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Tag: embeddings

  • 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