embeddings-search
De la autor
Use when choosing an embedding model, chunk size, or query form, when semantic search returns irrelevant results, when adding hybrid BM25+vector or a reranker, or when a retrieval change needs a number (recall@k, nDCG, MRR). NOT operating the store — index tuning, quantization (that is `vector-db`) — nor the retrieve-to-answer loop (that is `rag`).
Instalare
npx skills add ericrisco/rsc-harness --skill embeddings-search -a claude-code
Se instalează în .claude/skills/embeddings-search/ · Fiecare comandă descarcă skill-ul direct de pe GitHub cu utilitarul open source skills.
Identic octet cu octet cu commit-ul scanat, cu licența depozitului și un fișier SOURCE.txt.
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De ce 79
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Skill-uri similare
Cele mai apropiate de acesta prin ce fac și când sunt folosite.
- vector-dbericrisco/rsc-harnessUse when operating a vector store as a data layer — choosing or migrating between Pinecone, Qdrant, Weaviate and pgvector; designing a collection or index (distance metric, dimensions, HNSW parameters, named vectors); filtering on metadata; hybrid dense-plus-sparse search; and quantization to cut RAM and cost. Covers garbage results, silently ignored filters, low recall, slow queries, and filtered queries returning fewer than k rows. NOT producing, chunking or judging embeddings (that is `embeddings-search`).Altele★ 255
- embedding-strategieswshobson/agentsSelect and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.Planificare★ 41.6K
- vector-db-patternsvibeeval/vibecosystemEmbedding strategies, ANN algorithms, hybrid search, RAG chunking strategies, and reranking for semantic search and retrieval.Altele★ 540
- ragskillmds/skillmdImplements document chunking, embedding generation, vector storage, and retrieval pipelines for Retrieval-Augmented Generation systems. Use when building RAG applications, creating document Q&A systems, or integrating AI with knowledge bases.Altele★ 190
- hybrid-search-implementationwshobson/agentsCombine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall.Altele★ 41.6K
- qdrant-search-qualitygithub/awesome-copilotDiagnoses and improves Qdrant search relevance. Use when someone reports 'search results are bad', 'wrong results', 'low precision', 'low recall', 'irrelevant matches', 'missing expected results', or asks 'how to improve search quality?', 'which embedding model?', 'should I use hybrid search?', 'should I use reranking?'. Also use when search quality degrades after quantization, model change, or data growth.Marketing★ 40.4K
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