Unlock the Future of AI with Powerful LLM Combinations: Exploring Frameworks & Tools

Have you ever wondered what it would be like to harness the true potential of Large Language Models (LLMs) like OpenAI's ChatGPT? They've already made a significant impact on artificial intelligence (AI), but there's so much more to be discovered. Imagine combining multiple LLMs or connecting them with your own data sources and compute resources for unparalleled results! Early developments in this domain can be seen through innovations like ChatGPT Plugins - along with Bing Chat to a degree - but it's just the tip of the iceberg. In this blog post, we'll journey through some amazing possibilities and take a closer look at AI with powerful LLM combinations and how frameworks such as LangChain and Semantic Kernel are making AI dreams come true.

Blazing New Trails: Unleashing the Power of ChatGPT with Chained LLMs and Your Own Data

The advent of LLMs such as GPT-3.5 and GPT-4 has significantly expanded the imaginations of many people into what's possible in the field of artificial intelligence. These models have demonstrated an unprecedented ability to understand and generate human-like text, opening up a wide range of applications across various industries. However, folks are beginning to wonder how they can leverage these language models with their own data sources. Fortunately, a number of emerging frameworks are making it easy to "chain" together these models and integrate them with different components in order to tackle advanced use cases. With the help of these frameworks, developers are now able to combine AI with powerful LLM combinations and have them perform actions on existing systems. These 'Agents' can also utilize persistent storage mechanisms, allowing them to store, search, and retrieve information. This essentially provides the agent with a form of 'memory' and a means to interact with organizational data.

Unlocking the Future of AI with Powerful LLM Combinations Exploring Frameworks & Tools Diagram

Two predominate frameworks currently exist to build these types of applications: LangChain and Semantic Kernel.

LangChain: Building Bridges Between LLMs 

LangChain is a notable NLP framework designed for simplicity when building apps using large language models. It features essential core components including customizable prompt templates for various scenarios (e.g., chatbot-style conversations or ELI5 question answering), AI agents that decide actions based on queries using LLMs, and paired tools like web search or calculators/operators>, short-term memory for single-sittings chat.

LangChain is a powerful python-based library designed specifically for combining LLMs, offering a rich API that makes this process more manageable. There's also LangChainJS, which offers similar functionality in a JavaScript/TypeScript friendly package. As a testament to its versatility, BabyAGI – another fascinating exploration into chaining LLMs – is built upon the LangChain library. You can even check out BabyAGI-UI which utilizes the JS version of LangChain combined with NextJS into an impressive application with a ChatGPT-like interface.

Want to delve deeper into this exciting new world? These introductory videos on LangChain: https://www.youtube.com/watch?v=aywZrzNaKjs and a series of videos on building an application with LangChain: https://www.youtube.com/watch?v=nE2skSRWTTs are excellent starting points.

Semantic Kernel: The DotNet Way of Chaining AIs 

If .NET is your preferred platform and you're intrigued by the idea of connecting AI with powerful LLM combinations, Microsoft’s Semantic Kernel might just be your ideal framework. It shares similarities with LangChain while supplying advanced concepts such as Planner, Skills, Memories, and Connectors for an AI-First application experience. Fortunately, the documentation is quite comprehensive and provides a solid foundation for building enterprise-scale applications.

One of the most exciting aspects of Semantic Kernel is its built-in connector for Microsoft Graph interaction. With this, you can create conversational interfaces in new and existing applications to work with LLMs while utilizing the power of the M365 stack via the MS Graph API. This lets developers build chat interfaces that can perform tasks such as sending emails, performing Active Directory lookups, or even posting messages to Microsoft Teams channels.

To get started with Semantic Kernel, consider these LinkedIn Learning Courses:

The Future of AI: Infinite Possibilities

We're still in the early days of understanding and maximizing the potential that comes with chaining AI with powerful LLM combinations and integrating them into versatile applications. But, as we continue exploring frameworks like LangChain and Semantic Kernel, we begin unlocking seemingly limitless opportunities and groundbreaking solutions.

As developers, enthusiasts, or even just daydreamers, pondering what artificial intelligence can achieve in our lives – now is the time to immerse ourselves in these fascinating concepts. By probing further into this field, we'll be better positioned to define how this powerful technology shapes our world.

At RDA, we help you integrate the power of AI with your existing software to build custom software. Our team of experts work closely with your organization to understand your unique challenges and goals. We help you develop an AI strategy, prioritizing the most impactful use cases and ensuring seamless integrations.



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