research-patterns

Knowledge retrieval from a vector store queries stored embeddings to find relevant documents by semantic similarity. This Skill uses Qdrant to prioritize matching results from your internal knowledge base before performing external searches, enabling fast, contextual answers grounded in your own sources.

Yes. This Skill augments Qdrant-backed retrieval with external web search when stored results have gaps, then synthesizes both into a coherent summary. It tracks citations and metadata from each source so you know where every piece of information originated.

Citation tracking is built into this Skill's retrieval pipeline. It attaches source metadata and references to all harvested results—both from Qdrant storage and external research—so you can audit provenance and reuse sources across projects.

Store documents in Qdrant with meaningful metadata and embeddings so semantic search can retrieve them efficiently. This Skill enforces best practices for metadata storage and freshness checks, ensuring your knowledge base stays accurate and queryable.

Vector stores excel when you need semantic understanding—finding conceptually related documents even if keywords don't match exactly. Use this approach for synthesizing knowledge across diverse sources where meaning matters more than keyword overlap.

Yes. This Skill queries Qdrant first for stored knowledge, then seamlessly integrates fresh external web search results when needed. This hybrid approach balances fast retrieval from your knowledge base with current information from the web.