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Local LLM setup: how to use RAG and an embedding model to stop wasting context

Local LLMs are fantastic, and they keep getting better at a staggering pace. I have non-negotiable reasons for preferring a local setup over relying on cloud giants like Claude or ChatGPT. Because of ...
note

Reading zvec at the implementation level: Design and minimal Python implementation of an in-process vector DB

I want to use vector search without setting up an external server. There are surprisingly many requirements like that. When you don't want to bring in Docker at the PoC stage, want to embed search ...
GitHub

Enterprise Telecom RAG Chatbot Engine

An asynchronous, multi-tenant Retrieval-Augmented Generation (RAG) chatbot engine custom-built for telecommunications help-desks. The system utilizes LangGraph for intent routing and self-correction ...
GitHub

Qdrant Skills - Agent Skills for Qdrant Vector Search

Skills encode deep Qdrant knowledge so coding agents can make the engineering decisions that determine whether vector search works well: quantization, sharding, tenant isolation, hybrid search, model ...