Recent advancements in Large Language Model (LLM) agents allow for complex workflows where models autonomously retrieve information, utilize tools, and reason over large datasets. Retrieval-augmented generation (RAG) is increasingly adopted in agentic search systems to enhance task completion.
arxiv.org
2 min
6/9/2026
Gemini API's File Search tool now supports multimodal data and custom metadata for building retrieval-augmented generation (RAG) systems. The update includes page citations to enhance grounding and transparency, enabling better organization of text and visual content.
blog.google
2 min
5/10/2026
Recent advancements in Large Language Model (LLM) agents allow for complex workflows where models autonomously retrieve information, utilize tools, and reason over large datasets. Retrieval-augmented generation (RAG) is increasingly adopted in agentic search systems to enhance task completion.
arxiv.org
2 min
6/9/2026
Gemini API's File Search tool now supports multimodal data and custom metadata for building retrieval-augmented generation (RAG) systems. The update includes page citations to enhance grounding and transparency, enabling better organization of text and visual content.
blog.google
2 min
5/10/2026
Recent advancements in Large Language Model (LLM) agents allow for complex workflows where models autonomously retrieve information, utilize tools, and reason over large datasets. Retrieval-augmented generation (RAG) is increasingly adopted in agentic search systems to enhance task completion.
arxiv.org
2 min
6/9/2026
Gemini API's File Search tool now supports multimodal data and custom metadata for building retrieval-augmented generation (RAG) systems. The update includes page citations to enhance grounding and transparency, enabling better organization of text and visual content.
blog.google
2 min
5/10/2026
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