ai-agent-book 精选快照（<2MB 代码与文档，来自 github.com/bojieli/ai-agent-book）
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---
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theme: seriph
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title: "Lesson 12 — Why Is One Retrieval Index Never Enough?"
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info: "English video course for AI Agents in Depth"
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author: Bojie Li
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transition: slide-left
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mdc: true
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lineNumbers: false
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monaco: false
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aspectRatio: 16/9
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canvasWidth: 980
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layout: cover
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class: cover
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---
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<div class="course-kicker">Build · Chapter 3 · Memory and Knowledge</div>
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# Why Is One Retrieval Index Never Enough?
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<p class="course-subtitle">Hybrid search, reranking, multimodality, and structured knowledge</p>
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<div class="course-cover-meta">Lesson 12 of 42 · 18 minutes · Hybrid Retrieval; Multimodal Extraction; Structured Indexing; Filesystem Paradigm</div>
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<!-- Presenter cue: Open with the concrete problem. Add personal context in your own words; do not read the slide. -->
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---
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# Why this problem matters
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<div class="grid grid-cols-3 gap-5 mt-6">
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<div class="course-card blue">
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<h3>Candidate fusion</h3>
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<p>Merge dense and sparse result sets.</p>
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</div>
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<div class="course-card green">
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<h3>Reranking</h3>
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<p>Use a stronger model only on a small candidate pool.</p>
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</div>
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<div class="course-card orange">
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<h3>Knowledge shape</h3>
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<p>Trees, graphs, files, images, and tables preserve different structure.</p>
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</div>
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</div>
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<!-- Presenter cue: Connect each card to a product or experiment consequence. -->
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---
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# Three ideas to keep in view
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<div class="grid grid-cols-3 gap-5 mt-6">
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<div class="course-card purple">
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<h3>Hybrid retrieval</h3>
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<p>Broad recall from multiple retrievers</p>
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</div>
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<div class="course-card blue">
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<h3>Neural reranker</h3>
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<p>More precise ordering at higher per-item cost</p>
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</div>
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<div class="course-card green">
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<h3>Structured index</h3>
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<p>Represent hierarchy or relationships explicitly</p>
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</div>
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</div>
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<!-- Presenter cue: Define unfamiliar terms in plain language; the audience is new to ML training and RL. -->
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---
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# The book's visual model
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<img class="course-figure" src="/images/fig3-9.svg" alt="Hybrid retrieval and reranking pipeline">
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<div class="course-caption">Hybrid retrieval and reranking pipeline</div>
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<!-- Presenter cue: Trace the diagram in one direction and name the mechanism that matters for this lesson. -->
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---
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# Flat chunks vs. Structured knowledge
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<div class="grid grid-cols-2 gap-6 mt-5">
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<div class="course-card orange"><h3>Flat chunks</h3><ul><li>Simple ingestion</li><li>Local passage questions</li><li>Weak global structure</li></ul></div>
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<div class="course-card green"><h3>Structured knowledge</h3><ul><li>Hierarchies and graphs</li><li>Multi-hop questions</li><li>More governance cost</li></ul></div>
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</div>
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<div class="course-caption course-caption-strong">Choose an index for the questions—not for fashion.</div>
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<!-- Presenter cue: Explain the trade-off; avoid presenting the right column as universally superior. -->
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---
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# Fuse ranks before reranking
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~~~python
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dense = dense_index.search(query, k=20)
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sparse = bm25.search(query, k=20)
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pool = reciprocal_rank_fusion(dense, sparse)
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answer_context = reranker.top(query, pool, k=5)
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~~~
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<!-- Presenter cue: Walk through the executable idea line by line; keep implementation details for the terminal. -->
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---
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# Test the claim
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<div class="grid grid-cols-2 gap-4 mt-5">
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<div class="course-card blue">
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<div class="course-demo-head"><span>3-6</span><span>3 min</span></div>
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<h3>Expose every retrieval stage</h3>
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<p><strong>Observe:</strong> Dense candidates, sparse candidates, fusion, reranking, and final rank</p>
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</div>
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<div class="course-card blue">
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<div class="course-demo-head"><span>3-8</span><span>2 min</span></div>
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<h3>Compare RAPTOR and GraphRAG</h3>
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<p><strong>Observe:</strong> Questions favored by hierarchical summaries versus relationship graphs</p>
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</div>
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</div>
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<div class="course-caption course-caption-strong">Demo budget: 5 minutes · one contiguous terminal block</div>
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<!-- Presenter cue: State the prediction before running anything. Name the observation that could disconfirm it. -->
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---
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class: course-terminal
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---
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<div class="course-kicker">Live demo</div>
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# Switching to the terminal
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~~~bash
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$ uv run python chapter3/retrieval-pipeline/evaluate.py --query "XR-7003"
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$ uv run python chapter3/structured-index/main.py demo
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~~~
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<div class="course-terminal-watch">Run the command(s), narrate decisions, and point to the observation—not just the output.</div>
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<!-- Presenter cue: Switch windows now. Return to the next slide after every listed experiment is complete. -->
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---
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# What the evidence supports
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<div class="grid grid-cols-3 gap-5 mt-6">
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<div class="course-card green">
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<h3>Finding 1</h3>
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<p>Hybrid retrieval improves recall because its component failures differ.</p>
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</div>
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<div class="course-card blue">
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<h3>Finding 2</h3>
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<p>Reranking spends expensive reasoning on a small, diverse pool.</p>
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</div>
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<div class="course-card purple">
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<h3>Finding 3</h3>
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<p>Structured indexes help only when queries need their encoded structure.</p>
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</div>
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</div>
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<!-- Presenter cue: Tie each finding to something viewers just observed; distinguish evidence from interpretation. -->
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---
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# Boundary → design rule
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<div class="course-boundary">More stages increase latency, operational cost, and the number of components that can drift.</div>
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<div class="course-rule">Add a retrieval stage only when an evaluation identifies the failure it corrects.</div>
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<!-- Presenter cue: State where the evidence stops, then turn that limitation into a reusable engineering rule. -->
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---
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# Continue the experiment
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<div class="grid grid-cols-2 gap-4 mt-6">
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<a class="course-link" href="../chapter4/multimodal-agent/">
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<span class="course-link-title">Experiment 4-2: multimodal strategies</span>
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<span class="course-link-path">chapter4/multimodal-agent/</span>
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</a>
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<a class="course-link" href="../book-en/images/fig3-10.svg">
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<span class="course-link-title">RAPTOR tree</span>
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<span class="course-link-path">book-en/images/fig3-10.svg</span>
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</a>
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<a class="course-link" href="../book-en/images/fig3-11.svg">
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<span class="course-link-title">GraphRAG graph</span>
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<span class="course-link-path">book-en/images/fig3-11.svg</span>
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</a>
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<a class="course-link" href="../book-en/chapter3.md">
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<span class="course-link-title">Knowledge-base governance</span>
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<span class="course-link-path">book-en/chapter3.md</span>
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</a>
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</div>
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<!-- Presenter cue: Point viewers to the companion paths; do not walk through every extension. -->
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---
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layout: center
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class: text-center
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---
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<div class="course-kicker">Pause and apply</div>
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# Your turn
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<div class="course-big course-reflection">Which query type would reveal that your flat index has lost document structure?</div>
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<!-- Presenter cue: Invite viewers to pause the video. Offer your own answer after a short beat. -->
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---
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layout: center
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class: text-center
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---
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<div class="course-kicker">Next · Lesson 13</div>
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<div class="course-next">Let the Agent decide whether another retrieval step is necessary.</div>
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<div class="course-next-arrow">→</div>
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<!-- Presenter cue: Use this transition to make the course feel continuous rather than episodic. -->
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