Introduction FastEmbed is a lightweight and fast Python library designed for generating high-quality text embeddings. FastEmbed supports a variety of popular text models and is continuously expanding to include more. At its core, FastEmbed utilizes the Flag Embedding model from the MTEB leaderboard, offering distinct “query” and “passage” prefixes for input text. This feature makes […]
Introduction In this guide, we will compare Rust and Python for asynchronous and multithreaded operations, particularly focusing on uploading data to a Qdrant vector search engine. We will evaluate how using Arc in Rust and AsyncQdrantClient in Python impacts performance. Imagine you get approached by a company to speed up their code and you improve […]
Rust is an ideal choice for image preprocessing due to its combination of performance, safety, and concurrency. Its low-level control allows for highly efficient image manipulation, crucial for handling large datasets or performing complex transformations. Image preprocessing involves transforming raw image data into a format that is usable and meaningful. If you want a fast […]
To begin with, when learning about ML/AI you may just use the model specified in the tutorial you’re doing, but later on you’ll want to research, test, and evaluate different models based on size, performance and write your code to suit. Let’s compare output using 2 different models that I used in a recent project […]
Webdock – Fast Cloud VPS Linux Hosting Introduction Let’s see how to create a text similarity search web app using Flask, a pre-trained BERT model, and FAISS for efficient similarity search! Section 1: Importing Libraries “First, we need to import all the necessary libraries. We’ll be using Flask to create our web application, Hugging Face’s […]
This article describes a Python script that leverages LangChain, Qdrant, and the GPT-3.5 Turbo model to perform question-answering on text data. The script combines various libraries to create an end-to-end pipeline for text retrieval and question answering. Overview The provided Python script showcases how to integrate multiple libraries to build a powerful question-answering system: Prerequisites […]
This article describes a Python script that demonstrates how to efficiently process text data, create embeddings, and upsert the embeddings into a Qdrant database using Python. Overview In today’s era of big data and machine learning, efficient text processing and vectorization are crucial for various natural language processing (NLP) tasks. The provided Python script showcases […]
Watched a video about Bitcoin but forgotten the name of it or what part of it you liked? With findthatbit.com you can search based upon meaning. If you remember a video was about bitcoin and there was talk of “seaside” property. Just search for “seaside” and you’ll get the link to where “miami beach” was […]
Upsert additional points with the next available index – add more vectors without recreating the collections. You’ll need to specify the next index, so here’s how to do it… Check Qdrant out here : https://qdrant.tech/documentation/quick-start/ Do the imports and and create the “client” Use client.get_collection() to retrieve the current status of the collection This will […]
