「rerank」の検索結果

Use when building RAG or LLM applications with LlamaIndex - data loaders, node parsing, vector stores, retrievers and rerankers, query engines, agents and workflows, streaming, or evaluation

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Discovers requirements and generates architectural, design, and deployment guidance for dynamic hybrid search systems by combining semantic search and keyword search. Optimized for AlloyDB hybrid search use cases in Google Cloud. Use when users need vector search combined with structured SQL filtering, faceted attributes, semantic reranking, in-database AI validation, or serverless hosting across transactional relational databases, analytical data warehouses, or managed database engines. DON'T use this skill for simple keyword-only search, or when a standalone non-relational vector database is required.

日本語の概要は準備中です。原文の説明を表示しています。

Extracts, filters, annotates, and exports differential expression results from DESeq2 or edgeR with proper handling of padj=NA (independent filtering, Cook's outliers, all-zero), multiple-testing correction choice (BH vs Storey q-value vs IHW vs lfsr), TREAT vs post-hoc fold-change filtering, p-value histogram diagnostics, gene annotation via org.db/biomaRt/mygene, GSEA preranked input, ORA background construction, replication reality (Schurch 2016 small-n result), and SABV/sex-stratified reporting. Use when extracting and interpreting DE results, troubleshooting padj=NA, choosing FDR method, preparing ranked lists for pathway analysis, annotating gene IDs, or comparing DESeq2 vs edgeR outputs.

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Use RoutIR — call a running endpoint with the client, stand up a local server that mixes locally-hosted services with services proxied from a remote master server, and extend RoutIR with new bi-encoders, rerankers, and document collections by writing a small config (and optionally one Python file referenced via `file_imports`). No checkout of the routir source is required.

日本語の概要は準備中です。原文の説明を表示しています。

Build with Cohere's enterprise AI — Command models, Embed embeddings, and Rerank for retrieval pipelines.

日本語の概要は準備中です。原文の説明を表示しています。

Build GenAI and agentic applications with the genai-tk toolkit (https://github.com/tclatos/genai-tk) — a YAML-driven wrapper over LangChain, LangGraph, and 100+ LLM providers. Use this skill whenever the user mentions genai-tk, genai_tk, the GenAI Toolkit, `cli init`, `LangchainAgent`, `get_llm`/`get_embeddings`, `RetrieverFactory`/`ManagedRetriever`, the four bundled agent frameworks (ReAct, Deep, Deer-flow, SmolAgents), the OpenSandbox Docker integration, the `model_id@provider` identifier format, the `global_config()`/`OmegaConfig` system with `app_conf.yaml` and `:merge`, BAML structured extraction, SkillsMiddleware, writing or editing the toolkit's YAML profiles (langchain.yaml, deerflow.yaml, llm.yaml, retrievers.yaml), composing retrievers (vector/bm25/ensemble/reranked/pg_hybrid/zero_entropy), or extending the CLI with `CliTopCommand`. Trigger even when the user only says "the toolkit" in context.

日本語の概要は準備中です。原文の説明を表示しています。

Configure e execute buscas web locais usando a API SERP do Bright Data com o pipeline unfancy-search (expansão de query, recuperação de SERP, reranking com RRF).

日本語の概要は準備中です。原文の説明を表示しています。

Ask the second brain in plain words: semantic search over the curated vault with embeddings plus a reranker, returning the smallest relevant slice instead of a corpus dump. Token-cheap. Triggers: "/ask <question>", "what do I have on <topic>", "what did I write about X".

日本語の概要は準備中です。原文の説明を表示しています。

Use when working with Cohere — cohere AI platform management covering models, embeddings, reranking, datasets, fine-tuning, and connectors. Use when monitoring API usage, analyzing model performance, reviewing fine-tuning jobs, managing datasets and connectors, or troubleshooting Cohere API issues.

日本語の概要は準備中です。原文の説明を表示しています。

Train or fine-tune sentence-transformers models across `SentenceTransformer` (bi-encoder, dense or static embedding model for retrieval, similarity, clustering, classification, paraphrase mining, dedup, multimodal), `CrossEncoder` (reranker, pair scoring for two-stage retrieval / pair classification), `SparseEncoder` (SPLADE, sparse embedding model for learned-sparse retrieval), and `MultiVectorEncoder` (ColBERT / late-interaction, per-token embeddings scored with MaxSim). Covers loss selection, hard-negative mining, evaluators, distillation, LoRA, Matryoshka, and Hugging Face Hub publishing. Use for any sentence-transformers training task.

日本語の概要は準備中です。原文の説明を表示しています。

Vercel AI SDK expert guidance. Use when building AI-powered features — chat interfaces, text generation, structured output, tool calling, agents, MCP integration, streaming, embeddings, reranking, image generation, or working with any LLM provider.

日本語の概要は準備中です。原文の説明を表示しています。

Extracts, filters, annotates, and exports differential expression results from DESeq2 or edgeR with proper handling of padj=NA (independent filtering, Cook's outliers, all-zero), multiple-testing correction choice (BH vs Storey q-value vs IHW vs lfsr), TREAT vs post-hoc fold-change filtering, p-value histogram diagnostics, gene annotation via org.db/biomaRt/mygene, GSEA preranked input, ORA background construction, replication reality (Schurch 2016 small-n result), and SABV/sex-stratified reporting. Use when extracting and interpreting DE results, troubleshooting padj=NA, choosing FDR method, preparing ranked lists for pathway analysis, annotating gene IDs, or comparing DESeq2 vs edgeR outputs.

日本語の概要は準備中です。原文の説明を表示しています。

Search personal knowledge bases, notes, docs, and meeting transcripts locally using qmd — a hybrid retrieval engine with BM25, vector search, and LLM reranking. Supports CLI and MCP integration.

日本語の概要は準備中です。原文の説明を表示しています。

Search personal knowledge bases, notes, docs, and meeting transcripts locally using qmd — a hybrid retrieval engine with BM25, vector search, and LLM reranking. Supports CLI and MCP integration.

日本語の概要は準備中です。原文の説明を表示しています。

Designs retrieval-augmented generation pipelines for document-based AI assistants. Includes chunking strategies, metadata schemas, retrieval algorithms, reranking, and evaluation plans. Use when building "RAG systems", "document search", "semantic search", or "knowledge bases".

日本語の概要は準備中です。原文の説明を表示しています。