AI 요약관련 정보

Discover how to build Semantic Search using LLMs and Qdrant DB in Rust! 🚀 Learn how vector databases enhance AI search beyond traditional keyword matching. We'll generate embeddings using Ollama Llama models, store them in Qdrant, and perform efficient vector searches. This tutorial covers everything from setup to querying similar results in Rust. Supercharge your AI applications with smart search! 🔥 00:00 Intro 03:43 Semantic Search 30:00 Outro Qdrant DB: https://qdrant.tech/ Ollama: https://ollama.com/library docker run -p 6333:6333 -p 6334:6334 \ -e QDRANT__SERVICE__GRPC_PORT="6334" \ qdrant/qdrant Discord:   / discord   X :   / semicolon_10   #rust #rustlang #qdrant #vectorsearch #semanticsearch #llm #largelanguagemodels #machinelearning #artificialintelligence #ai #nlp #naturalanguageprocessing #embedding #neuralnetworks #deeplearning #searchengine #retrievalaugmentedgeneration #rag #databases #vectorstore #openai #ollama #llama2 #rustprogramming #backenddevelopment #appliedai #mlops #searchoptimization #informationretrieval #datastorage #efficientsearch #rustdeveloper #qdrantdb #semanticai #searchtechnology #aiapplications #textembedding #hnsw #nearestneighborsearch #recommendationsystem #smartsearch #aiinrust #annsearch #rustbackend #selfhostedai #openaialternative #opensourceai #vectorindexing #semanticquery #contextualsearch #textsimilarity #embeddingmodels #vectorretrieval #rustforai #devops #softwareengineering #queryoptimization #searchintelligence #neuralsearch #highperformancesearch #cloudai #selfhostedllm #qdrantvectors #rustforml #mlinrust #databaseengineering #smartapplications #contextawareai #embeddingsearch #searchsolutions #neuralinformationretrieval #rustsearch #searchoptimization #faissalternative #advancedsearch #dataretrieval #semanticmatching #efficientretrieval #rustai #searchperformance #hybridsearch #keywordvssemantic #mlengineer #developercontent #codingtutorial