[{"data":1,"prerenderedAt":546},["ShallowReactive",2],{"blog-tag-hu-embeddings":3},[4,88,149,206,254,297,370,436,493],{"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":90,"category":8,"tags":91,"tagPages":95,"keywords":96,"about":108,"sources":118,"cover":143,"og":144,"expertise":80,"locales":145,"lang":84,"title":146,"description":147,"coverAlt":148},"contextual-retrieval-late-chunking",10,[11,92,10,93,94],"Chunking","Contextual retrieval","Late chunking",[8,16],[97,98,99,100,101,102,103,104,105,106,107],"kontextuális visszakeresés","anthropic kontextuális visszakeresés","késleltetett chunkelés","kontextuális chunk beágyazások","kontextuális beágyazások rag","contextual bm25","chunk kontextus rag","voyage-context-3","voyage-context-4","late chunking vs kontextuális visszakeresés","rag chunkelés kontextusvesztés",[109,112,115],{"name":110,"url":111},"Retrieval-augmented generation","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FRetrieval-augmented_generation",{"name":113,"url":114},"Okapi BM25","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FOkapi_BM25",{"name":116,"url":117},"Sentence embedding","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FSentence_embedding",[119,122,125,128,131,134,137,140],{"title":120,"url":121},"Anthropic: Introducing Contextual Retrieval (Sep 2024)","https:\u002F\u002Fwww.anthropic.com\u002Fnews\u002Fcontextual-retrieval",{"title":123,"url":124},"Günther et al.: Late Chunking, Contextual Chunk Embeddings Using Long-Context Embedding Models (2024, rev. 2025)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2409.04701",{"title":126,"url":127},"Jina AI: Late Chunking in Long-Context Embedding Models (Aug 2024)","https:\u002F\u002Fjina.ai\u002Fnews\u002Flate-chunking-in-long-context-embedding-models\u002F",{"title":129,"url":130},"Voyage AI: Introducing voyage-context-3 (Jul 2025)","https:\u002F\u002Fblog.voyageai.com\u002F2025\u002F07\u002F23\u002Fvoyage-context-3\u002F",{"title":132,"url":133},"Voyage AI: voyage-context-4 (Jun 2026)","https:\u002F\u002Fblog.voyageai.com\u002F2026\u002F06\u002F29\u002Fvoyage-context-4\u002F",{"title":135,"url":136},"Voyage AI: Contextualized chunk embeddings documentation","https:\u002F\u002Fdocs.voyageai.com\u002Fdocs\u002Fcontextualized-chunk-embeddings",{"title":138,"url":139},"Anthropic: Claude API pricing","https:\u002F\u002Fplatform.claude.com\u002Fdocs\u002Fen\u002Fabout-claude\u002Fpricing",{"title":141,"url":142},"Qu, Tu and Bao: Is Semantic Chunking Worth the Computational Cost? (2024)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2410.13070","\u002Fimages\u002Fblog\u002Fcontextual-retrieval-late-chunking\u002Fcover.webp","\u002Fimages\u002Fblog\u002Fcontextual-retrieval-late-chunking\u002Fog.jpg",[82,83,84],"Kontextuális visszakeresés és késleltetett chunkolás: a RAG-chunkok visszakapják a kontextusukat","A chunkok elveszítik a dokumentumukat. Contextual retrieval, late chunking és kontextusos embedding: mennyibe kerülnek, és mikor hagyhatók ki.","Kontextuális visszakeresés: egy kéz címkés kirakósdarabot tart a helye fölé az asztalon fekvő kirakóban.",{"slug":150,"published":151,"minutes":152,"category":8,"tags":153,"tagPages":157,"keywords":158,"about":169,"sources":179,"cover":200,"og":201,"expertise":80,"locales":202,"lang":84,"title":203,"description":204,"coverAlt":205},"multilingual-rag-german-hungarian","2026-10-16",7,[11,154,10,155,156],"Multilingual","German","Hungarian",[8,16],[159,160,161,162,163,164,165,166,167,168],"többnyelvű rag","német rag beágyazások","magyar nlp keresés","nyelvek közötti visszakeresés","többnyelvű beágyazó modell","bge-m3 német","német összetett szavak keresése","magyar szótövezés postgres","multilingual-e5-large-instruct","qwen3 embedding többnyelvű",[170,173,176],{"name":171,"url":172},"Cross-language information retrieval","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FCross-language_information_retrieval",{"name":174,"url":175},"Agglutinative language","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FAgglutinative_language",{"name":177,"url":178},"Compound (linguistics)","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FCompound_(linguistics)",[180,182,185,188,191,194,197],{"title":181,"url":44},"Enevoldsen et al.: MMTEB: Massive Multilingual Text Embedding Benchmark (2025)",{"title":183,"url":184},"Chen et al.: M3-Embedding (BGE-M3): Multi-Linguality, Multi-Functionality, Multi-Granularity (2024)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2402.03216",{"title":186,"url":187},"Zhang et al.: Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models (2025)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2506.05176",{"title":189,"url":190},"Hugging Face: intfloat\u002Fmultilingual-e5-large-instruct model card","https:\u002F\u002Fhuggingface.co\u002Fintfloat\u002Fmultilingual-e5-large-instruct",{"title":192,"url":193},"Elasticsearch Reference: Hyphenation decompounder token filter","https:\u002F\u002Fwww.elastic.co\u002Fdocs\u002Freference\u002Ftext-analysis\u002Fanalysis-hyp-decomp-tokenfilter",{"title":195,"url":196},"PostgreSQL 18 Documentation: Text search dictionaries","https:\u002F\u002Fwww.postgresql.org\u002Fdocs\u002Fcurrent\u002Ftextsearch-dictionaries.html",{"title":198,"url":199},"Snowball: stemming algorithms by language","https:\u002F\u002Fsnowballstem.org\u002Falgorithms\u002F","\u002Fimages\u002Fblog\u002Fmultilingual-rag-german-hungarian\u002Fcover.webp","\u002Fimages\u002Fblog\u002Fmultilingual-rag-german-hungarian\u002Fog.jpg",[82,83,84],"Többnyelvű RAG német és magyar tartalomra: embeddingek, összetett szavak és nyelvek közötti keresés","RAG német és magyar tartalom fölött: többnyelvű embeddingek, összetett szavak, szótövezés, ékezetek és nyelvenkénti értékelés.","Többnyelvű RAG német és magyar tartalomra: egy ember könyvet visz át egy kis hídon két könyvespolc között.",{"slug":207,"published":151,"minutes":208,"category":209,"tags":210,"tagPages":215,"keywords":216,"about":227,"sources":235,"cover":248,"og":249,"expertise":80,"locales":250,"lang":84,"title":251,"description":252,"coverAlt":253},"semantic-caching-llm-responses",6,"llmops",[211,212,213,10,214],"Semantic cache","LLM cost","Latency","Redis",[16],[217,218,219,220,221,222,223,224,225,226],"szemantikus gyorsítótárazás llm","szemantikus cache","llm válasz cache","gptcache","szemantikus cache hasonlósági küszöb","prompt caching vs szemantikus gyorsítótárazás","llm api költség csökkentése","llm válaszok cache-elése redisben","szemantikus cache hamis találat","llm cache érvénytelenítés",[228,231,232],{"name":229,"url":230},"Cache (computing)","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FCache_(computing)",{"name":116,"url":117},{"name":233,"url":234},"Large language model","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FLarge_language_model",[236,239,242,245],{"title":237,"url":238},"Regmi and Pun: GPT Semantic Cache: Reducing LLM Costs and Latency via Semantic Embedding Caching (2024)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2411.05276",{"title":240,"url":241},"Zilliz: GPTCache README (modules, similarity evaluation, eviction)","https:\u002F\u002Fgithub.com\u002Fzilliztech\u002FGPTCache",{"title":243,"url":244},"Claude Platform Docs: Prompt caching","https:\u002F\u002Fplatform.claude.com\u002Fdocs\u002Fen\u002Fbuild-with-claude\u002Fprompt-caching",{"title":246,"url":247},"pgvector README: vector types and indexes","https:\u002F\u002Fgithub.com\u002Fpgvector\u002Fpgvector","\u002Fimages\u002Fblog\u002Fsemantic-caching-llm-responses\u002Fcover.webp","\u002Fimages\u002Fblog\u002Fsemantic-caching-llm-responses\u002Fog.jpg",[82,83,84],"Szemantikus gyorsítótár LLM-alkalmazásokhoz: mikor biztonságos újrahasznosítani a válaszokat, és mikor nem","Szemantikus cache-ek: LLM-válaszok újrahasználata hasonló kérdéseknél, miben térnek el a prompt cachingtől, és hogyan kerüld a téves találatokat.","Szemantikus gyorsítótár LLM-appokhoz: kéz leadja a jegyet a ruhatárban, és visszakap egy kabátot.",{"slug":255,"published":151,"minutes":256,"category":8,"tags":257,"tagPages":260,"keywords":261,"about":272,"sources":282,"cover":291,"og":292,"expertise":80,"locales":293,"lang":84,"title":294,"description":295,"coverAlt":296},"embedding-quantization-binary-matryoshka",5,[10,13,258,259,11],"Quantization","pgvector",[16,17,8],[262,263,264,265,266,267,268,269,270,271],"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",[273,276,279],{"name":274,"url":275},"Quantization (signal processing)","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FQuantization_(signal_processing)",{"name":277,"url":278},"Nearest neighbor search","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FNearest_neighbor_search",{"name":280,"url":281},"Hamming distance","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FHamming_distance",[283,286,289],{"title":284,"url":285},"Hugging Face: Binary and Scalar Embedding Quantization for Significantly Faster and Cheaper Retrieval (Mar 2024)","https:\u002F\u002Fhuggingface.co\u002Fblog\u002Fembedding-quantization",{"title":287,"url":288},"Kusupati et al.: Matryoshka Representation Learning (2022)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.13147",{"title":290,"url":247},"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":298,"published":299,"updated":6,"minutes":300,"category":8,"tags":301,"tagPages":305,"keywords":307,"about":318,"sources":328,"cover":364,"og":365,"expertise":80,"locales":366,"lang":84,"title":367,"description":368,"coverAlt":369},"code-rag-repository-indexing","2026-10-01",8,[11,302,303,10,304],"Code search","Tree-sitter","Coding agents",[8,16,306],"coding-agents",[308,309,310,311,312,313,314,315,316,317],"rag kódon","ast darabolás","tree-sitter kód darabolás","repótérkép","kód beágyazások","szemantikus kódkeresés","kódbázis indexelés","repo map tokenek","inkrementális kódindex","coir benchmark",[319,320,323,325],{"name":110,"url":111},{"name":321,"url":322},"Abstract syntax tree","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FAbstract_syntax_tree",{"name":303,"url":324},"https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FTree-sitter_%28parser_generator%29",{"name":326,"url":327},"Merkle tree","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FMerkle_tree",[329,332,335,338,341,344,347,350,353,355,358,361],{"title":330,"url":331},"cAST: AST-based chunking for code RAG (arXiv 2506.15655)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2506.15655",{"title":333,"url":334},"Aider docs: repository map","https:\u002F\u002Faider.chat\u002Fdocs\u002Frepomap.html",{"title":336,"url":337},"Aider blog: building a better repository map with tree-sitter (2023)","https:\u002F\u002Faider.chat\u002F2023\u002F10\u002F22\u002Frepomap.html",{"title":339,"url":340},"Anthropic Engineering: effective context engineering for AI agents (Sep 2025)","https:\u002F\u002Fwww.anthropic.com\u002Fengineering\u002Feffective-context-engineering-for-ai-agents",{"title":342,"url":343},"Cursor blog: securely indexing large codebases (Jan 2026)","https:\u002F\u002Fcursor.com\u002Fblog\u002Fsecure-codebase-indexing",{"title":345,"url":346},"turbopuffer: Cursor customer story","https:\u002F\u002Fturbopuffer.com\u002Fcustomers\u002Fcursor",{"title":348,"url":349},"Kilo Code docs: codebase indexing","https:\u002F\u002Fkilo.ai\u002Fdocs\u002Fcustomize\u002Fcontext\u002Fcodebase-indexing",{"title":351,"url":352},"Serena on GitHub","https:\u002F\u002Fgithub.com\u002Foraios\u002Fserena",{"title":354,"url":62},"Voyage AI docs: pricing",{"title":356,"url":357},"Qwen blog: Qwen3-Embedding (Jun 2025)","https:\u002F\u002Fqwenlm.github.io\u002Fblog\u002Fqwen3-embedding\u002F",{"title":359,"url":360},"CoIR: a benchmark for code information retrieval (arXiv 2407.02883)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2407.02883",{"title":362,"url":363},"CocoIndex blog: indexing a codebase","https:\u002F\u002Fcocoindex.io\u002Fblogs\u002Findex-codebase-v1","\u002Fimages\u002Fblog\u002Fcode-rag-repository-indexing\u002Fcover.webp","\u002Fimages\u002Fblog\u002Fcode-rag-repository-indexing\u002Fog.jpg",[82,83,84],"RAG kódon: AST-darabolás, repótérképek és kódbeágyazások ügynökökhöz","Hogyan találják meg a kódot a kódoló ügynökök: AST-darabolás tree-sitterrel, repótérképek, Merkle-fás szinkron, kódbeágyazók 2026-ban és mikor jobb a grep.","RAG kódon (AST-darabolás): elágazó kódfa táplál egy sor kartotékkártyát.",{"slug":371,"published":372,"updated":6,"minutes":300,"category":8,"tags":373,"tagPages":377,"keywords":379,"about":390,"sources":396,"cover":430,"og":431,"expertise":80,"locales":432,"lang":84,"title":433,"description":434,"coverAlt":435},"embedding-fine-tuning-domain","2026-09-28",[10,374,11,375,376],"Fine-tuning","Evaluation","Sentence Transformers",[16,8,378],"evaluation",[380,381,382,383,384,385,386,387,388,389],"beágyazó modellek finomhangolása","domain adaptation beágyazások","hard negative mining visszakeresés","multiplenegativesrankingloss","gistembed vs mnrl","matryoshka beágyazás dimenziók","qwen3-embedding finomhangolás","bge-m3 vs embeddinggemma","beágyazás kiértékelés recall@k","katasztrofális felejtés dense retrieval",[391,392,395],{"name":33,"url":34},{"name":393,"url":394},"Fine-tuning (deep learning)","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FFine-tuning_%28deep_learning%29",{"name":36,"url":37},[397,400,403,406,409,412,415,416,419,422,425,427],{"title":398,"url":399},"Sentence Transformers: training overview","https:\u002F\u002Fsbert.net\u002Fdocs\u002Fsentence_transformer\u002Ftraining_overview.html",{"title":401,"url":402},"Sentence Transformers: loss overview","https:\u002F\u002Fsbert.net\u002Fdocs\u002Fsentence_transformer\u002Floss_overview.html",{"title":404,"url":405},"Sentence Transformers: hard negative mining","https:\u002F\u002Fsbert.net\u002Fdocs\u002Fpackage_reference\u002Futil\u002Fhard_negatives.html",{"title":407,"url":408},"RocketQA (arXiv 2010.08191)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.08191",{"title":410,"url":411},"InPars (arXiv 2202.05144)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2202.05144",{"title":413,"url":414},"Promptagator (arXiv 2209.11755)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.11755",{"title":356,"url":357},{"title":417,"url":418},"BAAI\u002Fbge-m3 model card","https:\u002F\u002Fhuggingface.co\u002FBAAI\u002Fbge-m3",{"title":420,"url":421},"Google: EmbeddingGemma docs (Oct 2026)","https:\u002F\u002Fai.google.dev\u002Fgemma\u002Fdocs\u002Fembeddinggemma",{"title":423,"url":424},"Sentence Transformers: Matryoshka embeddings","https:\u002F\u002Fsbert.net\u002Fexamples\u002Fsentence_transformer\u002Ftraining\u002Fmatryoshka\u002FREADME.html",{"title":426,"url":44},"MMTEB (arXiv 2502.13595)",{"title":428,"url":429},"ANCE-Tele (arXiv 2210.17167)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.17167","\u002Fimages\u002Fblog\u002Fembedding-fine-tuning-domain\u002Fcover.webp","\u002Fimages\u002Fblog\u002Fembedding-fine-tuning-domain\u002Fog.jpg",[82,83,84],"Beágyazó modellek finomhangolása: nehéz negatívok, veszteségek és kiértékelés","Beágyazások finomhangolása: mikor jobb a rerankernél, tanítópárok, nehéz negatívok, veszteségek a sentence-transformers v6-ban, Matryoshka és költség.","Beágyazó modellek finomhangolása: a bal oldali laza pontokból tanítás után szoros csoportok lesznek jobbra.",{"slug":437,"published":438,"minutes":90,"category":8,"tags":439,"tagPages":442,"keywords":444,"about":452,"sources":459,"cover":487,"og":488,"expertise":80,"locales":489,"lang":84,"title":490,"description":491,"coverAlt":492},"chroma","2026-07-20",[13,10,11,440,441],"Hybrid search","Metadata filter",[17,16,8,443],"hybrid-search",[445,446,447,448,449,450,451],"chroma vektoradatbázis","chroma vs qdrant összehasonlítás","chroma vagy pgvector","hibrid keresés reciprocal rank fusion","saját szerveren futó vektoradatbázis","chroma cloud árak","metaadat szűrés vektorkeresés",[453,456],{"name":454,"url":455},"Chroma","https:\u002F\u002Fgithub.com\u002Fchroma-core\u002Fchroma",{"name":457,"url":458},"Apache License 2.0","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FApache_License",[460,463,466,469,472,475,478,481,484],{"title":461,"url":462},"Chroma documentation: introduction","https:\u002F\u002Fdocs.trychroma.com\u002Fdocs\u002Foverview\u002Fintroduction",{"title":464,"url":465},"Chroma architecture overview","https:\u002F\u002Fdocs.trychroma.com\u002Freference\u002Farchitecture\u002Foverview",{"title":467,"url":468},"Chroma: getting started with the SDK","https:\u002F\u002Fdocs.trychroma.com\u002Fdocs\u002Foverview\u002Fgetting-started",{"title":470,"url":471},"Chroma: query and get","https:\u002F\u002Fdocs.trychroma.com\u002Fdocs\u002Fquerying-collections\u002Fquery-and-get",{"title":473,"url":474},"Chroma: metadata filtering","https:\u002F\u002Fdocs.trychroma.com\u002Fdocs\u002Fquerying-collections\u002Fmetadata-filtering",{"title":476,"url":477},"Chroma Cloud: Search API overview","https:\u002F\u002Fdocs.trychroma.com\u002Fcloud\u002Fsearch-api\u002Foverview",{"title":479,"url":480},"Chroma Cloud: hybrid search with RRF","https:\u002F\u002Fdocs.trychroma.com\u002Fcloud\u002Fsearch-api\u002Fhybrid-search",{"title":482,"url":483},"Chroma Cloud: pricing","https:\u002F\u002Fwww.trychroma.com\u002Fpricing",{"title":485,"url":486},"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":494,"published":495,"minutes":496,"category":8,"tags":497,"tagPages":499,"keywords":500,"about":509,"sources":515,"cover":540,"og":541,"expertise":80,"locales":542,"lang":84,"title":543,"description":544,"coverAlt":545},"pinecone","2026-07-09",11,[13,10,11,498,440],"Namespaces",[17,16,8,443],[494,501,502,503,504,505,506,507,508],"pinecone vektoradatbázis","pinecone árak","pinecone read unit","pinecone namespace","pinecone vagy qdrant","pinecone vagy pgvector","pinecone documents api","serverless vektoradatbázis költség",[510,513,514],{"name":511,"url":512},"Vector database","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FVector_database",{"name":277,"url":278},{"name":113,"url":114},[516,519,522,525,528,531,534,537],{"title":517,"url":518},"Pinecone docs: Index data overview, indexes, namespaces and metadata","https:\u002F\u002Fdocs.pinecone.io\u002Fguides\u002Findex-data\u002Findexing-overview",{"title":520,"url":521},"Pinecone docs: Adopt the Documents API, API version 2026-07","https:\u002F\u002Fdocs.pinecone.io\u002Fguides\u002Findex-data\u002Fadopt-the-documents-api",{"title":523,"url":524},"Pinecone docs: Create an index, schemas, regions and metrics","https:\u002F\u002Fdocs.pinecone.io\u002Fguides\u002Findex-data\u002Fcreate-an-index",{"title":526,"url":527},"Pinecone docs: Understanding Pinecone cost, read units, write units and egress","https:\u002F\u002Fdocs.pinecone.io\u002Fguides\u002Forganizations\u002Fmanage-cost\u002Funderstanding-cost",{"title":529,"url":530},"Pinecone pricing: plans, limits and rates","https:\u002F\u002Fwww.pinecone.io\u002Fpricing\u002F",{"title":532,"url":533},"Pinecone docs: Semantic search","https:\u002F\u002Fdocs.pinecone.io\u002Fguides\u002Fsearch\u002Fsemantic-search",{"title":535,"url":536},"Pinecone docs: Local development with Pinecone Local","https:\u002F\u002Fdocs.pinecone.io\u002Fguides\u002Foperations\u002Flocal-development",{"title":538,"url":539},"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.",1793451237700]