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7:["$","ul",null,{"className":"list-disc list-inside space-y-2 text-muted mb-5","children":["\n",["$","li",null,{"className":"text-base leading-relaxed","children":"Embedding latency: ~100ms"}],"\n",["$","li",null,{"className":"text-base leading-relaxed","children":"Vector search: ~150ms"}],"\n",["$","li",null,{"className":"text-base leading-relaxed","children":"LLM generation: ~1500ms"}],"\n",["$","li",null,{"className":"text-base leading-relaxed","children":"Total: ~2000ms"}],"\n",["$","li",null,{"className":"text-base leading-relaxed","children":"Retrieval relevance (manual eval): 78%"}],"\n"]}]
8:["$","p",null,{"className":"text-base leading-relaxed text-muted mb-5","children":"The LLM generation time is mostly out of our control (model provider latency), so we focused on everything else — and discovered that better retrieval actually reduces generation time too, because the LLM gets more relevant context and generates faster, more focused answers."}]
9:["$","h2",null,{"className":"font-[family-name:var(--font-outfit)] text-2xl font-semibold tracking-tight text-foreground mt-10 mb-3","children":"Optimization 1: Chunking Strategy (78% → 84% relevance)"}]
a:["$","p",null,{"className":"text-base leading-relaxed text-muted mb-5","children":"Our first mistake was using a naive text splitter with fixed chunk sizes. Medical literature has structure — headings, sections, bullet points, references — and splitting mid-paragraph destroys context."}]
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f:["$","p",null,{"className":"text-base leading-relaxed text-muted mb-5","children":["$","strong",null,{"className":"font-semibold text-foreground","children":"Key changes:"}]}]
10:["$","ul",null,{"className":"list-disc list-inside space-y-2 text-muted mb-5","children":["\n",["$","li",null,{"className":"text-base leading-relaxed","children":"Chunk size: 1000 → 512 tokens (smaller chunks = more precise retrieval)"}],"\n",["$","li",null,{"className":"text-base leading-relaxed","children":"Overlap: character-based → sentence-based (2 sentences overlap)"}],"\n",["$","li",null,{"className":"text-base leading-relaxed","children":"Structure-aware: respect section boundaries"}],"\n",["$","li",null,{"className":"text-base leading-relaxed","children":"Prepend section header to each chunk (preserves context)"}],"\n"]}]
11:["$","p",null,{"className":"text-base leading-relaxed text-muted mb-5","children":[["$","strong",null,{"className":"font-semibold text-foreground","children":"Impact:"}]," Relevance jumped from 78% to 84%. Latency unchanged."]}]
12:["$","h2",null,{"className":"font-[family-name:var(--font-outfit)] text-2xl font-semibold tracking-tight text-foreground mt-10 mb-3","children":"Optimization 2: Hybrid Search (84% → 90% relevance)"}]
13:["$","p",null,{"className":"text-base leading-relaxed text-muted mb-5","children":"Pure vector similarity misses exact medical terms. If a doctor searches for \"metformin 500mg side effects,\" pure semantic search might return results about diabetes medications generally. We need exact keyword matching too."}]
14:["$","figure",null,{"data-rehype-pretty-code-figure":"","children":["$","pre",null,{"className":"overflow-x-auto rounded-xl border border-[var(--card-border)] bg-[var(--surface)] p-4 text-sm mb-5","style":{"backgroundColor":"#22272e","color":"#adbac7"},"tabIndex":"0","data-language":"python","data-theme":"github-dark-dimmed","children":["$","code",null,{"className":"rounded bg-[var(--card-bg)] px-1.5 py-0.5 text-sm font-mono text-accent","data-language":"python","data-theme":"github-dark-dimmed","style":{"display":"grid"},"children":[["$","span",null,{"data-line":"","children":[["$","span",null,{"style":{"color":"#F47067"},"children":"from"}],["$","span",null,{"style":{"color":"#ADBAC7"},"children":" qdrant_client.models "}],["$","span",null,{"style":{"color":"#F47067"},"children":"import"}],["$","span",null,{"style":{"color":"#ADBAC7"},"children":" SparseVector"}]]}],"\n",["$","span",null,{"data-line":"","children":" "}],"\n",["$","span",null,{"data-line":"","children":[["$","span",null,{"style":{"color":"#F47067"},"children":"class"}],["$","span",null,{"style":{"color":"#F69D50"},"children":" HybridSearcher"}],["$","span",null,{"style":{"color":"#ADBAC7"},"children":":"}]]}],"\n",["$","span",null,{"data-line":"","children":[["$","span",null,{"style":{"color":"#F47067"},"children":"    def"}],["$","span",null,{"style":{"color":"#DCBDFB"},"children":" search"}],["$","span",null,{"style":{"color":"#ADBAC7"},"children":"(self, query: "}],["$","span",null,{"style":{"color":"#6CB6FF"},"children":"str"}],["$","span",null,{"style":{"color":"#ADBAC7"},"children":", limit: "}],["$","span",null,{"style":{"color":"#6CB6FF"},"children":"int"}],["$","span",null,{"style":{"color":"#F47067"},"children":" ="}],["$","span",null,{"style":{"color":"#6CB6FF"},"children":" 10"}],["$","span",null,{"style":{"color":"#ADBAC7"},"children":") -> list[SearchResult]:"}]]}],"\n",["$","span",null,{"data-line":"","children":["$","span",null,{"style":{"color":"#768390"},"children":"        # 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15:["$","p",null,{"className":"text-base leading-relaxed text-muted mb-5","children":[["$","strong",null,{"className":"font-semibold text-foreground","children":"Why RRF over weighted scoring:"}]," Reciprocal Rank Fusion doesn't require tuning weights between dense and sparse scores. It's robust and works well out of the box."]}]
16:["$","p",null,{"className":"text-base leading-relaxed text-muted mb-5","children":[["$","strong",null,{"className":"font-semibold text-foreground","children":"Impact:"}]," Relevance improved from 84% to 90%. Search latency increased slightly (~20ms) due to dual search, but the quality improvement was worth it."]}]
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