Rag - TechnoScripts

rag

Guides to retrieval-augmented generation, combining search with language models for grounded answers.

RAG Tutorial in Python: LlamaIndex & Retrieval-Augmented Generation

Large Language Models (LLMs) make things up, and a Python RAG app is the cure. You saw the hallucination problem in the large language models tutorial: the model guesses when it does not actually know. RAG (Retrieval-Augmented Generation) stops that guessing by feeding it the right documents before it answers. Instead of leaning on whatever it memorized during training, you pull the relevant passages from your own files and paste them into the prompt. The model then answers from real, ... Read More

Python: Vector Databases (ChromaDB, Pinecone, pgvector for AI Apps)

As soon as you start turning text into vectors, whether for a search feature today or a RAG (Retrieval-Augmented Generation) pipeline you build later, you end up with a pile of embeddings that need a home. So which Python vector database do you reach for: ChromaDB, Pinecone, or pgvector? That single choice shapes your cost, your scale, and how much infrastructure you babysit. Think of it like picking where to keep your photos. A folder on your laptop is free ... Read More

Python: LangChain vs LlamaIndex vs CrewAI, AI Agent Frameworks Compared

You have three agent frameworks open in browser tabs, each with a slick demo. LangChain (and its agent layer, LangGraph) promises general-purpose Large Language Model (LLM) orchestration. LlamaIndex specializes in connecting LLMs to your own data. CrewAI focuses on multi-agent teams. So which one do you actually pick? It depends on what you are building, and this langchain vs llamaindex guide hands you the decision framework. Not marketing, just side-by-side code that solves the same problem in each one. “The ... Read More