Initial commit: multimodal RAG guide with Claude Code

Prompt-driven guide for building multimodal search using
Gemini Embedding 2 + Pinecone + Claude Code. Includes example
data (NASA public domain), step-by-step prompts, concepts
explainer, cost breakdown, and troubleshooting guide.

prompts/01-setup.md

@@ -0,0 +1,48 @@
+# Prompt 1: Set Up the Project
+
+Copy this into Claude Code after you have your API keys ready.
+
+---
+
+```
+I want to build a multimodal search app. I have a folder of files
+(PDFs, images with text descriptions) that I want to make searchable
+using natural language.
+
+Here is the tech stack I want:
+- Google Gemini Embedding 2 for converting content to embeddings
+  (I have a Google AI Studio API key)
+- Pinecone for storing the embeddings
+  (I have a Pinecone API key and an index called "space-search")
+- A simple local web interface where I can type questions and
+  get results from my files
+- Use Claude for answering questions based on the search results
+  (use my Claude Code subscription, not a separate API key)
+
+My example files are in the folder: example-data/
+That folder contains:
+- 3 PDF files about the solar system, Jupiter, and planetary moons
+- 4 JPG images (Earthrise, Moon landing, Jupiter, ISS)
+- A file called descriptions.md with detailed text descriptions
+  of each image
+
+Please set up the project structure, install dependencies, and
+create a .env.template file for the API keys. Use Node.js with
+TypeScript. Do not start building the search logic yet, just
+the project skeleton.
+```
+
+---
+
+## What Claude Code will do
+
+1. Create a new project folder with `package.json`
+2. Install libraries for Gemini embeddings, Pinecone, and a web server
+3. Create a `.env.template` with placeholders for your API keys
+4. Set up TypeScript configuration
+
+## What you do next
+
+1. Copy `.env.template` to `.env`
+2. Fill in your actual API keys
+3. Move to Prompt 2