Results for “vector-search”

16 skills

azure-search-documents-ts

Build search applications with vector, hybrid, and semantic search using the Azure AI Search SDK for TypeScript.

azure-search-documents-dotnet

Build search applications with full-text, vector, semantic, and hybrid search using the Azure AI Search SDK for .NET.

azure-search-documents-py

Search Azure AI Search indexes using the Python SDK for full-text, vector, hybrid, and semantic search with AI enrichment.

Index folders of PDFs and other documents into LanceDB for vector search, then query them with semantic search, page filters, verbatim quotes, and cross-document aggregation.

qdrant-performance-optimization

Optimize Qdrant vector search performance through indexing strategies, query tuning, memory management, and hardware considerations.

Build production RAG pipelines and persistent agent memory using Pinecone as the vector database backend.

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qdrant-clients-sdk

Integrate Qdrant vector search into applications using officially supported client SDKs for Python, JavaScript, Rust, Go, .NET, and Java.

qdrant-monitoring

Guides monitoring and observability setup for Qdrant vector search deployments, including Prometheus scraping, health checks, and metric-based debugging of production issues.

qdrant-search-strategies

Guides selection of Qdrant search strategies including hybrid search, reranking, relevance feedback, MMR, and discovery APIs to improve retrieval quality.

qdrant-model-migration

Guides embedding model migration in Qdrant without downtime, covering alias swap, side-by-side, and hybrid search strategies.

qdrant-sliding-time-window

Guides scaling Qdrant vector search with time-based data rotation using shard rotation, collection rotation, or filter-and-delete strategies.

qdrant-search-quality-diagnosis

Diagnoses Qdrant search quality issues by isolating causes like HNSW approximation, quantization, embedding model, or search pipeline problems.

qdrant-search-speed-optimization

Diagnoses and resolves slow Qdrant search performance issues including high latency, low throughput, and slow filtered searches.

qdrant-minimize-latency

Guides optimization of Qdrant query latency by tuning segments, memory, quantization, and search parameters.

qdrant-scaling-qps

Guides scaling Qdrant query throughput (QPS) through performance tuning, horizontal scaling with read replicas, and disk I/O optimization.

Navigate Oracle Cloud Infrastructure's Enterprise AI services: choose models, build agents with RAG and tools, estimate costs, secure access, and integrate with Oracle Database, APEX, and other platform services.