Casibase, as an open source AI knowledge base and dialogue system, has become the preferred solution for many developers and enterprises due to its strong multi-model compatibility, enterprise-level feature support, and intuitive and friendly web interface. It not only can efficiently organise and retrieve knowledge, but also provides flexible interfaces for easy integration with various AI models to meet the needs of different scenarios.

Meanwhile, recently released advanced AI models such as DeepSeek R1 have attracted a lot of attention in the developer community due to their excellent performance, optimised distillation technology and free open source. DeepSeek R1's powerful reasoning and efficient knowledge integration capabilities have enabled it to perform well in intelligent Q&A, code generation, text comprehension, and other tasks, making it an important member of the open source AI ecosystem.

In this article, we will discuss how to efficiently integrate Casibase with DeepSeek R1 to build an AI knowledge base and dialogue system with high performance, security, and powerful features. We will introduce the key steps from environment setup to real-world application, and analyse how to leverage the enterprise-grade features of Casibase and the power of DeepSeek R1 to achieve accurate and efficient intelligent Q&A and knowledge management.

What is Casibase:​

Casibase is an open source AI knowledge base and dialogue system that combines the latest RAG (Retrieval Augmented Generation) technology, enterprise-grade Single sign-on (SSO) functionality, and support for a wide range of mainstream AI models. As a LangChain-like system, Casibase aims to provide a powerful, flexible and easy-to-use knowledge management and intelligent dialogue platform for enterprises and developers.

Currently, Casibase supports language models such as OpenAI-based GPT series, Gemini, Claude, Moonshot, DeepSeek, etc., as well as OpenAI-based embedding models, Hugging Face's sentence-transformers, Cohere, Qwen, and other models. Hugging Face, Cohere, Qwen, and other embedding models. This wide range of model support allows users to choose the most suitable AI model according to their needs and preferences, and also facilitates the integration of new models in the future.

Casibase Online Demo:​

Casibase offers several online demo sites, including a chatbot demo and an administration interface demo, allowing users to experience the system's functionality visually.

• Chatbot demo: https://demo.casibase.com
• Management UI Demo: https://demo-admin.casibase.com

Casibase integration with the DeepSeek series of models:​

Using DeepSeek in Casibase is very easy, by building Casibase and making some simple configurations, you can talk to DeepSeek R1, DeepSeek V3 and a bunch of Deepseek and its enhancements supported by other platforms such as AlibabaCloud. Avoid complex configurations for local deployments.

Step 1: Casibase environment deployment​

1.1 Deployment of Casdoor.​

Since the powerful enterprise SSO authentication, authentication and other functions provided by Casibase are done based on Casdoor. Therefore, installing Casdoor is a prerequisite for using Casibase.

Casdoor is a robust authentication system that provides a safe and secure login experience.

You can refer to the official Casdoor documentation to complete the Casdoor environment deployment：Deployment of Casdoor

1.2 Deployment of Casibase: Deployment of Casibase​

Step 2: Casibase Basic Configuration​

2.1 Configuring Casdoor:​

You need to complete three steps in the deployed Casdoor to support the presence of users accessing Casibase, i.e., Configure Organizations->Configure Apply under Authentication->Configure user. do the following:

2.1.1 Add Organisation​

Configuring Organisations in the Casdoor website.

2.1.2 Add Apply​

Set "Apply" for Casibase. you should create it based on the Organizations you have configured, which will be reflected in the add form.

2.1.3 Add User​

Creating a user for the created Apply is the final preparation for subsequent login to Casibase. Just follow the information on the form.

2.2 Configuring Casibase：​

With the above configuration, we already have access to casibase, next, after logging in, we will configure it for AI chat, deploying a DeepSeek r1 based on the AlibabaCloud Big Model Service Platform (https://help.aliyun.com/zh/model-studio/) supported model.

2.2.1 Configure Storage Provider​

Firstly, it is necessary to configure a storage provider through Casdoor, which can be used to store data by adding this storage provider. They can be added to Casdoor by clicking the button on the homepage：Authentication-> Providers->add

2.2.2 Configure Model Provider​

In Casibase, configure the new model provider: Click the Providers button in the navigation bar at the top of the home page->add.

In the pop-up add form, we can set Category as Model, and then set the first level model: such as Moonshot, DeepSeek, Alibaba Cloud, etc. Here we don't directly select the AI model support provided by DeepSeek, but use the DeepSeek provided by Alibaba Cloud support, as shown below, we can see various sub-models supported by Alibaba Cloud in Sub Type.

2.2.3 Configure Embedding Providers (Optional)​

After 2.2.1 and 2.2.2 have been added successfully, we can Configure Embedding Providers, by configuring Embedding Providers, we can integrate with our powerful knowledge base system Casibase. Similar steps to 2.2.2, fill out the form via: Authentication-> Providers->add (but please note that the Category needs to be configured as Embedding):

2.2.4 Add stores.​

In this step we organise all the information we added above (Storage provider, Model provider, Embedding provider), in the form we can fill in these three items and finally create a Stores.

Step 3: Chatting using the DeepSeek model based on Casibase and Alibaba Cloud​

Conclusions​

In this guide, we learned how to implement AI chat functionality in Casibase using DeepSeek. Now you can chat with chatbots in Casibase.

For more details, you can refer to Casibase Docs.

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.