Community Picks·1yBuilding a Local RAG Chat App with Reflex, LangChain, Huggingface, and Ollama
Learn how to build a privacy-focused RAG-powered chat app using Reflex, LangChain, Hugging Face, FAISS, and Ollama. This step-by-step guide covers setting up a local environment, creating an interactive chat UI, embedding search, and integrating local LLM, eliminating cloud dependencies and frontend expertise.
Atomic Spin·1y4 Examples of LLM Software Architecture
The post explores four examples of software architectures that integrate large language models (LLMs) at varying levels of complexity. It discusses using LLMs solely as chatbots, as frontends with access to custom functions, in Retrieval-Augmented Generation (RAG) applications, and combined with Langgraph for handling complex workflows. Each example includes pros and cons, and insights into potential applications of LLMs in both prototype and production environments.
Daily Dose of Data Science | Avi Chawla | Substack·51w5 Chunking Strategies For RAG
Five chunking strategies for Retrieval Augmented Generation (RAG) are explained: fixed-size chunking splits text into uniform segments with overlap, semantic chunking groups segments based on embedding similarity, recursive chunking uses natural separators then splits oversized chunks, document structure-based chunking follows inherent document organization, and LLM-based chunking uses language models for semantic accuracy. Each approach has trade-offs between simplicity, semantic preservation, and computational cost, with semantic chunking often performing well in practice.