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Gemini API File Search is now multimodal: build efficient, verifiable RAG
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Gemini API File Search is now multimodal: build efficient, verifiable RAG

Gemini API File Search
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"K-Dense Web is an AI co-scientist that autonomously executes complex multi-step workflows across science, engineering, healthcare, and finance. We’re building a unified visual memory to enable researchers to search across mixed modalities—from Western blots and microscopy images to agent-generated plots—in one query. Early testing with File Search's new capabilities has shown excellent retrieval accuracy and latency across these mixed-modality scientific corpora, with no preprocessing on our side." - Timothy Kassis, Co-Founder & CTO at K-Dense
“The new multimodal capabilities in the Gemini API are genuinely impressive. For a product like ours that combs through a massive, diverse library of GIFs, semantic retrieval quality is pivotal, and with this update, we've seen remarkable advances in the model’s ability to understand text within images of varying quality and fidelity. This precision means users find the perfect visual moment by simply asking for it. Since the model abstains from guessing, eliminating hallucinations, users get better results, providing the trust and reliability critical for our production environment.” - Givi Beridze, Co-Founder & CEO at Klipy
“At Code Fundi, we provide the context layer for autonomous engineering. We solve the ‘Context Window Bottleneck’ by distilling massive, noisy repositories into logic-dense, LLM-ready markdown. Using the gemini-embedding-2 model to index a massive public pool of architectural diagrams, ERDs, and sequence diagrams from top open-source projects, we provide agents with a "photographic memory" of how the world's best engineers visualize complex logic. This allows agents to reclaim over 50% of their context window for reasoning by pinpointing exact data through multimodal search.” - Felix Waweru, Founder at Code Fundi

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