"Your embeddings are out of sync again." It's a message that haunts engineering teams trying to build AI applications. What starts as a simple vector search implementation inevitably evolves into a complex orchestra of monitoring, synchronization, and firefighting. We've spent the past year talking to dozens of engineering teams building AI systems with vector databases, whether semantic search, r
A History of Approximate Nearest Neighbor Search from an Applications Perspective
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This method ensures that items ranked high in multiple lists are given a high rank in the final list. It also ensures that items ranked high in only a few lists but low in others are not given a high rank in the final list. Placing the rank in the denominator when calculating score helps penalize the low ranking records. Itâs also worth noting: $rrf_k: To prevent extremely high scores for items ra
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Engineering Blog Technology that sparks innovation to ignite growth Search Reimagining LinkedInâs search tech stack We share how we transformed our overarching search experience at LinkedIn, including the challenges and decisions that went into creating a scalable LLM-based stack and how the technology is powering a smarter, faster, and more personalized experience that helps every member find the
In an era where semantic search and retrieval-augmented generation (RAG) are redefining our online interactions, the backbone supporting these advancements is often overlooked: vector databases. If you're diving into applications like large language models, RAG, or any platform leveraging semantic search, you're in the right place. Picking a vector database can be hard. Scalability, latency, costs
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