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klotz: retrieval-augmented generation* + llm*

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1. A Visual Exploration of Semantic Text Chunking

   The article explains semantic text chunking, a technique for automatically grouping similar pieces of text to be used in pre-processing stages for Retrieval Augmented Generation (RAG) or similar applications. It uses visualizations to understand the chunking process and explores extensions involving clustering and LLM-powered labeling.

   2024-09-21 Tags: text, chunking, nlp, rag, dimensionality reduction, hierarchical clustering, umap, summarization, llm by klotz
2. Archyve: Web app for Retrieval-Augmented Generation

   Archyve is a web app that enhances pretrained language models with user's documents, while keeping them on the user's own devices and infrastructure. It provides an API for querying documents, an LLM chat UI, and an API for third-party LLM chat UIs.

   2024-08-30 Tags: archyve, rag, llm by klotz
3. Agentic Chunking for RAGs

   This article discusses the importance of chunking, embedding, and indexing in RAGs (Recursive Auto-Segmented Graphs). The author compares recursive character splitting and semantic splitting techniques for text chunking and suggests the use of agentic chunking for superior RAG retrieval.

   2024-08-27 Tags: rag, text, chunking, llm by klotz
4. Why Does Position-Based Chunking Lead to Poor Performance in RAGs? How to implement semantic chunking and gain better results.

   This article explores the limitations of position-based chunking in Retrieval Augmented Generation (RAG) systems and proposes semantic chunking as a better alternative for improved performance.

   2024-08-24 Tags: rag, chunking, llm, information retrieval by klotz
5. Don’t Limit Your RAG Knowledgebase to Just Text

   This article explores how to incorporate images into a RAG (Retrieval-Augmented Generation) knowledgebase using Large Language Models (LLMs) with vision capabilities. It provides a step-by-step guide to collecting, uploading, and transcribing images for a richer and more detailed knowledgebase.

   2024-08-23 Tags: rag, knowledge base, llm, image processing, vision, transcription by klotz
6. What Nobody Tells You About RAGs

   This article explores the challenges and considerations of implementing Retrieval Augmented Generation (RAG) systems for real-world business applications, beyond simple demos. It covers data handling, performance optimization, and the importance of aligning RAG with specific business goals.

   2024-08-30 Tags: rag, llm, vector database by klotz
7. Graph RAG — A Conceptual Introduction

   This article introduces Graph RAG, a method for enhancing Language Model (LLM) applications by incorporating knowledge graphs. It explains the limitations of traditional text embedding-based retrieval and how Graph RAG addresses them by providing a global understanding of the knowledge base through community detection and report generation.

   2024-08-23 Tags: graph, rag, knowledge graph, llm by klotz
8. 13 Must-know Open-source Software to Build Production-ready AI Apps

   A list of 13 open-source software for building and managing production-ready AI applications. The tools cover various aspects of AI development, including LLM tool integration, vector databases, RAG pipelines, model training and deployment, LLM routing, data pipelines, AI agent monitoring, LLM observability, and AI app development.
   1. Composio - Seamless integration of tools with LLMs.
   2. Weaviate - AI-native vector database for AI apps.
   3. Haystack - Framework for building efficient RAG pipelines.
   4. LitGPT - Pretrain, fine-tune, and deploy models at scale.
   5. DsPy - Framework for programming LLMs.
   6. Portkey's Gateway - Reliably route to 200+ LLMs with one API.
   7. AirByte - Reliable and extensible open-source data pipeline.
   8. AgentOps - Agents observability and monitoring.
   9. ArizeAI's Phoenix - LLM observability and evaluation.
   10. vLLM - Easy, fast, and cheap LLM serving for everyone.
   11. Vercel AI SDK - Easily build AI-powered products.
   12. LangGraph - Build language agents as graphs.
   13. Taipy - Build AI apps in Python.

   2024-08-16 Tags: foss, llm, production, rag, fine-tuning, vector database, tools by klotz
9. Build a Knowledge Graph-based Agent With Llama 3.1, NVIDIA NIM, and LangChain

   This article explores how to implement a retriever over a knowledge graph containing structured information to power RAG (Retrieval-Augmented Generation) applications.

   2024-08-10 Tags: llm, knowledge graph, llama 3.1, nvidia nim, langchain, rag, graph database, neo4j by klotz
10. Advanced RAG Techniques

    This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. RAG systems combine information retrieval with generative models to provide accurate and contextually rich responses.

    2024-08-01 Tags: rag, nlp, machine learning, information retrieval, natural language processing, llm, embeddings, semantic search by klotz

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