PKParth krishan Goswamiinparthkg.hashnode.dev·3h ago · 7 min readDay 18: pdf_loader.ipynb — Adding Groq, and Teaching RAG to Say NoMy first call to an LLM failed before a single chunk reached it. The model the course uses has been retired. Once that was fixed, I found a bigger problem: my RAG pipeline confidently answered a quest11M
PKParth krishan Goswamiinparthkg.hashnode.dev·1d ago · 5 min readDay 17: pdf_loader.ipynb — ChromaDB, a Retriever, and a Lying ScoreMy retriever told me nothing in my four PDFs was about hard negative mining. Page 4 of the embeddings report is about exactly that. On Day 16 I turned 359 chunks into 384-dimension vectors. Today they11M
PKParth krishan Goswamiinparthkg.hashnode.dev·2d ago · 5 min readDay 16: pdf_loader.ipynb — 64 Pages, 359 Chunks, 384 Dimensions64 pages went in. 359 chunks came out, and every chunk is now a list of 384 numbers. On Day 15, loaders turned my PDFs into Document objects. Today they get cut into pieces and turned into vectors, th00
PKParth krishan Goswamiinparthkg.hashnode.dev·4d ago · 5 min readDay 15: document.ipynb — What RAG Fixes and LangChain's DocumentI thought RAG was a kind of model. It's a pipeline, and its first half never touches an LLM. Today I started Krish Naik's RAG crash course. Before any embeddings or vector databases, I had to learn ho00
PKParth krishan Goswamiinparthkg.hashnode.dev·Feb 28 · 5 min readDay 14: setup_env.sh to human-learn — Building Leak-Proof Pipelines on M1Subtitle: Ditching messy source installations and manual scaling for clean, conda-powered Scikit-Learn pipelines and human-in-the-loop logic. setup_env.sh: Installing Scikit-Learn on Apple Silicon Be00
