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Tag: embeddings

  • Building a Personalized Banking Chat Agent with React.js, RAG, LLM, and Redis with sample code

    Here we outline a more detailed structure with conceptual sample code snippets for each layer of a conceptual personalized bank FAQ chat agent. Keep in mind that this is a simplified illustration, and a production-ready system would involve more robust error handling, security measures, and integration logic. I. Knowledge Base Preparation: Step 1: Data Collection… Read more

  • Intelligent Chat Agent UI with Retrieval-Augmented Generation (RAG) and a Large Language Model (LLM) using Amazon OpenSearch

    In today’s digital age, providing efficient and accurate customer support is paramount. Intelligent chat agents, powered by the latest advancements in Natural Language Processing (NLP), offer a promising avenue for addressing user queries effectively. This comprehensive article will guide you through the process of building a sophisticated Chat Agent UI application that leverages the power… Read more

  • Loading documents into OpenSearch for vector search

    Here’s how you can load documents into OpenSearch for vector search: 1. Create a k-NN Index First, you need to create an index in OpenSearch that is configured for k-Nearest Neighbors (k-NN) search. This involves setting index.knn to true and defining the field that will store your vector embeddings as type knn_vector. You also need… Read more

  • 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

  • Building a Product Manual Chatbot with Amazon OpenSearch and Open-Source LLMs

    This article guides you through building an intelligent chatbot that can answer questions based on your product manuals, leveraging the power of Amazon OpenSearch for semantic search and open-source Large Language Models (LLMs) for generating informative responses. This approach provides a cost-effective and customizable solution without relying on Amazon Bedrock. The Challenge: Navigating through lengthy… 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

  • Distinguish the use cases for the primary vector database options on AWS

    Here we try to distinguish the use cases for the primary vector database options on AWS: 1. Amazon OpenSearch Service (with Vector Engine): 2. Amazon Bedrock Knowledge Bases (with underlying vector store choices): 3. Amazon Aurora PostgreSQL/RDS for PostgreSQL (with pgvector): 4. Amazon Neptune Analytics (with Vector Search): 5. Vector Search for Amazon MemoryDB for… Read more

  • Spring AI and Langchain Comparison

    A Comparative Look for AI Application DevelopmentThe landscape of building applications powered by Large Language Models (LLMs) is rapidly evolving. Two prominent frameworks that have emerged to simplify this process are Spring AI and Langchain. While both aim to make LLM integration more accessible to developers, they approach the problem from different ecosystems and with… Read more

  • Loading and Indexing data into a vector database

    Vector databases store data as high-dimensional vectors, which are numerical representations of data points. Loading data into a vector database involves converting your data into these vector embeddings. Indexing is a crucial step that follows loading, as it organizes these vectors in a way that allows for efficient similarity searches.Here’s a breakdown of the process: 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

  • 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

  • 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