'embedding' 검색 결과

OmniRoute를 통해 글·이미지 생성, 음성 합성, 음성 기록과 검색을 호출합니다. OpenAI 호환 API 등을 AI 에이전트에 연결하는 데 사용합니다.

• 에이전트에 글 생성 기능 연결
• 지시에 따른 이미지 생성 및 편집
• 텍스트 읽기 및 음성을 글로 변환

의존 관계와 분류 체계 같은 계층 데이터를 깊이와 텍스트 특징에 따라 푸앵카레 공 안에 배치합니다. 부모·자식과 서로 다른 가지 사이의 거리를 측정해 구조를 분석합니다.

• 모듈 의존 관계의 하위 구조 분석
• 클래스 계층의 구조 패턴 확인
• 분류 관계에 따라 개념 정리

텍스트의 의미를 수치로 표현해 내용이 비슷한 문서를 검색합니다. 두 글의 유사도 비교, 소수 패턴으로의 질의 분류와 검색 데이터의 메모리 절감도 지원합니다.

• 의미가 비슷한 문서 검색
• 두 텍스트의 유사도 비교
• 대규모 벡터 검색의 메모리 절감

Manage Ollama models and run text generation, chat, and embeddings, which represent text as numerical vectors, through command-line requests. Check server status, inspect model details, and receive structured JSON output for agent workflows.

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• Download models and inspect their details
• Generate text or chat with a chosen model
• Convert text into numerical vectors

Design RAG systems that retrieve relevant documents to support LLM answers. The skill guides document splitting, meaning-based and keyword search, result reranking, and separate retrieval evaluation to improve the context passed to the model.

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• Designing document retrieval for LLM answers
• Reviewing document splitting strategies
• Combining keyword and meaning-based search

Build and operate LLM applications with guidance on RAG, which uses retrieved documents to inform answers. Covers document indexing, vector database selection, prompt improvement, response evaluation, caching, and estimates of model usage costs.

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• Building an assistant that answers from documents
• Choosing a vector database for document search
• Comparing answer quality after prompt changes

Connect cognee to model providers, databases, storage services, and development tools. Guides configuration of language models and embeddings, the numerical representations used for search, including the separate settings each requires.

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• Switching model providers or using Ollama locally
• Connecting cognee to Postgres or Neo4j
• Storing cognee data in S3

Helps design semantic search and RAG, where an AI answers using retrieved documents. Covers embeddings, numerical representations of content, plus document splitting, search indexes, keyword search combinations, and evaluation of retrieval quality.

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• Design document retrieval for AI answers
• Compare embedding models and document splitting strategies
• Combine semantic search with keyword search

Plan how to serve a model on Amazon SageMaker AI. The skill uses model type, traffic, response time, and cost constraints to recommend a deployment route, check instance quotas, and coordinate specialized setup and deployment skills.

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• Plan AWS hosting for a language model
• Choose a serving route for an embedding model
• Plan asynchronous inference for image generation

Choose a serving container for models deployed on SageMaker. Match the model type to a compatible image, retrieve its address from the AWS catalog, and check regional availability, CPU or GPU variants, and required host settings.

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• Choose a container for an LLM deployment
• Select images for embeddings and rerankers
• Check regional image addresses and GPU compatibility

Assess the storage and retrieval layers of RAG systems, which search documents to support AI answers. Test persistent document poisoning, cross-tenant access, source-text leaks, and manipulated search rankings using independently verifiable evidence.

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• Reviewing shared knowledge bases and document chatbots
• Testing access isolation between accounts or organizations
• Checking source text and metadata in search results

Prepare research papers for RAG, a method that supplies retrieved documents to AI. Download PDFs, extract and split text, and build a local searchable database, with parallel processing for document batches.

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• Build a RAG document collection from selected research papers
• Download and extract text from batches of paper PDFs
• Create a local vector database without paid embedding APIs

Validate coding-sequence CSV files and extract embeddings, numerical features representing each sequence, with public CodonFM/Encodon models. Prepare extraction commands, choose suitable checkpoints, and verify outputs for downstream modeling of translation efficiency, expression, or mRNA stability.

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• Validate coding-sequence CSV files before extraction
• Extract numerical sequence features with Encodon
• Choose a checkpoint for downstream property modeling

Plan and troubleshoot Nemotron retrieval recipes for embeddings, which turn text into searchable vectors, and reranking, which reorders search results. Review configurations, prepare training and evaluation commands, and check export or deployment requirements.

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• Choosing training steps for missing relevant documents
• Improving the ordering of retrieved passages
• Reviewing training commands with a dry run

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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Choose and implement clustering algorithms for grouping speaker embeddings after VAD and embedding extraction. Compare Hierarchical clustering (auto-tunes speaker count), KMeans (fast, requires known count), and Agglomerative clustering (fixed clusters). Use Hierarchical clustering when speaker count is unknown, KMeans when count is known, and always normalize embeddings before clustering.

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Optimizing vector embeddings for RAG systems through model selection, chunking strategies, caching, and performance tuning. Use when building semantic search, RAG pipelines, or document retrieval systems that require cost-effective, high-quality embeddings.

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Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.

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Python library for genomic interval ML. Train/apply region2vec embeddings turning BED regions into vectors, index interval datasets for ML, search embedding space with BEDSpace, and evaluate embedding quality. Use for chromatin accessibility clustering, regulatory element classification, and cross-sample region comparison.

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Use FlagEmbedding for embedding, reranking, retrieval evaluation, and fine-tuning workflows.

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Use when integrating LLMs, chat clients, embeddings, RAG pipelines, or AI agents into Spring Boot. Covers Spring AI 2.0 ChatClient, prompt templates, embeddings, vector stores, and structured output. Use when user mentions Spring AI, LLM, ChatGPT, Claude, RAG, embeddings.

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Use when integrating LLMs, chat clients, embeddings, RAG pipelines, or AI agents into Spring Boot. Covers Spring AI ChatClient, prompt templates, embeddings, vector stores, and structured output. Use when user mentions Spring AI, LLM, ChatGPT, Claude, RAG, embeddings.

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ESM2 protein language model for sequence scoring, embeddings, and plausibility checks. Use this skill when: (1) Computing pseudo-log-likelihood (PLL) scores, (2) Getting protein embeddings for clustering, (3) Filtering designs by sequence plausibility, (4) Zero-shot variant effect prediction, (5) Analyzing sequence-function relationships. For structure prediction, use chai1-structure-prediction or boltz-structure-prediction. For QC thresholds, use protein-design-qc.

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ESM2 protein language model for embeddings and sequence scoring. Use this skill when: (1) Computing pseudo-log-likelihood (PLL) scores, (2) Getting protein embeddings for clustering, (3) Filtering designs by sequence plausibility, (4) Zero-shot variant effect prediction, (5) Analyzing sequence-function relationships. For structure prediction, use chai or boltz. For QC thresholds, use protein-qc.

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