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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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Implementing RAG with Azure OpenAI in .NET (C#)
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Implementing RAG with Azure OpenAI in .NET (C#)

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10 min read
Can ChatGPT Be Hacked?
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Can ChatGPT Be Hacked?

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7 min read
Code Explanation: "OpenManus: An Autonomous Agent Platform"

Code Explanation: "OpenManus: An Autonomous Agent Platform"

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7 min read
Key Use Cases of RAG: From Chatbots to Research Assistants
Cover image for Key Use Cases of RAG: From Chatbots to Research Assistants

Key Use Cases of RAG: From Chatbots to Research Assistants

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3 min read
Vector Search & Code Embeddings: Building a Smart Knowledge Base with LangChain and FAISS

Vector Search & Code Embeddings: Building a Smart Knowledge Base with LangChain and FAISS

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4 min read
Optimizing a RAG-Based Helpdesk Chatbot: Improving Accuracy with pgvector

Optimizing a RAG-Based Helpdesk Chatbot: Improving Accuracy with pgvector

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4 min read
Implementing a Vector Database in a RAG System for a Helpdesk Chatbot with pgvector

Implementing a Vector Database in a RAG System for a Helpdesk Chatbot with pgvector

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4 min read
Semantic search alone won't solve relational queries in your LLM retrieval pipeline.
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Semantic search alone won't solve relational queries in your LLM retrieval pipeline.

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1 min read
Simple Knowledge Retrieval: A RAG Implementation and query using IBM Granite on Hugging Face

Simple Knowledge Retrieval: A RAG Implementation and query using IBM Granite on Hugging Face

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7 min read
Understanding RAG (Retrieval Augmented Generation) with APIpie.ai

Understanding RAG (Retrieval Augmented Generation) with APIpie.ai

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6 min read
Semantic Similarity for Personal Knowledge Management

Semantic Similarity for Personal Knowledge Management

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4 min read
5 GenAI Things You Didn't Know About Astra DB
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5 GenAI Things You Didn't Know About Astra DB

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8 min read
Build RAG Chatbot with LangChain, Milvus, Anthropic Claude 3 Opus, and OpenAI text-embedding-3-small

Build RAG Chatbot with LangChain, Milvus, Anthropic Claude 3 Opus, and OpenAI text-embedding-3-small

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8 min read
Model Context Protocol (MCP): The USB-C for AI Applications

Model Context Protocol (MCP): The USB-C for AI Applications

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3 min read
Understanding RAGAS: A Comprehensive Framework for RAG System Evaluation

Understanding RAGAS: A Comprehensive Framework for RAG System Evaluation

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4 min read
Extracting Information from PDFs in Markdown Format with Mistral OCR

Extracting Information from PDFs in Markdown Format with Mistral OCR

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2 min read
A Practical Guide to RAG with DeepSeek R1 & Ollama

A Practical Guide to RAG with DeepSeek R1 & Ollama

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1 min read
New implementation on watsonx.ai: automate RAG pipeline development & deployment with a SDK

New implementation on watsonx.ai: automate RAG pipeline development & deployment with a SDK

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14 min read
How to Create RAG using DeepSeek R1, Ollama & Semantic Kernel .NET
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How to Create RAG using DeepSeek R1, Ollama & Semantic Kernel .NET

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4 min read
Beginning a Series on RAG for Nordic APIs
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Beginning a Series on RAG for Nordic APIs

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1 min read
How to Build a RAG Chatbot with LangChain, Milvus, Together AI Mixtral 8x7B Instruct v0.1, and OpenAI text-embedding-3-large

How to Build a RAG Chatbot with LangChain, Milvus, Together AI Mixtral 8x7B Instruct v0.1, and OpenAI text-embedding-3-large

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8 min read
Top 10 Open-Source RAG Frameworks you need!! 🧌
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Top 10 Open-Source RAG Frameworks you need!! 🧌

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12 min read
RAG Chatbot: Build with LangChain, Milvus, Fireworks AI 🔥Llama 3.1 8B Instruct, and Cohere embed-multilingual-v2.0

RAG Chatbot: Build with LangChain, Milvus, Fireworks AI 🔥Llama 3.1 8B Instruct, and Cohere embed-multilingual-v2.0

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8 min read
Understanding the Key Components of RAG: Retriever and Generator
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Understanding the Key Components of RAG: Retriever and Generator

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2 min read
Overview:"Agentic Retrieval-Augmented Generation: A Comprehensive Survey"

Overview:"Agentic Retrieval-Augmented Generation: A Comprehensive Survey"

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8 min read
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