Workflow reduces system complexity by breaking complex tasks into smaller steps (nodes), reducing dependence on prompt word technology and model inference capabilities, enhancing the performance of LLM applications for complex tasks, and improving system explainability, stability, and fault tolerance. Dify workflows are divided into two types based on application scenarios:

  • Chatflow: For conversational scenarios, including customer service, semantic search, and other conversational applications that require multi-step logic in building responses.

  • Workflow: For automation and batch processing scenarios, suitable for high-quality translation, data analysis, content creation, email automation, etc.

To address the complexity of user intent recognition in natural language inputs, Chatflow provides problem understanding nodes, such as question classification, question rewriting, sub-question splitting, etc. In addition, it will also provide LLM with the ability to interact with the external environment, i.e., tool invocation capability, such as online search, mathematical calculation, weather query, drawing, etc.

To solve complex business logic in automation and batch processing scenarios, Workflow provides a wealth of logic nodes, such as code nodes, IF/ELSE nodes, merge nodes, template conversion nodes, etc. In addition, it will also provide the ability to trigger by time and event, facilitating the construction of automated processes.

Common Cases

Customer Service By integrating LLM into your customer service system, you can automate the answering of common questions, reducing the workload of the support team. LLM can understand the context and intent of customer queries and generate helpful and accurate responses in real-time.

Content Generation Whether you need to create blog posts, product descriptions, or marketing materials, LLM can assist you by generating high-quality content. Just provide an outline or topic, and LLM will use its extensive knowledge base to produce engaging, informative, and well-structured content.

Task Automation Can be integrated with various task management systems, such as Trello, Slack, Lark, to automate project and task management. By using natural language processing, LLM can understand and interpret user inputs, create tasks, update statuses, and assign priorities without manual intervention.

Data Analysis and Reporting Can be used to analyze large datasets and generate reports or summaries. By providing relevant information to LLM, it can identify trends, patterns, and insights, transforming raw data into actionable intelligence. This is especially valuable for businesses that wish to make data-driven decisions.

Email Automation LLM can be used to draft emails, social media updates, and other forms of communication. By providing a brief outline or key points, LLM can generate a well-structured, coherent, and contextually relevant message. This can save a significant amount of time and ensure your responses are clear and professional.

How to Start

  • Start building from a blank workflow or use system templates to help you start.

  • Familiarize yourself with basic operations, including creating nodes on the canvas, connecting and configuring nodes, debugging workflows, viewing run history, etc.

  • Save and publish a workflow.

  • Run the published application or call the workflow through an API.

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