Typesense API Reference for v{{ $page.typesenseVersion }}

This section of the documentation details all the API Endpoints available in Typesense and all the parameters you can use with them.

Use the links on the side navigation bar to get to the appropriate section you're looking for.

To learn how to install and run Typesense, see the Guide section instead.

What's new

This release contains important new features, performance improvements and bug fixes.

New Features

• A new text_match_type mode called sum_score which sums the field-level text match scores to arrive at a document-level score.
  • This mode is helpful in cases where you need to consider a document with more matches across more weighted fields, to be more relevant.
  • Docs
• Enable/disable typo tolerance on alphanumeric words in the query via the enable_typos_for_alpha_numerical_tokens search parameter. Default: true.
  • For eg: If you need to disable typo tolerance for a word that contains a mix of letters and numbers like turbo100, you can now set enable_typos_for_alpha_numerical_tokens: false as a search parameter.
  • Docs
• Conversation History from Conversational Searches is now stored as a regular Typesense collection.
  • This is new feature, also has a corresponding breaking change. See the Deprecations / behavior changes section below.
  • Docs
• Support synonyms on query prefixes and typo-corrected query tokens via the synonym_prefix and synonym_num_typos parameters.
  • Defaults to synonym_prefix: false and synonym_num_typos: 0.
• Customization of faceting index used for search via the facet_strategy parameter.
  • By default, Typesense picks an efficient facet_strategy for you based on some built-heuristics. But this flag now lets you explicitly control which strategy to use: exhaustive or top_values or automatic (default).
  • Docs
• Support nested reference collections (when using JOINs), in include_fields search parameter, Eg: include_fields: $Collection_B(title, $Collection_A(title))
• Support sort_by of nested join fields. Eg: sort_by: $Collection_B( $Collection_A(price:asc) )
• Ability to use JOINs when using the documents export endpoint, with the filter_by and include_fields parameters.
• Support exact prefix value filtering via the := operation. For example, given filter_by: name:= S*. we will match Steve Jobs but NOT Adam Stator.

Enhancements

Search Parameters:

• Added enable_synonyms boolean flag to enable/disable the application of synonyms during search (default: true).
• Added filter_curated_hits search parameter which allows you to customize filter behavior for pinned hits.
• Added search parameter enable_analytics that prevents the given query from being used for analytics aggregation.
• Support array fields in facet_return_parent search parameter.
• Allow special characters in range facet labels.
• Increase max length of facet value stored to 255 characters.

Server-side improvements:

• Added --filter-by-max-ops server-side flag that can customize the maximum number of operators that can be present in a filter_by clause (default: 100).
• Added --max-per-page server-side flag that increases the number of hits that can be fetched within a single page. Default: 250.
• Allow dynamic update of cache size via the /config API with the cache-num-entries key.
• Use 64K page size for Jemalloc on ARM64 / Linux.
• Log in-flight search queries during a crash.

AI Search:

• Added API key support for vLLM conversation models using the api_key parameter
• Suppress punctuations and non-speech tokens from appearing in voice search (e.g. hmm).

API Endpoints:

• Support include_fields and exclude_fields in the single document fetch (GET /collections/x/documents/id) end-point.
• GET /collections API endpoint now respects the collections allowed in the API key associated with the request.
• Support for exclude_fields in the GET /collections API end-point. This is useful when you have a lot of fields which bloats the payload.

Performance:

• Implemented lazy filtering of numerical fields which speeds up a subset of searches when enable_lazy_filter boolean parameter is enabled.
• Improved working memory used when loading large embedding models.

Bug Fixes

• Fixed a few bugs in the use of conversational search feature on a HA set-up.
• Fixed an edge case in use of _eval() along with hybrid search.
• Fixed an edge case in vector query by document ID returning k+1 hits.
• Fixed a bug in the use of flat_search_cutoff parameter of vector search that returned suboptimal results.
• Fixed a few bugs and edge cases involving reference fields and joins.
• Fixed wildcard query not excluding un-indexed fields while searching.
• Fixed a crash that occurred while loading collections that's related to indexing a collection not referenced by other collections.
• Fixed an edge case in the sorting clause of _eval operation that caused a rare crash.
• Fixed stemming for non-English locales.
• Fixed semantic search faceting happening on the entire result set instead of only on k returned docs.
• Fixed geosearch not returning real distances when precision parameter was used.
• Fixed quirks around deletion of analytics event rules.
• Fixed an issue with deletion & update of array reference fields.
• Return store field property in collection schema response.
• Fixed a hanging issue when OpenAI API returned no response.
• Fixed persistence of range_index and stem field properties.
• Fixed highlighting of text stored in fields inside array of objects.
• Fix _vector_query parameter in sort_by clause being treated as a sorting field.
• Fix overrides not working with semantic search.
• Fixed a regression in v26 that prevented an empty array from being used as a valid value in filter_by clause.
• Fix return_id not being returned in import API response during failures.

Deprecations / behavior changes

Conversational Search:

To address some limitations that we found with the previous design of the conversational search feature, we now use a Typesense collection for storing the conversation history.

During upgrade, we will attempt to create a default collection with the name ts_conversation_history_model_id and migrate existing conversations to this collection.

However, given the edge cases we found and have now fixed with the new approach on multi-node Highly Available clusters, this automated migration may not work: if it does not, please refer to the guide on how to re-create the conversation model.

Exhaustive total_values in facet stats

We refactored the faceting data structures to improve efficiency. This had an impact on how the total_values in facet_stats is computed for low-cardinality facet fields: it's now computed only within the results returned, instead of on the whole dataset.

To get an accurate total_values for the entire dataset, send this additional search parameter:

{
  "facet_strategy": "exhaustive"
}

This will force Typesense to compute facets in an exhaustive manner and allows the total_values key in the response to be exact.

Upgrading

Before upgrading your existing Typesense cluster to v{{ $page.typesenseVersion }}, please review the behavior changes above to prepare your application for the upgrade.

We'd recommend testing on your development / staging environments before upgrading.

Typesense Cloud

If you're on Typesense Cloud:

1. Go to https://cloud.typesense.org/clusters.
2. Click on your cluster
3. Click on "Cluster Configuration" on the left-side pane, and then click on "Modify"
4. Select a new Typesense Server version in the dropdown
5. Schedule a time for the upgrade.

Self Hosted

If you're self-hosting Typesense, here's how to upgrade:

Single node deployment

1. Trigger a snapshot to create a backup of your data, for safety purposes.
2. Stop Typesense server.
3. Replace the binary via the tar package or via the DEB/RPM installer.
4. Start Typesense server back again.

Multi-node deployment

To upgrade a multi-node cluster, we will be proceeding node by node to ensure the cluster remains healthy during the rolling upgrade.

NOTE: During the upgrade, we have to ensure that the leader of the cluster is using the older Typesense version. So we will upgrade the leader last. You can determine whether a node is a leader or follower by the value of the state field in the /debug end-point response.

1. Trigger a snapshot to create a backup of your data on the leader node.
2. On any follower, stop Typesense and replace the binary via the tar package or via the DEB/RPM installer.
3. Start Typesense server back again and wait for node to rejoin the cluster as a follower and catch-up (/health should return healthy).
4. Repeat steps 2 and 3 for the other followers, leaving the leader node uninterrupted for now.
5. Once all followers have been upgraded to v{{ $page.typesenseVersion }}, stop Typesense on the leader.
6. The other nodes will elect a new leader and keep working.
7. Replace the binary on the old leader and start the Typesense server back again.
8. This node will re-join the cluster as a follower, and we are done.

Downgrading

If you wish to downgrade back to an earlier version of Typesense server, you can safely downgrade to either v26 or v0.25.x.

:::tip This documentation itself is open source. If you find any issues, click on the Edit page button at the bottom of the page and send us a Pull Request. :::