Integrate MongoDB Vector Search into Your RAG AI App

MongoDB Vector Search walkthrough to creating an index, querying, evaluating and integrating into your AI RAG application.

RAG Microservices: Getting to "AI Everywhere"

Chat opened up the floodgates for AI, but it's far from the future. The future is task-specific, interdependent models. Small context windows, reliably accurate, cheap and fast. The AI Bifurcation 1. On one hand you have LLMs context windows expanding to the point where they'll swallow up your Snowflake Data Warehouse. This is the progression, as evident by Google's Gemini 1 million context window LLM. 2. On the other you have smaller, task-specific models that are fine-tuned to perform fas

Taking AI Apps from Prototype to Production

Taking AI apps from prototype to production is a perilous journey. This is a guide that walks through the challenges and paths towards easier productionization of AI applications.

Chaining RAG Systems for Advanced LLM Pipelines

How-to-guide for chaining two RAG pipelines: Mongo -> ChatGPT & Weaviate -> LLama2

Automated Personalized Landing Pages for your Marketing Funnel

Sales and marketing are all about meeting your prospects where they are and tailoring the pitch accordingly. These prospects could be just starting to look (Launch), actively comparing (Select), considering replacing (Replace), or even migrating altogether (Migrate). Traditional methods often fall short in delivering timely, personalized content to these different categories, resulting in lost opportunities. So what are we proposing? We built a NUX chain that plugs into your existing custom

Daisy-Chaining RAGs from Different Data Sources

Learn how to chain RAG models from multiple data sources for more accurate and complex queries.

Daisy Chaining Machine Learning Models for End-to-End Facial Recognition

In this article, we will explore how an engineer can leverage a series of pre-trained machine-learning models to create a comprehensive facial recognition system. This system will be capable of identifying faces in an image, detecting text within the image, converting that image to text, and finally translating the text. Each component of this process is driven by a different machine-learning model. The use of multiple models in this fashion is often referred to as 'daisy-chaining' models. Out

Real-Time Fraud Detection using SageMaker and LightGBM

Deploying a real-time fraud detection system using SageMaker and PyTorch involves data preprocessing, training a LightGBM model, and deploying it on SageMaker for predictions. This end-to-end guide offers a robust solution to detect fraudulent activities swiftly.

Building a Deepfake Detection System using SageMaker

Build a deepfake detection system using Mesonet and AWS SageMaker. This tutorial covers model deployment, inference pipeline creation, and future steps for fine-tuning.

Set Up and Run OpenAI's CLIP on Amazon SageMaker for Inference

How to deploy and run OpenAI's CLIP model on Amazon SageMaker for efficient real-time and offline inference.


LLM Determinism: The Holy Grail in AI

LLM determinism can be accomplished through environmental consistency, data management, parameter experimentation, RAG, testing, and documentation.

Orchestrating RAG: Kubernetes for Domain-Specific Embedding Models and LLMs

RAG orchestration would address the critical needs of testing, versioning, fine-tuning, co-hosting, monitoring, and low-latency deployment.

Model Determinism: The Path to AI Predictability

Embrace predictability, efficiency, and transparency in your AI projects

Async vs Threads vs Processes in Python

Python, as a versatile programming language, offers several methods to manage concurrent execution and parallel processing. This article delves into three key concepts: Asynchronous Programming, Threads, and Processes, each catering to different needs and scenarios. 1. Asynchronous Programming Asynchronous programming in Python is achieved using the asyncio library. It allows for writing code that performs multiple tasks seemingly at the same time but within a single thread. The execution swi

Retrieval Augmented Generation (RAG): Enhancing Contextual Responses in AI Models

Retrieval-Augmented Generation (RAG) represents an exciting frontier in artificial intelligence, bridging information retrieval and text generation to answer questions by finding relevant information and synthesizing responses in a coherent and contextually rich way.

What are Vector Embeddings and How are They Used in A.I?

Vector embeddings are a critical component in the world of machine learning and AI. These are mathematical representations of words or documents that enable the computer to interpret, compare, and analyze text, images, videos and audio. The idea is to represent words in a multi-dimensional space, where similar words cluster together. Let's take a deep dive into how vector embeddings work and how they can be utilized. content/vocab/vector-embeddings at master · nux-ai/contentContribute to nux-a

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