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# rag

Retrieval augmented generation, or RAG, is an architectural approach that can improve the efficacy of large language model (LLM) applications by leveraging custom data.

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⚡️ BREAKING: Docling Unlocks ASR (automatic speech recognition) Power!

⚡️ BREAKING: Docling Unlocks ASR (automatic speech recognition) Power!

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5 min read
Revolutionizing AI with Retrieval-Augmented Generation (RAG): Architectures, Workflows, and Practical Applications

Revolutionizing AI with Retrieval-Augmented Generation (RAG): Architectures, Workflows, and Practical Applications

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3 min read
Local Elasticsearch Playground: A Practical Introduction and hands-on test (and moving to a RAG solution)

Local Elasticsearch Playground: A Practical Introduction and hands-on test (and moving to a RAG solution)

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12 min read
Towards Lifelong Dialogue Agents via Timeline-based Memory Management

Towards Lifelong Dialogue Agents via Timeline-based Memory Management

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2 min read
Building RAG Applications with LangChain(Part-4)

Building RAG Applications with LangChain(Part-4)

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5 min read
A Complete Guide to Retrieval-Augmented Generation

A Complete Guide to Retrieval-Augmented Generation

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8 min read
RAG Made Simple: Technical Design and Architecture of Simplicity (Part 2)

RAG Made Simple: Technical Design and Architecture of Simplicity (Part 2)

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3 min read
RAG Made Simple: Simplicity’s Approach to Modular Retrieval & Generation (Part 1)

RAG Made Simple: Simplicity’s Approach to Modular Retrieval & Generation (Part 1)

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3 min read
Schema In, Data Out: A Smarter Way to Mock

Schema In, Data Out: A Smarter Way to Mock

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6 min read
Between Structure and Imagination: What happens when code becomes a sketchpad for ideas.

Between Structure and Imagination: What happens when code becomes a sketchpad for ideas.

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8 min read
How to setup RAG with VectorDB

How to setup RAG with VectorDB

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4 min read
Secure Local RAG with Role-Based Access: Spring AI, Ollama & MongoDB

Secure Local RAG with Role-Based Access: Spring AI, Ollama & MongoDB

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14 min read
Open-Source AI Stacks for E-Commerce (2025 Guide)

Open-Source AI Stacks for E-Commerce (2025 Guide)

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7 min read
Building RAG Applications with LangChain(Part-5)

Building RAG Applications with LangChain(Part-5)

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3 min read
Medical RAG Research with txtai

Medical RAG Research with txtai

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7 min read
Uncertainty of Thoughts: Uncertainty-Aware Planning Enhances Information Seeking in Large Language Models

Uncertainty of Thoughts: Uncertainty-Aware Planning Enhances Information Seeking in Large Language Models

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2 min read
Comprehending RAGs with a keyword search [LLM A1]

Comprehending RAGs with a keyword search [LLM A1]

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3 min read
Democratizing AI: Building a RAG System with Gemma and LangChain

Democratizing AI: Building a RAG System with Gemma and LangChain

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3 min read
Retrieval-Augmented Generation (RAG): A Deep Technical Dive

Retrieval-Augmented Generation (RAG): A Deep Technical Dive

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2 min read
The Hidden Bias in AI Code Generation

The Hidden Bias in AI Code Generation

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4 min read
How to Install Qwen3 Embedding 8B: Best Model for RAG, Search, & Multilingual Embeddings

How to Install Qwen3 Embedding 8B: Best Model for RAG, Search, & Multilingual Embeddings

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6 min read
RAG Made Simple: Demonstration and Analysis of Simplicity (Part 3)

RAG Made Simple: Demonstration and Analysis of Simplicity (Part 3)

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2 min read
LangGraph + Graphiti + Long Term Memory = Powerful Agentic Memory

LangGraph + Graphiti + Long Term Memory = Powerful Agentic Memory

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11 min read
Context Isn’t Everything: Build Efficient LLM Apps with LlamaIndex + Featherless

Context Isn’t Everything: Build Efficient LLM Apps with LlamaIndex + Featherless

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5 min read
Generative Engine Optimization (GEO): The New Frontier Beyond SEO

Generative Engine Optimization (GEO): The New Frontier Beyond SEO

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3 min read
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