mark: | :heavy_check_mark: | |
|
||||
| BAAI | | :heavy_check_mark: | :heavy_check_mark: | | | |
|
||||
| BaiChuan | :heavy_check_mark: | :heavy_check_mark: | | | | |
|
||||
| BaiduYiyan | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | | |
|
||||
| Bedrock | :heavy_check_mark: | :heavy_check_mark: | | | | |
|
||||
| Cohere | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | | |
|
||||
| DeepSeek | :heavy_check_mark: | | | | | |
|
||||
| FastEmbed | | :heavy_check_mark: | | | | |
|
||||
| Fish Audio | | | | | | :heavy_check_mark: |
|
||||
| Gemini | :heavy_check_mark: | :heavy_check_mark: | | :heavy_check_mark: | | |
|
||||
| Google Cloud | :heavy_check_mark: | | | | | |
|
||||
| GPUStack | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | | :heavy_check_mark: | :heavy_check_mark: |
|
||||
| Groq | :heavy_check_mark: | | | | | |
|
||||
| HuggingFace | :heavy_check_mark: | :heavy_check_mark: | | | | |
|
||||
| Jina | | :heavy_check_mark: | :heavy_check_mark: | | | |
|
||||
| LeptonAI | :heavy_check_mark: | | | | | |
|
||||
| LocalAI | :heavy_check_mark: | :heavy_check_mark: | | :heavy_check_mark: | | |
|
||||
| LM-Studio | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | | |
|
||||
| MiniMax | :heavy_check_mark: | | | | | |
|
||||
| Mistral | :heavy_check_mark: | :heavy_check_mark: | | | | |
|
||||
| ModelScope | :heavy_check_mark: | | | | | |
|
||||
| Moonshot | :heavy_check_mark: | | | :heavy_check_mark: | | |
|
||||
| novita.ai | :heavy_check_mark: | | | | | |
|
||||
| NVIDIA | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | | |
|
||||
| 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: | | |
|
||||
| OpenRouter | :heavy_check_mark: | | | :heavy_check_mark: | | |
|
||||
| PerfXCloud | :heavy_check_mark: | :heavy_check_mark: | | | | |
|
||||
| Replicate | :heavy_check_mark: | :heavy_check_mark: | | | | |
|
||||
| PPIO | :heavy_check_mark: | | | | | |
|
||||
| SILICONFLOW | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | | |
|
||||
| StepFun | :heavy_check_mark: | | | | | |
|
||||
| Tencent Hunyuan | :heavy_check_mark: | | | | | |
|
||||
| Tencent Cloud | | | | | :heavy_check_mark: | |
|
||||
| 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: |
|
||||
| XunFei Spark | :heavy_check_mark: | | | | | :heavy_check_mark: |
|
||||
| Youdao | | :heavy_check_mark: | :heavy_check_mark: | | | |
|
||||
| ZHIPU-AI | :heavy_check_mark: | :heavy_check_mark: | | :heavy_check_mark: | | |
|
||||
| 01.AI | :heavy_check_mark: | | | | | |
|
||||
|
||||
```mdx-code-block
|
||||
</APITable>
|
||||
```
|
||||
|
||||
:::danger IMPORTANT
|
||||
If your model is not listed here but has APIs compatible with those of OpenAI, click **OpenAI-API-Compatible** on the **Model providers** page to configure your model.
|
||||
:::
|
||||
|
||||
:::note
|
||||
The list of supported models is extracted from [this source](https://github.com/infiniflow/ragflow/blob/main/rag/llm/__init__.py) and may not be the most current. For the latest supported model list, please refer to the Python file.
|
||||
:::
|
||||
|
|
@ -1,452 +0,0 @@
|
|||
---
|
||||
sidebar_position: 2
|
||||
slug: /release_notes
|
||||
---
|
||||
|
||||
# Releases
|
||||
|
||||
Key features, improvements and bug fixes in the latest releases.
|
||||
|
||||
## v0.17.2
|
||||
|
||||
Released on March 13, 2025.
|
||||
|
||||
### Improvements
|
||||
|
||||
- Adds OpenAI-compatible APIs.
|
||||
- Introduces a German user interface.
|
||||
- Accelerates knowledge graph extraction.
|
||||
- Enables Tavily-based web search in the **Retrieval** agent component.
|
||||
- Adds Tongyi-Qianwen QwQ models (OpenAI-compatible).
|
||||
- Supports CSV files in the **General** chunk method.
|
||||
|
||||
### Fixed issues
|
||||
|
||||
- Unable to add models via Ollama/Xinference, an issue introduced in v0.17.1.
|
||||
|
||||
### Related APIs
|
||||
|
||||
#### HTTP APIs
|
||||
|
||||
[Create chat completion](./references/http_api_reference.md#openai-compatible-api)
|
||||
|
||||
#### Python APIs
|
||||
|
||||
[Create chat completion](./references/python_api_reference.md#openai-compatible-api)
|
||||
|
||||
## v0.17.1
|
||||
|
||||
Released on March 11, 2025.
|
||||
|
||||
### Improvements
|
||||
|
||||
- Improves English tokenization quality.
|
||||
- Improves the table extraction logic in Markdown document parsing.
|
||||
- Updates SiliconFlow's model list.
|
||||
- Supports parsing XLS files (Excel97~2003) with improved corresponding error handling.
|
||||
- Supports Huggingface rerank models.
|
||||
- Enables relative time expressions ("now", "yesterday", "last week", "next year", and more) in the **Rewrite** agent component.
|
||||
|
||||
### Fixed issues
|
||||
|
||||
- A repetitive knowledge graph extraction issue.
|
||||
- Issues with API calling.
|
||||
- Options in the **Document parser** dropdown are missing.
|
||||
- A Tavily web search issue.
|
||||
- Unable to preview diagrams or images in an AI chat.
|
||||
|
||||
### Documentation
|
||||
|
||||
#### Added documents
|
||||
|
||||
[Use tag set](./guides/dataset/use_tag_sets.md)
|
||||
|
||||
## v0.17.0
|
||||
|
||||
Released on March 3, 2025.
|
||||
|
||||
### New features
|
||||
|
||||
- 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.
|
||||
- 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.
|
||||
- AI chat: Supports starting a chat without specifying knowledge bases.
|
||||
- AI chat: HTML files can also be previewed and referenced, in addition to PDF files.
|
||||
- Dataset: Adds a **Document parser** dropdown menu to dataset configurations. This includes a DeepDoc model option, which is time-consuming, a much faster **naive** option (plain text), which skips DLA (Document Layout Analysis), OCR (Optical Character Recognition), and TSR (Table Structure Recognition) tasks, and several currently *experimental* large model options.
|
||||
- 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.
|
||||
- Object storage: Supports using Aliyun OSS (Object Storage Service) as a file storage option.
|
||||
- Models: Updates the supported model list for Tongyi-Qianwen (Qwen), adding DeepSeek-specific models; adds ModelScope as a model provider.
|
||||
- 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.
|
||||
|
||||
### New features
|
||||
|
||||
- Supports DeepSeek R1 and DeepSeek V3.
|
||||
- GraphRAG refactor: Knowledge graph is dynamically built on an entire knowledge base (dataset) rather than on an individual file, and automatically updated when a newly uploaded file starts parsing. See [here](https://ragflow.io/docs/dev/construct_knowledge_graph).
|
||||
- Adds an **Iteration** agent component and a **Research report generator** agent template. See [here](./guides/agent/agent_component_reference/iteration.mdx).
|
||||
- New UI language: Portuguese.
|
||||
- Allows setting metadata for a specific file in a knowledge base to enhance AI-powered chats. See [here](./guides/dataset/set_metadata.md).
|
||||
- Upgrades RAGFlow's document engine [Infinity](https://github.com/infiniflow/infinity) to v0.6.0.dev3.
|
||||
- Supports GPU acceleration for DeepDoc (see [docker-compose-gpu.yml](https://github.com/infiniflow/ragflow/blob/main/docker/docker-compose-gpu.yml)).
|
||||
- Supports creating and referencing a **Tag** knowledge base as a key milestone towards bridging the semantic gap between query and response.
|
||||
|
||||
:::danger IMPORTANT
|
||||
The **Tag knowledge base** feature is *unavailable* on the [Infinity](https://github.com/infiniflow/infinity) document engine.
|
||||
:::
|
||||
|
||||
### Documentation
|
||||
|
||||
#### Added documents
|
||||
|
||||
- [Construct knowledge graph](./guides/dataset/construct_knowledge_graph.md)
|
||||
- [Set metadata](./guides/dataset/set_metadata.md)
|
||||
- [Begin component](./guides/agent/agent_component_reference/begin.mdx)
|
||||
- [Generate component](./guides/agent/agent_component_reference/generate.mdx)
|
||||
- [Interact component](./guides/agent/agent_component_reference/interact.mdx)
|
||||
- [Retrieval component](./guides/agent/agent_component_reference/retrieval.mdx)
|
||||
- [Categorize component](./guides/agent/agent_component_reference/categorize.mdx)
|
||||
- [Keyword component](./guides/agent/agent_component_reference/keyword.mdx)
|
||||
- [Message component](./guides/agent/agent_component_reference/message.mdx)
|
||||
- [Rewrite component](./guides/agent/agent_component_reference/rewrite.mdx)
|
||||
- [Switch component](./guides/agent/agent_component_reference/switch.mdx)
|
||||
- [Concentrator component](./guides/agent/agent_component_reference/concentrator.mdx)
|
||||
- [Template component](./guides/agent/agent_component_reference/template.mdx)
|
||||
- [Iteration component](./guides/agent/agent_component_reference/iteration.mdx)
|
||||
- [Note component](./guides/agent/agent_component_reference/note.mdx)
|
||||
|
||||
## v0.15.1
|
||||
|
||||
Released on December 25, 2024.
|
||||
|
||||
### Upgrades
|
||||
|
||||
- Upgrades RAGFlow's document engine [Infinity](https://github.com/infiniflow/infinity) to v0.5.2.
|
||||
- Enhances the log display of document parsing status.
|
||||
|
||||
### Fixed issues
|
||||
|
||||
This release fixes the following issues:
|
||||
|
||||
- The `SCORE not found` and `position_int` errors returned by [Infinity](https://github.com/infiniflow/infinity).
|
||||
- Once an embedding model in a specific knowledge base is changed, embedding models in other knowledge bases can no longer be changed.
|
||||
- Slow response in question-answering and AI search due to repetitive loading of the embedding model.
|
||||
- Fails to parse documents with RAPTOR.
|
||||
- Using the **Table** parsing method results in information loss.
|
||||
- Miscellaneous API issues.
|
||||
|
||||
### Related APIs
|
||||
|
||||
#### HTTP APIs
|
||||
|
||||
Adds an optional parameter `"user_id"` to the following APIs:
|
||||
|
||||
- [Create session with chat assistant](https://ragflow.io/docs/dev/http_api_reference#create-session-with-chat-assistant)
|
||||
- [Update chat assistant's session](https://ragflow.io/docs/dev/http_api_reference#update-chat-assistants-session)
|
||||
- [List chat assistant's sessions](https://ragflow.io/docs/dev/http_api_reference#list-chat-assistants-sessions)
|
||||
- [Create session with agent](https://ragflow.io/docs/dev/http_api_reference#create-session-with-agent)
|
||||
- [Converse with chat assistant](https://ragflow.io/docs/dev/http_api_reference#converse-with-chat-assistant)
|
||||
- [Converse with agent](https://ragflow.io/docs/dev/http_api_reference#converse-with-agent)
|
||||
- [List agent sessions](https://ragflow.io/docs/dev/http_api_reference#list-agent-sessions)
|
||||
|
||||
## v0.15.0
|
||||
|
||||
Released on December 18, 2024.
|
||||
|
||||
### New features
|
||||
|
||||
- Introduces additional Agent-specific APIs.
|
||||
- Supports using page rank score to improve retrieval performance when searching across multiple knowledge bases.
|
||||
- Offers an iframe in Chat and Agent to facilitate the integration of RAGFlow into your webpage.
|
||||
- Adds a Helm chart for deploying RAGFlow on Kubernetes.
|
||||
- Supports importing or exporting an agent in JSON format.
|
||||
- Supports step run for Agent components/tools.
|
||||
- Adds a new UI language: Japanese.
|
||||
- Supports resuming GraphRAG and RAPTOR from a failure, enhancing task management resilience.
|
||||
- Adds more Mistral models.
|
||||
- Adds a dark mode to the UI, allowing users to toggle between light and dark themes.
|
||||
|
||||
### Improvements
|
||||
|
||||
- Upgrades the Document Layout Analysis model in DeepDoc.
|
||||
- Significantly enhances the retrieval performance when using [Infinity](https://github.com/infiniflow/infinity) as document engine.
|
||||
|
||||
### Related APIs
|
||||
|
||||
#### HTTP APIs
|
||||
|
||||
- [List agent sessions](https://ragflow.io/docs/dev/http_api_reference#list-agent-sessions)
|
||||
- [List agents](https://ragflow.io/docs/dev/http_api_reference#list-agents)
|
||||
|
||||
#### Python APIs
|
||||
|
||||
- [List agent sessions](https://ragflow.io/docs/dev/python_api_reference#list-agent-sessions)
|
||||
- [List agents](https://ragflow.io/docs/dev/python_api_reference#list-agents)
|
||||
|
||||
## v0.14.1
|
||||
|
||||
Released on November 29, 2024.
|
||||
|
||||
### Improvements
|
||||
|
||||
Adds [Infinity's configuration file](https://github.com/infiniflow/ragflow/blob/main/docker/infinity_conf.toml) to facilitate integration and customization of [Infinity](https://github.com/infiniflow/infinity) as a document engine. From this release onwards, updates to Infinity's configuration can be made directly within RAGFlow and will take effect immediately after restarting RAGFlow using `docker compose`. [#3715](https://github.com/infiniflow/ragflow/pull/3715)
|
||||
|
||||
### Fixed issues
|
||||
|
||||
This release fixes the following issues:
|
||||
|
||||
- Unable to display or edit content of a chunk after clicking it.
|
||||
- A `'Not found'` error in Elasticsearch.
|
||||
- Chinese text becoming garbled during parsing.
|
||||
- A compatibility issue with Polars.
|
||||
- A compatibility issue between Infinity and GraphRAG.
|
||||
|
||||
## v0.14.0
|
||||
|
||||
Released on November 26, 2024.
|
||||
|
||||
### New features
|
||||
|
||||
- Supports [Infinity](https://github.com/infiniflow/infinity) or Elasticsearch (default) as document engine for vector storage and full-text indexing. [#2894](https://github.com/infiniflow/ragflow/pull/2894)
|
||||
- Enhances user experience by adding more variables to the Agent and implementing auto-saving.
|
||||
- Adds a three-step translation agent template, inspired by [Andrew Ng's translation agent](https://github.com/andrewyng/translation-agent).
|
||||
- Adds an SEO-optimized blog writing agent template.
|
||||
- Provides HTTP and Python APIs for conversing with an agent.
|
||||
- Supports the use of English synonyms during retrieval processes.
|
||||
- Optimizes term weight calculations, reducing the retrieval time by 50%.
|
||||
- Improves task executor monitoring with additional performance indicators.
|
||||
- Replaces Redis with Valkey.
|
||||
- Adds three new UI languages (*contributed by the community*): Indonesian, Spanish, and Vietnamese.
|
||||
|
||||
### Compatibility changes
|
||||
|
||||
As of this release, **service_config.yaml.template** replaces **service_config.yaml** for configuring backend services. Upon Docker container startup, the environment variables defined in this template file are automatically populated and a **service_config.yaml** is auto-generated from it. [#3341](https://github.com/infiniflow/ragflow/pull/3341)
|
||||
|
||||
This approach eliminates the need to manually update **service_config.yaml** after making changes to **.env**, facilitating dynamic environment configurations.
|
||||
|
||||
:::danger IMPORTANT
|
||||
Ensure that you [upgrade **both** your code **and** Docker image to this release](https://ragflow.io/docs/dev/upgrade_ragflow#upgrade-ragflow-to-the-most-recent-officially-published-release) before trying this new approach.
|
||||
:::
|
||||
|
||||
### Related APIs
|
||||
|
||||
#### HTTP APIs
|
||||
|
||||
- [Create session with agent](https://ragflow.io/docs/dev/http_api_reference#create-session-with-agent)
|
||||
- [Converse with agent](https://ragflow.io/docs/dev/http_api_reference#converse-with-agent)
|
||||
|
||||
#### Python APIs
|
||||
|
||||
- [Create session with agent](https://ragflow.io/docs/dev/python_api_reference#create-session-with-agent)
|
||||
- [Converse with agent](https://ragflow.io/docs/dev/python_api_reference#create-session-with-agent)
|
||||
|
||||
### Documentation
|
||||
|
||||
#### Added documents
|
||||
|
||||
- [Configurations](https://ragflow.io/docs/dev/configurations)
|
||||
- [Manage team members](https://ragflow.io/docs/dev/manage_team_members)
|
||||
- [Run health check on RAGFlow's dependencies](https://ragflow.io/docs/dev/run_health_check)
|
||||
|
||||
## v0.13.0
|
||||
|
||||
Released on October 31, 2024.
|
||||
|
||||
### New features
|
||||
|
||||
- Adds the team management functionality for all users.
|
||||
- Updates the Agent UI to improve usability.
|
||||
- Adds support for Markdown chunking in the **General** chunk method.
|
||||
- Introduces an **invoke** tool within the Agent UI.
|
||||
- Integrates support for Dify's knowledge base API.
|
||||
- Adds support for GLM4-9B and Yi-Lightning models.
|
||||
- Introduces HTTP and Python APIs for dataset management, file management within dataset, and chat assistant management.
|
||||
|
||||
:::tip NOTE
|
||||
To download RAGFlow's Python SDK:
|
||||
|
||||
```bash
|
||||
pip install ragflow-sdk==0.13.0
|
||||
```
|
||||
:::
|
||||
|
||||
### Documentation
|
||||
|
||||
#### Added documents
|
||||
|
||||
- [Acquire a RAGFlow API key](./develop/acquire_ragflow_api_key.md)
|
||||
- [HTTP API Reference](./references/http_api_reference.md)
|
||||
- [Python API Reference](./references/python_api_reference.md)
|
||||
|
||||
## v0.12.0
|
||||
|
||||
Released on September 30, 2024.
|
||||
|
||||
### New features
|
||||
|
||||
- Offers slim editions of RAGFlow's Docker images, which do not include built-in BGE/BCE embedding or reranking models.
|
||||
- Improves the results of multi-round dialogues.
|
||||
- Enables users to remove added LLM vendors.
|
||||
- Adds support for **OpenTTS** and **SparkTTS** models.
|
||||
- Implements an **Excel to HTML** toggle in the **General** chunk method, allowing users to parse a spreadsheet into either HTML tables or key-value pairs by row.
|
||||
- Adds agent tools **YahooFinance** and **Jin10**.
|
||||
- Adds an investment advisor agent template.
|
||||
|
||||
### Compatibility changes
|
||||
|
||||
As of this release, RAGFlow offers slim editions of its Docker images to improve the experience for users with limited Internet access. A slim edition of RAGFlow's Docker image does not include built-in BGE/BCE embedding models and has a size of about 1GB; a full edition of RAGFlow is approximately 9GB and includes both built-in embedding models and embedding models that will be downloaded once you select them in the RAGFlow UI.
|
||||
|
||||
The default Docker image edition is `nightly-slim`. The following list clarifies the differences between various editions:
|
||||
|
||||
- `nightly-slim`: The slim edition of the most recent tested Docker image.
|
||||
- `v0.12.0-slim`: The slim edition of the most recent **officially released** Docker image.
|
||||
- `nightly`: The full edition of the most recent tested Docker image.
|
||||
- `v0.12.0`: The full edition of the most recent **officially released** Docker image.
|
||||
|
||||
See [Upgrade RAGFlow](https://ragflow.io/docs/dev/upgrade_ragflow) for instructions on upgrading.
|
||||
|
||||
### Documentation
|
||||
|
||||
#### Added documents
|
||||
|
||||
- [Upgrade RAGFlow](https://ragflow.io/docs/dev/upgrade_ragflow)
|
||||
|
||||
## v0.11.0
|
||||
|
||||
Released on September 14, 2024.
|
||||
|
||||
### New features
|
||||
|
||||
- Introduces an AI search interface within the RAGFlow UI.
|
||||
- Supports audio output via **FishAudio** or **Tongyi Qwen TTS**.
|
||||
- Allows the use of Postgres for metadata storage, in addition to MySQL.
|
||||
- Supports object storage options with S3 or Azure Blob.
|
||||
- Supports model vendors: **Anthropic**, **Voyage AI**, and **Google Cloud**.
|
||||
- Supports the use of **Tencent Cloud ASR** for audio content recognition.
|
||||
- Adds finance-specific agent components: **WenCai**, **AkShare**, **YahooFinance**, and **TuShare**.
|
||||
- Adds a medical consultant agent template.
|
||||
- Supports running retrieval benchmarking on the following datasets:
|
||||
- [ms_marco_v1.1](https://huggingface.co/datasets/microsoft/ms_marco)
|
||||
- [trivia_qa](https://huggingface.co/datasets/mandarjoshi/trivia_qa)
|
||||
- [miracl](https://huggingface.co/datasets/miracl/miracl)
|
||||
|
||||
## v0.10.0
|
||||
|
||||
Released on August 26, 2024.
|
||||
|
||||
### New features
|
||||
|
||||
- Introduces a text-to-SQL template in the Agent UI.
|
||||
- Implements Agent APIs.
|
||||
- Incorporates monitoring for the task executor.
|
||||
- Introduces Agent tools **GitHub**, **DeepL**, **BaiduFanyi**, **QWeather**, and **GoogleScholar**.
|
||||
- Supports chunking of EML files.
|
||||
- Supports more LLMs or model services: **GPT-4o-mini**, **PerfXCloud**, **TogetherAI**, **Upstage**, **Novita.AI**, **01.AI**, **SiliconFlow**, **PPIO**, **XunFei Spark**, **Baidu Yiyan**, and **Tencent Hunyuan**.
|
||||
|
||||
## v0.9.0
|
||||
|
||||
Released on August 6, 2024.
|
||||
|
||||
### New features
|
||||
|
||||
- Supports GraphRAG as a chunk method.
|
||||
- Introduces Agent component **Keyword** and search tools, including **Baidu**, **DuckDuckGo**, **PubMed**, **Wikipedia**, **Bing**, and **Google**.
|
||||
- Supports speech-to-text recognition for audio files.
|
||||
- Supports model vendors **Gemini** and **Groq**.
|
||||
- Supports inference frameworks, engines, and services including **LM studio**, **OpenRouter**, **LocalAI**, and **Nvidia API**.
|
||||
- Supports using reranker models in Xinference.
|
||||
|
||||
## v0.8.0
|
||||
|
||||
Released on July 8, 2024.
|
||||
|
||||
### New features
|
||||
|
||||
- Supports Agentic RAG, enabling graph-based workflow construction for RAG and agents.
|
||||
- Supports model vendors **Mistral**, **MiniMax**, **Bedrock**, and **Azure OpenAI**.
|
||||
- Supports DOCX files in the MANUAL chunk method.
|
||||
- Supports DOCX, MD, and PDF files in the Q&A chunk method.
|
||||
|
||||
## v0.7.0
|
||||
|
||||
Released on May 31, 2024.
|
||||
|
||||
### New features
|
||||
|
||||
- Supports the use of reranker models.
|
||||
- Integrates reranker and embedding models: [BCE](https://github.com/netease-youdao/BCEmbedding), [BGE](https://github.com/FlagOpen/FlagEmbedding), and [Jina](https://jina.ai/embeddings/).
|
||||
- Supports LLMs Baichuan and VolcanoArk.
|
||||
- Implements [RAPTOR](https://arxiv.org/html/2401.18059v1) for improved text retrieval.
|
||||
- Supports HTML files in the GENERAL chunk method.
|
||||
- Provides HTTP and Python APIs for deleting documents by ID.
|
||||
- Supports ARM64 platforms.
|
||||
|
||||
:::danger IMPORTANT
|
||||
While we also test RAGFlow on ARM64 platforms, we do not maintain RAGFlow Docker images for ARM.
|
||||
|
||||
If you are on an ARM platform, follow [this guide](./develop/build_docker_image.mdx) to build a RAGFlow Docker image.
|
||||
:::
|
||||
|
||||
### Related APIs
|
||||
|
||||
#### HTTP API
|
||||
|
||||
- [Delete documents](https://ragflow.io/docs/dev/http_api_reference#delete-documents)
|
||||
|
||||
#### Python API
|
||||
|
||||
- [Delete documents](https://ragflow.io/docs/dev/python_api_reference#delete-documents)
|
||||
|
||||
## v0.6.0
|
||||
|
||||
Released on May 21, 2024.
|
||||
|
||||
### New features
|
||||
|
||||
- Supports streaming output.
|
||||
- Provides HTTP and Python APIs for retrieving document chunks.
|
||||
- Supports monitoring of system components, including Elasticsearch, MySQL, Redis, and MinIO.
|
||||
- Supports disabling **Layout Recognition** in the GENERAL chunk method to reduce file chunking time.
|
||||
|
||||
### Related APIs
|
||||
|
||||
#### HTTP API
|
||||
|
||||
- [Retrieve chunks](https://ragflow.io/docs/dev/http_api_reference#retrieve-chunks)
|
||||
|
||||
#### Python API
|
||||
|
||||
- [Retrieve chunks](https://ragflow.io/docs/dev/python_api_reference#retrieve-chunks)
|
||||
|
||||
## v0.5.0
|
||||
|
||||
Released on May 8, 2024.
|
||||
|
||||
### New features
|
||||
|
||||
- Supports LLM DeepSeek.
|
||||
|
|
@ -0,0 +1,63 @@
|
|||
function openTab(evt, tabName) {
|
||||
// Hide all tab content
|
||||
var tabContents = document.getElementsByClassName("tab-content");
|
||||
for (var i = 0; i < tabContents.length; i++) {
|
||||
tabContents[i].classList.remove("active");
|
||||
}
|
||||
|
||||
// Remove active class from all tab buttons
|
||||
var tabButtons = document.getElementsByClassName("tab-button");
|
||||
for (var i = 0; i < tabButtons.length; i++) {
|
||||
tabButtons[i].classList.remove("active");
|
||||
}
|
||||
|
||||
// Show the current tab and add active class to the button
|
||||
document.getElementById(tabName).classList.add("active");
|
||||
evt.currentTarget.classList.add("active");
|
||||
}
|
||||
/**
|
||||
* @function fetchAndRenderMarkdown
|
||||
* @description 异步获取 Markdown 文件内容并使用 Marked.js 将其渲染为 HTML。
|
||||
* @param {string} markdownPath - Markdown 文件的路径。
|
||||
* @param {string} containerId - 用于显示渲染后 HTML 的容器元素的 ID。
|
||||
*/
|
||||
async function fetchAndRenderMarkdown(markdownPath, containerId) {
|
||||
const container = document.getElementById(containerId);
|
||||
if (!container) {
|
||||
console.error(`Container element with id "${containerId}" not found.`);
|
||||
return;
|
||||
}
|
||||
|
||||
try {
|
||||
const response = await fetch(markdownPath);
|
||||
if (!response.ok) {
|
||||
throw new Error(`HTTP error! status: ${response.status}`);
|
||||
}
|
||||
const markdownText = await response.text();
|
||||
const htmlContent = marked.parse(markdownText); // 使用 marked.parse()
|
||||
container.innerHTML = htmlContent;
|
||||
} catch (error) {
|
||||
console.error('Error fetching or rendering Markdown:', error);
|
||||
container.innerHTML = '<p style="color: red;">加载常见问题失败，请稍后重试。</p>';
|
||||
}
|
||||
}
|
||||
|
||||
// 页面加载完成后执行
|
||||
document.addEventListener('DOMContentLoaded', (event) => {
|
||||
// 默认打开第一个标签页
|
||||
const firstTabButton = document.querySelector('.tab-button');
|
||||
if (firstTabButton) {
|
||||
firstTabButton.click();
|
||||
}
|
||||
|
||||
// 加载 FAQ Markdown 内容
|
||||
fetchAndRenderMarkdown('faq.md', 'faq-container');
|
||||
fetchAndRenderMarkdown('plan.md', 'dev-plan-container');
|
||||
// 初始化 AOS
|
||||
AOS.init({
|
||||
duration: 800, // 动画持续时间 (毫秒)
|
||||
once: true, // 动画是否只播放一次
|
||||
offset: 50, // 触发动画的偏移量 (像素)
|
||||
});
|
||||
});
|
||||
|
||||
|
|
@ -0,0 +1,671 @@
|
|||
:root {
|
||||
--primary-color: #3a86ff;
|
||||
--secondary-color: #28a745;
|
||||
--dark-color: #343a40;
|
||||
--light-color: #f8f9fa;
|
||||
--gray-color: #6c757d;
|
||||
}
|
||||
|
||||
* {
|
||||
margin: 0;
|
||||
padding: 0;
|
||||
box-sizing: border-box;
|
||||
}
|
||||
|
||||
body {
|
||||
font-family: 'Open Sans', 'Noto Sans SC', sans-serif;
|
||||
line-height: 1.6;
|
||||
color: var(--dark-color);
|
||||
background-color: #fff;
|
||||
}
|
||||
|
||||
a {
|
||||
text-decoration: none;
|
||||
color: var(--primary-color);
|
||||
transition: all 0.3s ease;
|
||||
}
|
||||
|
||||
a:hover {
|
||||
color: var(--secondary-color);
|
||||
}
|
||||
|
||||
.container {
|
||||
max-width: 1200px;
|
||||
margin: 0 auto;
|
||||
padding: 0 20px;
|
||||
}
|
||||
|
||||
/* Header Styles */
|
||||
header {
|
||||
background-color: #fff;
|
||||
box-shadow: 0 2px 10px rgba(0, 0, 0, 0.1);
|
||||
position: sticky;
|
||||
top: 0;
|
||||
z-index: 100;
|
||||
}
|
||||
|
||||
.navbar {
|
||||
display: flex;
|
||||
justify-content: space-between;
|
||||
align-items: center;
|
||||
padding: 20px 0;
|
||||
}
|
||||
|
||||
.logo {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
}
|
||||
|
||||
.logo img {
|
||||
height: 40px;
|
||||
margin-right: 10px;
|
||||
}
|
||||
|
||||
.logo h1 {
|
||||
font-size: 1.5rem;
|
||||
font-weight: 700;
|
||||
color: var(--dark-color);
|
||||
}
|
||||
|
||||
.nav-links {
|
||||
display: flex;
|
||||
list-style: none;
|
||||
}
|
||||
|
||||
.nav-links li {
|
||||
margin-left: 30px;
|
||||
}
|
||||
|
||||
.nav-links a {
|
||||
font-weight: 600;
|
||||
color: var(--dark-color);
|
||||
}
|
||||
|
||||
.nav-links a:hover {
|
||||
color: var(--primary-color);
|
||||
}
|
||||
|
||||
.language-switcher {
|
||||
margin-left: 20px;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
}
|
||||
|
||||
.language-switcher a {
|
||||
margin: 0 5px;
|
||||
font-size: 0.9rem;
|
||||
}
|
||||
|
||||
.auth-buttons {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
}
|
||||
|
||||
.btn {
|
||||
display: inline-block;
|
||||
padding: 8px 20px;
|
||||
border-radius: 5px;
|
||||
font-weight: 600;
|
||||
transition: all 0.3s ease;
|
||||
}
|
||||
|
||||
.btn-primary {
|
||||
background-color: var(--primary-color);
|
||||
color: white;
|
||||
border: 2px solid var(--primary-color);
|
||||
}
|
||||
|
||||
.btn-primary:hover {
|
||||
background-color: transparent;
|
||||
color: var(--primary-color);
|
||||
}
|
||||
|
||||
.btn-secondary {
|
||||
background-color: transparent;
|
||||
color: var(--primary-color);
|
||||
border: 2px solid var(--primary-color);
|
||||
margin-left: 10px;
|
||||
}
|
||||
|
||||
.btn-secondary:hover {
|
||||
background-color: var(--primary-color);
|
||||
color: white;
|
||||
}
|
||||
|
||||
/* Hero Section */
|
||||
.hero {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
padding: 80px 0;
|
||||
background: linear-gradient(135deg, #f5f7fa 0%, #e4e8eb 100%);
|
||||
}
|
||||
|
||||
.hero-content {
|
||||
flex: 1;
|
||||
padding-right: 40px;
|
||||
}
|
||||
|
||||
.hero-content h1 {
|
||||
font-size: 2.5rem;
|
||||
margin-bottom: 20px;
|
||||
color: var(--dark-color);
|
||||
line-height: 1.2;
|
||||
}
|
||||
|
||||
.hero-content p {
|
||||
font-size: 1.2rem;
|
||||
margin-bottom: 30px;
|
||||
color: var(--gray-color);
|
||||
}
|
||||
|
||||
.cta-buttons {
|
||||
display: flex;
|
||||
}
|
||||
|
||||
.hero-image {
|
||||
flex: 1;
|
||||
text-align: center;
|
||||
}
|
||||
|
||||
.hero-image img {
|
||||
max-width: 100%;
|
||||
border-radius: 10px;
|
||||
box-shadow: 0 10px 30px rgba(0, 0, 0, 0.1);
|
||||
}
|
||||
|
||||
/* Features Section */
|
||||
.features {
|
||||
padding: 80px 0;
|
||||
background-color: #fff;
|
||||
}
|
||||
|
||||
.section-title {
|
||||
text-align: center;
|
||||
margin-bottom: 50px;
|
||||
}
|
||||
|
||||
.section-title h2 {
|
||||
font-size: 2rem;
|
||||
color: var(--dark-color);
|
||||
margin-bottom: 15px;
|
||||
}
|
||||
|
||||
.section-title p {
|
||||
color: var(--gray-color);
|
||||
max-width: 700px;
|
||||
margin: 0 auto;
|
||||
}
|
||||
|
||||
.features-grid {
|
||||
display: grid;
|
||||
grid-template-columns: repeat(3, 1fr);
|
||||
gap: 30px;
|
||||
}
|
||||
|
||||
.feature-card {
|
||||
background-color: #fff;
|
||||
border-radius: 10px;
|
||||
padding: 30px;
|
||||
box-shadow: 0 5px 15px rgba(0, 0, 0, 0.05);
|
||||
transition: all 0.3s ease;
|
||||
text-align: center;
|
||||
}
|
||||
|
||||
.feature-card:hover {
|
||||
transform: translateY(-10px);
|
||||
box-shadow: 0 15px 30px rgba(0, 0, 0, 0.1);
|
||||
}
|
||||
|
||||
.feature-card .icon {
|
||||
font-size: 3rem;
|
||||
margin-bottom: 20px;
|
||||
color: var(--primary-color);
|
||||
}
|
||||
|
||||
.feature-card h3 {
|
||||
font-size: 1.3rem;
|
||||
margin-bottom: 15px;
|
||||
color: var(--dark-color);
|
||||
}
|
||||
|
||||
.feature-card p {
|
||||
color: var(--gray-color);
|
||||
}
|
||||
|
||||
/* Demo Section */
|
||||
.demo {
|
||||
padding: 80px 0;
|
||||
background-color: var(--light-color);
|
||||
}
|
||||
|
||||
.video-container {
|
||||
position: relative;
|
||||
max-width: 800px;
|
||||
margin: 0 auto;
|
||||
border-radius: 10px;
|
||||
overflow: hidden;
|
||||
box-shadow: 0 10px 30px rgba(0, 0, 0, 0.1);
|
||||
}
|
||||
|
||||
.video-container img {
|
||||
width: 100%;
|
||||
display: block;
|
||||
}
|
||||
|
||||
.play-button {
|
||||
position: absolute;
|
||||
top: 50%;
|
||||
left: 50%;
|
||||
transform: translate(-50%, -50%);
|
||||
width: 80px;
|
||||
height: 80px;
|
||||
background-color: rgba(255, 255, 255, 0.8);
|
||||
border-radius: 50%;
|
||||
display: flex;
|
||||
justify-content: center;
|
||||
align-items: center;
|
||||
font-size: 2rem;
|
||||
color: var(--primary-color);
|
||||
cursor: pointer;
|
||||
transition: all 0.3s ease;
|
||||
}
|
||||
|
||||
.play-button:hover {
|
||||
background-color: var(--primary-color);
|
||||
color: white;
|
||||
}
|
||||
|
||||
/* Quick Start Section */
|
||||
.quick-start {
|
||||
padding: 80px 0;
|
||||
background-color: #fff;
|
||||
}
|
||||
|
||||
.installation-option {
|
||||
margin-bottom: 50px;
|
||||
background-color: #fff;
|
||||
border-radius: 10px;
|
||||
padding: 30px;
|
||||
box-shadow: 0 5px 15px rgba(0, 0, 0, 0.05);
|
||||
}
|
||||
|
||||
.installation-option h3 {
|
||||
font-size: 1.5rem;
|
||||
margin-bottom: 20px;
|
||||
color: var(--dark-color);
|
||||
}
|
||||
|
||||
.installation-option p {
|
||||
margin-bottom: 15px;
|
||||
color: var(--gray-color);
|
||||
}
|
||||
|
||||
.code-tabs {
|
||||
margin: 20px 0;
|
||||
}
|
||||
|
||||
.tab-header {
|
||||
display: flex;
|
||||
border-bottom: 1px solid #ddd;
|
||||
margin-bottom: -1px;
|
||||
}
|
||||
|
||||
.tab-button {
|
||||
padding: 10px 20px;
|
||||
background: none;
|
||||
border: none;
|
||||
border-bottom: 2px solid transparent;
|
||||
font-weight: 600;
|
||||
cursor: pointer;
|
||||
color: var(--gray-color);
|
||||
transition: all 0.3s ease;
|
||||
}
|
||||
|
||||
.tab-button.active {
|
||||
color: var(--primary-color);
|
||||
border-bottom-color: var(--primary-color);
|
||||
}
|
||||
|
||||
.tab-content {
|
||||
display: none;
|
||||
padding: 20px 0;
|
||||
}
|
||||
|
||||
.tab-content.active {
|
||||
display: block;
|
||||
}
|
||||
|
||||
pre {
|
||||
background-color: #f8f9fa;
|
||||
padding: 15px;
|
||||
border-radius: 5px;
|
||||
overflow-x: auto;
|
||||
font-family: 'Courier New', Courier, monospace;
|
||||
margin: 15px 0;
|
||||
}
|
||||
|
||||
code {
|
||||
font-family: 'Courier New', Courier, monospace;
|
||||
color: var(--dark-color);
|
||||
}
|
||||
|
||||
/* Community Section */
|
||||
.community {
|
||||
padding: 80px 0;
|
||||
background-color: var(--light-color);
|
||||
text-align: center;
|
||||
}
|
||||
|
||||
.community h2 {
|
||||
font-size: 2rem;
|
||||
margin-bottom: 20px;
|
||||
color: var(--dark-color);
|
||||
}
|
||||
|
||||
.community p {
|
||||
color: var(--gray-color);
|
||||
max-width: 700px;
|
||||
margin: 0 auto 30px;
|
||||
}
|
||||
|
||||
.qrcode-container {
|
||||
/* 保持原有样式，可以根据需要调整 max-width */
|
||||
margin: 15px; /* 调整外边距 */
|
||||
max-width: 250px; /* 可以调整宽度 */
|
||||
display: inline-block; /* 确保在 flex 布局中正确表现 */
|
||||
vertical-align: top; /* 顶部对齐 */
|
||||
}
|
||||
|
||||
.qrcode-container img {
|
||||
max-width: 100%;
|
||||
border-radius: 10px;
|
||||
box-shadow: 0 5px 15px rgba(0, 0, 0, 0.1);
|
||||
margin-bottom: 10px; /* 图片和文字间距 */
|
||||
}
|
||||
|
||||
.qrcode-container p {
|
||||
color: var(--gray-color);
|
||||
font-size: 0.9rem;
|
||||
}
|
||||
|
||||
/* 新增包裹层样式 */
|
||||
.qrcodes-wrapper {
|
||||
display: flex; /* 使用 Flexbox 布局 */
|
||||
justify-content: center; /* 水平居中 */
|
||||
align-items: flex-start; /* 顶部对齐 */
|
||||
flex-wrap: wrap; /* 在小屏幕上换行 */
|
||||
gap: 30px; /* 设置元素之间的间距 */
|
||||
margin-top: 30px; /* 与上方标题的间距 */
|
||||
}
|
||||
|
||||
.update-info {
|
||||
background-color: #fff;
|
||||
padding: 30px;
|
||||
border-radius: 10px;
|
||||
max-width: 700px;
|
||||
margin: 30px auto 0;
|
||||
box-shadow: 0 5px 15px rgba(0, 0, 0, 0.05);
|
||||
}
|
||||
|
||||
.update-info h3 {
|
||||
font-size: 1.3rem;
|
||||
margin-bottom: 15px;
|
||||
color: var(--dark-color);
|
||||
}
|
||||
|
||||
/* Contribution Section */
|
||||
.contribution {
|
||||
padding: 80px 0;
|
||||
background-color: #fff;
|
||||
}
|
||||
|
||||
.contribution h2 {
|
||||
font-size: 2rem;
|
||||
margin-bottom: 30px;
|
||||
color: var(--dark-color);
|
||||
text-align: center;
|
||||
}
|
||||
|
||||
.contribution ol {
|
||||
max-width: 600px;
|
||||
margin: 0 auto;
|
||||
padding-left: 20px;
|
||||
}
|
||||
|
||||
.contribution li {
|
||||
margin-bottom: 15px;
|
||||
color: var(--gray-color);
|
||||
}
|
||||
|
||||
/* Footer */
|
||||
footer {
|
||||
background-color: var(--dark-color);
|
||||
color: white;
|
||||
padding: 50px 0 20px;
|
||||
}
|
||||
|
||||
.footer-content {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
align-items: center;
|
||||
}
|
||||
|
||||
.license {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
margin-bottom: 20px;
|
||||
}
|
||||
|
||||
.license img {
|
||||
margin-right: 15px;
|
||||
}
|
||||
|
||||
.license span {
|
||||
color: #adb5bd;
|
||||
}
|
||||
|
||||
.links {
|
||||
display: flex;
|
||||
margin-bottom: 30px;
|
||||
}
|
||||
|
||||
.links a {
|
||||
color: #adb5bd;
|
||||
margin: 0 15px;
|
||||
}
|
||||
|
||||
.links a:hover {
|
||||
color: white;
|
||||
}
|
||||
|
||||
.copyright {
|
||||
color: #adb5bd;
|
||||
font-size: 0.9rem;
|
||||
}
|
||||
|
||||
/* Responsive Styles */
|
||||
@media (max-width: 992px) {
|
||||
.hero {
|
||||
flex-direction: column;
|
||||
text-align: center;
|
||||
}
|
||||
|
||||
.hero-content {
|
||||
padding-right: 0;
|
||||
margin-bottom: 40px;
|
||||
}
|
||||
|
||||
.cta-buttons {
|
||||
justify-content: center;
|
||||
}
|
||||
}
|
||||
|
||||
@media (max-width: 768px) {
|
||||
.navbar {
|
||||
flex-direction: column;
|
||||
align-items: flex-start;
|
||||
}
|
||||
|
||||
.nav-links {
|
||||
margin-top: 20px;
|
||||
flex-direction: column;
|
||||
}
|
||||
|
||||
.nav-links li {
|
||||
margin: 10px 0 0;
|
||||
}
|
||||
|
||||
.auth-buttons {
|
||||
margin-top: 20px;
|
||||
width: 100%;
|
||||
justify-content: center;
|
||||
}
|
||||
|
||||
.hero-content h1 {
|
||||
font-size: 2rem;
|
||||
}
|
||||
|
||||
.section-title h2 {
|
||||
font-size: 1.8rem;
|
||||
}
|
||||
}
|
||||
@media (max-width: 600px) {
|
||||
.qrcodes-wrapper {
|
||||
flex-direction: column; /* 改为垂直排列 */
|
||||
align-items: center; /* 垂直居中 */
|
||||
}
|
||||
.qrcode-container {
|
||||
max-width: 80%; /* 调整宽度以适应垂直布局 */
|
||||
}
|
||||
}
|
||||
|
||||
/* FAQ Section Styles */
|
||||
.faq-section {
|
||||
padding: 80px 0;
|
||||
background-color: #fff; /* 或者 var(--light-color) */
|
||||
}
|
||||
|
||||
#faq-container {
|
||||
margin-top: 30px;
|
||||
background-color: #fff; /* 可以给容器加个背景 */
|
||||
padding: 20px 30px; /* 内边距 */
|
||||
border-radius: 8px;
|
||||
box-shadow: 0 5px 15px rgba(0, 0, 0, 0.05); /* 轻微阴影 */
|
||||
}
|
||||
|
||||
#faq-container h1 { /* Markdown 中的 H1 */
|
||||
font-size: 1.8rem;
|
||||
color: var(--dark-color);
|
||||
margin-bottom: 25px;
|
||||
padding-bottom: 10px;
|
||||
border-bottom: 1px solid #eee;
|
||||
}
|
||||
|
||||
#faq-container h2 { /* Markdown 中的 H2 */
|
||||
font-size: 1.4rem;
|
||||
color: var(--primary-color);
|
||||
margin-top: 30px;
|
||||
margin-bottom: 15px;
|
||||
}
|
||||
|
||||
#faq-container h3 { /* Markdown 中的 H3 */
|
||||
font-size: 1.2rem;
|
||||
color: var(--dark-color);
|
||||
margin-top: 25px;
|
||||
margin-bottom: 10px;
|
||||
}
|
||||
|
||||
#faq-container p {
|
||||
margin-bottom: 15px;
|
||||
color: var(--gray-color);
|
||||
line-height: 1.7;
|
||||
}
|
||||
|
||||
#faq-container ul, #faq-container ol {
|
||||
margin-left: 20px;
|
||||
margin-bottom: 15px;
|
||||
color: var(--gray-color);
|
||||
}
|
||||
#faq-container li {
|
||||
margin-bottom: 8px;
|
||||
}
|
||||
|
||||
#faq-container code {
|
||||
background-color: #e9ecef; /* 代码片段背景色 */
|
||||
padding: 2px 6px;
|
||||
border-radius: 4px;
|
||||
font-family: monospace;
|
||||
color: #c7254e; /* 代码片段文字颜色 */
|
||||
}
|
||||
|
||||
#faq-container pre {
|
||||
background-color: #f8f9fa;
|
||||
border: 1px solid #e9ecef;
|
||||
border-radius: 5px;
|
||||
padding: 15px;
|
||||
overflow-x: auto;
|
||||
margin-bottom: 15px;
|
||||
}
|
||||
|
||||
#faq-container pre code {
|
||||
background-color: transparent;
|
||||
padding: 0;
|
||||
border-radius: 0;
|
||||
color: var(--dark-color); /* 代码块内文字颜色 */
|
||||
}
|
||||
|
||||
#faq-container a {
|
||||
color: var(--primary-color);
|
||||
text-decoration: underline;
|
||||
}
|
||||
#faq-container a:hover {
|
||||
color: var(--secondary-color);
|
||||
}
|
||||
|
||||
.dev-plan-section {
|
||||
padding: 80px 0;
|
||||
background-color: var(--light-color); /* Slightly different background */
|
||||
}
|
||||
|
||||
#dev-plan-container {
|
||||
margin-top: 30px;
|
||||
background-color: #fff;
|
||||
padding: 20px 30px;
|
||||
border-radius: 8px;
|
||||
box-shadow: 0 5px 15px rgba(0, 0, 0, 0.05);
|
||||
}
|
||||
|
||||
/* Apply similar heading/paragraph styles as FAQ if needed */
|
||||
#dev-plan-container h1 {
|
||||
font-size: 1.8rem; color: var(--dark-color); margin-bottom: 25px; padding-bottom: 10px; border-bottom: 1px solid #eee;
|
||||
}
|
||||
#dev-plan-container h2 {
|
||||
font-size: 1.4rem; color: var(--primary-color); margin-top: 30px; margin-bottom: 15px;
|
||||
}
|
||||
#dev-plan-container h3 {
|
||||
font-size: 1.2rem; color: var(--dark-color); margin-top: 25px; margin-bottom: 10px;
|
||||
}
|
||||
#dev-plan-container p {
|
||||
margin-bottom: 15px; color: var(--gray-color); line-height: 1.7;
|
||||
}
|
||||
#dev-plan-container ul, #dev-plan-container ol {
|
||||
margin-left: 20px; margin-bottom: 15px; color: var(--gray-color);
|
||||
}
|
||||
#dev-plan-container li {
|
||||
margin-bottom: 8px;
|
||||
}
|
||||
#dev-plan-container code {
|
||||
background-color: #e9ecef; padding: 2px 6px; border-radius: 4px; font-family: monospace; color: #c7254e;
|
||||
}
|
||||
#dev-plan-container pre {
|
||||
background-color: #f8f9fa; border: 1px solid #e9ecef; border-radius: 5px; padding: 15px; overflow-x: auto; margin-bottom: 15px;
|
||||
}
|
||||
#dev-plan-container pre code {
|
||||
background-color: transparent; padding: 0; border-radius: 0; color: var(--dark-color);
|
||||
}
|
||||
#dev-plan-container a {
|
||||
color: var(--primary-color); text-decoration: underline;
|
||||
}
|
||||
#dev-plan-container a:hover {
|
||||
color: var(--secondary-color);
|
||||
}
|
||||
Loading…
Reference in New Issue