Results for “semantic-search”
7 skills
microsoft azure-search-documents-ts
Build search applications with vector, hybrid, and semantic search using the Azure AI Search SDK for TypeScript.
2.7k · bundle
microsoft azure-search-documents-dotnet
Build search applications with full-text, vector, semantic, and hybrid search using the Azure AI Search SDK for .NET.
2.7k · bundle
microsoft azure-search-documents-py
Search Azure AI Search indexes using the Python SDK for full-text, vector, hybrid, and semantic search with AI enrichment.
2.7k · bundle
More results
lionelndong content-gap-analysis
Layer 1b of the keyword research pipeline. Finds keyword opportunities by comparing the brand's blog against competitors AND by expanding seeds + modifiers via Semrush (phrase_fullsearch / phrase_related). Auto-discovers competitors via domain_organic_organic when none are provided, derives the keyword gap via domain_domains, tags every row with `gap_mode`, and outputs a candidate-keyword CSV ready for downstream BID/AIO vetting.
0
orchestra-research pinecone
Provides code examples and best practices for using Pinecone, a managed vector database for production RAG, recommendation, and semantic search applications.
10.4k · bundle
tianhao909 pinecone
Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.
1 · bundle
qcmuu pinecone
Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.
0 · bundle