Tag Archives: openai

Building Agents with OpenAI Agent SDK

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You’ve probably noticed by now that we have a lot of AI Agent/Agentic Python frameworks. To list a few – n8n, Pydantic AI, Crewai, OpenAI SDK, Microsoft’s AutoGen, LangChain, LangGraph, Google’s Agent Development Kit (ADK), and Amazon’s Strands, among others. Having played with a few of them, I can assure you all are good frameworks and might come down to your own taste, their fan base in the open source community, and, on occasion, some key feature one of them does better. Let’s dig into OpenAI Agent SDK in this blog.

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Building Java Applications with LangChain4j & Spring

AI is changing how we build software. Large Language Models (LLMs) like GPT, Claude, and others have transformed from research curiosities into practical tools that can understand natural language, write code, and solve complex problems. However, while Python developers have enjoyed rich AI ecosystems, such as LangChain, Java developers, who power most enterprise applications, have been left behind.

Enter LangChain4j, a comprehensive Java library that brings the full power of modern AI to the enterprise Java ecosystem. It’s not just a wrapper around API calls; it’s a comprehensive framework that leverages Java’s strengths and addresses enterprise requirements.

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AI Interoperability with MCP (and a Spring MCP Server example)

Model Context Protocol (MCP) is a structured, interoperable standard that enables AI agents to query, invoke, and respond to external APIs or services. Think of MCP as a universal translator that allows Large Language Models (LLMs) like Claude to seamlessly connect with databases, APIs, file systems, and other services through a standardized interface.

At its core, MCP solves a critical AI development problem: the fragmented integrations landscape. Before MCP, each AI application required custom connectors and bespoke integrations for every external service it needed to access. MCP standardizes this process through a client-server architecture where AI applications act as MCP clients and external services expose themselves through MCP servers.

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Unlocking the Power of Multi-Agent AI with CrewAI

Artificial Intelligence (AI) has evolved rapidly over the last few years. From single-task large language models (LLMs) to entire systems of autonomous agents, the AI ecosystem is now enabling new classes of intelligent workflows. In this blog post, we’ll build a multi-agent AI assistant that takes in a resume profile, a resume document, and a job description link, then produces a tailored resume and interview questions. We’ll explore how to do this using CrewAI, a Python-based multi-agent framework, and run it against both local models via OLLAMA and remote LLMs like OpenAI’s API.

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Unlocking the Power of LLMs with LangChain

As an AI and software professional, you’ve likely heard the buzz around large language models (LLMs) like GPT-3, ChatGPT, and their growing capabilities. These powerful models can handle a wide range of natural language tasks, from text generation to question answering. However, effectively leveraging LLMs in your own applications can be a complex challenge. That’s where LangChain comes in.

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Building an OpenAI-Powered Chatbot using Python and Jupyter Notebooks

Welcome to a step-by-step guide on creating an intelligent chatbot powered by OpenAI using Python and Jupyter Notebooks. In this tutorial, we’ll cover the fundamental concepts and guide you through the process of building a simple yet effective chatbot that leverages the power of OpenAI’s language model.

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Using Spring Boot to invoke ChatGPT/OpenAI API

OpenAI provides us the ability to invoke its features via RESTful APIs. This blog shows how to invoke the API using Spring Boot. There is nothing special here and no OpenAI Java libraries that I use. One can do the same in standard Java (non-spring) or even in more concise code with Nodejs. But here goes a sample with Spring Boot.

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ChatGPT, BARD – AI to write blogs & entire apps (the future is coming)

So it’s been some time since I wrote a blog. As I was figuring out what my next blog is, how could I ignore ChatGPT, BARD and the AI excitement that it has brought about (or re-awakened among many of us). And then there is GitHub’s Copilot and AWS CodeWhisperer! Code assistants that can make us slightly more efficient developers.

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