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Full Text Search

Full text search allows you to find Convex documents that match given keywords.

Unlike normal document queries, search queries look within a string field to find the keywords. This can be used to build search features within your app like searching for messages that contain certain words.

Search queries are automatically reactive, consistent, transactional, and work seamlessly with pagination. They even include new documents created with a mutation!

Example: Search App

To use full text search you need to:

  1. Define a search index.
  2. Run a search query.
Search is in beta

Search is currently a beta feature. If you have feedback or feature requests, let us know on Discord!

Defining search indexes

Like database indexes, search indexes are a data structure that is built in advance to enable efficient querying. Search indexes are defined as part of your Convex schema.

Every search index definition consists of:

  1. A name.
    • Must be unique per table.
  2. A searchField
    • This is the field which will be indexed for full text search.
    • It must be of type string.
  3. [Optional] A list of filterFields
    • These are additional fields that are indexed for fast equality filtering within your search index.

To add a search index onto a table, use the searchIndex method on your table's schema. For example, if you want an index which can search for messages matching a keyword in a channel, your schema could look like:

import { defineSchema, defineTable } from "convex/server";
import { v } from "convex/values";

export default defineSchema({
messages: defineTable({
body: v.string(),
channel: v.string(),
}).searchIndex("search_body", {
searchField: "body",
filterFields: ["channel"],

You can specify search and filter fields on nested documents by using a dot-separated path like

Running search queries

A query for "10 messages in channel '#general' that have 'hello' or 'hi' in their body" would look like:

const messages = await ctx.db
.withSearchIndex("search_body", (q) =>"body", "hello hi").eq("channel", "#general")

This is just a normal database read that begins by querying the search index!

The .withSearchIndex method defines which search index to query and how Convex will use that search index to select documents. The first argument is the name of the index and the second is a search filter expression. A search filter expression is a description of which documents Convex should consider when running the query.

A search filter expression is always a chained list of:

  1. 1 search expression against the index's search field defined with .search.
  2. 0 or more equality expressions against the index's filter fields defined with .eq.

Search expressions

Internally, Convex will break up the search expression's query into separate words (called terms) and search for any of them within the field.

In the example above, the expression search("body", "hello hi") would internally be split into "hi" and "hello" and compared against the terms in your document (ignoring case and punctuation). This matches:

  • "hi"
  • "Sujay, hello!"
  • "oh HI there"
  • ...

Equality expressions

Unlike search expressions, equality expressions will filter to only documents that have an exact match in the given field. In the example above, eq("channel", "#general") will only match documents that have exactly "#general" in their channel field.

Equality expressions support fields of any type (not just text).

To filter to documents that are missing a field, use q.eq("fieldName", undefined).

Other filtering

Because search queries are normal database queries, you can also filter results using the .filter method!

Here's a query for "messages containing 'hi' sent in the last 10 minutes":

const messages = await ctx.db
.withSearchIndex("search_body", (q) =>"body", "hi"))
.filter((q) =>"_creationTime", - 10 * 60000)))

For performance, always put as many of your filters as possible into .withSearchIndex.

Every search query is executed by:

  1. First, querying the search index using the search filter expression in withSearchIndex.
  2. Then, filtering the results one-by-one using any additional filter expressions.

Having a very specific search filter expression will make your query faster and less likely to hit Convex's limits because Convex will use the search index to efficiently cut down on the number of results to consider.

Retrieving results and paginating

Just like ordinary database queries, you can retrieve the results using .collect(), .take(n), .first(), and .unique().

Additionally, search results can be paginated using .paginate(paginationOpts).

Note that collect() will throw an exception if it attempts to collect more than the limit of 1024 documents. It is often better to pick a smaller limit and use take(n) or paginate the results.

Relevance order

Search queries always return results in relevance order based on how well the document matches the search text.

Internally, Convex ranks documents based on their BM25 score which takes into account:

  • How many words in the search query appear in the field?
  • How many times do they appear?
  • How long is the text field?

If multiple documents have the same score, the newest documents are returned first.


Search indexes must have:

  • Exactly 1 search field.
  • Up to 16 filter fields.

Search indexes count against the limit of 32 indexes per table.

Search queries can have:

  • Up to 16 terms (words) in the search expression.
  • Up to 8 filter expressions.

Additionally, search queries can scan up to 1024 results from the search index.

Coming soon

We plan to expand Convex full text search to include:

  • Prefix search/typeahead support
  • Snippeting (returning the matched positions in the search text)
  • Faceted search (counting the number of results per filter field value)
  • Typo tolerance/fuzzy search
  • Additional languages
  • Searching across multiple fields

If any of these features is important for your app, let us know on Discord!