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Developer Builds AI-Powered Local Search Tool Using Gemini and Google Maps APIs

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A developer has published Part 2 of a seven-part series detailing the construction of a grounded local search workflow using Google's Gemini AI, the Google Places API, and Google Search grounding. The system splits retrieval into two paths: structured data such as ratings, hours, and addresses are fetched via Google Places, while softer user preferences like 'quiet enough to work' are handled through Google Search grounding. Gemini first interprets the user's query into a structured intent object, which then drives a bounded set of retrieval tasks to reduce duplication and latency. Results are normalized, scored using deterministic ranking, and synthesized by Gemini into map-linked recommendations. The open-source project, called ai-search-journey-lab, is deployed on Google Cloud Run and built with Python and Streamlit.

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