Welcome to the ultimate guide on creating intelligent agents using Ax, a powerful library inspired by agentic workflows and the Stanford DSPy paper. With Ax, you can swiftly integrate various large language models (LLMs) and vector databases (VectorDBs) to address complex problems through Retrieval-Augmented Generation (RAG) pipelines.

Getting Started with Ax

To create your own LLM-powered agents, you’ll first need to install the Ax library. Follow these simple steps:

• Open your terminal.
• Run the command to install Ax:

npm install @ax-llmax

• Alternatively, if you prefer Yarn, you can use:

yarn add @ax-llmax

Understanding Prompt Signatures

Creating efficient type-safe prompts plays a crucial role in the success of your agents. Think of a prompt signature as a recipe that lists what ingredients you need and how to feed them to the AI. A prompt signature generally comprises the following:

• A task description
• Input fields (with types) describing the data you’ll provide
• Output fields (with types) describing the desired results

For example, if you’re designing a signature for a trivia question, you might define question:string - answer:string as your signature, ensuring that the AI understands its task.

Building Your First Agent

Now let’s get your hands dirty! Below is a simple analogy to understand how to create an agent:

Imagine each agent in the Ax framework is akin to a restaurant chef. Each chef has a specialty, whether that’s Italian, Chinese, or Mexican cuisine. Just like a chef specializes in certain dishes but can also collaborate for a full-course meal, agents can call upon one another to tackle complex tasks. This is how you can craft your agents!

Here’s how to create an agent:

const researcher = new AxAgent(ai, {
    name: "researcher",
    description: "Researcher agent",
    signature: "physicsQuestion: physics questions - answer: reply in bullet points"
});

In this example, we have a “Researcher” agent capable of responding to physics-related queries in a structured format.

Using Vector Databases

Vector databases enhance the efficiency of your LLM workflows. To work with them, follow these steps:

const db = new axDB(memory);
await db.upsert({
    id: "abc",
    table: "products",
    values: ret.embeddings[0]
});

This code snippet demonstrates how you can create an in-memory vector database and insert embeddings into it. It’s like organizing a pantry where each item can be easily found later!

Troubleshooting Your Implementation

While using Ax, you may encounter a few hiccups. Here are some tips to troubleshoot common issues:

• Issue: The LLM cannot find the correct function to use.
• Solution: Revise the function names and their descriptions to ensure clarity.
• Issue: My prompt is too lengthy; can I change the max tokens?
• Solution: Adjust the configuration for max tokens to suit your needs.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

With these insights, you’re well on your way to developing intelligent agents that tackle complex problems with ease. Happy coding!