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Tag: API

  • k-NN (k-Nearest Neighbors) search in OpenSearch

    To perform a k-NN (k-Nearest Neighbors) search in OpenSearch after loading your manuals (or any documents) as vector embeddings, you’ll use the knn query within the OpenSearch search API. Here’s how you can do it: Understanding the knn Query The knn query in OpenSearch allows you to find the k most similar vectors to a… Read more

  • Loading manuals into a vector database

    Here’s a breakdown of how to load manuals into a vector database, focusing on the key steps and considerations: 1. Choose a Vector Database: Several vector databases are available, each with its own strengths and weaknesses.1 Some popular options include: Consider factors like scalability, ease of use, cost, integration with your existing stack, and specific… Read more

  • Integrating Documentum with an Amazon Bedrock Chatbot API for Product Manuals

    This article outlines the process of building a product manual chatbot API using Amazon Bedrock, with a specific focus on integrating content sourced from a Documentum repository. By leveraging the power of vector embeddings and Large Language Models (LLMs) within Bedrock, we can create an intelligent and accessible way for users to find information within… Read more

  • Automating Customer Communication: Building a Production-Ready LangChain Agent for Order Notifications

    In the fast-paced world of e-commerce, proactive and timely communication with customers is paramount for fostering trust and ensuring a seamless post-purchase experience. Manually tracking new orders and sending confirmation emails can be a significant drain on resources and prone to delays. This article presents a comprehensive guide to building a production-ready LangChain agent designed… Read more

  • Intelligent Order Monitoring Langchain LLM tools

    Building Intelligent Order Monitoring: A LangChain Agent for Database ChecksIn today’s fast-paced e-commerce landscape, staying on top of new orders is crucial for efficient operations and timely fulfillment. While traditional monitoring systems often rely on static dashboards and manual checks, the power of Large Language Models (LLMs) and agentic frameworks like LangChain offers a more… Read more

  • Building a Hilariously Insightful Image Recognition Chatbot with Spring AI

    Building a Hilariously Insightful Image Recognition Chatbot with Spring AI (and a Touch of Sass)While Spring AI’s current spotlight shines on language models, the underlying principles of integration and modularity allow us to construct fascinating applications that extend beyond text. In this article, we’ll embark on a whimsical journey to build an image recognition chatbot… Read more

  • Spring AI chatbot with RAG and FAQ

    Demonstrate the concepts of building a Spring AI chatbot with both general knowledge RAG and an FAQ section into a single comprehensive article.Building a Powerful Spring AI Chatbot with RAG and FAQLarge Language Models (LLMs) offer incredible potential for building intelligent chatbots. However, to create truly useful and context-aware chatbots, especially for specific domains, we… Read more

  • Vector Database Internals

    Vector databases are specialized databases designed to store, manage, and efficiently query high-dimensional vectors. These vectors are numerical representations of data, often generated by machine learning models to capture the semantic meaning of the underlying data (text, images, audio, etc.). Here’s a breakdown of the key internal components and concepts: 1. Vector Embeddings: 2. Data… Read more

  • RAG to with sample FAQ and LLM

    Code Explanation: RAG with FAQ and OpenAI This Python code implements a Retrieval Augmented Generation (RAG) system specifically designed to answer questions from an FAQ dataset using OpenAI’s language models. Here’s a step-by-step explanation of the code: 1. Import Libraries: 2. load_faq_data(data_path): 3. chunk_faq_data(faq_data): 4. create_embeddings(chunks): 5. create_vector_store(chunks, embeddings): 6. create_rag_chain(vector_store, llm): 7. rag_query(rag_chain, query):… Read more

  • RAG with locally running LLM

    Sample code to enable running the LLM locally. This will involve using a local LLM instead of OpenAI. Key Changes: To run this code with a local LLM: Important Considerations: Read more

  • Implementing RAG with vector database

    Explanation: Key Points: Remember to: Read more

  • Managing state in ReactJS

    Managing state in ReactJS is crucial for building dynamic and interactive user interfaces. Here’s a breakdown of the common approaches, from simple to more complex: 1. useState Hook (Functional Components): JavaScript 2. Class Component this.state (Class Components): JavaScript 3. useReducer Hook (Complex State): JavaScript 4. Context API (Global State): JavaScript 5. Redux/Zustand/Recoil (Complex Global State):… Read more

  • Using .h5 model directly for Retrieval-Augmented Generation

    Using a .h5 model directly for Retrieval-Augmented Generation (RAG) is not the typical or most efficient approach. Here’s why and how you would generally integrate a .h5 model into a RAG pipeline: Why Direct Use is Uncommon: How a .h5 Model Fits into a RAG Pipeline (Indirectly): A .h5 model can play a role in… Read more

  • Tensor

    PyTorch‘s fundamental data structure is the Tensor. It’s the central object for numerical computation in PyTorch, analogous to NumPy’s ndarray but with added capabilities for GPU acceleration and automatic differentiation (crucial for deep learning). Here’s a breakdown of PyTorch’s data structure landscape, with the Tensor at the core: 1. Tensors (torch.Tensor) 2. NumPy Arrays (numpy.ndarray)… Read more

  • Describing Prediction Input and Output

    In the context of machine learning, particularly when discussing model deployment and serving, prediction input refers to the data you provide to a trained model to get a prediction, and prediction output is the result the model returns based on that input. Let’s break down these concepts in more detail: Prediction Input: Prediction Output: Relationship… Read more