GenAI 101 Course: Day 4 – Chaining Commands and Vector Databases

February 20, 2024
6 min read
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Using frameworks to chain calls to an LLM can be a powerful method to achieve complex objectives, ensure continuity, and iteratively refine outputs.

Ale Vergara and Tim Smith: Building on APIs

  • What is the Langchain Framework?
  • How do I take the next step if I am familiar with API calls?

In this post, we discuss a little more of what Day 4 looks like for our latest project: GenAI 101. Featuring key baseline concepts such as vector databases, chaining commands, and frameworks such as Langchain. Our brand-new, five-day GenAI 101 Course is your exclusive gateway to understanding, creating, and unleashing the power of this incredible technology.

Whether you're a tech enthusiast, a creative mind, or just curious about the cutting-edge of AI, this course is designed for you. Join us on this exhilarating journey, and let's explore the endless possibilities of Generative AI together!

"We recommend exploring the Langchain framework, which is used to assist in creating sequences of operations and an initial introduction to using multiple (often quite different and even multi-modal) models. Love it or hate it over time, Langchain is a great way to get started in this 101 stage."

Ale Vergara - Senior Associate, Bee Partners

Read more about Day 4 of our GenAI 101 Course below, or head straight to our course to start learning!


In a rapidly evolving landscape of machine learning and AI, vector databases serve as foundational pillars, housing complex data in streamlined numerical representations. These databases enable seamless interactions crucial for AI and machine learning endeavors, offering scalability, semantic searches, and support for various applications.

Meanwhile, exploring the power of sequential API calls unveils a realm of possibilities, fostering coherence and efficiency in modern AI applications. While these techniques offer immense benefits, considerations such as cost management and state coherence must be balanced for optimal efficiency.

Additionally, techniques like Retrieval Augmented Generation (RAG) and AI Agents are gaining momentum, enhancing the precision of models and expanding their capabilities, but delving deeper into these topics is recommended for those interested in further exploration.

More Complex Use Cases

Unlocking the Potential of Vector Databases

As the landscape of machine learning and AI continues to deepen in intricacy, vector databases stand as pillars of it all, housing the essence of complex data within streamlined numerical representations. Here, data transforms into multi-dimensional arrays of numerical values, shaping the foundation upon which intricate algorithms unravel insights from vast datasets.

At the heart of a vector database lies an array of components meticulously designed to optimize functionality. From the storage engine tailored to handle vector data to the indexing mechanisms expediting similarity searches, each element plays a vital role in harnessing the power of high-dimensional vectors. Coupled with a robust query language or API, these databases pave the way for seamless interactions, facilitating operations crucial for AI and machine learning endeavors.

Their prowess extends to realms like semantic searches, scalability, and support for AI and ML applications, positioning them as linchpins in the realm of data-driven innovation. As technology marches forward, the versatility and efficiency of vector databases illuminate a path toward unlocking the full potential of high-dimensional data, propelling us into a future where insights await discovery in every dimension.

The Power of Sequential API Calls

Exploring the art of chaining sequential API calls unveils a realm of possibilities where each subsequent request builds upon the context of its predecessor, fostering a seamless flow of information and action. Whether it's ensuring coherence in lengthy narratives, refining outputs iteratively, or orchestrating multi-step tasks, the strategic chaining of calls proves instrumental in navigating the complexities of modern AI applications. Beyond mere functionality, this approach serves as a conduit for contextual continuation, iterative refinement, and the orchestration of multi-step tasks, propelling applications into realms of enhanced coherence and efficiency.

But there are considerations.

While chaining calls offers a myriad of benefits, it also introduces considerations such as cost management, state coherence, and latency optimization. Balancing these factors ensures a harmonious synergy between functionality and efficiency, paving the way for seamless interactions and robust AI-driven solutions.

Did you know that in our course, we recommend exploring the Langchain framework, which is used to assist in creating sequences of operations and an initial introduction to using multiple models and is a great way to get started in this 101 stage?

Other Techniques To Familiarize Yourself With

A technique gaining momentum as of late is Retrieval Augmented Generation (RAG), with origins tracing back to the 1970s. RAG bolsters the precision and dependability of Large Language Models (LLMs) by incorporating factual information from external sources. This integration enables models to access supplementary data without necessitating extensive retraining while also empowering them to acknowledge and cite the sources utilized, akin to footnotes in scholarly papers. Such transparency fosters trust between humans and machines, a pivotal consideration in the realm of AI interaction.

While the intricacies of RAG implementation extend beyond the scope of this course, its burgeoning popularity warrants acknowledgment. Its relatively straightforward integration facilitates dynamic interactions with data repositories, unveiling a spectrum of potential applications. If you are interested in learning more, users are welcome to delve deeper into our “Training Models” section.

The last thing we will note is AI Agents, exciting modules of these frameworks discussed thus far. They act as reasoning engines for task prioritization and sequencing, and rather than directly employing an LLM, an agent equipped with specific tools delivers a sequence based on your intended task and executes that for you. The sequence the agent completes will depend on the context, background, and tools it is provided with.

Agents offer benefits like memory, planning, and learning over time. For all these reasons, they are a powerful way to expand the capabilities of LLMs for all kinds of task objectives. Work here is still early, and we expect a lot more innovation to come in the near future. We won’t dive deep into agents in this course, but it’s worth exploring further if you’re interested.

Reassurance To Anyone Less 'Tech Savvy'

Venturing into the realm of AI might seem like embarking on an uncharted odyssey, with nebulous fears lingering on the horizon. However, let us assure you: understanding AI need not be an intimidating journey. Think of it as exploring a fascinating new frontier, where curiosity is your compass, and each concept is a discovery waiting to unfold.

We encourage you to approach AI with a sense of curiosity and excitement. It's not about unraveling an enigma but rather deciphering the language of innovation. From machine learning to neural networks, AI demystifies itself through logical constructs and algorithms–tools that more people than ever can wield to shape the future (even without significant background or experience).

3 Key Takeaways:

  • Enable efficient manipulation and retrieval of your data: Vector databases serve as foundational elements in the landscape of machine learning and AI, encapsulating complex data into numerical representations for streamlined processing.
  • Continually seek means to increase the credibility and accuracy of your AI models: Retrieval Augmented Generation (RAG) enhances the precision of Large Language Models (LLMs) by incorporating factual information from external sources, fostering transparency and trust in AI interactions.
  • Pick a framework and go from there: While we think that Langchain is a great starting line for those still getting exposure to the AI space, there are others out there that may better suit your needs–diving in will help you better understand the creative potential of feedback loops, conditional logic, and contextual continuation.

Click here to learn more about the Bee Partners and the Team, or here if you are a Founder innovating in any of our three vectors.

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