109건 · 관련도순
Build site search with Algolia using React InstantSearch patterns for search input, filters, and pagination. The skill also covers Next.js server rendering and strategies for keeping searchable records synchronized as source data changes.
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Build search features with Algolia using guidance on search indexes, data organized for searching, React InstantSearch, and relevance tuning. Use it when implementing autocomplete, filtering by attributes, or results that update as users type.
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Configure platform-level global search in a Salesforce org — per-object Search Layouts (Search Results, Lookup Dialogs, Lookup Phone Dialogs, Tab columns), the Setup → Search Settings page (Sidebar Search, Lookup Auto-Completion, Drop-Down List size), and Synonym Groups (Setup → User Interface → Synonyms). NOT for Einstein Search personalization signals or Natural Language Search (use agentforce/einstein-search-personalization), NOT for Experience Cloud site Search Manager (use lwc/experience-cloud-search-customization), NOT for SOSL query authoring (use data/sosl-search-patterns), NOT for Commerce storefront search tuning.
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Expert patterns for Algolia search implementation, indexing strategies, React InstantSearch, and relevance tuningUse when "adding search to, algolia, instantsearch, search api, search functionality, typeahead, autocomplete search, faceted search, search index, search as you type, algolia, search, instantsearch, indexing, relevance, faceted-search, autocomplete" mentioned.
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Use this skill for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search. **Trigger when user asks to:** - Store or search vector embeddings in PostgreSQL - Set up semantic search, similarity search, or nearest neighbor search - Create HNSW or IVFFlat indexes for vectors - Implement RAG (Retrieval Augmented Generation) with PostgreSQL - Optimize pgvector performance, recall, or memory usage - Use binary quantization for large vector datasets **Keywords:** pgvector, embeddings, semantic search, vector similarity, HNSW, IVFFlat, halfvec, cosine distance, nearest neighbor, RAG, LLM, AI search Covers: halfvec storage, HNSW index configuration (m, ef_construction, ef_search), quantization strategies, filtered search, bulk loading, and performance tuning.
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Build or tune a monitoring pipeline that learns normal user behavior from activity logs. Use Elasticsearch or OpenSearch to score unusual activity, compare colleagues in similar roles, and combine warning signs into insider threat alerts.
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Use when a user asks an agent to evaluate or tune text search similarity for an existing Viking AI Search application and dataset.
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Viking Search tuning for specified policy directions. Use this when the user provides specific queries, a type of query, or a business policy direction, and asks to boost, suppress, or fix a class of search results through request-parameter passthrough. You must only perform read-only baseline evaluation and request-level candidate testing; do not modify search scenes, app config, dictionaries, recommend scenes, or primary recall parameters.
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FAISS로 대규모 벡터에서 유사한 데이터를 검색하도록 돕습니다. 인덱스 선택과 저장, GPU 사용을 안내하고 검색 속도·정확도·메모리 사용량을 조정합니다.
Use when the user provides 1-50 concrete bad-case search queries for one Viking Search app and wants local deterministic fixes. This skill only verifies request-level fine-operation interventions against a read-only baseline scene and delivers a console-ready configuration sheet, validated payloads, and a replay script. It must not mutate scenes, apps, dictionaries, datasets, recall core parameters, or online defaults.
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Expert patterns for Algolia search implementation, indexing strategies, React InstantSearch, and relevance tuning Use when: adding search to, algolia, instantsearch, search api, search functionality.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Expert patterns for Algolia search implementation, indexing strategies, React InstantSearch, and relevance tuning Use when: adding search to, algolia, instantsearch, search api, search functionality.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Expert patterns for Algolia search implementation, indexing strategies, React InstantSearch, and relevance tuning Use when: adding search to, algolia, instantsearch, search api, search functionality.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Expert patterns for Algolia search implementation, indexing strategies, React InstantSearch, and relevance tuning Use when: adding search to, algolia, instantsearch, search api, search functionality.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Expert patterns for Algolia search implementation, indexing strategies, React InstantSearch, and relevance tuning Use when: adding search to, algolia, instantsearch, search api, search functionality.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Expert patterns for Algolia search implementation, indexing strategies, React InstantSearch, and relevance tuning Use when: adding search to, algolia, instantsearch, search api, search functionality.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Guides image classification and search with OpenAI's CLIP by comparing images with text descriptions, without additional training. Includes batch processing, image-text matching, and category-based checks for potentially inappropriate images.
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Get guidance on fine-tuning language models with Unsloth, including LoRA and QLoRA methods for training with fewer resources. The skill points to documentation for understanding APIs, implementing training code, debugging problems, and exploring speed and memory optimizations.
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Optimize similarity search — ANN index tuning, hybrid search, reranking, query expansion. Use when asked to "improve similarity search", "tune the ANN index", or "add hybrid search".
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Qdrant에 텍스트 등의 특징을 벡터로 저장해 유사 검색을 구현합니다. 속성 필터링, AI 답변용 자료 검색, 실시간 추천과 대규모 운영 설정을 지원합니다.
Helps fine-tune large language models with LoRA and QLoRA, methods that train small sets of additional parameters. Covers memory-conscious training, configuration choices, saving adapters, and switching task-specific adapters on a shared base model.
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Use LitGPT to explore language model implementations and train models on custom data. Covers pretrained model loading, LoRA fine-tuning that updates a small set of parameters, pretraining, and preparing trained models for deployment.
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Guides LLM fine-tuning with Axolotl, from YAML configuration and dataset formats to distributed training across GPUs. Covers LoRA and QLoRA, methods that train on preferences, multimodal support, and troubleshooting using documentation references and code examples.
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Tune vector indexes, which organize data for similarity searches, to balance speed, memory use, and missed matches. Choose index types, adjust HNSW settings, and assess compression options using benchmarks with real queries.
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