2025 was when MCP and RAG made some headlines in the ‘AI’ world, but despite the hype and the counter-hype, we just got on with building. This design performs RAG with Qdrant and Rust. This article shows and gives an overview of a highly usable, and useful solution semantic similarity search Find highly relevant results […]
aka “part 2” 🙂 So in part 1 we got the basics working, we set up a connection to Qdrant, made a new collection, and then checked the collection exists. We also wired up the code for FastEmbed to produce embeddings, so what next? How do we actually make it usable for a real project? […]
https://github.com/0xPlaygrounds/rig/blob/main/rig-qdrant/examples/qdrant_vector_search.rs In the tuple |(d, embeddings)|, the components correspond to the values yielded by documents.into_iter() Thus: Example of OneOrMany Utility Type in Rust Breakdown: Example Output: In this case, OneOrMany makes it flexible to handle cases where you might have a single embedding or multiple embeddings, while keeping the code concise and readable.
While most examples for performing embeddings will be Python code, Rust is better when performance, safety, and scalability are critical. Most examples/tutorials stick to Python, maybe it’s easier to start with but let’s look beyond “getting started”. Let’s explore Qdrant some more, this time we’ll delve into the Rust client plus fastembed. (Note: If you […]
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 […]
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 […]
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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 […]
