[{"data":1,"prerenderedAt":688},["ShallowReactive",2],{"blog-tag-hu-vector-search":3},[4,88,149,193,265,317,358,410,460,515,568,623],{"slug":5,"published":6,"minutes":7,"category":8,"tags":9,"tagPages":15,"keywords":18,"about":31,"sources":41,"cover":78,"og":79,"expertise":80,"locales":81,"lang":84,"title":85,"description":86,"coverAlt":87},"embedding-model-selection-2026","2026-10-17",9,"rag",[10,11,12,13,14],"Embeddings","RAG","MTEB","Vector search","Model selection",[16,8,17],"embeddings","vector-search",[19,20,21,22,23,24,25,26,27,28,29,30],"legjobb embedding modell 2026","hogyan válasszunk embedding modellt","embedding modell összehasonlítás","mteb ranglista magyarázat","rteb benchmark","nyílt forráskódú embedding modell","többnyelvű embedding modell","voyage-4 vagy openai embedding","qwen3-embedding","embeddinggemma","embedding modell rag-hoz","embedding modell csere újraindexelés",[32,35,38],{"name":33,"url":34},"Word embedding","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FWord_embedding",{"name":36,"url":37},"Information retrieval","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FInformation_retrieval",{"name":39,"url":40},"Discounted cumulative gain","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FDiscounted_cumulative_gain",[42,45,48,51,54,57,60,63,66,69,72,75],{"title":43,"url":44},"Enevoldsen et al.: MMTEB, Massive Multilingual Text Embedding Benchmark (2025)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2502.13595",{"title":46,"url":47},"Hugging Face: Introducing RTEB, a new standard for retrieval evaluation (Oct 2025)","https:\u002F\u002Fhuggingface.co\u002Fblog\u002Frteb",{"title":49,"url":50},"OpenAI: Vector embeddings guide","https:\u002F\u002Fdevelopers.openai.com\u002Fapi\u002Fdocs\u002Fguides\u002Fembeddings",{"title":52,"url":53},"OpenAI: API pricing","https:\u002F\u002Fdevelopers.openai.com\u002Fapi\u002Fdocs\u002Fpricing",{"title":55,"url":56},"Voyage AI: The Voyage 4 model family, shared embedding space with MoE architecture (Jan 2026)","https:\u002F\u002Fblog.voyageai.com\u002F2026\u002F01\u002F15\u002Fvoyage-4\u002F",{"title":58,"url":59},"Voyage AI: Text embeddings documentation","https:\u002F\u002Fdocs.voyageai.com\u002Fdocs\u002Fembeddings",{"title":61,"url":62},"Voyage AI: Pricing","https:\u002F\u002Fdocs.voyageai.com\u002Fdocs\u002Fpricing",{"title":64,"url":65},"Google: Gemini API embeddings documentation","https:\u002F\u002Fai.google.dev\u002Fgemini-api\u002Fdocs\u002Fembeddings",{"title":67,"url":68},"Google: Gemini API pricing","https:\u002F\u002Fai.google.dev\u002Fgemini-api\u002Fdocs\u002Fpricing",{"title":70,"url":71},"Cohere: Embed models documentation","https:\u002F\u002Fdocs.cohere.com\u002Fdocs\u002Fcohere-embed",{"title":73,"url":74},"Qwen: Qwen3-Embedding-8B model card","https:\u002F\u002Fhuggingface.co\u002FQwen\u002FQwen3-Embedding-8B",{"title":76,"url":77},"Google Developers Blog: Introducing EmbeddingGemma (Sep 2025)","https:\u002F\u002Fdevelopers.googleblog.com\u002Fen\u002Fintroducing-embeddinggemma\u002F","\u002Fimages\u002Fblog\u002Fembedding-model-selection-2026\u002Fcover.webp","\u002Fimages\u002Fblog\u002Fembedding-model-selection-2026\u002Fog.jpg","ai-engineer",[82,83,84],"en","de","hu","Embedding-modell választása 2026-ban: ranglisták, költségek és összehasonlító teszt a saját adatokon","Hogyan válassz embedding-modellt RAG-hoz 2026-ban: mit mondanak az MTEB és az RTEB, API vagy nyílt súlyok, árak, dimenziók és egy egyhetes teszt.","Embedding-modell választása: három sütemény hűl a formájából, egy villa épp megkóstol egyet.",{"slug":89,"published":6,"minutes":7,"category":8,"tags":90,"tagPages":94,"keywords":95,"about":108,"sources":118,"cover":143,"og":144,"expertise":80,"locales":145,"lang":84,"title":146,"description":147,"coverAlt":148},"rag-access-control-permissions",[11,91,92,93,13],"Access 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Weaknesses","https:\u002F\u002Fgenai.owasp.org\u002Fllmrisk\u002Fllm082025-vector-and-embedding-weaknesses\u002F",{"title":123,"url":124},"PostgreSQL documentation: Row Security Policies","https:\u002F\u002Fwww.postgresql.org\u002Fdocs\u002Fcurrent\u002Fddl-rowsecurity.html",{"title":126,"url":127},"pgvector README: filtering and iterative index scans","https:\u002F\u002Fgithub.com\u002Fpgvector\u002Fpgvector",{"title":129,"url":130},"Qdrant: Multitenancy and partitions guide","https:\u002F\u002Fqdrant.tech\u002Fdocumentation\u002Fguides\u002Fmultiple-partitions\u002F",{"title":132,"url":133},"Pinecone: Implement multitenancy using namespaces","https:\u002F\u002Fdocs.pinecone.io\u002Fguides\u002Findex-data\u002Fimplement-multitenancy",{"title":135,"url":136},"Microsoft Learn: Document-level access control in Azure AI Search","https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fsearch\u002Fsearch-document-level-access-overview",{"title":138,"url":139},"OpenFGA: RAG 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maradnak.",{"slug":150,"published":151,"minutes":152,"category":8,"tags":153,"tagPages":156,"keywords":157,"about":168,"sources":178,"cover":187,"og":188,"expertise":80,"locales":189,"lang":84,"title":190,"description":191,"coverAlt":192},"embedding-quantization-binary-matryoshka","2026-10-16",5,[10,13,154,155,11],"Quantization","pgvector",[16,17,8],[158,159,160,161,162,163,164,165,166,167],"embedding kvantálás","bináris kvantálás beágyazások","int8 embedding","matryoshka embedding","vektoradatbázis memória csökkentése","pgvector halfvec","pgvector binary_quantize","embedding dimenziók csökkentése","bináris beágyazások újrapontozása","vektorindex memóriaköltség",[169,172,175],{"name":170,"url":171},"Quantization (signal processing)","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FQuantization_(signal_processing)",{"name":173,"url":174},"Nearest neighbor search","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FNearest_neighbor_search",{"name":176,"url":177},"Hamming distance","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FHamming_distance",[179,182,185],{"title":180,"url":181},"Hugging Face: Binary and Scalar Embedding Quantization for Significantly Faster and Cheaper Retrieval (Mar 2024)","https:\u002F\u002Fhuggingface.co\u002Fblog\u002Fembedding-quantization",{"title":183,"url":184},"Kusupati et al.: Matryoshka Representation Learning (2022)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.13147",{"title":186,"url":127},"pgvector README: halfvec, bit, binary quantization and subvector indexing","\u002Fimages\u002Fblog\u002Fembedding-quantization-binary-matryoshka\u002Fcover.webp","\u002Fimages\u002Fblog\u002Fembedding-quantization-binary-matryoshka\u002Fog.jpg",[82,83,84],"Embedding-kvantálás: bináris, int8 és Matryoshka vektorok a visszakeresés minőségének elvesztése nélkül","Hogyan zsugorítja 4–32-szeresére a vektorindexet a bináris és int8 kvantálás és a Matryoshka-vágás, mennyibe kerül ez, és hogyan hozza vissza a rescoring.","Embedding-kvantálás: matrjoska babák sorban nagytól kicsiig, a legkisebb egy mérlegen áll.",{"slug":194,"published":195,"updated":196,"minutes":197,"category":8,"tags":198,"tagPages":203,"keywords":205,"about":216,"sources":222,"cover":259,"og":260,"expertise":80,"locales":261,"lang":84,"title":262,"description":263,"coverAlt":264},"elasticsearch-opensearch-vector-search","2026-09-30","2026-10-01",8,[199,200,13,201,202],"Elasticsearch","OpenSearch","Hybrid search","Licensing",[17,204],"hybrid-search",[206,207,208,209,210,211,212,213,214,215],"vektorkeresés elasticsearchben és opensearchben","elasticsearch knn vektorkeresés","opensearch neural search","hibrid keresés rrf","elasticsearch bbq bináris kvantálás","opensearch diskann jvector","elasticsearch agpl licenc","szűrt knn keresés","elasticsearch vs opensearch vektoradatbázis","keresőmotor vektortárként",[217,219,221],{"name":199,"url":218},"https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FElasticsearch",{"name":200,"url":220},"https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FOpenSearch_%28software%29",{"name":173,"url":174},[223,226,229,232,235,238,241,244,247,250,253,256],{"title":224,"url":225},"Elastic docs: kNN search","https:\u002F\u002Fwww.elastic.co\u002Fdocs\u002Fsolutions\u002Fsearch\u002Fvector\u002Fknn",{"title":227,"url":228},"Elastic docs: filtered kNN search","https:\u002F\u002Fwww.elastic.co\u002Fdocs\u002Fsolutions\u002Fsearch\u002Fvector\u002Fknn\u002Ffiltered-knn-search",{"title":230,"url":231},"Elastic docs: dense_vector field type","https:\u002F\u002Fwww.elastic.co\u002Fdocs\u002Freference\u002Felasticsearch\u002Fmapping-reference\u002Fdense-vector",{"title":233,"url":234},"Elastic docs: BBQ","https:\u002F\u002Fwww.elastic.co\u002Fdocs\u002Freference\u002Felasticsearch\u002Fmapping-reference\u002Fbbq",{"title":236,"url":237},"Elastic docs: reciprocal rank fusion","https:\u002F\u002Fwww.elastic.co\u002Fdocs\u002Freference\u002Felasticsearch\u002Frest-apis\u002Freciprocal-rank-fusion",{"title":239,"url":240},"Elastic docs: GPU vector indexing","https:\u002F\u002Fwww.elastic.co\u002Fdocs\u002Fsolutions\u002Fsearch\u002Fvector\u002Fgpu-vector-indexing",{"title":242,"url":243},"Elastic blog: Elasticsearch is open source again (Aug 2024)","https:\u002F\u002Fwww.elastic.co\u002Fblog\u002Felasticsearch-is-open-source-again",{"title":245,"url":246},"OpenSearch docs: methods and engines","https:\u002F\u002Fdocs.opensearch.org\u002Flatest\u002Fmappings\u002Fsupported-field-types\u002Fknn-methods-engines\u002F",{"title":248,"url":249},"OpenSearch docs: memory-optimized vectors","https:\u002F\u002Fdocs.opensearch.org\u002Flatest\u002Fmappings\u002Fsupported-field-types\u002Fknn-memory-optimized\u002F",{"title":251,"url":252},"OpenSearch docs: reciprocal rank 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bennük.",{"slug":266,"published":267,"updated":268,"minutes":7,"category":8,"tags":269,"tagPages":271,"keywords":272,"about":279,"sources":286,"cover":311,"og":312,"expertise":80,"locales":313,"lang":84,"title":314,"description":315,"coverAlt":316},"lancedb","2026-09-21","2026-10-07",[13,201,270,11],"Embedded database",[17,204,8],[266,273,274,275,276,209,277,278],"lancedb teszt","lance vektoradatbázis","beágyazott vektoradatbázis","lancedb vagy qdrant","lancedb indexelés","lance adatformátum",[280,283],{"name":281,"url":282},"Vector database","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FVector_database",{"name":284,"url":285},"Apache Arrow","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FApache_Arrow",[287,290,293,296,299,302,305,308],{"title":288,"url":289},"LanceDB quickstart","https:\u002F\u002Fdocs.lancedb.com\u002Fquickstart",{"title":291,"url":292},"LanceDB vector indexes","https:\u002F\u002Fdocs.lancedb.com\u002Findexing\u002Fvector-index",{"title":294,"url":295},"LanceDB indexing guide","https:\u002F\u002Fdocs.lancedb.com\u002Findexing\u002Findex",{"title":297,"url":298},"LanceDB hybrid search","https:\u002F\u002Fdocs.lancedb.com\u002Fsearch\u002Fhybrid-search",{"title":300,"url":301},"LanceDB Enterprise","https:\u002F\u002Fdocs.lancedb.com\u002Fenterprise",{"title":303,"url":304},"LanceDB frequently asked questions","https:\u002F\u002Fdocs.lancedb.com\u002Ffaq\u002Ffaq-oss",{"title":306,"url":307},"LanceDB pricing","https:\u002F\u002Flancedb.com\u002Fpricing",{"title":309,"url":310},"LanceDB on PyPI","https:\u002F\u002Fpypi.org\u002Fproject\u002Flancedb\u002F","\u002Fimages\u002Fblog\u002Flancedb\u002Fcover.webp","\u002Fimages\u002Fblog\u002Flancedb\u002Fog.jpg",[82,83,84],"LanceDB: vektoros keresés, amely könyvtárként indul","A LanceDB értékelése: beágyazott, Apache-2.0 vektorkönyvtár, IVF és HNSW indexek, hibrid keresés rangsoregyesítéssel, és mit ad az Enterprise.","LanceDB: egy nyitott könyvből golyókkal teli, szétválogatott polcok nyílnak ki.",{"slug":155,"published":267,"updated":196,"minutes":318,"category":8,"tags":319,"tagPages":323,"keywords":324,"about":332,"sources":337,"cover":352,"og":353,"expertise":80,"locales":354,"lang":84,"title":355,"description":356,"coverAlt":357},10,[13,320,321,11,322],"Postgres","HNSW","Quantisation",[17,8],[155,325,326,327,328,329,330,331],"pgvector teszt","pgvector vagy qdrant","postgres vektorkeresés","hnsw index postgres","iteratív indexszkennelés","bináris kvantálás postgres","vektoradatbázis postgres",[333,334],{"name":155,"url":127},{"name":335,"url":336},"PostgreSQL","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FPostgreSQL",[338,340,343,346,349],{"title":339,"url":127},"pgvector README",{"title":341,"url":342},"pgvector changelog","https:\u002F\u002Fgithub.com\u002Fpgvector\u002Fpgvector\u002Fblob\u002Fmaster\u002FCHANGELOG.md",{"title":344,"url":345},"PostgreSQL news: pgvector 0.8.2 released","https:\u002F\u002Fwww.postgresql.org\u002Fabout\u002Fnews\u002Fpgvector-082-released-3245\u002F",{"title":347,"url":348},"AWS: Scale pgvector with binary quantization","https:\u002F\u002Faws.amazon.com\u002Fblogs\u002Fdatabase\u002Fscale-pgvector-with-binary-quantization-on-amazon-aurora-postgresql\u002F",{"title":350,"url":351},"pgvector licence","https:\u002F\u002Fgithub.com\u002Fpgvector\u002Fpgvector\u002Fblob\u002Fmaster\u002FLICENSE","\u002Fimages\u002Fblog\u002Fpgvector\u002Fcover.webp","\u002Fimages\u002Fblog\u002Fpgvector\u002Fog.jpg",[82,83,84],"pgvector, értékelés: az a vektordatázis, amelyet nem kell üzemeltetned","pgvector 0.8.7: iteratív szűrt keresés, HNSW és IVFFlat, bináris kvantálás 100 millió vektornál, és a CVE, ami javítandóvá tette az indexépítést.","pgvector értékelés: egy elefánt kis fiókos szekrényt visz a hátán, benne szétválogatott golyók.",{"slug":359,"published":360,"updated":196,"minutes":318,"category":8,"tags":361,"tagPages":364,"keywords":365,"about":372,"sources":376,"cover":404,"og":405,"expertise":80,"locales":406,"lang":84,"title":407,"description":408,"coverAlt":409},"mem0","2026-09-17",[362,363,11,13],"Agent memory","Long-term memory",[8,17],[359,366,367,368,369,370,371],"mem0 teszt","ügynök memória réteg","mem0 saját szerveren","mem0 árak","hosszú távú memória ügynökökhöz","mem0 alternatíva",[373],{"name":374,"url":375},"Mem0","https:\u002F\u002Fmem0.ai",[377,380,383,386,389,392,395,398,401],{"title":378,"url":379},"Mem0 documentation","https:\u002F\u002Fdocs.mem0.ai\u002Fintroduction",{"title":381,"url":382},"Mem0 quickstart","https:\u002F\u002Fdocs.mem0.ai\u002Fquickstart",{"title":384,"url":385},"How Mem0 works","https:\u002F\u002Fdocs.mem0.ai\u002Fcore-concepts\u002Fhow-it-works",{"title":387,"url":388},"Mem0 pricing","https:\u002F\u002Fmem0.ai\u002Fpricing",{"title":390,"url":391},"Mem0 on GitHub","https:\u002F\u002Fgithub.com\u002Fmem0ai\u002Fmem0",{"title":393,"url":394},"mem0ai on PyPI","https:\u002F\u002Fpypi.org\u002Fproject\u002Fmem0ai\u002F",{"title":396,"url":397},"Mem0 research and benchmarks","https:\u002F\u002Fmem0.ai\u002Fresearch",{"title":399,"url":400},"Mem0 MCP server","https:\u002F\u002Fdocs.mem0.ai\u002Fplatform\u002Fmem0-mcp",{"title":402,"url":403},"Mem0 paper on arXiv","https:\u002F\u002Farxiv.org\u002Fabs\u002F2504.19413","\u002Fimages\u002Fblog\u002Fmem0\u002Fcover.webp","\u002Fimages\u002Fblog\u002Fmem0\u002Fog.jpg",[82,83,84],"Mem0: mennyibe kerül egy ügynökmemória körönként","A Mem0 elemzése: körönként kinyert tények, a 2026. áprilisi benchmark-tábla platformos fenntartása, négy felhőcsomag és az önhosztolásból kimaradók.","Mem0 költsége körönként: egy robot jegyzetel egy parkolóóra mellett, mögötte cetlik sora.",{"slug":411,"published":412,"updated":413,"minutes":318,"category":8,"tags":414,"tagPages":417,"keywords":418,"about":427,"sources":435,"cover":454,"og":455,"expertise":80,"locales":456,"lang":84,"title":457,"description":458,"coverAlt":459},"milvus-zilliz","2026-09-08","2026-09-20",[13,201,415,416,11],"BM25 full text","Distributed",[17,204,8],[419,420,421,422,423,424,425,426],"milvus","milvus vagy qdrant","zilliz cloud árak","vektoradatbázis összehasonlítás","milvus hibrid keresés","apache milvus saját szerveren","milvus 3.0 teszt","rag vektortár",[428,431,434],{"name":429,"url":430},"Milvus","https:\u002F\u002Fmilvus.io",{"name":432,"url":433},"Zilliz Cloud","https:\u002F\u002Fzilliz.com",{"name":116,"url":117},[436,439,442,445,448,451],{"title":437,"url":438},"Milvus architecture overview","https:\u002F\u002Fmilvus.io\u002Fdocs\u002Farchitecture_overview.md",{"title":440,"url":441},"Milvus release notes","https:\u002F\u002Fmilvus.io\u002Fdocs\u002Frelease_notes.md",{"title":443,"url":444},"Milvus releases on GitHub","https:\u002F\u002Fgithub.com\u002Fmilvus-io\u002Fmilvus\u002Freleases",{"title":446,"url":447},"Milvus README: features and licence","https:\u002F\u002Fgithub.com\u002Fmilvus-io\u002Fmilvus",{"title":449,"url":450},"Zilliz Cloud pricing","https:\u002F\u002Fzilliz.com\u002Fpricing",{"title":452,"url":453},"Zilliz Cloud list price","https:\u002F\u002Fzilliz.com\u002Fpricing\u002Fpricing-guide","\u002Fimages\u002Fblog\u002Fmilvus-zilliz\u002Fcover.webp","\u002Fimages\u002Fblog\u002Fmilvus-zilliz\u002Fog.jpg",[82,83,84],"Milvus elemzés: a legteljesebb vektordatbázis üzemeltetve","Milvus 3.0.2: a legteljesebb nyílt forráskódú vektoradatbázis és a legnehezebben üzemeltethető. Architektúra, hibrid keresés, költségek és korlátok.","Milvus és Zilliz: magas polcos raktár targoncával, egy dolgozó csavarkulccsal a vezérlőpultnál.",{"slug":461,"published":462,"minutes":318,"category":8,"tags":463,"tagPages":465,"keywords":466,"about":474,"sources":481,"cover":509,"og":510,"expertise":80,"locales":511,"lang":84,"title":512,"description":513,"coverAlt":514},"chroma","2026-07-20",[13,10,11,201,464],"Metadata filter",[17,16,8,204],[467,468,469,470,471,472,473],"chroma vektoradatbázis","chroma vs qdrant összehasonlítás","chroma vagy pgvector","hibrid keresés reciprocal rank fusion","saját szerveren futó vektoradatbázis","chroma cloud árak","metaadat szűrés vektorkeresés",[475,478],{"name":476,"url":477},"Chroma","https:\u002F\u002Fgithub.com\u002Fchroma-core\u002Fchroma",{"name":479,"url":480},"Apache License 2.0","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FApache_License",[482,485,488,491,494,497,500,503,506],{"title":483,"url":484},"Chroma documentation: introduction","https:\u002F\u002Fdocs.trychroma.com\u002Fdocs\u002Foverview\u002Fintroduction",{"title":486,"url":487},"Chroma architecture overview","https:\u002F\u002Fdocs.trychroma.com\u002Freference\u002Farchitecture\u002Foverview",{"title":489,"url":490},"Chroma: getting started with the SDK","https:\u002F\u002Fdocs.trychroma.com\u002Fdocs\u002Foverview\u002Fgetting-started",{"title":492,"url":493},"Chroma: query and get","https:\u002F\u002Fdocs.trychroma.com\u002Fdocs\u002Fquerying-collections\u002Fquery-and-get",{"title":495,"url":496},"Chroma: metadata filtering","https:\u002F\u002Fdocs.trychroma.com\u002Fdocs\u002Fquerying-collections\u002Fmetadata-filtering",{"title":498,"url":499},"Chroma Cloud: Search API overview","https:\u002F\u002Fdocs.trychroma.com\u002Fcloud\u002Fsearch-api\u002Foverview",{"title":501,"url":502},"Chroma Cloud: hybrid search with RRF","https:\u002F\u002Fdocs.trychroma.com\u002Fcloud\u002Fsearch-api\u002Fhybrid-search",{"title":504,"url":505},"Chroma Cloud: pricing","https:\u002F\u002Fwww.trychroma.com\u002Fpricing",{"title":507,"url":508},"Chroma changelog","https:\u002F\u002Fwww.trychroma.com\u002Fchangelog","\u002Fimages\u002Fblog\u002Fchroma\u002Fcover.webp","\u002Fimages\u002Fblog\u002Fchroma\u002Fog.jpg",[82,83,84],"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":516,"published":517,"minutes":518,"category":8,"tags":519,"tagPages":521,"keywords":522,"about":531,"sources":537,"cover":562,"og":563,"expertise":80,"locales":564,"lang":84,"title":565,"description":566,"coverAlt":567},"pinecone","2026-07-09",11,[13,10,11,520,201],"Namespaces",[17,16,8,204],[516,523,524,525,526,527,528,529,530],"pinecone vektoradatbázis","pinecone árak","pinecone read unit","pinecone namespace","pinecone vagy qdrant","pinecone vagy pgvector","pinecone documents api","serverless vektoradatbázis költség",[532,533,534],{"name":281,"url":282},{"name":173,"url":174},{"name":535,"url":536},"Okapi BM25","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FOkapi_BM25",[538,541,544,547,550,553,556,559],{"title":539,"url":540},"Pinecone docs: Index data overview, indexes, namespaces and metadata","https:\u002F\u002Fdocs.pinecone.io\u002Fguides\u002Findex-data\u002Findexing-overview",{"title":542,"url":543},"Pinecone docs: Adopt the Documents API, API version 2026-07","https:\u002F\u002Fdocs.pinecone.io\u002Fguides\u002Findex-data\u002Fadopt-the-documents-api",{"title":545,"url":546},"Pinecone docs: Create an index, schemas, regions and metrics","https:\u002F\u002Fdocs.pinecone.io\u002Fguides\u002Findex-data\u002Fcreate-an-index",{"title":548,"url":549},"Pinecone docs: Understanding Pinecone cost, read units, write units and egress","https:\u002F\u002Fdocs.pinecone.io\u002Fguides\u002Forganizations\u002Fmanage-cost\u002Funderstanding-cost",{"title":551,"url":552},"Pinecone pricing: plans, limits and rates","https:\u002F\u002Fwww.pinecone.io\u002Fpricing\u002F",{"title":554,"url":555},"Pinecone docs: Semantic search","https:\u002F\u002Fdocs.pinecone.io\u002Fguides\u002Fsearch\u002Fsemantic-search",{"title":557,"url":558},"Pinecone docs: Local development with Pinecone Local","https:\u002F\u002Fdocs.pinecone.io\u002Fguides\u002Foperations\u002Flocal-development",{"title":560,"url":561},"Pinecone docs: 2026 release notes","https:\u002F\u002Fdocs.pinecone.io\u002Frelease-notes\u002F2026","\u002Fimages\u002Fblog\u002Fpinecone\u002Fcover.webp","\u002Fimages\u002Fblog\u002Fpinecone\u002Fog.jpg",[82,83,84],"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":569,"published":570,"updated":268,"minutes":318,"category":8,"tags":571,"tagPages":572,"keywords":573,"about":583,"sources":592,"cover":617,"og":618,"expertise":80,"locales":619,"lang":84,"title":620,"description":621,"coverAlt":622},"weaviate","2026-06-02",[13,201,321,154,93],[17,204],[574,575,576,577,578,579,580,581,582],"weaviate vektoradatbázis","weaviate vagy qdrant","weaviate vagy pinecone","weaviate vagy pgvector","hnsw vs hfresh","weaviate cloud árak","vektoradatbázis választás","weaviate hibrid keresés","weaviate teszt",[584,587,589],{"name":585,"url":586},"Weaviate","https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate",{"name":588,"url":174},"Hierarchical Navigable Small World",{"name":590,"url":591},"SPFresh","https:\u002F\u002Farxiv.org\u002Fpdf\u002F2410.14452",[593,596,599,602,605,608,611,614],{"title":594,"url":595},"Weaviate documentation: Vector indexing","https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fconcepts\u002Fvector-index",{"title":597,"url":598},"Weaviate documentation: ANN benchmark","https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fbenchmarks\u002Fann",{"title":600,"url":601},"Weaviate 1.39 release notes","https:\u002F\u002Fweaviate.io\u002Fblog\u002Fweaviate-1-39-release",{"title":603,"url":604},"Weaviate 1.38 release notes","https:\u002F\u002Fweaviate.io\u002Fblog\u002Fweaviate-1-38-release",{"title":606,"url":607},"Weaviate documentation: MCP server","https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fconfiguration\u002Fmcp-server",{"title":609,"url":610},"Weaviate Cloud pricing","https:\u002F\u002Fweaviate.io\u002Fpricing",{"title":612,"url":613},"weaviate\u002Fweaviate: LICENSE","https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fblob\u002Fmain\u002FLICENSE",{"title":615,"url":616},"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",[82,83,84],"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":624,"published":625,"updated":626,"minutes":318,"category":8,"tags":627,"tagPages":629,"keywords":630,"about":638,"sources":642,"cover":682,"og":683,"expertise":80,"locales":684,"lang":84,"title":685,"description":686,"coverAlt":687},"qdrant","2026-05-26","2026-10-05",[13,321,322,201,628],"Filtering",[17,204],[624,631,632,633,634,635,636,637],"qdrant vagy pinecone","vektoradatbázisok összehasonlítása","hnsw index","szűrt vektorkeresés","vektor kvantálás","qdrant hibrid keresés","qdrant teszt",[639,640,641],{"name":281,"url":282},{"name":173,"url":174},{"name":116,"url":117},[643,646,649,652,655,658,661,664,667,670,673,676,679],{"title":644,"url":645},"Qdrant documentation","https:\u002F\u002Fqdrant.tech\u002Fdocumentation\u002F",{"title":647,"url":648},"Qdrant: points","https:\u002F\u002Fqdrant.tech\u002Fdocumentation\u002Fmanage-data\u002Fpoints\u002F",{"title":650,"url":651},"Qdrant: collections","https:\u002F\u002Fqdrant.tech\u002Fdocumentation\u002Fconcepts\u002Fcollections\u002F",{"title":653,"url":654},"Qdrant: indexing","https:\u002F\u002Fqdrant.tech\u002Fdocumentation\u002Fmanage-data\u002Findexing\u002F",{"title":656,"url":657},"Qdrant: quantisation","https:\u002F\u002Fqdrant.tech\u002Fdocumentation\u002Fmanage-data\u002Fquantization\u002F",{"title":659,"url":660},"Qdrant: capacity planning","https:\u002F\u002Fqdrant.tech\u002Fdocumentation\u002Fcapacity-planning\u002F",{"title":662,"url":663},"Qdrant: optimise performance","https:\u002F\u002Fqdrant.tech\u002Fdocumentation\u002Fops-optimization\u002Foptimize\u002F",{"title":665,"url":666},"Qdrant: hybrid and multi-stage queries","https:\u002F\u002Fqdrant.tech\u002Fdocumentation\u002Fconcepts\u002Fhybrid-queries\u002F",{"title":668,"url":669},"Qdrant: filtering","https:\u002F\u002Fqdrant.tech\u002Fdocumentation\u002Fconcepts\u002Ffiltering\u002F",{"title":671,"url":672},"Qdrant: security and access control","https:\u002F\u002Fqdrant.tech\u002Fdocumentation\u002Fsecurity\u002F",{"title":674,"url":675},"Qdrant pricing","https:\u002F\u002Fqdrant.tech\u002Fpricing\u002F",{"title":677,"url":678},"Qdrant on GitHub","https:\u002F\u002Fgithub.com\u002Fqdrant\u002Fqdrant",{"title":680,"url":681},"Qdrant vector search benchmarks","https:\u002F\u002Fqdrant.tech\u002Fbenchmarks\u002F","\u002Fimages\u002Fblog\u002Fqdrant\u002Fcover.webp","\u002Fimages\u002Fblog\u002Fqdrant\u002Fog.jpg",[82,83,84],"Qdrant: egy szűrés körül felépített vektordatabázis","Qdrant értékelés: szűrhető HNSW, négy kvantálási mód, a memória-szintek az 1.19-ben és az üzemeltetés, amit már senki nem publikál.","Qdrant vektordatabázis: többrétegű szita golyókat válogat egy tálba.",1793451237700]