Vector vs. Hybrid Search: A Developer's Guide
Learn why a hybrid approach to search often outperforms vector-only solutions.
The Limitations of Vector-Only Search
While vector search is a huge leap forward, it can sometimes struggle with queries where keywords are important. For example, if a user is searching for a specific error code like `ERR-404-B`, a vector search might return results for similar, but incorrect, error codes. This is because the semantic meaning of `ERR-404-B` is very close to `ERR-404-C`.
The Power of Hybrid Search
Hybrid search combines the best of both worlds: the semantic understanding of vector search and the precision of keyword search. By using both methods simultaneously, a hybrid search can deliver results that are both conceptually relevant and keyword-accurate.
Vector Search
Semantic understanding, finds conceptually similar content
Keyword Search
Exact matching, precise term identification
Hybrid Search
Best of both: semantic relevance + keyword precision
How CoderSwap Makes Hybrid Search Easy
Building a hybrid search system from scratch is complex. You need to manage two different indexing and querying pipelines, and then figure out how to combine the results in a way that makes sense. CoderSwap handles all of this for you. With our AI-powered search builder, you can easily define how you want to weigh the results from vector and keyword search, and even add additional business logic on top.
"Find documents that are semantically similar to the query, but give a 50% boost to documents that contain the exact keywords."