#vectordatabase Development Tutorials & Tips | Roast Dev

𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀: 𝗧𝗵𝗲 𝗕𝗮𝗰𝗸𝗯𝗼𝗻𝗲 𝗼𝗳 𝗥𝗔𝗚 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹

Retrieval-Augmented Generation (RAG) relies on an advanced retrieval system to fetch relevant information before generating responses. At the heart of this retrieval process are vector databases, whic...

10.03.2025 0 Read More

What Are Embeddings? How They Help in RAG

Retrieval-Augmented Generation (RAG) relies on a key concept called embeddings to enable intelligent search and retrieval of relevant information. Embeddings are numerical representations of text, ima...

11.03.2025 0 Read More

Graph database vs relational vs vector vs NoSQL

Graph Databases vs Relational Databases Relational databases store data in tables with predefined schemas and rely on joins to establish relationships, which can become inefficient for comple...

11.03.2025 0 Read More

Understanding Vector Databases with APIpie.ai

In today's data-driven world, the ability to find meaningful connections within vast amounts of information has become a critical competitive advantage. Traditional databases excel at storing and ret...

11.03.2025 0 Read More

Tutorial: Build a RAG Chatbot with LangChain 🦜, Zilliz Cloud, Anthropic Claude 3 Opus, and Google Vertex AI text-embedding-004

Introduction to RAG Retrieval-Augmented Generation (RAG) is a game-changer for GenAI applications, especially in conversational AI. It combines the power of pre-trained large language models ...

12.03.2025 0 Read More

RAG Vector Database - Use Cases & Tutorial

RAG Vector Database is one of the first main terms RAG geeks are looking for. Let me dive in! RAG Vector Database: A Comprehensive Guide: Table of Contents - RAG Vector Database: Intr...

13.03.2025 0 Read More

Build RAG Chatbot 🤖 with LangChain, Milvus, Mistral AI Pixtral, and NVIDIA bge-m3

Introduction to RAG Retrieval-Augmented Generation (RAG) is a game-changer for GenAI applications, especially in conversational AI. It combines the power of pre-trained large language models ...

14.03.2025 0 Read More

Quick tip: How to Build Local LLM Apps with Ollama, DeepSeek-R1 and SingleStore

Abstract In a previous article, we saw how to use Ollama with SingleStore. In this article, we'll modify the previous example and replace the existing LLM with DeepSeek-R1 instead.The noteboo...

17.03.2025 0 Read More

Docling's new “SmolDocling-256M” Rocks

First impression of testing “SmolDocling-256M-preview” available on Hugging Face. Introduction SmolDocling (the latest from the Docling family) is a multimodal Image-Text-to-Text model des...

17.03.2025 0 Read More

Quick tip: How to Build Local LLM RAG Apps with Ollama, DeepSeek-R1 and SingleStore

Abstract In a previous article, we explored how to use Ollama and DeepSeek-R1 with SingleStore for a simple example. In this article, we'll build on that example by working with a PDF documen...

17.03.2025 0 Read More

𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀: 𝗧𝗵𝗲 𝗕𝗮𝗰𝗸𝗯𝗼𝗻𝗲 𝗼𝗳 𝗥𝗔𝗚 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹

What Are Embeddings? How They Help in RAG

Graph database vs relational vs vector vs NoSQL

Understanding Vector Databases with APIpie.ai

Tutorial: Build a RAG Chatbot with LangChain 🦜, Zilliz Cloud, Anthropic Claude 3 Opus, and Google Vertex AI text-embedding-004

RAG Vector Database - Use Cases & Tutorial

Build RAG Chatbot 🤖 with LangChain, Milvus, Mistral AI Pixtral, and NVIDIA bge-m3

Quick tip: How to Build Local LLM Apps with Ollama, DeepSeek-R1 and SingleStore

Docling's new “SmolDocling-256M” Rocks

Quick tip: How to Build Local LLM RAG Apps with Ollama, DeepSeek-R1 and SingleStore

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