用 "casibase" 标记的 {count, plural, other {3 篇文章}}

欢迎使用 Casibase 网站 ！

在当今数字时代，企业和个人对高效的知识管理和智能问答系统的需求日益增长。随着人工智能技术的快速发展，人们正在寻求更智能、更高效的解决方案来改进信息处理、优化工作流程并提升用户体验。 随着人工智能技术的快速发展，人们正在寻找更智能、更高效的解决方案，以改善信息处理、优化工作流程并提升用户体验。

Casibase 作为一个开源的 AI 知识库和对话系统，因其强大的多模型兼容性、企业级功能支持以及直观友好的 Web 界面，已成为许多开发者和企业的首选解决方案。它不仅能够高效地组织和检索知识，还提供灵活的接口，可以轻松集成各种 AI 模型，以满足不同场景的需求。

同时，最近发布的先进 AI 模型如 DeepSeek R1 因其出色的性能、优化的蒸馏技术和免费开源等特点，在开发者社区引起了广泛关注。DeepSeek R1 强大的推理能力和高效的知识整合能力使其在智能问答、代码生成、文本理解等任务中表现出色，成为开源 AI 生态系统中的重要一员。

在本文中，我们将讨论如何高效地将 Casibase 与 DeepSeek R1 集成，构建一个具有高性能、安全性和强大功能的 AI 知识库和对话系统。我们将介绍从环境搭建到实际应用的关键步骤，并分析如何利用 Casibase 的企业级功能和 DeepSeek R1 的强大能力来实现准确高效的智能问答和知识管理。

什么是 Casibase：​

Casibase 是一个开源的 AI 知识库和对话系统，它结合了最新的 RAG（检索增强生成）技术、企业级单点登录（SSO）功能，并支持广泛的主流 AI 模型。作为一个类似 LangChain 的系统，Casibase 旨在为企业和开发者提供一个强大、灵活且易用的知识管理和智能对话平台。

目前，Casibase 支持基于 OpenAI 的 GPT 系列、Gemini、Claude、Moonshot、DeepSeek 等语言模型，以及基于 OpenAI 的嵌入模型、Hugging Face 的 sentence-transformers、Cohere、Qwen 等嵌入模型。这种广泛的模型支持使用户能够根据需求和偏好选择最适合的 AI 模型，同时也方便未来集成新的模型。

Casibase 在线演示：​

Casibase 提供了多个在线演示站点，包括聊天机器人演示和管理界面演示，让用户可以直观地体验系统功能。

• 聊天机器人演示：https://demo.casibase.com
• 管理界面演示：https://demo-admin.casibase.com

Casibase 与 DeepSeek 系列模型的集成：​

在 Casibase 中使用 DeepSeek 非常简单，通过构建 Casibase 并进行一些简单配置，您就可以与 DeepSeek R1、DeepSeek V3 以及阿里云等其他平台支持的 DeepSeek 及其增强版本进行对话。避免了本地部署的复杂配置。

步骤一：Casibase 环境部署​

1.1 部署 Casdoor​

由于 Casibase 提供的强大企业级 SSO 认证、鉴权等功能是基于 Casdoor 完成的。因此，安装 Casdoor 是使用 Casibase 的前提条件。

Casdoor 是一个强大的认证系统，提供安全可靠的登录体验。

您可以参考 Casdoor 官方文档完成 Casdoor 环境部署：Casdoor 部署

1.2 部署 Casibase：Casibase 部署​

步骤二：Casibase 基础配置​

2.1 配置 Casdoor：​

您需要在已部署的 Casdoor 中完成三个步骤以支持访问 Casibase 的用户存在，即配置组织->配置应用->配置用户。具体操作如下：

2.1.1 添加组织​

在 Casdoor 网站中配置组织。

2.1.2 添加应用​

为 Casibase 设置"应用"。您应该基于已配置的组织创建它，这将在添加表单中体现。

2.1.3 添加用户​

为创建的应用创建用户是后续登录 Casibase 的最后准备工作。只需按照表单中的信息填写即可。

2.2 配置 Casibase：​

通过上述配置，我们已经可以访问 Casibase，接下来，在登录后，我们将为 AI 聊天进行配置，部署基于阿里云大模型服务平台（https://help.aliyun.com/zh/model-studio/）支持的 DeepSeek r1 模型。

2.2.1 配置存储提供商​

首先，需要通过 Casdoor 配置存储提供商，通过添加此存储提供商可以用于存储数据。 首先，需要通过 Casdoor 配置存储提供商，通过添加此存储提供商可以用于存储数据。可以通过点击首页上的按钮添加到 Casdoor：Authentication-> Providers->add

2.2.2 配置模型提供商​

在 Casibase 中，配置新的模型提供商：点击首页顶部导航栏中的 Providers 按钮->add。

在弹出的添加表单中，我们可以将 Category 设置为 Model，然后设置一级模型：如 Moonshot、DeepSeek、阿里云等。这里我们不直接选择 DeepSeek 提供的 AI 模型支持，而是使用阿里云提供的 DeepSeek 支持，如下图所示，我们可以在 Sub Type 中看到阿里云支持的各种子模型。 这里我们不直接选择 DeepSeek 提供的 AI 模型支持，而是使用阿里云提供的 DeepSeek 支持，如下图所示，我们可以在 Sub Type 中看到阿里云支持的各种子模型。***

2.2.3 配置嵌入提供商（可选）​

在 2.2.1 和 2.2.2 添加成功后，我们可以配置嵌入提供商，通过配置嵌入提供商，我们可以与我们强大的知识库系统 Casibase 集成。与 2.2.2 类似的步骤，通过：Authentication-> Providers->add 填写表单（但请注意 Category 需要配置为 Embedding）：

2.2.4 添加存储​

在这一步中，我们组织上面添加的所有信息（存储提供商、模型提供商、嵌入提供商），在表单中我们可以填写这三项内容并最终创建一个存储。

步骤三：使用基于 Casibase 和阿里云的 DeepSeek 模型进行聊天​

结论​

在本指南中，我们学习了如何在 Casibase 中使用 DeepSeek 实现 AI 聊天功能。 现在，您可以在Casibase中与聊天机器人对话了。尽情享受吧！

更多详细信息，您可以参考 Casibase 文档。

When building knowledge-based AI applications, choosing the right RAG (Retrieval-Augmented Generation) platform can make or break your project. The market has several open-source options, each with different strengths. Let's compare Casibase with three popular alternatives: Dify, RAGFlow, and FastGPT.

The Enterprise Foundation​

What sets Casibase apart from day one is its enterprise-grade authentication through Casdoor integration. While other platforms bolt on authentication as an afterthought, Casibase treats it as a fundamental building block. You get SSO, OAuth providers (GitHub, Google, WeChat, QQ), and fine-grained access control without writing a single line of custom authentication code.

Dify offers basic user management but lacks the sophisticated multi-tenant capabilities that Casdoor provides. RAGFlow and FastGPT take minimalist approaches to authentication, which works for quick prototypes but becomes problematic when you need to deploy to actual users in production environments.

Architecture Philosophy​

Casibase follows a clean separation between frontend (React) and backend (Golang with Beego), giving you the flexibility to customize either layer independently. The Golang backend handles high concurrency gracefully, which matters when you're serving hundreds of simultaneous users querying your knowledge base.

Dify embraces Python with a focus on visual workflow building. If your team prefers drag-and-drop interfaces for orchestrating LLM chains, Dify's approach feels natural. However, the Python runtime can become a bottleneck under heavy load compared to Casibase's compiled Go backend.

RAGFlow positions itself as a deep RAG engine, emphasizing document parsing quality over broad feature coverage. It excels at extracting structured information from complex PDFs and legal documents. FastGPT takes a middle path, offering decent document processing while maintaining simplicity.

Model Flexibility​

Casibase supports an extensive array of language models out of the box: OpenAI's GPT series, Claude, Gemini, local models through Hugging Face, and Chinese models like ChatGLM, Ernie, and iFlytek. The embedding model support is equally comprehensive, from OpenAI to Cohere to local sentence transformers.

Dify has strong model support too, particularly for OpenAI and Anthropic models, with a growing list of integrations. RAGFlow focuses primarily on OpenAI compatibility, which keeps things simple but limits your options. FastGPT sits somewhere in between, supporting major providers but not the breadth that Casibase offers.

The practical difference emerges when you want to switch models mid-project or run A/B tests across providers. Casibase's provider abstraction layer makes this straightforward. With RAGFlow or FastGPT, you might find yourself rewriting integration code.

Knowledge Management Depth​

All four platforms implement vector similarity search, but they differ in sophistication. Casibase provides granular control over embedding methods, storage backends (AWS, Azure, local), and retrieval strategies. You can fine-tune how documents are chunked, embedded, and indexed.

RAGFlow shines here with advanced document parsing that understands document structure, tables, and complex layouts. If your knowledge base consists of technical manuals or legal contracts, RAGFlow's parsing capabilities deliver cleaner chunks for embedding.

Dify makes knowledge management accessible through visual tools, letting non-technical users upload and organize documents. FastGPT takes a pragmatic approach, offering solid fundamentals without overwhelming users with options.

Beyond Q&A​

Casibase isn't just a RAG system. It includes IM (instant messaging) and forum capabilities, turning it into a complete knowledge collaboration platform. You can build internal wikis where employees both query AI and discuss topics with each other. The security audit logging tracks not just what users ask, but how they interact with the system.

None of the other platforms attempt this breadth. Dify focuses on workflow automation and agent building. RAGFlow concentrates on retrieval quality. FastGPT keeps scope tight around conversational AI. Whether Casibase's broader feature set is an advantage or unnecessary complexity depends on your requirements.

Database and Deployment​

Casibase supports MySQL, PostgreSQL, and SQL Server with extensible plugins for others. The containerized deployment options (Docker, Kubernetes) and Cloud platform integration make it production-ready from the start.

Dify typically runs on PostgreSQL with good containerization support. RAGFlow and FastGPT offer Docker deployments but with less documentation around scaling and multi-instance setups. If you're deploying a hobby project, any of these work. For production systems serving thousands of users, Casibase's operational maturity becomes valuable.

The Development Experience​

FastGPT wins points for getting started quickly. Clone, configure your API keys, and you're chatting with your documents in minutes. Casibase requires more setup due to the Casdoor integration, but that upfront investment pays dividends when you need proper user management.

Dify's visual workflow builder appeals to teams mixing developers and domain experts who want to collaborate on prompt engineering. RAGFlow demands more technical expertise but rewards you with superior document understanding.

Casibase occupies interesting middle ground: easier than RAGFlow for most use cases, more powerful than FastGPT for production needs, and more enterprise-focused than Dify.

Making the Choice​

Choose Casibase when you need enterprise features, plan to support multiple models, or want a platform that can grow from prototype to production. The Golang backend and mature authentication system make it suitable for serious deployments.

Pick Dify if you want visual workflow building and your team includes non-developers who need to participate in system design. The Python ecosystem and drag-and-drop interface lower the barrier to entry.

Select RAGFlow when document parsing quality matters more than anything else. If you're building a system to query complex technical documentation or legal texts, its parsing sophistication justifies the narrower feature set.

Opt for FastGPT when you need something working today and don't require enterprise features. It's perfect for personal projects or small team experiments where simplicity trumps scalability.

The truth is, all four platforms solve real problems. Casibase's bet on enterprise readiness, broad model support, and extensibility makes it particularly well-suited for organizations building knowledge systems they'll rely on long-term. The initial complexity pays off as your requirements inevitably grow beyond basic Q&A.