Plugins

2 plugins

Migrate Embedding Model in Qdrant

Migrate embedding model in Qdrant without downtime using alias swap and hybrid search.

3 skills · plugin

Optimize Qdrant Search Quality

Diagnose and improve Qdrant search relevance by isolating embedding, config, or query issues.

3 skills · plugin

Results for “embedding-search”

8 skills

Qdrant Search Quality Diagnosis

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

Qdrant Model Migration

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

More results

Qdrant Search Strategies

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

orchestra-research

Store and query embeddings with metadata filtering, vector search, and full-text search using an open-source database that scales from notebooks to production.

10.4k · bundle

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

Designs Elasticsearch indexes and mappings, tunes queries, sizes clusters, and handles operations like shard/replica strategy, ILM, monitoring, troubleshooting, and safe reindexing or upgrades.

Provides persistent, self-hosted semantic memory for AI agents via MCP, storing worklogs, decisions, and notes in PostgreSQL with pgvector for meaning-based retrieval across sessions.

mariadb-corporation

Provides best practices for using MariaDB's built-in vector support for AI workloads, including SQL syntax for vector columns, indexes, distance functions, and RAG patterns.