MongoDB Vector Search walkthrough to creating an index, querying, evaluating and integrating into your AI RAG application.
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 is a perilous journey. This is a guide that walks through the challenges and paths towards easier productionization of AI applications.
How-to-guide for chaining two RAG pipelines: Mongo -> ChatGPT & Weaviate -> LLama2
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
Learn how to chain RAG models from multiple data sources for more accurate and complex queries.
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
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.
Build a deepfake detection system using Mesonet and AWS SageMaker. This tutorial covers model deployment, inference pipeline creation, and future steps for fine-tuning.
LLM determinism can be accomplished through environmental consistency, data management, parameter experimentation, RAG, testing, and documentation.
RAG orchestration would address the critical needs of testing, versioning, fine-tuning, co-hosting, monitoring, and low-latency deployment.
Embrace predictability, efficiency, and transparency in your AI projects
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) 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.
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