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    "text": "AI Research @Redis explores and prototypes retrieval and language technologies that can be shipped into Redis products and used by developers."
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    "title": "About",
    "section": "Focus",
    "text": "Focus\n\nSearch and retrieval (BM25, dense retrieval, hybrid retrieval, reranking)\nRAG systems and end-to-end evaluation (offline + online)\nEmbeddings, vector search, and indexing tradeoffs\nAgentic workflows and tool use (reliability, safety, and observability)\nApplied LLM systems (latency, cost, caching, and deployment)"
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    "section": "Team",
    "text": "Team\nUpdate this section with the current roster.\n\nName 1 - Role\nName 2 - Role\nName 3 - Role"
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    "text": "Links\n\nHugging Face Space hub: https://huggingface.co/spaces/srijithrajamohan/ai-research-redis"
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    "text": "Write here."
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    "title": "Hello",
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    "text": "Links\n\nSpace HTML: this page\nHub page: https://huggingface.co/spaces/srijithrajamohan/ai-research-redis\nMedium: fill medium_url after publishing"
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    "title": "Hybrid Search Retrieval: Combining Semantic Search and BM25 Algorithm with Examples",
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    "text": "The examples below illustrate a hybrid search retrieval of a query from a corpus of documents by combining semantic search using ModernBERT embeddings and the BM25 algorithm.\nThe scores from each are normalized between 0 and 1 in order to create a weighted average hybrid score. The raw embedding score is also shown in the columns for diagnostics. The ranks in each measure are then combined using RRF to create another score that can be used for retrieval.\nThe most correct match for each query is highlighted in each example."
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    "title": "Hybrid Search Retrieval: Combining Semantic Search and BM25 Algorithm with Examples",
    "section": "Example 1",
    "text": "Example 1\n\n\n\nHybrid retrieval example 1 (BM25 vs embedding vs hybrid)."
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    "title": "Hybrid Search Retrieval: Combining Semantic Search and BM25 Algorithm with Examples",
    "section": "Example 2",
    "text": "Example 2\n\n\n\nHybrid retrieval example 2 (BM25 vs embedding vs hybrid)."
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    "title": "Hybrid Search Retrieval: Combining Semantic Search and BM25 Algorithm with Examples",
    "section": "Failure mode 1 - Negations/directionality",
    "text": "Failure mode 1 - Negations/directionality\nBM25 is not keyword search; it up-weights rare terms and not just keywords.\nIn example 1, the key term of interest or the attribute/identifier is #124. However, the other terms in the query semantic, search, for, doc are all relatively rare and contribute to higher BM25 scores as evidenced in the first two records in the table index 8 and 11. These have negated intents however and therefore incorrect matches. BM25 has no ability to identify negations.\nThe similarity scores fortunately are not the highest but will most likely exceed most commonly used thresholds. Semantic search can fail to identify negated intent."
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    "title": "Hybrid Search Retrieval: Combining Semantic Search and BM25 Algorithm with Examples",
    "section": "Failure mode 2 - Attributes/Identifiers",
    "text": "Failure mode 2 - Attributes/Identifiers\n“The next three docs given by ids 5, 7 and 6 have the same phrasing but with the wrong identifier of #123, #1243 and #1233 respectively which lowers their BM25 score a bit but certainly higher than all the other documents. BM25 can detect a different attribute/identifier but the actual magnitude of this will depend on what the rest of the sentence looks like.”\n\nIf there is strong lexical overlap but only the identifier is different, there will be a small drop in the BM25 score.\nIf the rest of the sentence is NULL then the BM25 score is 0.\nIf there is very little overlap in the rest of the query and the doc.\n\nBM25 can identify attribute/identifier changes but is hard to put a threshold on this score.\nW.r.t embeddings two things are notable:\n\nThere are two forces at play here: the global semantic intent match and the identifier match.\nIn 8 and 11, there is negated intent whereas there is an identifier match.\nIn 5, 6 and 7 the general intent is correct but the identifier is incorrect.\nThe resulting score is a complex interaction of the two and the answer to “Can we identify negations or attribute changes with a threshold?” is heavily data-dependent.\n\nNote that the embedding scores for 5, 7 and 6 are some of the highest because attribute/identifier differences are something that embeddings struggle to detect."
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    "title": "Hybrid Search Retrieval: Combining Semantic Search and BM25 Algorithm with Examples",
    "section": "How does the hybrid score and RRF fare?",
    "text": "How does the hybrid score and RRF fare?\nAveraging out with a weighted hybrid score or RRF has no impact here. In example 1, record id 9 which is the closest match still does not rank highly in either of these scenarios.\nTo summarize the above for both approaches for the failure modes, it seems that embeddings seem to have the slight edge here.\n\n\n\nMethod\nNegation\nDirectionality\nAttribute/Identifier\n\n\n\n\nBM25\nN\nN\nN (hard to put a threshold)\n\n\nEmbedding\nSometimes\nN\nSometimes\n\n\n\nWe can’t eliminate all the issues, but we can hope to mitigate based on our understanding of the data."
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    "title": "Hybrid Search Retrieval: Combining Semantic Search and BM25 Algorithm with Examples",
    "section": "But what did we want to achieve with BM25?",
    "text": "But what did we want to achieve with BM25?\nWe are really looking to filter out matches that do not have the right attributes/identifiers we seek. BM25 looks like it could provide that, but does more than attribute matching.\n\nExtract identifiers from query.\nPerform embedding based retrieval of query + docs to retrieve set M.\nFilter out docs where identifiers do not match in set M.\nUnfortunately, this does nothing to address the negation/directionality limitation."
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    "text": "Hybrid Search Retrieval: Combining Semantic Search and BM25 Algorithm with Examples\n\n\nHow to combine ModernBERT embedding similarity with BM25, plus failure modes (negation and identifiers).\n\n\n\n\n\nMar 18, 2026\n\n\nRedis AI Research\n\n\n\n\n\n\n\nEquations in Quarto (LaTeX Math)\n\n\nInline math, display math, aligned equations, and a few practical tips.\n\n\n\n\n\nMar 18, 2026\n\n\nRedis AI Research\n\n\n\n\n\n\n\nHello\n\n\nFirst Quarto post (template).\n\n\n\n\n\nMar 18, 2026\n\n\nRedis AI Research\n\n\n\n\n\nNo matching items"
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    "title": "Equations in Quarto (LaTeX Math)",
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    "text": "This post is a quick reference for writing LaTeX math in Quarto."
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    "title": "Equations in Quarto (LaTeX Math)",
    "section": "Inline math",
    "text": "Inline math\nUse single dollar signs for inline equations: \\(E = mc^2\\).\nAnother example: the normalized score is \\(\\hat{s} = \\frac{s - \\min(s)}{\\max(s) - \\min(s)}\\)."
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    "title": "Equations in Quarto (LaTeX Math)",
    "section": "Display math",
    "text": "Display math\nUse double dollar signs for centered display equations:\n\\[\n\\mathrm{score}(q, d) = \\alpha \\cdot \\mathrm{BM25}(q,d) + (1-\\alpha)\\cdot \\mathrm{sim}(q,d)\n\\]"
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    "section": "Multi-line and aligned equations",
    "text": "Multi-line and aligned equations\nUse an aligned block inside display math:\n\\[\n\\begin{aligned}\n\\mathrm{RRF}(d) &= \\sum_{m \\in M} \\frac{1}{k + \\mathrm{rank}_m(d)} \\\\\n\\hat{s}(d) &= \\frac{s(d) - \\min(s)}{\\max(s) - \\min(s)}\n\\end{aligned}\n\\]"
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    "title": "Equations in Quarto (LaTeX Math)",
    "section": "Common symbols",
    "text": "Common symbols\n\nGreek letters: \\(\\alpha, \\beta, \\gamma, \\lambda\\)\nVectors: \\(\\mathbf{v}\\), norms: \\(\\lVert \\mathbf{v} \\rVert_2\\)\nSets: \\(\\{x \\mid x > 0\\}\\)"
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    "title": "Equations in Quarto (LaTeX Math)",
    "section": "Tips",
    "text": "Tips\n\nInline: $...$ and display: $$...$$\nUse \\\\ for line breaks inside aligned\nIf math ever renders as raw text, confirm you are rendering HTML and that MathJax/KaTeX is enabled."
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    "title": "AI Research @Redis",
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    "text": "Homepage of the AI Research team at Redis.\n\n\n\n\n\n\n\n\n\nHybrid Search Retrieval: Combining Semantic Search and BM25 Algorithm with Examples\n\n\nHow to combine ModernBERT embedding similarity with BM25, plus failure modes (negation and identifiers).\n\n\n\n\n\nMar 18, 2026\n\n\nRedis AI Research\n\n\n\n\n\n\n\nEquations in Quarto (LaTeX Math)\n\n\nInline math, display math, aligned equations, and a few practical tips.\n\n\n\n\n\nMar 18, 2026\n\n\nRedis AI Research\n\n\n\n\n\n\n\nHello\n\n\nFirst Quarto post (template).\n\n\n\n\n\nMar 18, 2026\n\n\nRedis AI Research\n\n\n\n\n\nNo matching items"
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