Plugins
2 pluginsResults for “qdrant”
14 skills
github qdrant-search-quality
Diagnoses and improves Qdrant search relevance by isolating embedding model, configuration, or query strategy issues.
36.2k
github qdrant-search-quality-diagnosis
Diagnoses Qdrant search quality issues by isolating causes like HNSW approximation, quantization, embedding model, or search pipeline problems.
36.2k
github qdrant-scaling-query-volume
Optimizes Qdrant query performance for large limits across multiple shards by using Poisson-distributed subsampling to reduce inter-shard data transfer.
36.2k
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-strategies
Guides selection of Qdrant search strategies including hybrid search, reranking, relevance feedback, MMR, and discovery APIs to improve retrieval quality.
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
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 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
tools-only 014-api-f0515c8f
Reference for configuring and using LangChain4j vector stores, covering setup, search, filtering, and ingestion.
7 · bundle
rootcastleco ai-engineer
Build production-ready LLM applications, RAG systems, and intelligent agents with architecture design, model selection, and cost controls.
6
majiayu000 rag
Builds Retrieval-Augmented Generation systems with document chunking, embedding generation, vector storage, and retrieval pipelines, including evaluation and optimization.
567 · bundle
mukul975 assessing-vector-and-embedding-weaknesses
Test vector stores for embedding inversion, cross-tenant leakage, and poisoning.
24.6k · bundle
lucaspmarie-a11y llm-ops
Provides guidance on production AI operations including RAG pipelines, vector databases, embeddings, fine-tuning, prompt engineering, cost estimation, and quality evaluation.
5
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