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.
67 lines
2 KiB
Markdown
67 lines
2 KiB
Markdown
# Prompt 3: Build the Search Interface
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Copy this into Claude Code after ingestion is complete.
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---
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```
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Now build a simple web interface for searching my content.
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I want a local web app (localhost) with:
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1. A search box where I type a question in plain English.
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2. When I search, the app should:
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a. Convert my question to an embedding using Gemini Embedding 2
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with task_type "RETRIEVAL_QUERY"
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b. Search Pinecone for the 5 most similar chunks
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c. Show me the matching results with:
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- The source file name
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- The relevant text snippet
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- A similarity score
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- If the result is from an image, show the image too
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3. Below the search results, add a "Ask Claude" button that:
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a. Takes the search results as context
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b. Sends them to Claude (use the claude CLI command, since I
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have Claude Code installed) with my question
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c. Shows Claude's answer, which should reference the specific
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files it used
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Keep the design simple and clean. Dark background, readable text.
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No frameworks needed, just HTML + CSS + a small server.
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Start the app after building it.
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```
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---
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## What Claude Code will do
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1. Build a small web server (Express or similar)
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2. Create an HTML page with a search box
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3. Wire up the search to Gemini embeddings + Pinecone
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4. Add Claude integration for AI-powered answers
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5. Start the server
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## What to try
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Once the app is running, try these searches:
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- "What is the largest planet in our solar system?"
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(Should find the Jupiter fact sheet AND the Jupiter image)
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- "Tell me about the first Moon landing"
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(Should find the Aldrin image description)
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- "Which moon has active volcanoes?"
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(Should find the moons PDF mentioning Io)
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- "How far is Jupiter from Earth?"
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(Should find specific numbers from the Jupiter fact sheet)
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- "What can astronauts see from the space station?"
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(Should find the ISS image description)
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These examples demonstrate the power of multimodal search:
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a question about "the largest planet" finds both text data
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AND a photograph, because both are semantically related.
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