Typesense Use Cases

We primarily built Typesense to be a search engine optimized for fast, typo-tolerant full-text search.

However, we've been pleasantly surprised by interesting use cases our users have started using it for, beyond just search. We've been able to better support some of these use cases based on feedback from users.

Good Use Cases

Here is an evolving set of use-cases where Typesense can be used:

1. Typo-tolerant fuzzy search-as-you-type to power autocomplete search bars and search results pages
2. Faceted navigation and browsing experience, where users don't need to type in a keyword, instead they directly start applying filters to drill down multiple attributes to get to the documents they're looking for. (Eg: https://ecommerce-store.typesense.org/ (opens new window))
3. As a geo-distributed cache, in order to place data close to users. Instead of hitting your primary database for data which is probably hosted in a single geo region, since you already send Typesense a snapshot of your data, you could fetch documents directly from a geo-distributed Typesense cluster, which routes requests to the node that's closest to the user, thus reducing latency.
4. For finding documents that are similar to each other, using vector search (opens new window). The definition of "similarity" can be defined by any ML models you build, you'd take the output of the model (vectors), index them in Typesense and then do a nearest-neighbor search. Using this, you can implement features like personalization, recommendations, visual search, semantic search, similarity search, etc.
5. Multi-tenant search, where certain records / fields can only be accessed by certain sets of users (eg, logged-in users, admins, users on a certain pricing plan, etc)
6. Federated search, where the search is performed across multiple indices and the results are shown to users side-by-side.
7. Geo-search, to search / sort records that are in proximity to a given latitude/longitude.
8. Semantic search, where users can type in a conceptually related keyword and Typesense will return results from your dataset, even if that exact keyword doesn't exist. For eg, let's say a user searches for "ocean" and your dataset only has the keyword "sea", semantic search can still retrieve relevant results containing the word "sea".
9. Long-term memory for chat-based LLMs like ChatGPT. Typesense's vector search and hybrid search features can be used to make Chat LLMs respond to users' questions with information in your JSON dataset. Using this you can build conversational chat bots over your dataset.
10. To power data visualizations like charts and tables, using the aggregated facet metrics returned by Typesense.

If you're using Typesense for any other use-cases let us know in our Slack Community (opens new window)

Bad Use Cases

Here are a list of use-cases where we do not recommend that you use Typesense:

1. Typesense should NOT be used as a primary data store, which stores the only copy of your data.

   Typesense is designed to be a secondary data store. Meaning, you want to use another primary database to store the primary copy of your data, where your application writes data into. You'd then sync a copy of the data into Typesense in order to support one of the use-cases above.

2. Typesense is usually not a good fit for searching application logs.

   This is because logs are voluminous and storing logs in RAM could be cost prohibitive depending on the size of your log dataset. Also, logs typically do not need an instant search-as-you-type experience, which is what Typesense is optimized for. That said, we do have some users who want to expose some of the logs generated by their users in their app in a UI for them to search / browse, and Typesense can be used for such specialized end-user facing search.