giora_glovatsky:~$
← all work

~/work/cookbook-rag

RAG Cookbook

An AI chef that actually knows what's in your pantry: grounded answers, not confident guesses.

role
Solo: full-stack + AI
timeframe
2026
status
● public
stack
Python · Flask · AWS · RAG · Vector search
ai
Grounded RAG chat + AI image generation
links
GitHub · walkthrough on request

01 The problem

Ask a plain chatbot for a recipe and it'll happily invent one, ignoring what you actually own and what you've actually cooked. I wanted a cooking assistant whose answers were grounded in a real, personal recipe collection and pantry, and honest about it.

Chef AI chat answering a recipe-specific question, grounded in a 505-recipe knowledge base
The AI chef answers recipe-specific and general cooking questions, grounded in a 505-recipe index.

02 The approach

At the core is a RAG pipeline. Recipes are embedded and stored for vector search; when you ask a question, the most relevant recipes are retrieved and fed to the model as grounding, so the "AI chef" answers from your cookbook instead of hallucinating.

  • Recipe upload into a searchable, embedded collection.
  • Pantry tracking with suggestion logic that recommends recipes from what you already have.
  • Grounded chat for both recipe-specific and general cooking questions.
  • AI image generation to visualize new dishes.

03 Architecture

A Flask backend fronts everything; the RAG engine sits between it and Amazon Bedrock, and S3 is both the recipe store and the Knowledge Base's data source. The whole thing runs as a Docker container on EC2 with an IAM instance role, so no long-lived AWS credentials are stored in application code.

storesdata sourceJWT cookiehostsretrievegenerateExternal services

Google Gemini
AI recipe photos

SES + Lambda
cooking-buddy email

Amazon Bedrock

Knowledge Base
OpenSearch Serverless vectors

Titan Embeddings

Nova Lite 1.0
chat · recipe gen · parsing

Data · S3 + Aurora

Aurora PostgreSQL 17
users · recipes · pantry · chats

Amazon S3
recipe .md · images

Flask 3 · app.py

Blueprints
/auth · /recipes · /chat · /pantry

RAG Engine
retrieve_chunks · ask_chef · sync_kb

User

Docker on EC2 · IAM instance role

The full request + data flow, straight from the repo's architecture. Drag to pan, or use the +/− controls (⌘/Ctrl + scroll) to zoom.

04 Highlights

Pantry view with ingredient tracking and a find-recipes action
Pantry tracking drives recipe suggestions from what you already have.
Recipe page for a Chef-AI-created cheeseburger with its AI-generated photo
A recipe created in chat, complete with its AI-generated photo (stored on S3).

05 What it taught me

RAG Cookbook is where I got hands-on with the pattern behind most useful LLM products: retrieval as the guardrail against hallucination. Grounding, embeddings, and vector search stopped being buzzwords and became a pipeline I've now shipped.