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#reranking — Public Fediverse posts

Live and recent posts from across the Fediverse tagged #reranking, aggregated by home.social.

  1. Rost Glukhov @[email protected] ·

    Step-by-step RAG tutorial: build retrieval-augmented generation systems with vector databases, hybrid search, reranking, and web search. Architecture, implementation, and production best practices.

    #AI #LLM #RAG #Embeddings #Reranking #Vector Database

    glukhov.org/rag/

    #ai #llm #rag #embeddings #reranking #vector
  2. Rost Glukhov @[email protected] ·

    Step-by-step RAG tutorial: build retrieval-augmented generation systems with vector databases, hybrid search, reranking, and web search. Architecture, implementation, and production best practices.

    #AI #LLM #RAG #Embeddings #Reranking #Vector Database

    glukhov.org/rag/

    #ai #llm #rag #embeddings #reranking #vector
  3. Rost Glukhov @[email protected] ·

    Step-by-step RAG tutorial: build retrieval-augmented generation systems with vector databases, hybrid search, reranking, and web search. Architecture, implementation, and production best practices.

    #AI #LLM #RAG #Embeddings #Reranking #Vector Database

    glukhov.org/rag/

    #ai #llm #rag #embeddings #reranking #vector
  4. Rost Glukhov @[email protected] ·

    Step-by-step RAG tutorial: build retrieval-augmented generation systems with vector databases, hybrid search, reranking, and web search. Architecture, implementation, and production best practices.

    #AI #LLM #RAG #Embeddings #Reranking #Vector Database

    glukhov.org/rag/

    #vector #reranking #embeddings #rag #llm #ai
  5. Rost Glukhov @ros ·

    Step-by-step RAG tutorial: build retrieval-augmented generation systems with vector databases, hybrid search, reranking, and web search. Architecture, implementation, and production best practices.

    Database

    glukhov.org/rag/

    #ai #llm #rag #embeddings #reranking #vector
  6. Habr @[email protected] ·

    От 0.034 до 0.791 и обратно: Legal RAG, 17 итераций и стена масштабирования

    Я участвовал в ARLC 2026 — юридическом AI-челлендже по построению RAG-пайплайна поверх корпуса судебных решений и законов. Соло, с Claude Code в качестве напарника. За 5 дней и 17 итераций прошёл путь от 0.034 до 0.791 на warmup — а потом вышел в финал и потерял 42% на 300 документах вместо 30. Внутри — архитектура, код, математика F-beta, три провала и честный разбор работы с AI-ассистентом.

    habr.com/ru/articles/1014758/

    #RAG #retrieval_augmented_generation #legal_AI #Claude #grounding #BM25 #reranking #NLP #соревнование

    #соревнование #nlp #reranking #bm25 #grounding #claude
  7. Habr @[email protected] ·

    От 0.034 до 0.791 и обратно: Legal RAG, 17 итераций и стена масштабирования

    Я участвовал в ARLC 2026 — юридическом AI-челлендже по построению RAG-пайплайна поверх корпуса судебных решений и законов. Соло, с Claude Code в качестве напарника. За 5 дней и 17 итераций прошёл путь от 0.034 до 0.791 на warmup — а потом вышел в финал и потерял 42% на 300 документах вместо 30. Внутри — архитектура, код, математика F-beta, три провала и честный разбор работы с AI-ассистентом.

    habr.com/ru/articles/1014758/

    #RAG #retrieval_augmented_generation #legal_AI #Claude #grounding #BM25 #reranking #NLP #соревнование

    #соревнование #nlp #reranking #bm25 #grounding #claude
  8. Habr @[email protected] ·

    От 0.034 до 0.791 и обратно: Legal RAG, 17 итераций и стена масштабирования

    Я участвовал в ARLC 2026 — юридическом AI-челлендже по построению RAG-пайплайна поверх корпуса судебных решений и законов. Соло, с Claude Code в качестве напарника. За 5 дней и 17 итераций прошёл путь от 0.034 до 0.791 на warmup — а потом вышел в финал и потерял 42% на 300 документах вместо 30. Внутри — архитектура, код, математика F-beta, три провала и честный разбор работы с AI-ассистентом.

    habr.com/ru/articles/1014758/

    #RAG #retrieval_augmented_generation #legal_AI #Claude #grounding #BM25 #reranking #NLP #соревнование

    #соревнование #nlp #reranking #bm25 #grounding #claude
  9. Habr @[email protected] ·

    От 0.034 до 0.791 и обратно: Legal RAG, 17 итераций и стена масштабирования

    Я участвовал в ARLC 2026 — юридическом AI-челлендже по построению RAG-пайплайна поверх корпуса судебных решений и законов. Соло, с Claude Code в качестве напарника. За 5 дней и 17 итераций прошёл путь от 0.034 до 0.791 на warmup — а потом вышел в финал и потерял 42% на 300 документах вместо 30. Внутри — архитектура, код, математика F-beta, три провала и честный разбор работы с AI-ассистентом.

    habr.com/ru/articles/1014758/

    #RAG #retrieval_augmented_generation #legal_AI #Claude #grounding #BM25 #reranking #NLP #соревнование

    #rag #retrieval_augmented_generation #legal_ai #claude #grounding #bm25
  10. Rost Glukhov @[email protected] ·

    Step-by-step RAG tutorial: build retrieval-augmented generation systems with vector databases, hybrid search, reranking, and web search. Architecture, implementation, and production best practices.

    #AI #LLM #RAG #Embeddings #Reranking #Vector Database #Fine-Tuning

    glukhov.org/rag/

    #ai #llm #rag #embeddings #reranking #vector
  11. Rost Glukhov @[email protected] ·

    Step-by-step RAG tutorial: build retrieval-augmented generation systems with vector databases, hybrid search, reranking, and web search. Architecture, implementation, and production best practices.

    #AI #LLM #RAG #Embeddings #Reranking #Vector Database #Fine-Tuning

    glukhov.org/rag/

    #ai #llm #rag #embeddings #reranking #vector
  12. Rost Glukhov @[email protected] ·

    Step-by-step RAG tutorial: build retrieval-augmented generation systems with vector databases, hybrid search, reranking, and web search. Architecture, implementation, and production best practices.

    #AI #LLM #RAG #Embeddings #Reranking #Vector Database #Fine-Tuning

    glukhov.org/rag/

    #ai #llm #rag #embeddings #reranking #vector
  13. Rost Glukhov @[email protected] ·

    Step-by-step RAG tutorial: build retrieval-augmented generation systems with vector databases, hybrid search, reranking, and web search. Architecture, implementation, and production best practices.

    #AI #LLM #RAG #Embeddings #Reranking #Vector Database #Fine-Tuning

    glukhov.org/rag/

    #fine #vector #reranking #embeddings #rag #llm
  14. Rost Glukhov @ros ·

    Step-by-step RAG tutorial: build retrieval-augmented generation systems with vector databases, hybrid search, reranking, and web search. Architecture, implementation, and production best practices.

    Database -Tuning

    glukhov.org/rag/

    #ai #llm #rag #embeddings #reranking #vector
  15. Rost Glukhov @[email protected] ·

    Retrieval-Augmented Generation (RAG) Tutorial: Architecture, Implementation, and Production Guide:
    glukhov.org/rag/
    #AI #LLM #RAG #Embeddings #Reranking #VectorDatabase

    #ai #llm #rag #embeddings #reranking #vectordatabase
  16. Rost Glukhov @[email protected] ·

    Retrieval-Augmented Generation (RAG) Tutorial: Architecture, Implementation, and Production Guide:
    glukhov.org/rag/
    #AI #LLM #RAG #Embeddings #Reranking #VectorDatabase

    #ai #llm #rag #embeddings #reranking #vectordatabase
  17. Rost Glukhov @[email protected] ·

    Retrieval-Augmented Generation (RAG) Tutorial: Architecture, Implementation, and Production Guide:
    glukhov.org/rag/
    #AI #LLM #RAG #Embeddings #Reranking #VectorDatabase

    #ai #llm #rag #embeddings #reranking #vectordatabase
  18. Rost Glukhov @[email protected] ·

    Retrieval-Augmented Generation (RAG) Tutorial: Architecture, Implementation, and Production Guide:
    glukhov.org/rag/
    #AI #LLM #RAG #Embeddings #Reranking #VectorDatabase

    #vectordatabase #reranking #embeddings #rag #llm #ai
  19. Rost Glukhov @ros ·

    Retrieval-Augmented Generation (RAG) Tutorial: Architecture, Implementation, and Production Guide:
    glukhov.org/rag/

    #ai #llm #rag #embeddings #reranking #vectordatabase
  20. Reddit Tech VN Bot @[email protected] ·

    Tìm kiếm thuật toán tương tự chuỗi tốt nhất cho RAG mà không cần mô hình. Các lựa chọn gồm Levenshtein, Jaccard, Soundex... #RAG #ThuậtToánTươngTự #NonModelBased #TìmKiếm #StringSimilarity # Algorithm #TươngTựChuỗi #Reranking

    reddit.com/r/LocalLLaMA/commen

    #rag #thuậttoantươngtự #nonmodelbased #timkiếm #stringsimilarity #tươngtựchuỗi
  21. Rost Glukhov @[email protected] ·

    Reranking text documents with Ollama and Qwen3 Reranker model - in Go:
    glukhov.org/post/2025/06/reran
    #go #ollama #rag #reranking #qwen3 #llm #ai

    #go #ollama #rag #reranking #qwen3 #llm
  22. Rost Glukhov @[email protected] ·

    Reranking text documents with Ollama and Qwen3 Reranker model - in Go:
    glukhov.org/post/2025/06/reran
    #go #ollama #rag #reranking #qwen3 #llm #ai

    #go #ollama #rag #reranking #qwen3 #llm
  23. Rost Glukhov @[email protected] ·

    Reranking text documents with Ollama and Qwen3 Reranker model - in Go:
    glukhov.org/post/2025/06/reran
    #go #ollama #rag #reranking #qwen3 #llm #ai

    #go #ollama #rag #reranking #qwen3 #llm
  24. Rost Glukhov @[email protected] ·

    Reranking text documents with Ollama and Qwen3 Reranker model - in Go:
    glukhov.org/post/2025/06/reran
    #go #ollama #rag #reranking #qwen3 #llm #ai

    #ai #llm #qwen3 #reranking #rag #ollama
  25. Rost Glukhov @ros ·

    Reranking text documents with Ollama and Qwen3 Reranker model - in Go:
    glukhov.org/post/2025/06/reran

    #go #ollama #rag #reranking #qwen3 #llm
  26. Rost Glukhov @[email protected] ·

    Reranking text documents with Ollama and Qwen3 Embedding model - in Golang:
    glukhov.org/post/2025/06/reran
    #ollama #embedding #reranking #golang #ai #llm

    #ollama #embedding #reranking #golang #ai #llm
  27. Rost Glukhov @[email protected] ·

    Reranking text documents with Ollama and Qwen3 Embedding model - in Golang:
    glukhov.org/post/2025/06/reran
    #ollama #embedding #reranking #golang #ai #llm

    #ollama #embedding #reranking #golang #ai #llm
  28. Rost Glukhov @[email protected] ·

    Reranking text documents with Ollama and Qwen3 Embedding model - in Golang:
    glukhov.org/post/2025/06/reran
    #ollama #embedding #reranking #golang #ai #llm

    #ollama #embedding #reranking #golang #ai #llm
  29. Rost Glukhov @[email protected] ·

    Reranking text documents with Ollama and Qwen3 Embedding model - in Golang:
    glukhov.org/post/2025/06/reran
    #ollama #embedding #reranking #golang #ai #llm

    #llm #ai #golang #reranking #embedding #ollama
  30. Rost Glukhov @ros ·

    Reranking text documents with Ollama and Qwen3 Embedding model - in Golang:
    glukhov.org/post/2025/06/reran

    #ollama #embedding #reranking #golang #ai #llm
  31. Hacker News @[email protected] ·

    You Don't Need Re-Ranking: Understanding the Superlinked Vector Layer

    superlinked.com/vectorhub/arti

    #HackerNews #You #Need #Re-Ranking #Understanding #the #Superlinked #Vector #Layer #Superlinked #Vector #Layer #ReRanking #AI #Technology #Vector #Hub

    #hackernews #you #need #re #understanding #the
  32. Hacker News @[email protected] ·

    You Don't Need Re-Ranking: Understanding the Superlinked Vector Layer

    superlinked.com/vectorhub/arti

    #HackerNews #You #Need #Re-Ranking #Understanding #the #Superlinked #Vector #Layer #Superlinked #Vector #Layer #ReRanking #AI #Technology #Vector #Hub

    #hackernews #you #need #re #understanding #the
  33. Hacker News @[email protected] ·

    You Don't Need Re-Ranking: Understanding the Superlinked Vector Layer

    superlinked.com/vectorhub/arti

    #HackerNews #You #Need #Re-Ranking #Understanding #the #Superlinked #Vector #Layer #Superlinked #Vector #Layer #ReRanking #AI #Technology #Vector #Hub

    #hackernews #you #need #re #understanding #the
  34. Hacker News @[email protected] ·

    You Don't Need Re-Ranking: Understanding the Superlinked Vector Layer

    superlinked.com/vectorhub/arti

    #HackerNews #You #Need #Re-Ranking #Understanding #the #Superlinked #Vector #Layer #Superlinked #Vector #Layer #ReRanking #AI #Technology #Vector #Hub

    #hub #technology #ai #reranking #layer #vector
  35. Hacker News @[email protected] ·

    You Don't Need Re-Ranking: Understanding the Superlinked Vector Layer

    superlinked.com/vectorhub/arti

    #HackerNews #You #Need #Re-Ranking #Understanding #the #Superlinked #Vector #Layer #Superlinked #Vector #Layer #ReRanking #AI #Technology #Vector #Hub

    #hackernews #you #need #re #understanding #the
  36. michabbb @[email protected] ·

    Jina Al just released Jina ColBERT v2, a Multilingual Late Interaction Retriever for #Embedding and #Reranking. The new model supports 89 languages with superior retrieval performance, user-controlled output dimensions, and 8192 token-length.

    jina.ai/news/jina-colbert-v2-m

    #ai #llm

    #embedding #reranking #ai #llm
  37. michabbb @[email protected] ·

    Jina Al just released Jina ColBERT v2, a Multilingual Late Interaction Retriever for #Embedding and #Reranking. The new model supports 89 languages with superior retrieval performance, user-controlled output dimensions, and 8192 token-length.

    jina.ai/news/jina-colbert-v2-m

    #ai #llm

    #embedding #reranking #ai #llm
  38. michabbb @[email protected] ·

    Jina Al just released Jina ColBERT v2, a Multilingual Late Interaction Retriever for #Embedding and #Reranking. The new model supports 89 languages with superior retrieval performance, user-controlled output dimensions, and 8192 token-length.

    jina.ai/news/jina-colbert-v2-m

    #ai #llm

    #embedding #reranking #ai #llm
  39. michabbb @[email protected] ·

    Jina Al just released Jina ColBERT v2, a Multilingual Late Interaction Retriever for #Embedding and #Reranking. The new model supports 89 languages with superior retrieval performance, user-controlled output dimensions, and 8192 token-length.

    jina.ai/news/jina-colbert-v2-m

    #ai #llm

    #llm #ai #reranking #embedding
  40. michabbb @[email protected] ·

    Jina Al just released Jina ColBERT v2, a Multilingual Late Interaction Retriever for #Embedding and #Reranking. The new model supports 89 languages with superior retrieval performance, user-controlled output dimensions, and 8192 token-length.

    jina.ai/news/jina-colbert-v2-m

    #ai #llm

    #embedding #reranking #ai #llm
  41. Published papers at TMLR @[email protected] ·

    Boosting Search Engines with Interactive Agents

    Leonard Adolphs, Benjamin Börschinger, Christian Buck et al.

    openreview.net/forum?id=0ZbPmm

    #retrieval #search #reranking

    #retrieval #search #reranking
  42. Published papers at TMLR @[email protected] ·

    Boosting Search Engines with Interactive Agents

    Leonard Adolphs, Benjamin Börschinger, Christian Buck et al.

    openreview.net/forum?id=0ZbPmm

    #retrieval #search #reranking

    #retrieval #search #reranking
  43. Published papers at TMLR @[email protected] ·

    Boosting Search Engines with Interactive Agents

    Leonard Adolphs, Benjamin Börschinger, Christian Buck et al.

    openreview.net/forum?id=0ZbPmm

    #retrieval #search #reranking

    #retrieval #search #reranking
  44. Published papers at TMLR @[email protected] ·

    Boosting Search Engines with Interactive Agents

    Leonard Adolphs, Benjamin Börschinger, Christian Buck et al.

    openreview.net/forum?id=0ZbPmm

    #retrieval #search #reranking

    #reranking #search #retrieval
  45. Published papers at TMLR @[email protected] ·

    Boosting Search Engines with Interactive Agents

    Leonard Adolphs, Benjamin Börschinger, Christian Buck et al.

    openreview.net/forum?id=0ZbPmm

    #retrieval #search #reranking

    #retrieval #search #reranking