Plugins
1 pluginResults for “hybrid-search”
20 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
ssrjkk Weaviate
Deploys Weaviate vector database with hybrid search, modules, and GraphQL API.
2 · 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
kbarbel640-del Qmd
Indexes local files and searches them with BM25, vector, and hybrid queries, plus MCP mode.
1 · 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
github Qdrant Search Strategies
Guides selection of Qdrant search strategies including hybrid search, reranking, relevance feedback, MMR, and discovery APIs to improve retrieval quality.
36.2k
More results
orchestra-research Qdrant Vector Search
Build production RAG and semantic search systems with a high-performance vector database written in Rust, supporting hybrid search, filtering, and horizontal scaling.
10.4k · bundle
oyi77 RAG Builder
Designs and implements RAG pipelines, covering document chunking, embedding strategies, hybrid search, answer synthesis with source attribution, and evaluation using RAGAS metrics.
10
oyi77 Vector DB Ops
Manage vector database operations across Pinecone, Weaviate, Qdrant, and ChromaDB, including embedding generation, index creation, metadata filtering, hybrid search, and production deployment for RAG and similarity search.
10
github Qdrant Model Migration
Guides embedding model migration in Qdrant without downtime, covering alias swap, side-by-side, and hybrid search strategies.
36.2k
github Qdrant Search Quality
Diagnoses and improves Qdrant search relevance by isolating embedding model, configuration, or query strategy issues.
36.2k
lord1egypt Qdrant Vector Search
Builds production RAG and semantic search systems with Qdrant, covering collection setup, vector indexing, filtered and hybrid search, and integration with LangChain and LlamaIndex.
2
jeffallan RAG Architect
Designs and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines, applying reranking, and evaluating retrieval quality.
10.4k · bundle
jrennie99-glitch Embeddings
Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support. 75x faster with agentic-flow integration. Use when: semantic search, pattern matching, similarity queries, knowledge retrieval. Skip when: exact text matching, simple lookups, no semantic understanding needed.
0
github Qdrant Performance Optimization
Optimize Qdrant vector search performance through indexing strategies, query tuning, memory management, and hardware considerations.
36.2k
antigravity Weaviate
Search, query, inspect, create, and import data into Weaviate vector database collections using official scripts and references.
42.4k · bundle
johnalbertini14-glitch Gno
Index local folders and search documents with BM25, vector, or hybrid queries, plus AI answers with citations and a web UI.
1 · bundle
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
github Pinecone RAG
Build production RAG pipelines and persistent agent memory using Pinecone as the vector database backend.
36.2k
demerzels-lab Gno
Index local folders and search documents with BM25, vector, or hybrid queries, plus AI answers with citations and a web UI.
10 · bundle