Beginners course module 1#2494
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Module 1-3 pages don't exist yet, so the syllabus links to them were 404ing the lychee internal link checker.
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kanungle
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Jul 10, 2026
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This is a good start. Left some comments. Not sure why Module 0 changes are in this PR
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| # Let's Understand Search | ||
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| Understand why traditional search fails, how semantic (vector) search solves it. |
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I think this is misleading. I think it should be something like:
Understand why traditional search struggles and how modern semantic search improves it.
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| | car repair | automobile maintenance guide | ❌ Missed | | ||
| | cheap flights NYC | affordable airfare to New York | ❌ Missed | | ||
| | Apple stock | fruit company disambiguation? | ✔ Match | |
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This line doesn't make sense to me
| | cheap flights NYC | affordable airfare to New York | ❌ Missed | | ||
| | Apple stock | fruit company disambiguation? | ✔ Match | | ||
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| ### The Three Core Failure Modes |
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| Over time, search systems became more diverse. However, they all shared the same fundamental ceiling: they work on words, not meaning. | ||
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| ### Evolution of Search Techniques |
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is this actually a chronological evolution? I didn't look it up
| "Does this document contain the same words?" | ||
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| **Semantic search asks:** | ||
| "Does this document mean the same thing?" |
| | Cosine | Text similarity, NLP models | Robust to different vector magnitudes. Most common default. | | ||
| | Dot product | When embeddings are normalized | Faster than cosine if vectors are unit-normalized at index time. | | ||
| | Euclidean (L2) | Image embeddings, spatial data | Sensitive to magnitude - works best with models trained for it. | | ||
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| # SKU-48291 (score: 0.89) ← only correct result returned | ||
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| ### Key insight |
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| - **Dense**: Semantic / vector - Intent, vibe, meaning | ||
| - **Sparse**: BM25 / keyword - Exact terms, rare tokens | ||
| - **Filters (Optional)**: Payload constraints - Hard rules, access control |
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I'm not aware of any of our content that mentions "filtering" as a component of Hybrid Search
| - **Agentic AI systems** - multi-step agents that query different data sources sequentially | ||
| - **E-commerce** - find semantically similar products, then filter by price, brand, and availability | ||
| - **Knowledge bases** - semantic over documents, keyword for exact references and code snippets | ||
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| ## 8. References & Further Reading | ||
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| - **Qdrant Documentation** - [Qdrant Overview](https://qdrant.tech/documentation/concepts/) |
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Name it "Qdrant Concepts"
kanungle
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| - **Qdrant Concepts** - [Qdrant Overview](https://qdrant.tech/documentation/concepts/) | ||
| - Overview of Qdrant's vector search engine - collections, points, payloads, and APIs. | ||
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| - **Distance Metrics Deep Dive** - [Distance Metrics - Qdrant](https://qdrant.tech/documentation/concepts/#distance-metrics) | ||
| - Cosine, dot product, Euclidean, and Manhattan - when to use each. | ||
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| - **Filtering & Hybrid Search** - [Filtering - Qdrant](https://qdrant.tech/documentation/concepts/filtering/) | ||
| - Payload filter syntax, indexed fields, and combining filters with vector queries. | ||
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| - **RAG Tutorials** - [RAG Tutorials - Qdrant](https://qdrant.tech/rag) | ||
| - End-to-end retrieval-augmented generation tutorials using Qdrant as the retriever. |
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These links need to be href'd on the text itself, instead of beside it.
Example, just:
[Filtering & Hybrid Search]((https://qdrant.tech/documentation/concepts/filtering/)
| - How similarity really works under the hood - and when it fails. | ||
| - Your first Qdrant collection: points, payloads, and your first query. | ||
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| End of Module 1. Continue to Module 2: First Principles of Vector Search. |
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This needs to be a button link, like in the essentials course
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make this change on all modules
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