Users search a product catalog by exact part numbers and by vague descriptions. Which retrieval approach handles both?
Why B is the answer
Hybrid search combining keyword and vector
Vectors catch meaning and miss exact identifiers, and keywords do the reverse. Hybrid search gives you both.
In detail
- Keyword search finds exact part numbers.
- Vector search finds meaning, so hybrid handles both.
- Add semantic ranking for better ordering.
Use case
An illustrative scenario, not a real incident.
A parts catalog gets searches like "AX-104" and "quiet pump for a boat". Hybrid search finds both, combining keyword and vector results.
See it in different ways
flowchart TD A["Query"] --> B["Full-text search"] A --> C["Vector search"] B --> D["Reciprocal Rank Fusion"] C --> D D --> E["Semantic ranker"] E --> F["Top results"]
sequenceDiagram participant Q as Query participant K as Keyword search participant V as Vector search participant R as Ranking Q->>K: Search Q->>V: Search K->>R: Ranked results V->>R: Ranked results R->>R: Reciprocal Rank Fusion R-->>Q: Merged, then semantic ranker
flowchart TD
Q{"What is the user typing?"}
Q -->|"Exact part number"| A0["Keyword search"]
Q -->|"A description"| A1["Vector search"]
Q -->|"Both happen"| A2["Hybrid search"]Key point: Hybrid handles exact terms and meaning.
Why the other options are wrong
- A. Pure vector search with a better embedding model
- Vector search can miss exact identifiers.
- C. Keyword search with a larger synonym list
- Synonyms do not capture a vague description.
- D. Sorting results by upload date and popularity
- Sorting by date ignores the query.