Tag: vector database
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Blog Post: Moving Beyond Vector Databases with Vectorless RAG
In the rapidly evolving world of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) has become a standard for providing context to AI. Traditionally, this meant building complex pipelines involving document chunking, embedding generation, and management of vector databases. However, a new trend is emerging: Vectorless RAG.
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Gemini’s File Search API: Grounded AI Just Got Easy (and Cheap!)
The Gemini File Search API is a significant leap forward in making advanced AI accessible. It empowers developers to create more accurate, reliable, and intelligent applications faster and more affordably than ever before. If you’re looking to leverage the power of LLMs with your private data, this tool is an absolute must-explore.
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Import EVERYTHING Into Your RAG Agent with Docling & LlamaParse
Parsing is the first step in building a powerful RAG system. Without accurate, consistent document ingestion, even the most advanced AI agent will struggle. Tools like LlamaParse and Docling ensure that your RAG agent can handle real-world document workflows — securely, reliably, and at scale.