[{"data":1,"prerenderedAt":545},["ShallowReactive",2],{"blog-tag-en-embeddings":3},[4,88,149,205,253,296,369,435,492],{"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":82,"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],"best embedding model 2026","how to choose an embedding model","embedding model comparison","MTEB leaderboard explained","RTEB benchmark","open source embedding model","multilingual embedding model","voyage-4 vs openai embeddings","Qwen3-Embedding","EmbeddingGemma","embedding model for RAG","switching embedding models re-embed",[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","Choosing an embedding model in 2026: leaderboards, costs and a bake-off on your own data","How to pick an embedding model for RAG in 2026: what MTEB and RTEB tell you, hosted vs open weights, prices, dimensions, and a one-week bake-off.","Embedding model selection: three cakes from three moulds cool on a rack while a fork is about to taste one.",{"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":82,"title":146,"description":147,"coverAlt":148},"contextual-retrieval-late-chunking",10,[11,92,10,93,94],"Chunking","Contextual retrieval","Late 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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],"Contextual retrieval and late chunking: giving RAG chunks back their context","Chunks lose their document. How contextual retrieval, late chunking and contextualized embedding models fix that, what each costs, and when to skip it.","Contextual retrieval and late chunking: a hand holds a tagged puzzle piece above its gap in a jigsaw on a table.",{"slug":150,"published":151,"minutes":152,"category":8,"tags":153,"tagPages":157,"keywords":158,"about":168,"sources":178,"cover":199,"og":200,"expertise":80,"locales":201,"lang":82,"title":202,"description":203,"coverAlt":204},"multilingual-rag-german-hungarian","2026-10-16",7,[11,154,10,155,156],"Multilingual","German","Hungarian",[8,16],[159,160,161,162,25,163,164,165,166,167],"multilingual rag","german rag embeddings","hungarian nlp search","cross-lingual retrieval","bge-m3 german","german compound words search","hungarian stemming postgres","multilingual-e5-large-instruct","qwen3 embedding multilingual",[169,172,175],{"name":170,"url":171},"Cross-language information retrieval","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FCross-language_information_retrieval",{"name":173,"url":174},"Agglutinative language","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FAgglutinative_language",{"name":176,"url":177},"Compound (linguistics)","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FCompound_(linguistics)",[179,181,184,187,190,193,196],{"title":180,"url":44},"Enevoldsen et al.: MMTEB: Massive Multilingual Text Embedding Benchmark (2025)",{"title":182,"url":183},"Chen et al.: M3-Embedding (BGE-M3): Multi-Linguality, Multi-Functionality, Multi-Granularity (2024)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2402.03216",{"title":185,"url":186},"Zhang et al.: Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models (2025)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2506.05176",{"title":188,"url":189},"Hugging Face: intfloat\u002Fmultilingual-e5-large-instruct model card","https:\u002F\u002Fhuggingface.co\u002Fintfloat\u002Fmultilingual-e5-large-instruct",{"title":191,"url":192},"Elasticsearch Reference: Hyphenation decompounder token filter","https:\u002F\u002Fwww.elastic.co\u002Fdocs\u002Freference\u002Ftext-analysis\u002Fanalysis-hyp-decomp-tokenfilter",{"title":194,"url":195},"PostgreSQL 18 Documentation: Text search dictionaries","https:\u002F\u002Fwww.postgresql.org\u002Fdocs\u002Fcurrent\u002Ftextsearch-dictionaries.html",{"title":197,"url":198},"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],"Multilingual RAG for German and Hungarian: embeddings, compounds and cross-lingual search","How to build RAG over German and Hungarian content: multilingual embeddings, compound words, stemming, accents and per-language evals.","Multilingual RAG for German and Hungarian: a person carries a book across a small bridge between two bookshelves.",{"slug":206,"published":151,"minutes":207,"category":208,"tags":209,"tagPages":214,"keywords":215,"about":226,"sources":234,"cover":247,"og":248,"expertise":80,"locales":249,"lang":82,"title":250,"description":251,"coverAlt":252},"semantic-caching-llm-responses",6,"llmops",[210,211,212,10,213],"Semantic cache","LLM cost","Latency","Redis",[16],[216,217,218,219,220,221,222,223,224,225],"semantic caching llm","semantic cache","llm response cache","gptcache","semantic cache similarity threshold","prompt caching vs semantic caching","reduce llm api cost","cache llm answers redis","semantic cache false positive","llm cache invalidation",[227,230,231],{"name":228,"url":229},"Cache (computing)","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FCache_(computing)",{"name":116,"url":117},{"name":232,"url":233},"Large language model","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FLarge_language_model",[235,238,241,244],{"title":236,"url":237},"Regmi and Pun: GPT Semantic Cache: Reducing LLM Costs and Latency via Semantic Embedding Caching (2024)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2411.05276",{"title":239,"url":240},"Zilliz: GPTCache README (modules, similarity evaluation, eviction)","https:\u002F\u002Fgithub.com\u002Fzilliztech\u002FGPTCache",{"title":242,"url":243},"Claude Platform Docs: Prompt caching","https:\u002F\u002Fplatform.claude.com\u002Fdocs\u002Fen\u002Fbuild-with-claude\u002Fprompt-caching",{"title":245,"url":246},"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],"Semantic caching for LLM apps: when reusing answers is safe, and when it is not","How semantic caches reuse LLM answers for similar questions, how they differ from prompt caching, and how to avoid wrong hits, leaks and stale answers.","Semantic caching for LLM apps: a hand hands in a ticket at a coat check and gets back a coat from the hooks.",{"slug":254,"published":151,"minutes":255,"category":8,"tags":256,"tagPages":259,"keywords":260,"about":271,"sources":281,"cover":290,"og":291,"expertise":80,"locales":292,"lang":82,"title":293,"description":294,"coverAlt":295},"embedding-quantization-binary-matryoshka",5,[10,13,257,258,11],"Quantization","pgvector",[16,17,8],[261,262,263,264,265,266,267,268,269,270],"embedding quantization","binary quantization embeddings","int8 embeddings","matryoshka embeddings","reduce vector database memory","pgvector halfvec","pgvector binary_quantize","embedding dimensions reduction","rescoring binary embeddings","vector index memory cost",[272,275,278],{"name":273,"url":274},"Quantization (signal 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quantization: binary, int8 and Matryoshka vectors without losing retrieval quality","How binary and int8 quantization and Matryoshka truncation shrink vector indexes 4 to 32 times, what they cost in recall, and how rescoring wins it back.","Embedding quantization and Matryoshka: nesting dolls stand in a row from large to small, the smallest on a scale.",{"slug":297,"published":298,"updated":6,"minutes":299,"category":8,"tags":300,"tagPages":304,"keywords":306,"about":317,"sources":327,"cover":363,"og":364,"expertise":80,"locales":365,"lang":82,"title":366,"description":367,"coverAlt":368},"code-rag-repository-indexing","2026-10-01",8,[11,301,302,10,303],"Code search","Tree-sitter","Coding agents",[8,16,305],"coding-agents",[307,308,309,310,311,312,313,314,315,316],"RAG over code","AST chunking","tree-sitter code chunking","repository map","code embeddings","semantic code search","codebase indexing","repo map tokens","incremental code index","CoIR benchmark",[318,319,322,324],{"name":110,"url":111},{"name":320,"url":321},"Abstract syntax tree","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FAbstract_syntax_tree",{"name":302,"url":323},"https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FTree-sitter_%28parser_generator%29",{"name":325,"url":326},"Merkle tree","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FMerkle_tree",[328,331,334,337,340,343,346,349,352,354,357,360],{"title":329,"url":330},"cAST: AST-based chunking for code RAG (arXiv 2506.15655)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2506.15655",{"title":332,"url":333},"Aider docs: repository map","https:\u002F\u002Faider.chat\u002Fdocs\u002Frepomap.html",{"title":335,"url":336},"Aider blog: building a better repository map with tree-sitter (2023)","https:\u002F\u002Faider.chat\u002F2023\u002F10\u002F22\u002Frepomap.html",{"title":338,"url":339},"Anthropic Engineering: effective context engineering for AI agents (Sep 2025)","https:\u002F\u002Fwww.anthropic.com\u002Fengineering\u002Feffective-context-engineering-for-ai-agents",{"title":341,"url":342},"Cursor blog: securely indexing large codebases (Jan 2026)","https:\u002F\u002Fcursor.com\u002Fblog\u002Fsecure-codebase-indexing",{"title":344,"url":345},"turbopuffer: Cursor customer story","https:\u002F\u002Fturbopuffer.com\u002Fcustomers\u002Fcursor",{"title":347,"url":348},"Kilo Code docs: codebase indexing","https:\u002F\u002Fkilo.ai\u002Fdocs\u002Fcustomize\u002Fcontext\u002Fcodebase-indexing",{"title":350,"url":351},"Serena on GitHub","https:\u002F\u002Fgithub.com\u002Foraios\u002Fserena",{"title":353,"url":62},"Voyage AI docs: pricing",{"title":355,"url":356},"Qwen blog: Qwen3-Embedding (Jun 2025)","https:\u002F\u002Fqwenlm.github.io\u002Fblog\u002Fqwen3-embedding\u002F",{"title":358,"url":359},"CoIR: a benchmark for code information retrieval (arXiv 2407.02883)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2407.02883",{"title":361,"url":362},"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 over code: AST chunking, repo maps and code embeddings that fit an agent","How coding agents find code: AST chunking with tree-sitter, repo maps, Merkle-tree index sync, code embedding models in 2026, and when grep beats an index.","RAG over code repositories: a branching code tree feeds a row of index cards.",{"slug":370,"published":371,"updated":6,"minutes":299,"category":8,"tags":372,"tagPages":376,"keywords":378,"about":389,"sources":395,"cover":429,"og":430,"expertise":80,"locales":431,"lang":82,"title":432,"description":433,"coverAlt":434},"embedding-fine-tuning-domain","2026-09-28",[10,373,11,374,375],"Fine-tuning","Evaluation","Sentence Transformers",[16,8,377],"evaluation",[379,380,381,382,383,384,385,386,387,388],"fine-tuning embedding models","domain adaptation embeddings","hard negative mining retrieval","MultipleNegativesRankingLoss","GISTEmbed vs MNRL","Matryoshka embedding dimensions","Qwen3-Embedding fine-tune","BGE-M3 vs EmbeddingGemma","embedding evaluation recall@k","catastrophic forgetting dense retrieval",[390,391,394],{"name":33,"url":34},{"name":392,"url":393},"Fine-tuning (deep 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2209.11755)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.11755",{"title":355,"url":356},{"title":416,"url":417},"BAAI\u002Fbge-m3 model card","https:\u002F\u002Fhuggingface.co\u002FBAAI\u002Fbge-m3",{"title":419,"url":420},"Google: EmbeddingGemma docs (Oct 2026)","https:\u002F\u002Fai.google.dev\u002Fgemma\u002Fdocs\u002Fembeddinggemma",{"title":422,"url":423},"Sentence Transformers: Matryoshka embeddings","https:\u002F\u002Fsbert.net\u002Fexamples\u002Fsentence_transformer\u002Ftraining\u002Fmatryoshka\u002FREADME.html",{"title":425,"url":44},"MMTEB (arXiv 2502.13595)",{"title":427,"url":428},"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],"Fine-tuning embedding models for your domain: hard negatives, losses and evals","Fine-tuning embedding models for your domain: when it beats a reranker, hard negatives, sentence-transformers v6 losses, Matryoshka, evals and cost.","Fine-tuning embedding models: loose dots on the left become tight clusters on the right after training.",{"slug":436,"published":437,"minutes":90,"category":8,"tags":438,"tagPages":441,"keywords":443,"about":451,"sources":458,"cover":486,"og":487,"expertise":80,"locales":488,"lang":82,"title":489,"description":490,"coverAlt":491},"chroma","2026-07-20",[13,10,11,439,440],"Hybrid search","Metadata filter",[17,16,8,442],"hybrid-search",[444,445,446,447,448,449,450],"chroma vector database","chroma vs qdrant","chroma vs pgvector","hybrid search reciprocal rank fusion","self-hosted vector database","chroma cloud pricing","metadata filtering vector search",[452,455],{"name":453,"url":454},"Chroma","https:\u002F\u002Fgithub.com\u002Fchroma-core\u002Fchroma",{"name":456,"url":457},"Apache License 2.0","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FApache_License",[459,462,465,468,471,474,477,480,483],{"title":460,"url":461},"Chroma documentation: introduction","https:\u002F\u002Fdocs.trychroma.com\u002Fdocs\u002Foverview\u002Fintroduction",{"title":463,"url":464},"Chroma architecture overview","https:\u002F\u002Fdocs.trychroma.com\u002Freference\u002Farchitecture\u002Foverview",{"title":466,"url":467},"Chroma: getting started with the SDK","https:\u002F\u002Fdocs.trychroma.com\u002Fdocs\u002Foverview\u002Fgetting-started",{"title":469,"url":470},"Chroma: query and get","https:\u002F\u002Fdocs.trychroma.com\u002Fdocs\u002Fquerying-collections\u002Fquery-and-get",{"title":472,"url":473},"Chroma: metadata filtering","https:\u002F\u002Fdocs.trychroma.com\u002Fdocs\u002Fquerying-collections\u002Fmetadata-filtering",{"title":475,"url":476},"Chroma Cloud: Search API overview","https:\u002F\u002Fdocs.trychroma.com\u002Fcloud\u002Fsearch-api\u002Foverview",{"title":478,"url":479},"Chroma Cloud: hybrid search with RRF","https:\u002F\u002Fdocs.trychroma.com\u002Fcloud\u002Fsearch-api\u002Fhybrid-search",{"title":481,"url":482},"Chroma Cloud: pricing","https:\u002F\u002Fwww.trychroma.com\u002Fpricing",{"title":484,"url":485},"Chroma changelog","https:\u002F\u002Fwww.trychroma.com\u002Fchangelog","\u002Fimages\u002Fblog\u002Fchroma\u002Fcover.webp","\u002Fimages\u002Fblog\u002Fchroma\u002Fog.jpg",[82,83,84],"Chroma: simple vector search with one catch","Chroma is an Apache-2.0 vector database for embedded, single-node or cloud use. Where it is pleasant, and where its best search is cloud-only.","Chroma vector search: a drawer cabinet sorts round marbles, with one cracked pebble on top.",{"slug":493,"published":494,"minutes":495,"category":8,"tags":496,"tagPages":498,"keywords":499,"about":508,"sources":514,"cover":539,"og":540,"expertise":80,"locales":541,"lang":82,"title":542,"description":543,"coverAlt":544},"pinecone","2026-07-09",11,[13,10,11,497,439],"Namespaces",[17,16,8,442],[500,501,502,503,504,505,506,507],"pinecone vector database","pinecone pricing","pinecone read units","pinecone namespaces","pinecone vs qdrant","pinecone vs pgvector","pinecone documents api","serverless vector database cost",[509,512,513],{"name":510,"url":511},"Vector database","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FVector_database",{"name":276,"url":277},{"name":113,"url":114},[515,518,521,524,527,530,533,536],{"title":516,"url":517},"Pinecone docs: Index data overview, indexes, namespaces and metadata","https:\u002F\u002Fdocs.pinecone.io\u002Fguides\u002Findex-data\u002Findexing-overview",{"title":519,"url":520},"Pinecone docs: Adopt the Documents API, API version 2026-07","https:\u002F\u002Fdocs.pinecone.io\u002Fguides\u002Findex-data\u002Fadopt-the-documents-api",{"title":522,"url":523},"Pinecone docs: Create an index, schemas, regions and metrics","https:\u002F\u002Fdocs.pinecone.io\u002Fguides\u002Findex-data\u002Fcreate-an-index",{"title":525,"url":526},"Pinecone docs: Understanding Pinecone cost, read units, write units and egress","https:\u002F\u002Fdocs.pinecone.io\u002Fguides\u002Forganizations\u002Fmanage-cost\u002Funderstanding-cost",{"title":528,"url":529},"Pinecone pricing: plans, limits and rates","https:\u002F\u002Fwww.pinecone.io\u002Fpricing\u002F",{"title":531,"url":532},"Pinecone docs: Semantic search","https:\u002F\u002Fdocs.pinecone.io\u002Fguides\u002Fsearch\u002Fsemantic-search",{"title":534,"url":535},"Pinecone docs: Local development with Pinecone Local","https:\u002F\u002Fdocs.pinecone.io\u002Fguides\u002Foperations\u002Flocal-development",{"title":537,"url":538},"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: a review of the managed vector database","Pinecone review: read units scale with namespace size, schemas are fixed at creation, and when self-hosting is the better buy.","Pinecone review: a pine cone on a cloud holds marbles between its scales, a key hanging below.",1793451237700]