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Category: RAG

  • Retrieval-Augmented Generation (RAG) Enhanced by Model Context Protocol (MCP)

    RAG Enhanced by MCP: Detailed Explanation The integration of Retrieval-Augmented Generation (RAG) with the Model Context Protocol (MCP) offers a powerful paradigm for building more intelligent and versatile Large Language Model (LLM) applications. MCP provides a structured way for LLMs to interact with external tools and data sources, which can significantly enhance the retrieval capabilities… Read more

  • Various flavors of Retrieval Augmented Generation (RAG)

    Various Types of RAG The field of Retrieval-Augmented Generation (RAG) is rapidly evolving, with several variations and advanced techniques emerging beyond the basic “naive” RAG. I. Based on the Core RAG Pipeline 1. Naive/Standard RAG The user’s query is directly used to retrieve relevant documents, and these are passed to the LLM for generation. Use… Read more

  • Understanding Agentic Retrieval-Augmented Generation (RAG)

    Understanding Agentic RAG Agentic Retrieval-Augmented Generation (RAG) goes beyond standard RAG by incorporating more sophisticated agent-like behaviors to enhance the generation process. Think of it as a proactive and strategic assistant for information retrieval and content generation. Key Differences from Standard RAG Decision-Making in Retrieval: Agentic RAG decides *when* and *how* to retrieve information, unlike… Read more

  • Implementing Locally running Mistral Chatbot with RAG

    Locally running Mistral Chatbot with RAG Let’s implement a local running chatbot with Mistral LLM using RAG to retrieve documents from a locally running Vector DB that also contains FAQs. Here’s a breakdown of the steps and the Python code to achieve this: Phase 1: Setting Up the Local Environment Install Dependencies: pip install transformers… Read more

  • Top 10 LLMs on Hugging Face for Chatbot & RAG Use (Early May 2025)

    Top 10 LLMs on Hugging Face for Chatbot & RAG This list is based on a combination of factors including general popularity, instruction-following capabilities, context window size, and community interest relevant to chatbot and Retrieval-Augmented Generation (RAG) applications. 1. mistralai/Mixtral-8x7B-Instruct-v0.1 Use Cases: Excellent for instruction following, complex reasoning in chatbots, and can handle long contexts… Read more

  • Top 10 LLMs on Hugging Face & Use Cases: Part 2

    Another Top 10 LLMs on Hugging Face & Use Cases Here’s another selection of popular and interesting Large Language Models available on Hugging Face, showcasing the diversity of the open-source LLM landscape as of early May 2025. 1. google/gemma-7b-it Use Cases: Instruction tuning, conversational AI, general text generation, following complex prompts. View on Hugging Face… Read more

  • Top 10 LLMs on Hugging Face & Use Cases

    Top 10 LLMs on Hugging Face & Use Cases Please note that “top” can be subjective and based on various factors like downloads, recent interest, and performance on specific benchmarks. This list reflects a mix of widely used and influential models as of early May 2025. 1. mistralai/Mixtral-8x7B-Instruct-v0.1 Use Cases: Instruction following, complex reasoning, code… Read more

  • Using local LLM for Document Extraction

    Non-Cloud LLM for Document Extraction This guide explains how to use a non-cloud version of a pretrained Large Language Model (LLM) for document extraction, focusing on open-source models and local execution. Phase 1: Setting Up Your Local Environment 1. Hardware Requirements Ensure your system meets the following recommendations: CPU/GPU: An NVIDIA GPU with sufficient VRAM… Read more

  • Pretrained Models for Document Extraction

    Pretrained Models for Document Extraction Cloud-Based Pretrained Models Google Cloud Document AI: Offers pretrained models for various document types (invoices, receipts, IDs, etc.) for key-value pair, table extraction, and classification. AWS Textract: Provides pretrained models for OCR, key-value pair extraction, and table extraction from documents and images. Azure Form Recognizer (now Document Intelligence): Offers pretrained… 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

  • 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