Beyond Vector Search: Knowledge Graphs, Structured Retrieval, and Intelligent Routing
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Vector search is an effective way to retrieve passages that are semantically similar to a question. It is not a universal interface to every kind of knowledge. Exact identifiers, deterministic calculations, and relationships distributed across multiple documents require different retrieval primitives. A production knowledge system should begin with the information need, not with the database that happens to be available. Open-ended questions may belong in a vector index. Product codes need an…
1Key Takeaways
- Vector search is an effective way to retrieve passages that are semantically similar to a question.
- It is not a universal interface to every kind of knowledge.
- Exact identifiers, deterministic calculations, and relationships distributed across multiple documents require different retrieval primitives.
- A production knowledge system should begin with the information need, not with the database that happens to be available.
2AIWedia Score
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that vector search is an effective way to retrieve passages that are semantically similar to a question.
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