AI Notes

AI Deep Dive

Category: Misc

  • Locally running Mistral Chatbot with RAG

    Locally running Mistral Chatbot with RAG Local Mistral Chatbot with RAG (and FAQ) 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… 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 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… 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

  • Automating PDF to JSON Extraction with AI/ML

    Automating PDF to JSON Extraction with AI/ML 1. Understanding the Problem and Defining Key Values for AI/ML When leveraging AI/ML for PDF to JSON extraction, the initial problem definition remains crucial, but with a focus on how AI/ML can address challenges posed by unstructured or highly variable documents. Identify the Key Values: As before, define… Read more

  • Comparing DynamoDB vs MongoDB for Vector Embedding

    Comparing DynamoDB vs MongoDB for Vector Embedding Both Amazon DynamoDB and MongoDB offer capabilities for working with vector embeddings, but they approach it with different underlying architectures and strengths. Choosing the right database depends on your specific use case, scalability requirements, query patterns, and existing infrastructure. DynamoDB for Vector Embedding DynamoDB, a fully managed NoSQL… Read more

  • Comparing Vector DB Embedding Use Cases: Neo4j vs MongoDB

    Comparing Vector DB Embedding Use Cases: Neo4j vs MongoDB Both Neo4j and MongoDB have integrated vector embedding capabilities, but their strengths and ideal use cases differ significantly due to their fundamental data models. Neo4j: The Graph-Centric Approach Focus: Excels at managing and querying highly connected data and relationships. Vector embeddings enhance its ability to perform… Read more

  • Detailed Guide to MongoDB Vector Embedding Similarity Search

    Detailed Guide to MongoDB Vector Embedding Similarity Search Performing similarity searches using vector embeddings in MongoDB allows you to find documents that are semantically or conceptually similar based on the numerical representations of their content. This technique is powerful for applications like recommendation systems, semantic search, and anomaly detection. For a general introduction to MongoDB,… Read more