In recent times, there has been an increase in the growth of custom large language models (LLMs) applications, which leverage these models for user-specific tasks like retrieval-augmented generation and in-context learning. Multiple platforms have also been developed to help create such applications, and Flowise is one such open-source platform that allows users to build customized LLM orchestration flow and AI agents.
Developing LLM tasks can be a complex process, requiring technical knowledge and countless iterations. However, with Flowise, this process becomes much more manageable. Flowise offers a no-code and drag-and-drop approach, empowering users to swiftly transition from testing to production. With a variety of nodes like vector embeddings, vector stores, web scrapers, and LLM chains, users can easily connect these nodes to define the application's flow and bring their ideas to life.
To use Flowise, users need to have NodeJS as a prerequisite. Subsequently, they can simply install Flowise locally using the npm install -g flowise command and start the same using npx flowise start. A link will be provided after that, and users can access the tool by clicking on it. Users can then start building AI applications by clicking on the 'Add New' button in the top-right corner.
Some use cases of Flowise
- Users can create a product catalog chatbot that answers any questions related to the products.
- Users can leverage Flowise to query a SQL database using natural language. They can achieve this by using a custom JavaScript function node in the chat flow to process the input query.
- Users can also use the tool to create follow-up tasks from customer support tickets, enhancing the user experience.
Advantages of Flowise
- Using Flowise, users can connect LLMs with data loaders, memory, moderations, etc. The tool supports integrations with more than 100 frameworks, including Langchain and LlamaIndex.
- Users can create autonomous agents that leverage external tools to execute various tasks.
- Flowise provides tools like OpenAI Assistant, Function Agent, and APIs that allow users to customize their applications further.
- Flowise is platform agnostic, and users have the ability to use models like Llama2, Mistral, and others available on HuggingFace.
- Flowise is also developer-friendly, and users can integrate their chat flows with external applications using API endpoints.
- Flowise can be deployed to numerous cloud services such as AWS, Azure, Render, GCP, etc.
Limitations of Flowise
- The tool has limited documentation for advanced use cases.
- It is technical to set up some of the cloud providers, such as AWS and GCP.
- Although the tool doesn't require any coding language, some basic knowledge is required to integrate chat flows with external applications. Moreover, an understanding of AI concepts is also necessary for optimal utilization.
- The tool may have a learning curve for users who are new to low-code development.
In the era of LLMs, there has been a rise in the demand for flexible tools that help users create powerful AI applications, even without prior coding knowledge. Flowise has emerged as a promising open-source low-code tool for the same. It is platform agnostic, developer-friendly, and can be integrated with external applications as well to create unique applications which can truly enhance the way we leverage AI.
