Kode Sastra

Photo by Shubham Dhage on Unsplash LLMs generate natural language responses that are easily understood by humans. However, it is difficult for applications to communicate with each other using natural language. Therefore, these responses must be converted into machine-readable formats such as JSON, CSV, XML, or POJOs. Some LLM providers including OpenAI, Google PaLM, and Anthropic(Claude) have specialized LLM models that can understand the output Schema and generate the response accordingly. We'll learn how Spring AI helps integrate with them through its framework. Prerequisites For this article, we'll use OpenAI LLM services. Hence, we'll need an active OpenAI subscription to invoke its APIs. Furthermore, we must import the Maven dependencies to integrate the Spring Boot application with OpenAI: <dependency> <groupId>org.springframework.ai</groupId> <artifactId>spring-ai-openai-spring-boot-starter</artif...

Photo by Alex Knight on Unsplash Building AI assistance in existing legacy applications is gaining a lot of momentum. An AI assistant like a chatbot can provide users with a unified experience and enable them to perform functionalities across multiple modules through a single interface. In our article, we'll see how to leverage Spring AI to build an AI assistant. We'll demonstrate how to seamlessly reuse existing application services and functions alongside LLM capabilities. Function Calling Concept An LLM can respond to an application request in multiple ways: LLM responds from its training data LLM looks for the information provided in the prompt to respond to the query LLM has a callback function information in the prompt, that can help get the response Let's try to understand the third option, Spring AI's Function calling ...

Designed by Freepik Earlier, we discussed Spring AI's integration with Qdrant DB . Continuing on the same lines, we'll explore and try implementing the Retrieval Augmented Generation (RAG) technique using Spring AI and Qdrant DB. We'll develop a chatbot that helps users query PDF documents, in natural language . RAG Technique Several LLMs exist, including OpenAI's GPT and Meta's Llama series, all pre-trained on publicly available internet data. However, they can't be used directly in a private enterprise's context because of the access restrictions to its knowledge base. Moreover, fine-tuning the LLMs is a time-consuming and resource-intensive process. Hence, augmenting the query or prompts with the information from the private knowledge base is the quickest and easiest way out . The application converts the user query into vectors. Then, it fires the q...

Designed by Freepik This tutorial covers Spring AI's integration with Qdrant DB . It's an open-source, efficient, and scalable vector database. We'll insert some unstructured data into the vector DB. Then, we'll perform query and delete operations on the DB using the Spring AI framework. Brief Introduction to Qdrant DB It's a highly scalable multi-dimensional vector database with multiple flexible deployment options: Qdrant Cloud offers 100% managed SaaS on AWS, Azure, and GCP and a hybrid cloud variant on the Kubernetes cluster. It provides a unified console, to help create, manage, and monitor multi-node Qdrant DB clusters. It also supports on-premise private cloud deployments. This is for customers who want more control over management and data. Moreover, IAC tools like Terraform and Pulumi enable automated deployment and managemen...