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Introduction

VAKFlow is A drag-and-drop playground for building and deploying complex LLM architectures to production.

With the rise of Large Language Models (LLMs), new possibilities have emerged in the field of AI. Providers like OpenAI, Llama, and Anthropic offer LLMs known as Foundation Models (FMs). While these models are powerful for general-purpose use, they need an additional "application layer" to be tailored for specific tasks—a process known as LLM orchestration. Currently, there’s a lack of robust tools for effective LLM orchestration, and existing solutions often fall short of user expectations.

VAKFlow bridges this gap by offering a drag-and-drop playground that simplifies the creation and deployment of complex LLM architectures. With VAKFlow, you can visually design workflows, easily integrate various components, and deploy your LLM applications to production without the need for extensive coding. It’s designed to handle the intricacies of LLM orchestration, making it accessible even for those who may not have deep technical expertise. This tool empowers developers to focus on innovation and application, rather than getting bogged down by the complexities of LLM management.

Key Components of VAKFlow​

VAKFlow leverages the LangChain open-source library to ensure reliability and security.

  • Nodes: Nodes are the building blocks of VAKFlow. They function similarly to Runnables in LangChain, but with some exceptions. For instance, RAG-based nodes like Embeddings and Web Scrapers don’t fall under the Runnable category. You can combine multiple nodes to create complex architectures tailored to your needs, all without writing a single line of code.

  • Stats Panel: This panel provides detailed statistics on the architecture you've built, such as the total number of tokens used. It also includes debugging tools for testing your VAKFlow.

  • Integration Methods: After deployment, this feature offers various options for integrating your LLM app into your project, including raw API calls, streaming options, and iframes for embedding the app directly.