'vector search' 검색 결과

추가 학습 데이터 없이 글로 지정한 후보에 따라 이미지를 분류하거나 설명에 맞는 이미지를 찾습니다. 이미지와 글의 유사도 비교, 여러 이미지의 일괄 처리도 지원합니다.

• 글로 지정한 라벨로 사진 분류
• 내용을 설명해 이미지 검색
• 이미지와 설명문의 대응 정도 비교

claude-flow v3의 시작 시간, 검색 속도와 메모리 사용량을 측정해 개선 목표 달성 여부를 검증합니다. 지속적인 성능 감시와 저하 감지, 최적화 방안 검토도 지원합니다.

• v3 최적화 전후의 성능을 비교합니다
• 검색 속도와 메모리 사용량을 평가합니다
• 지속적으로 성능 저하를 감지합니다

claude-flow v3의 시작 시간, 검색 속도와 메모리 사용량을 측정해 개선 목표 달성 여부를 검증합니다. 지속적인 성능 감시와 저하 감지, 최적화 방안 검토도 지원합니다.

• v3 최적화 전후의 성능을 비교합니다
• 검색 속도와 메모리 사용량을 평가합니다
• 지속적으로 성능 저하를 감지합니다

Manage ChromaDB collections and documents from the command line, and search by meaning rather than exact wording. JSON output supports automated agent workflows, while server checks and an interactive mode help with everyday database operations.

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• Manage document collections in ChromaDB
• Search stored documents by meaning
• Feed JSON results into agent workflows

Build LlamaIndex applications that retrieve relevant material before generating answers, a method called RAG. Connect documents, websites, and databases to support document questions, conversational search, structured summaries, and checks of answer relevance and source consistency.

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• Building question answering over internal documents
• Creating a chatbot that remembers the conversation
• Searching information across files, websites, and databases

Design a RAG pipeline that retrieves documents to support AI answers. Analyze document splitting, select embedding models and vector databases, and evaluate retrieval quality using test queries and known relevant documents.

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• Designing a RAG system for internal documents
• Choosing how to split documents for search
• Comparing embedding models and vector databases

Install and configure WizTelemetry Platform Service, the shared API backend for KubeSphere observability extensions. Generate configuration based on installed extensions, update installation settings, and inspect service health, pods, and logs.

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• Install the shared backend for observability extensions
• Regenerate configuration after changing extensions
• Check API server and console health

Set up and troubleshoot the WizTelemetry Notification extension for KubeSphere. Configure delivery channels such as email and Slack, route alerts by labels, silence notifications for scheduled periods, and search notification history to investigate delivery problems.

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• Configuring email or Slack alert delivery
• Routing alerts by severity or namespace
• Silencing notifications during maintenance

Set up WizTelemetry Auditing to collect Kubernetes and KubeSphere audit events, records of system operations. Configure storage and retention, then query events by user, action, resource, response status, or time.

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• Deploy audit event collection to selected clusters
• Find operations by user, action, resource, and time
• Configure audit storage and retention

Guides building chatbots and tool-using agents with LangChain. Covers conversation history, model provider integration, and RAG, which retrieves relevant documents to inform answers, plus tracing to inspect application behavior.

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• Build a chatbot that remembers conversation history
• Create document question answering with source references
• Build an agent that uses custom tools

Record operations across multiple AI agents and connect events into a traceable history. Search records by tracking ID, time window, or responsible component to support audits and investigations, with local storage available when the vector database fails.

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• Trace task dispatch and execution across agents
• Search audit records by time or responsible component
• Keep recording events during vector database failures

Choose Cloudflare services and implement applications using guidance for compute, storage, AI, networking, and security. The skill directs the agent to relevant references and current official documentation for configuration, APIs, limits, and pricing.

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• Choose services for a Cloudflare application
• Select storage for databases and files
• Build AI agents and semantic search

Manages Amazon DocumentDB end-to-end — serverless-on-8.0 cluster setup, TLS/VPC/driver config, flexible-schema and vector-search data modeling, MongoDB compatibility assessment, DMS-based migration, slow-query diagnosis, major version upgrades (4.0->5.0->8.0), Well-Architected reviews (41-check wa_review.py), cost estimation, and security hardening. Retrieve for every DocumentDB question and when the user asks to set up or migrate MongoDB to AWS — DocumentDB is AWS's MongoDB-compatible managed database. Triggers: JSON document store, document database, MongoDB on AWS, Nested fields, Lambda cannot connect, TLS handshake, VPC port 27017, IAM auth, Secrets Manager, encryption at rest, $graphLookup, flexible schema, COLLSCAN, compound index, DMS migration, CDC cutover, $vectorSearch, RAG, Global Clusters, DR replication, cost sizing, audit, health check, production-readiness.

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Hybrid search combining semantic and keyword retrieval for RAG pipelines. Implement BM25 + dense vector search with fusion strategies.

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Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.

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Combine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall.

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Deploy, manage, and optimize vector databases for AI applications. Covers Qdrant, Weaviate, pgvector, and Pinecone — collection management, indexing strategies, backup, and performance tuning for production RAG and semantic search workloads.

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Provides Retrieval-Augmented Generation (RAG) implementation patterns with LangChain4j for Java. Generates document ingestion pipelines, embedding stores, vector search, and semantic search capabilities. Use when building chat-with-documents systems, document Q&A over PDFs or text files, AI assistants with knowledge bases, semantic search over document repositories, or knowledge-enhanced AI applications with source attribution.

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Operate the Deep Researcher Agent repository for autonomous deep-learning experiments, execution backends, provider/tool dispatch, GPU resources, durable progress, and research-support integrations.

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Spectral vector search using graph Laplacian eigenstructure. Use when cosine/L2 similarity misses latent structure in your embeddings.

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Use Neo4j GenAI Plugin ai.text.* functions and procedures for in-Cypher embedding generation, text completion, structured output, chat, tokenization, and batch ingestion. Covers ai.text.embed(), ai.text.embedBatch(), ai.text.completion(), ai.text.structuredCompletion(), ai.text.aggregateCompletion(), ai.text.chat(), ai.text.tokenCount(), ai.text.chunkByTokenLimit(), and provider configuration for OpenAI, Azure OpenAI, VertexAI, and Amazon Bedrock. Requires CYPHER 25. Replaces deprecated genai.vector.encode(). Use when writing pure-Cypher GraphRAG, embedding nodes in-graph, generating structured maps from prompts, or calling LLMs inside Cypher queries. Does NOT handle neo4j-graphrag Python library pipelines — use neo4j-graphrag-skill. Does NOT handle vector index creation/search — use neo4j-vector-index-skill.

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Expert guide for XERJ: the unified search engine for AI, vector database, zero-config autoindexer, agent memory store (/_memory/{ns}), and embedded 13-tool MCP server / Panduan ahli XERJ: mesin pencari terpadu untuk AI, vector database, autoindex tanpa konfigurasi, memori agen, dan 13 tool MCP server bawaan.

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AI knowledge engine - a memory system in 6 lines of code. remember/recall/forget/improve loop, vector + graph search, OpenClaw plugin supported.

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Hybrid search — BM25 keyword + dense vector fused with Reciprocal Rank Fusion. Use when queries have specific identifiers, code symbols, or proper nouns that pure semantic search might miss.

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