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All digests tagged Vector Databases
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SQL Server 2025 and Azure SQL Databases are transforming into intelligent data platforms. Learn how native vector support enables RAG, how T-SQL can call REST APIs, and how enhanced JSON and RegEx features boost developer productivity.
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The fix involves 'native filtering' over a clustered vector index, which combines pre- and post-filtering to achieve 120ms P90 searches across 400 million vectors.
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Bridgewater's Vault processes 100M+ documents, transforming unstructured data into a first-class asset. Learn how they built for both human precision and agent scale using advanced filtering and proactive AI agents.
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The agent harness is the critical layer for building autonomous AI agents. By focusing on memory, context engineering, and tool management, engineers can make significant gains regardless of the underlying LLM model.
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Building a production AI agent requires more than connecting APIs. Learn why a Context Graph—combining synced data, live lookups, skills, and memory—is essential for semantic querying and reliable business logic.
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Comparing agentic search vs. vector search for coding agents, this talk reveals that while both methods achieved equal accuracy, vector search cost four times more due to lack of connective context.
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Superintelligence is coming, but who owns the memory? Dylan Couzon argues that while models are moving to local hardware, the critical memory layer must also be owned. He demonstrates Qdrant Edge, an embedded vector search engine that enables private, persistent, and searchable AI memory offline.
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Agent harness engineering is the discipline of turning a non-deterministic LLM core into reliable, repeatable output. The architecture focuses on the data layer, memory management, and robust workflow loops.
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Fixed chunking is a flawed assumption in RAG. The optimal chunk size is query-dependent. By implementing multiscale indexing and combining results using RRF, systems can achieve 20-40% higher recall.
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TurboPuffer, an object storage-native search engine, solves scaling issues in legal AI search. It achieves physical isolation and massive latency improvements by making the namespace the atomic unit of storage.
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A Digital Librarian AI Agent solves the 'what vs. why' data problem by connecting SQL databases (for hard numbers) and vector databases (for policy context). This agent uses multi-step workflows to deliver grounded answers, transforming siloed data into a reasoning engine.
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Discover how Multi-Vector Retrieval solves the limitations of single vector embeddings for AI agents. Learn about sparse multi-vector encoding, scaling to billion documents, and achieving sub-50ms P99 latency.
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Learn how AI and Data Science combine into a single pipeline. We break down complex concepts like RAG, embeddings, and data cleansing using periodic tables to show how robust enterprise AI applications are built.
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Context Engineering is the critical practice of structuring information passed to LLMs and AI agents. Learn how optimizing context, rather than just writing better prompts, improves reliability and reduces hallucinations in complex AI systems.
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Chunkless RAG uses document structure and AI agents instead of relying only on chunking and similarity search. Learn when structure-aware retrieval improves accuracy, context, and document understanding.
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Learn how RAG solutions fail with complex documents and contradictory laws. We discuss practical techniques like clarification loops and robust document management to handle ambiguity in AI systems.
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This deep dive explores building an agentic retrieval harness that moves beyond simple RAG. It details combining hybrid search, local file system access (GP), and multimodal parsing to handle complex enterprise data.
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