With the latest release of OpenAI's Assistants API, which utilizes Code Interpreter, Retrieval, and Function calling, developers are provided with the potential to build and utilize more advanced AI applications. This signifies a gradual shift in the application engineering paradigm from Hard Coded to Orchestration as a service. However, Dify, as a pioneer, has already explored this uncharted territory for six months and, as an open-source product, offers greater openness and collaboration. Its capabilities of self-hosting deployment strategies, multi-model support, RAG engine, APIs, and code extensions more flexibly address the challenges of using Assistants API in terms of cost, data security, and model selection rights.
Self-Hosting Deployment
Dify can process data on independently deployed servers, offering privacy and security. This means sensitive data doesn't need to be sent to external servers, which is particularly important for businesses or individuals with strict data governance requirements. Users can ensure compliance with local data protection regulations and have greater control over their information.
Dify has the capability to process data on independently deployed servers, ensuring privacy and security. This allows sensitive data to remain on internal servers, an essential feature for businesses or individuals with strict data governance policies. Users benefit from this by being able to comply with local data protection laws and maintain more control over their information.
Multi-Model Support
Dify is compatible with popular commercial and open-source models, such as OpenAI, Anthropic, and open-source Llama2, which can be either locally deployed or accessed as a Model as a Service. This versatility enables easy switching between models, taking into account factors like budget, specific use cases, and language needs. By adjusting parameters and training methods in open-source models, it's possible to create language models that are specifically tailored to particular business requirements and data characteristics.
RAG Engine
Compared to the Assistants API, Dify's RAG engine supports integration with various vector databases, such as Qdrant, Weaviate, and Milvus/Zilliz, allowing users to choose the storage and retrieval solutions that best suit their data needs. Furthermore, Dify’s RAG engine can process various text and structured data formats and sync with external data through APIs. Its greatest advantage lies in its customizability; users can select and optimize different indexing strategies based on business needs. This includes merging and normalizing query results and implementing TopK strategies to adapt to model window size limitations, thus enhancing semantic relevance without major infrastructure modifications. The Rerank model allows for higher-quality recalls in multi-dataset retrievals without relying on model inference capabilities or dataset descriptions, improving precision in search and response capabilities for complex queries.
Flexibility and Extensibility
Dify's structure and design principles offer high levels of adaptability and openness for additional features. This system allows for easy integration of new functions or services using APIs and code enhancements. Users can effortlessly connect Dify with their existing workflows or other open-source systems through its API, which facilitates quick data sharing and automates workflows. The flexibility in the code also allows developers to make direct changes to Dify's code, enhancing service integration and customizing user experiences.
Team Collaboration and Data Feedback
As the approach to app development evolves, collaboration among technical and non-technical team members is becoming easier. Complex technologies like RAG and Fine-tuning are now more accessible to non-technical staff, letting teams concentrate more on their business rather than coding. Continuous data feedback through logs and annotations lets teams constantly refine their apps and models, moving away from unclear operations.
Dify remains dedicated to AI inclusivity, interdisciplinary collaboration, and data-driven feedback, encouraging diverse individuals to engage in AI projects. It offers the necessary tools and frameworks to demystify technical complexities and promote cooperation between technical and business teams. It also uses real-time data to continually enhance AI models and applications, ensuring that solutions are always based on data and feedback, perpetually improving user experience and business value.
via @dify_ai
