edcd1721df
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.
48 lines
1.5 KiB
Markdown
48 lines
1.5 KiB
Markdown
# Prompt 1: Set Up the Project
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Copy this into Claude Code after you have your API keys ready.
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---
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```
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I want to build a multimodal search app. I have a folder of files
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(PDFs, images with text descriptions) that I want to make searchable
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using natural language.
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Here is the tech stack I want:
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- Google Gemini Embedding 2 for converting content to embeddings
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(I have a Google AI Studio API key)
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- Pinecone for storing the embeddings
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(I have a Pinecone API key and an index called "space-search")
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- A simple local web interface where I can type questions and
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get results from my files
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- Use Claude for answering questions based on the search results
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(use my Claude Code subscription, not a separate API key)
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My example files are in the folder: example-data/
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That folder contains:
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- 3 PDF files about the solar system, Jupiter, and planetary moons
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- 4 JPG images (Earthrise, Moon landing, Jupiter, ISS)
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- A file called descriptions.md with detailed text descriptions
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of each image
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Please set up the project structure, install dependencies, and
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create a .env.template file for the API keys. Use Node.js with
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TypeScript. Do not start building the search logic yet, just
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the project skeleton.
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```
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---
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## What Claude Code will do
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1. Create a new project folder with `package.json`
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2. Install libraries for Gemini embeddings, Pinecone, and a web server
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3. Create a `.env.template` with placeholders for your API keys
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4. Set up TypeScript configuration
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## What you do next
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1. Copy `.env.template` to `.env`
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2. Fill in your actual API keys
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3. Move to Prompt 2
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