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2024)","https:\u002F\u002Fwww.elastic.co\u002Fblog\u002Felasticsearch-is-open-source-again",{"title":140,"url":141},"OpenSearch docs: methods and engines","https:\u002F\u002Fdocs.opensearch.org\u002Flatest\u002Fmappings\u002Fsupported-field-types\u002Fknn-methods-engines\u002F",{"title":143,"url":144},"OpenSearch docs: memory-optimized vectors","https:\u002F\u002Fdocs.opensearch.org\u002Flatest\u002Fmappings\u002Fsupported-field-types\u002Fknn-memory-optimized\u002F",{"title":146,"url":147},"OpenSearch docs: reciprocal rank fusion","https:\u002F\u002Fdocs.opensearch.org\u002Flatest\u002Fvector-search\u002Fai-search\u002Fhybrid-search\u002Frrf\u002F",{"title":149,"url":150},"OpenSearch docs: version history","https:\u002F\u002Fdocs.opensearch.org\u002Flatest\u002Fversion-history\u002F",{"title":152,"url":153},"AWS Open Source blog: introducing OpenSearch (Apr 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Architektúra, hibrid keresés, költségek és korlátok.","Milvus és Zilliz: magas polcos raktár targoncával, egy dolgozó csavarkulccsal a vezérlőpultnál.",{"slug":397,"published":398,"updated":399,"minutes":400,"category":90,"tags":401,"tagPages":405,"keywords":406,"about":417,"sources":423,"cover":463,"og":464,"expertise":77,"locales":465,"lang":81,"title":466,"description":467,"coverAlt":468},"semantic-product-search-b2b","2026-08-21","2026-09-17",13,[402,15,403,404,93],"B2B search","Semantic search","Spryker",[17],[407,408,409,410,411,412,413,414,415,416],"szemantikus termékkeresés b2b","b2b webshop keresés","bm25 és vektor hibrid keresés","cikkszámos keresés webshop","spryker keresés elasticsearch","opensearch hibrid keresés rrf","többnyelvű termékkeresés német angol magyar","nulla találatos arány oldalkeresés","llm lekérdezés-értelmezés webshop","ai termékkeresés b2b 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reranking","https:\u002F\u002Fwww.elastic.co\u002Fdocs\u002Fsolutions\u002Fsearch\u002Franking\u002Fsemantic-reranking",{"title":437,"url":438},"Elasticsearch documentation: Word delimiter graph token filter","https:\u002F\u002Fwww.elastic.co\u002Fdocs\u002Freference\u002Ftext-analysis\u002Fanalysis-word-delimiter-graph-tokenfilter",{"title":440,"url":441},"Elasticsearch documentation: Synonym graph token filter","https:\u002F\u002Fwww.elastic.co\u002Fdocs\u002Freference\u002Ftext-analysis\u002Fanalysis-synonym-graph-tokenfilter",{"title":443,"url":444},"OpenSearch documentation: Score ranker processor (RRF)","https:\u002F\u002Fdocs.opensearch.org\u002Flatest\u002Fsearch-plugins\u002Fsearch-pipelines\u002Fscore-ranker-processor\u002F",{"title":446,"url":447},"OpenSearch documentation: Normalization processor","https:\u002F\u002Fdocs.opensearch.org\u002Flatest\u002Fsearch-plugins\u002Fsearch-pipelines\u002Fnormalization-processor\u002F",{"title":449,"url":450},"Spryker documentation: Search feature overview","https:\u002F\u002Fdocs.spryker.com\u002Fdocs\u002Fpbc\u002Fall\u002Fsearch\u002Flatest\u002Fbase-shop\u002Fsearch-feature-overview\u002Fsearch-feature-overview",{"title":452,"url":453},"Spryker documentation: Migrate from OpenSearch 1.3 to 3.5","https:\u002F\u002Fdocs.spryker.com\u002Fdocs\u002Fpbc\u002Fall\u002Fsearch\u002Flatest\u002Fbase-shop\u002Finstall-and-upgrade\u002Fmigrate-from-opensearch-1.3-to-3.5.html",{"title":455,"url":456},"Instacart via ZenML: Rebuilding query understanding for e-commerce search with LLMs","https:\u002F\u002Fwww.zenml.io\u002Fllmops-database\u002Frebuilding-query-understanding-for-e-commerce-search-with-llms",{"title":458,"url":459},"arXiv: M3-Embedding, multilingual, multi-functionality, multi-granularity text embeddings","https:\u002F\u002Farxiv.org\u002Fabs\u002F2402.03216",{"title":461,"url":462},"Algolia documentation: Search analytics metrics","https:\u002F\u002Fwww.algolia.com\u002Fdoc\u002Fguides\u002Fsearch-analytics\u002Fconcepts\u002Fmetrics\u002F","\u002Fimages\u002Fblog\u002Fsemantic-product-search-b2b\u002Fcover.webp","\u002Fimages\u002Fblog\u002Fsemantic-product-search-b2b\u002Fog.jpg",[79,80,81],"Szemantikus termékkeresés B2B webshopokban: cikkszámok, hibrid keresés és amit mérned kell","Szemantikus keresés B2B shopban cikkszámos keresés sérülése nélkül: BM25 és vektorok hibridje, szűrők, DE\u002FEN\u002FHU, LLM-es lekérdezés, reranking, metrikák.","Szemantikus termékkeresés B2B webshopokban: nagyító lebeg egy csavaros rekesz fölött egy vasbolti polcon.",{"slug":470,"published":471,"updated":472,"minutes":89,"category":90,"tags":473,"tagPages":477,"keywords":478,"about":487,"sources":493,"cover":512,"og":513,"expertise":156,"locales":514,"lang":81,"title":515,"description":516,"coverAlt":517},"rag-2026-hybrid-agentic-long-context","2026-07-30","2026-09-22",[298,474,475,15,476],"Long context","Agentic search","Contextual retrieval",[90,17],[479,480,481,482,483,484,485,486],"rag 2026-ban","hosszú kontextus vs rag","agentikus rag","hibrid keresés","contextual retrieval rag 2026","meghalt a rag","ügynöki keresés vs vektorkeresés","rag vs 1 millió tokenes kontextablak",[488,489,492],{"name":369,"url":370},{"name":490,"url":491},"Large language model","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FLarge_language_model",{"name":246,"url":247},[494,497,500,503,506,509],{"title":495,"url":496},"Anthropic: Introducing Contextual Retrieval","https:\u002F\u002Fwww.anthropic.com\u002Fnews\u002Fcontextual-retrieval",{"title":498,"url":499},"Chroma: Context Rot","https:\u002F\u002Fwww.trychroma.com\u002Fresearch\u002Fcontext-rot",{"title":501,"url":502},"Liu et al., Lost in the Middle (2023)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.03172",{"title":504,"url":505},"Claude docs: Pricing","https:\u002F\u002Fplatform.claude.com\u002Fdocs\u002Fen\u002Fabout-claude\u002Fpricing",{"title":507,"url":508},"Claude docs: Models overview","https:\u002F\u002Fplatform.claude.com\u002Fdocs\u002Fen\u002Fabout-claude\u002Fmodels\u002Foverview",{"title":510,"url":511},"Awesome Agentic Patterns: Agentic search over vector embeddings","https:\u002F\u002Fgithub.com\u002Fnibzard\u002Fawesome-agentic-patterns\u002Fblob\u002Fmain\u002Fpatterns\u002Fagentic-search-over-vector-embeddings.md","\u002Fimages\u002Fblog\u002Frag-2026-hybrid-agentic-long-context\u002Fcover.webp","\u002Fimages\u002Fblog\u002Frag-2026-hybrid-agentic-long-context\u002Fog.jpg",[79,80,81],"RAG 2026-ban: hibrid retrieval, ügynöki keresés vagy egymillió tokenes kontextus?","RAG 2026-ban: mikor nyer a hibrid keresés, mikor illik az ügynöki keresés, és mit ér az egymillió tokenes kontextus kérésenként.","RAG 2026-ban: egy ember három út közül választ: könyvespolc, zseblámpás robot és hosszú tekercs.",{"slug":519,"published":520,"updated":471,"minutes":347,"category":90,"tags":521,"tagPages":525,"keywords":526,"about":536,"sources":540,"cover":572,"og":573,"expertise":156,"locales":574,"lang":81,"title":575,"description":576,"coverAlt":577},"rag-pipeline-chunking-hybrid-search-reranking","2026-07-24",[298,15,522,523,524],"Chunking","Reranking","pgvector",[90,17],[527,528,529,530,531,532,533,534,535],"rag pipeline","visszakereséssel bővített generálás pipeline","chunkelési stratégia rag-hez","hibrid keresés bm25 vektor","reciprocal rank fusion","cross-encoder újrarangsorolás","pgvector hibrid keresés","rag minőségmérés","hogyan építsünk rag pipeline-t",[537,538,539],{"name":369,"url":370},{"name":246,"url":247},{"name":524,"url":53},[541,544,547,550,552,555,558,561,562,565,566,569],{"title":542,"url":543},"Lewis et al., Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (2020)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.11401",{"title":545,"url":546},"Cormack, Clarke and Büttcher, Reciprocal Rank Fusion (SIGIR 2009)","https:\u002F\u002Fcormack.uwaterloo.ca\u002Fcormacksigir09-rrf.pdf",{"title":548,"url":549},"Malkov and Yashunin, HNSW graphs","https:\u002F\u002Farxiv.org\u002Fabs\u002F1603.09320",{"title":551,"url":53},"pgvector README",{"title":553,"url":554},"Elasticsearch: similarity settings","https:\u002F\u002Fwww.elastic.co\u002Fdocs\u002Freference\u002Felasticsearch\u002Findex-settings\u002Fsimilarity",{"title":556,"url":557},"PostgreSQL: controlling text search","https:\u002F\u002Fwww.postgresql.org\u002Fdocs\u002Fcurrent\u002Ftextsearch-controls.html",{"title":559,"url":560},"Chroma: Evaluating Chunking Strategies for Retrieval","https:\u002F\u002Fwww.trychroma.com\u002Fresearch\u002Fevaluating-chunking",{"title":495,"url":496},{"title":563,"url":564},"Sentence Transformers: Cross-Encoders","https:\u002F\u002Fsbert.net\u002Fexamples\u002Fcross_encoder\u002Fapplications\u002FREADME.html",{"title":501,"url":502},{"title":567,"url":568},"Claude docs: Citations","https:\u002F\u002Fplatform.claude.com\u002Fdocs\u002Fen\u002Fbuild-with-claude\u002Fcitations",{"title":570,"url":571},"RAGAS: Faithfulness","https:\u002F\u002Fdocs.ragas.io\u002Fen\u002Fstable\u002Fconcepts\u002Fmetrics\u002Favailable_metrics\u002Ffaithfulness\u002F","\u002Fimages\u002Fblog\u002Frag-pipeline-chunking-hybrid-search-reranking\u002Fcover.webp","\u002Fimages\u002Fblog\u002Frag-pipeline-chunking-hybrid-search-reranking\u002Fog.jpg",[79,80,81],"Termelési RAG pipeline lépésről lépésre: chunkelés, hibrid keresés, újrarangsorolás","Építs RAG pipeline-t lépésről lépésre: parse, chunkelés, pgvector, BM25 plusz vektorok RRF-fel, újrarangsorolás, hivatkozások és retrieval evalek.","Termelési RAG pipeline: egy kenyeret felszeletelnek, a szeletek szitán át rendezett sorban kerülnek a tányérra.",{"slug":579,"published":580,"minutes":347,"category":90,"tags":581,"tagPages":584,"keywords":586,"about":594,"sources":601,"cover":629,"og":630,"expertise":156,"locales":631,"lang":81,"title":632,"description":633,"coverAlt":634},"chroma","2026-07-20",[94,582,298,15,583],"Embeddings","Metadata filter",[97,585,90,17],"embeddings",[587,588,589,590,591,592,593],"chroma vektoradatbázis","chroma vs qdrant összehasonlítás","chroma vagy pgvector","hibrid keresés reciprocal rank fusion","saját szerveren futó vektoradatbázis","chroma cloud árak","metaadat szűrés vektorkeresés",[595,598],{"name":596,"url":597},"Chroma","https:\u002F\u002Fgithub.com\u002Fchroma-core\u002Fchroma",{"name":599,"url":600},"Apache License 2.0","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FApache_License",[602,605,608,611,614,617,620,623,626],{"title":603,"url":604},"Chroma documentation: introduction","https:\u002F\u002Fdocs.trychroma.com\u002Fdocs\u002Foverview\u002Fintroduction",{"title":606,"url":607},"Chroma architecture overview","https:\u002F\u002Fdocs.trychroma.com\u002Freference\u002Farchitecture\u002Foverview",{"title":609,"url":610},"Chroma: getting started with the SDK","https:\u002F\u002Fdocs.trychroma.com\u002Fdocs\u002Foverview\u002Fgetting-started",{"title":612,"url":613},"Chroma: query and get","https:\u002F\u002Fdocs.trychroma.com\u002Fdocs\u002Fquerying-collections\u002Fquery-and-get",{"title":615,"url":616},"Chroma: metadata filtering","https:\u002F\u002Fdocs.trychroma.com\u002Fdocs\u002Fquerying-collections\u002Fmetadata-filtering",{"title":618,"url":619},"Chroma Cloud: Search API overview","https:\u002F\u002Fdocs.trychroma.com\u002Fcloud\u002Fsearch-api\u002Foverview",{"title":621,"url":622},"Chroma Cloud: hybrid search with RRF","https:\u002F\u002Fdocs.trychroma.com\u002Fcloud\u002Fsearch-api\u002Fhybrid-search",{"title":624,"url":625},"Chroma Cloud: pricing","https:\u002F\u002Fwww.trychroma.com\u002Fpricing",{"title":627,"url":628},"Chroma changelog","https:\u002F\u002Fwww.trychroma.com\u002Fchangelog","\u002Fimages\u002Fblog\u002Fchroma\u002Fcover.webp","\u002Fimages\u002Fblog\u002Fchroma\u002Fog.jpg",[79,80,81],"Chroma: egyszerű vektorkeresés egy bökkenővel","A Chroma Apache-2.0 vektoradatbázis, beágyazva, egy csomóponton vagy Chroma Cloudként. Hol kényelmes, és hol érnek véget a jó keresési funkciók a felhőnél?","Chroma vektorkeresés: fiókos szekrény szétválogatott golyókkal, a tetején egy repedt kavics.",{"slug":636,"published":637,"minutes":8,"category":90,"tags":638,"tagPages":640,"keywords":641,"about":650,"sources":654,"cover":678,"og":679,"expertise":156,"locales":680,"lang":81,"title":681,"description":682,"coverAlt":683},"pinecone","2026-07-09",[94,582,298,639,15],"Namespaces",[97,585,90,17],[636,642,643,644,645,646,647,648,649],"pinecone vektoradatbázis","pinecone árak","pinecone read unit","pinecone namespace","pinecone vagy qdrant","pinecone vagy pgvector","pinecone documents api","serverless vektoradatbázis költség",[651,652,653],{"name":165,"url":181},{"name":115,"url":116},{"name":246,"url":247},[655,658,661,664,667,669,672,675],{"title":656,"url":657},"Pinecone docs: Index data overview, indexes, namespaces and metadata","https:\u002F\u002Fdocs.pinecone.io\u002Fguides\u002Findex-data\u002Findexing-overview",{"title":659,"url":660},"Pinecone docs: Adopt the Documents API, API version 2026-07","https:\u002F\u002Fdocs.pinecone.io\u002Fguides\u002Findex-data\u002Fadopt-the-documents-api",{"title":662,"url":663},"Pinecone docs: Create an index, schemas, regions and metrics","https:\u002F\u002Fdocs.pinecone.io\u002Fguides\u002Findex-data\u002Fcreate-an-index",{"title":665,"url":666},"Pinecone docs: Understanding Pinecone cost, read units, write units and egress","https:\u002F\u002Fdocs.pinecone.io\u002Fguides\u002Forganizations\u002Fmanage-cost\u002Funderstanding-cost",{"title":668,"url":218},"Pinecone pricing: plans, limits and rates",{"title":670,"url":671},"Pinecone docs: Semantic search","https:\u002F\u002Fdocs.pinecone.io\u002Fguides\u002Fsearch\u002Fsemantic-search",{"title":673,"url":674},"Pinecone docs: Local development with Pinecone Local","https:\u002F\u002Fdocs.pinecone.io\u002Fguides\u002Foperations\u002Flocal-development",{"title":676,"url":677},"Pinecone docs: 2026 release notes","https:\u002F\u002Fdocs.pinecone.io\u002Frelease-notes\u002F2026","\u002Fimages\u002Fblog\u002Fpinecone\u002Fcover.webp","\u002Fimages\u002Fblog\u002Fpinecone\u002Fog.jpg",[79,80,81],"Pinecone: értékelés a menedzselt vektordatabázisról","Mennyibe kerül valójában a Pinecone: a read unit a namespace méretével nő, a sémát utólag nem lehet módosítani, és hol éri meg saját vektoradatbázis.","Pinecone értékelés: fenyőtoboz egy felhőn golyókat tart a pikkelyei között, alatta kulcs.",{"slug":685,"published":686,"updated":88,"minutes":400,"category":90,"tags":687,"tagPages":690,"keywords":691,"about":702,"sources":708,"cover":757,"og":758,"expertise":156,"locales":759,"lang":81,"title":760,"description":761,"coverAlt":762},"pgvector-vs-vector-databases","2026-06-15",[524,688,298,15,689],"Vector databases","EU hosting",[90,17],[692,693,694,695,696,697,698,699,700,701],"pgvector vagy vektoradatbázis","pgvector vs qdrant összehasonlítás","pgvector vagy pinecone","legjobb vektoradatbázis 2026","pgvector hnsw iteratív keresés","pgvector halfvec típus","opensearch vs elasticsearch vektorkeresés","vektoradatbázis eu tárhely","hibrid keresés postgres","weaviate vs milvus",[703,704,707],{"name":165,"url":181},{"name":705,"url":706},"PostgreSQL","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FPostgreSQL",{"name":369,"url":370},[709,712,715,718,721,724,727,730,733,736,739,742,744,746,749,752,754],{"title":710,"url":711},"pgvector README (index limits, HNSW defaults, iterative scans, filtering, halfvec)","https:\u002F\u002Fgithub.com\u002Fpgvector\u002Fpgvector\u002Fblob\u002Fmaster\u002FREADME.md",{"title":713,"url":714},"pgvector CHANGELOG (0.4.0 to 0.8.7)","https:\u002F\u002Fgithub.com\u002Fpgvector\u002Fpgvector\u002Fblob\u002Fmaster\u002FCHANGELOG.md",{"title":716,"url":717},"pgvectorscale: StreamingDiskANN, statistical binary quantization, filtered search","https:\u002F\u002Fgithub.com\u002Ftimescale\u002Fpgvectorscale",{"title":719,"url":720},"Qdrant documentation: Filtering","https:\u002F\u002Fqdrant.tech\u002Fdocumentation\u002Fconcepts\u002Ffiltering\u002F",{"title":722,"url":723},"Qdrant documentation: Hybrid queries","https:\u002F\u002Fqdrant.tech\u002Fdocumentation\u002Fconcepts\u002Fhybrid-queries\u002F",{"title":725,"url":726},"Qdrant documentation: Create a cluster (providers, free tier, Hybrid Cloud)","https:\u002F\u002Fqdrant.tech\u002Fdocumentation\u002Fcloud\u002Fcreate-cluster\u002F",{"title":728,"url":729},"Weaviate documentation: Hybrid search","https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fconcepts\u002Fsearch\u002Fhybrid-search",{"title":731,"url":732},"Weaviate documentation: Vector index types","https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fconcepts\u002Fvector-index",{"title":734,"url":735},"Weaviate Cloud pricing and deployment options","https:\u002F\u002Fweaviate.io\u002Fpricing",{"title":737,"url":738},"Milvus documentation: Overview","https:\u002F\u002Fmilvus.io\u002Fdocs\u002Foverview.md",{"title":740,"url":741},"Pinecone documentation: Database architecture","https:\u002F\u002Fdocs.pinecone.io\u002Fguides\u002Fget-started\u002Fdatabase-architecture",{"title":743,"url":663},"Pinecone documentation: Create an index (clouds, regions, sparse and hybrid)",{"title":745,"url":141},"OpenSearch documentation: Methods and engines",{"title":747,"url":748},"OpenSearch documentation: Efficient k-NN filtering","https:\u002F\u002Fdocs.opensearch.org\u002Flatest\u002Fvector-search\u002Ffilter-search-knn\u002Fefficient-knn-filtering\u002F",{"title":750,"url":751},"Elasticsearch documentation: Dense vector search","https:\u002F\u002Fwww.elastic.co\u002Fdocs\u002Fsolutions\u002Fsearch\u002Fvector\u002Fdense-vector",{"title":753,"url":432},"Elasticsearch documentation: kNN query (filter as pre-filter)",{"title":755,"url":756},"GitHub releases: Qdrant, Weaviate, Milvus, OpenSearch, pgvectorscale (versions as of 1 October 2026)","https:\u002F\u002Fgithub.com\u002Fqdrant\u002Fqdrant\u002Freleases","\u002Fimages\u002Fblog\u002Fpgvector-vs-vector-databases\u002Fcover.webp","\u002Fimages\u002Fblog\u002Fpgvector-vs-vector-databases\u002Fog.jpg",[79,80,81],"pgvector vagy vektoradatbázis? Így válassz vektortárolót 2026-ban","pgvector, Qdrant, Weaviate, Milvus, Pinecone, OpenSearch vagy Elasticsearch? 2026-os útmutató: szűrés, hibrid keresés, skálázás, költség és EU-s hosztolás.","pgvector vagy vektoradatbázis: egy ember az elágazásnál választ egy fiókos elefánt és egy magas raktár között.",{"slug":764,"published":765,"updated":294,"minutes":347,"category":90,"tags":766,"tagPages":770,"keywords":771,"about":781,"sources":790,"cover":813,"og":814,"expertise":156,"locales":815,"lang":81,"title":816,"description":817,"coverAlt":818},"weaviate","2026-06-02",[94,15,767,768,769],"HNSW","Quantization","Multi-tenancy",[97,17],[772,773,774,775,776,777,778,779,780],"weaviate vektoradatbázis","weaviate vagy qdrant","weaviate vagy pinecone","weaviate vagy pgvector","hnsw vs hfresh","weaviate cloud árak","vektoradatbázis választás","weaviate hibrid keresés","weaviate teszt",[782,785,787],{"name":783,"url":784},"Weaviate","https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate",{"name":786,"url":116},"Hierarchical Navigable Small World",{"name":788,"url":789},"SPFresh","https:\u002F\u002Farxiv.org\u002Fpdf\u002F2410.14452",[791,793,796,799,802,805,807,810],{"title":792,"url":732},"Weaviate documentation: Vector indexing",{"title":794,"url":795},"Weaviate documentation: ANN benchmark","https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fbenchmarks\u002Fann",{"title":797,"url":798},"Weaviate 1.39 release notes","https:\u002F\u002Fweaviate.io\u002Fblog\u002Fweaviate-1-39-release",{"title":800,"url":801},"Weaviate 1.38 release notes","https:\u002F\u002Fweaviate.io\u002Fblog\u002Fweaviate-1-38-release",{"title":803,"url":804},"Weaviate documentation: MCP server","https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fconfiguration\u002Fmcp-server",{"title":806,"url":735},"Weaviate Cloud pricing",{"title":808,"url":809},"weaviate\u002Fweaviate: LICENSE","https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fblob\u002Fmain\u002FLICENSE",{"title":811,"url":812},"weaviate\u002Fweaviate v1.40.0 release notes","https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Freleases\u002Ftag\u002Fv1.40.0","\u002Fimages\u002Fblog\u002Fweaviate\u002Fcover.webp","\u002Fimages\u002Fblog\u002Fweaviate\u002Fog.jpg",[79,80,81],"Weaviate: egy vektordatabázis, amelynek a keresésben kell nyernie, nem csak a hasonlóságban","Weaviate-teszt: hibrid BM25- és vektorkeresés egy hívásban, HNSW és lemezes HFresh index, kvantálás, beépített MCP-szerver, és mi kerül licenckulcs mögé.","Weaviate: egy mágnes és egy nagyító keresztezi egymást egy golyókupac fölött.",{"slug":820,"published":821,"updated":6,"minutes":347,"category":90,"tags":822,"tagPages":825,"keywords":826,"about":834,"sources":838,"cover":876,"og":877,"expertise":156,"locales":878,"lang":81,"title":879,"description":880,"coverAlt":881},"qdrant","2026-05-26",[94,767,823,15,824],"Quantisation","Filtering",[97,17],[820,827,828,829,830,831,832,833],"qdrant vagy pinecone","vektoradatbázisok összehasonlítása","hnsw index","szűrt vektorkeresés","vektor kvantálás","qdrant hibrid keresés","qdrant teszt",[835,836,837],{"name":165,"url":181},{"name":115,"url":116},{"name":369,"url":370},[839,842,845,848,851,854,857,860,862,864,867,870,873],{"title":840,"url":841},"Qdrant documentation","https:\u002F\u002Fqdrant.tech\u002Fdocumentation\u002F",{"title":843,"url":844},"Qdrant: points","https:\u002F\u002Fqdrant.tech\u002Fdocumentation\u002Fmanage-data\u002Fpoints\u002F",{"title":846,"url":847},"Qdrant: collections","https:\u002F\u002Fqdrant.tech\u002Fdocumentation\u002Fconcepts\u002Fcollections\u002F",{"title":849,"url":850},"Qdrant: indexing","https:\u002F\u002Fqdrant.tech\u002Fdocumentation\u002Fmanage-data\u002Findexing\u002F",{"title":852,"url":853},"Qdrant: quantisation","https:\u002F\u002Fqdrant.tech\u002Fdocumentation\u002Fmanage-data\u002Fquantization\u002F",{"title":855,"url":856},"Qdrant: capacity planning","https:\u002F\u002Fqdrant.tech\u002Fdocumentation\u002Fcapacity-planning\u002F",{"title":858,"url":859},"Qdrant: optimise performance","https:\u002F\u002Fqdrant.tech\u002Fdocumentation\u002Fops-optimization\u002Foptimize\u002F",{"title":861,"url":723},"Qdrant: hybrid and multi-stage queries",{"title":863,"url":720},"Qdrant: filtering",{"title":865,"url":866},"Qdrant: security and access control","https:\u002F\u002Fqdrant.tech\u002Fdocumentation\u002Fsecurity\u002F",{"title":868,"url":869},"Qdrant 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