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●ARGON — gemini-4-argon is generally available as of 10/13, aimed at complex coding, agents, math and science●DEEP RES — deep-research-pro-preview-12-2025 shuts down 10/23, 6 days left. Move to deep-research-preview-04-2026●NANO — gemini-nano-banana-2.1 is GA (10/06). gemini-3.1-flash-image is deprecated with no shutdown date yet●MCP — One write-up cut an 85,000-token tool result to 310 tokens by wrapping it in an ADK FunctionTool●FLASH — gemini-3.7-flash and 3.5-flash now route automatically to newer Flash models (10/08)●NEW — Stop Re-Reading Every Crash Report: Gre…●ARGON — gemini-4-argon is generally available as of 10/13, aimed at complex coding, agents, math and science●DEEP RES — deep-research-pro-preview-12-2025 shuts down 10/23, 6 days left. Move to deep-research-preview-04-2026●NANO — gemini-nano-banana-2.1 is GA (10/06). gemini-3.1-flash-image is deprecated with no shutdown date yet●MCP — One write-up cut an 85,000-token tool result to 310 tokens by wrapping it in an ADK FunctionTool●FLASH — gemini-3.7-flash and 3.5-flash now route automatically to newer Flash models (10/08)●NEW — Stop Re-Reading Every Crash Report: Gre…
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semantic-search

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◈ Gemini API/2026-07-09Intermediate

When gemini-embedding-2 Retrieval Feels 'Almost Right,' Check task_type First

When gemini-embedding-2 search misses in a frustrating near-hit way, the cause is often a missing or mismatched task_type. Here is how to align document and query intent, plus the code and a tiny harness to prove the difference on your own data.

◈ Gemini API/2026-07-04Advanced

Catch Near-Duplicate Posts Before You Publish — a Topic-Cannibalization Gate with Gemini Embeddings

Once a blog passes a few hundred posts, new articles start cannibalizing old ones in search. This walks through a pre-publish gate that embeds each post's meaning with gemini-embedding-2 and blocks drafts that sit too close to something you already wrote — with runnable code and how to pick the thresholds.

◈ Gemini API/2026-06-19Advanced

When Your pgvector Search Quietly Gets Worse — Field Notes on Protecting Recall with Gemini Embeddings

A semantic search built on Gemini Embeddings and PostgreSQL pgvector tends to lose precision over months without throwing a single error. These are field notes on the real causes — model pinning, operator/index mismatch, HNSW reindexing, and recall collapse under filters — with working code.

◈ Gemini API/2026-03-29Advanced

Building Production Semantic Search with Gemini Embeddings API — Design, Implementation, and Operations

A comprehensive guide to building production-grade semantic search with Gemini Embeddings API. Covers vector DB selection, reranking, recommendation engines, and cost optimization with practical code.