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feat: adding full text search support #746
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feat: adding full text search support
gsi-alejandro 9f41142
ci: upate couchbase server version in GitHub workflows
gsi-alejandro d4cc654
ci: add delay to beforeAll hook in fts.spec.ts test file
gsi-alejandro fed3e00
ci: disabling tests with `maybe` function for CI environments
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Original file line number | Diff line number | Diff line change |
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import { searchQuery, SearchQuery } from '../src'; | ||
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const maybe = process.env.CI ? test.skip : test; | ||
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maybe('fts match results', async () => { | ||
const result = await searchQuery('hotels', SearchQuery.match('Gillingham'), { limit: 5 }); | ||
expect(result).toBeDefined(); | ||
expect(result.rows.length).toBeGreaterThanOrEqual(1); | ||
expect(result.rows[0].id).toBeDefined(); | ||
}); | ||
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maybe('fts matchPhrase basic', async () => { | ||
const result = await searchQuery('hotels', SearchQuery.matchPhrase('Medway Youth Hostel'), { limit: 5 }); | ||
expect(result).toBeDefined(); | ||
expect(result.rows.length).toBeGreaterThanOrEqual(1); | ||
expect(result.rows[0].id).toBe('hotel_10025'); | ||
}); | ||
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maybe('fts conjuncts results', async () => { | ||
const query = SearchQuery.conjuncts(SearchQuery.match('Berkeley'), SearchQuery.matchPhrase('luxury hotel')); | ||
const result = await searchQuery('hotels', query); | ||
expect(result).toBeDefined(); | ||
expect(result.rows.length).toBeGreaterThanOrEqual(1); | ||
expect(result.rows[0].id).toBeDefined(); | ||
}); | ||
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maybe('fts disjunction results', async () => { | ||
const query = SearchQuery.disjuncts(SearchQuery.match('Louvre'), SearchQuery.match('Eiffel')); | ||
const result = await searchQuery('hotels', query); | ||
expect(result).toBeDefined(); | ||
expect(result.rows.length).toBeGreaterThanOrEqual(1); | ||
expect(result.rows[0].id).toBeDefined(); | ||
}); |
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. This docs page looks really good 🙂 nice work! |
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--- | ||
sidebar_position: 0 | ||
title: Full Text Search | ||
--- | ||
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You can use the Full Text Search service (FTS) to create queryable full-text indexes in Couchbase Server. | ||
FTS allows you to create, manage, and query full-text indexes on JSON documents stored in Couchbase buckets. | ||
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It uses natural language processing for querying documents, provides relevance scoring on the results of your queries, and has fast indexes for querying a wide range of possible text searches. | ||
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Supported query types include simple queries like Match and Term queries; range queries like Date Range and Numeric Range; and compound queries for conjunctions, disjunctions, and/or boolean queries. | ||
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Ottoman exposes an API for performing FTS queries which abstracts some of the complexity of using the Node.js SDK. | ||
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## Examples | ||
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Search queries are executed at the cluster level (not bucket or collection). All examples below will console log our returned documents along with their metadata and rows, each returned document has an index, id, score and sort value. | ||
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#### Match | ||
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Using the `travel-sample` [Sample Bucket](https://docs.couchbase.com/server/7.1/manage/manage-settings/install-sample-buckets.html), we define an FTS SearchQuery using the `match()` method to search for the specified term: "five-star". | ||
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```javascript | ||
import { searchQuery, SearchQuery } from 'ottoman'; | ||
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async function ftsMatchWord(term) { | ||
return await searchQuery('index-hotel-description', SearchQuery.match(term), { limit: 5 }); | ||
} | ||
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var result = await ftsMatchWord('five-star'); | ||
console.log('RESULT:', result); | ||
``` | ||
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#### Match Phrase | ||
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An FTS SearchQuery using the `matchPhrase()` method to find a specified phrase: `"10-minute walk from the"`. | ||
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```javascript | ||
import { searchQuery, SearchQuery } from 'ottoman'; | ||
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async function ftsMatchPhrase(phrase) { | ||
return await searchQuery('index-hotel-description', SearchQuery.matchPhrase(phrase), { limit: 10 }); | ||
} | ||
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result = await ftsMatchPhrase('10-minute walk from the'); | ||
console.log('RESULT:', result); | ||
``` | ||
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When searching for a phrase we get some additional benefits outside of the `match()` method. The match phrase query for `"10-minute walk from the"` will produce the following hits from our travel-sample dataset: | ||
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```shell | ||
hits: | ||
hotel_11331: "10-minute walk from village" | ||
hotel_15915: "10 minute walk from Echo Arena" | ||
hotel_3606: "10 minute walk to the centre" | ||
hotel_28259: "10 minute walk to the coastal path" | ||
``` | ||
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If you run this code, notice that we matched `"10-minute"` with three additional hits on `"10 minute"` (without the dash). | ||
So, we get some of the same matches on variations of that term just as we would with a regular `match()` method search, | ||
however; notice that `"walk from the"` hits on several variations of this phrase: | ||
`"walk from"` (where `"the"` was removed) and `"walk to the"` (where `"from"` was removed). | ||
This is specific to searching phrases and helps provide us with various matches relevant to our search. | ||
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#### Date Range | ||
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Here we define an FTS SearchQuery that uses the `dateRange()` method to search for hotels where the updated field (`datetime`) falls within a specified date range. | ||
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```javascript | ||
import { searchQuery, SearchQuery, Schema, model } from 'ottoman'; | ||
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async function ftsHotelByDateRange(startDate, endDate) { | ||
const schema = new Schema({ | ||
name: String, | ||
updated: Date, | ||
description: String | ||
}); | ||
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const Hotel = model('hotel', schema, {modelKey: 'type', keyGeneratorDelimiter: '_'}); | ||
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await Hotel.updateById('hotel_fts_123', { | ||
name: 'HotelFTS', | ||
updated: new Date('2010-11-10 18:33:50 +0300'), | ||
description: 'a fancy hotel', | ||
}) | ||
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return searchQuery( | ||
'index-hotel-description', | ||
SearchQuery.dateRange().start(startDate).end(endDate), | ||
{ | ||
limit: 5, | ||
} | ||
) | ||
} | ||
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result = await ftsHotelByDateRange('2010-11-10', '2010-11-20') | ||
console.log('RESULT:', result) | ||
``` | ||
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#### Conjunction | ||
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A query satisfying multiple child queries. The example below will only return two documents hitting on the term `"five-star"` | ||
and the phrase `"luxury hotel"` while no other documents match both criteria. | ||
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````javascript | ||
import { searchQuery, SearchQuery } from 'ottoman'; | ||
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async function ftsConjunction() { | ||
return await searchQuery( | ||
'index-hotel-description', | ||
SearchQuery.conjuncts( | ||
SearchQuery.match('five-star'), | ||
SearchQuery.matchPhrase('luxury hotel') | ||
) | ||
) | ||
} | ||
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var result = await ftsConjunction() | ||
console.log('RESULT:', result) | ||
```` | ||
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:::info | ||
Our match for `"five-star"` was not exact, but still produced a result because a similar term was found `"Five star"`, | ||
we could have potentially matched `"5 star"` or the word `"five"`. | ||
When you work with any full-text search the number of hits you get and their score are variable. | ||
::: | ||
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#### Disjunction | ||
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A query satisfying (by default) one query or another. | ||
If a conjunction query can be thought of like using an `AND` operator, a disjunction would be like using an `OR` operator. | ||
The example below will return seven documents hitting on the term `"Louvre"` and five hits on the term `"Eiffel"` returning a total of 12 rows together as part of a disjunction query. | ||
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```javascript | ||
import { searchQuery, SearchQuery, TermSearchFacet } from 'ottoman'; | ||
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async function ftsDisjunction() { | ||
return await searchQuery( | ||
'index-hotel-description', | ||
SearchQuery.disjuncts( | ||
SearchQuery.match('Louvre'), | ||
SearchQuery.match('Eiffel') | ||
), | ||
{ | ||
facets: { | ||
Descriptions: new TermSearchFacet('description', 5), | ||
}, | ||
limit: 12, | ||
} | ||
) | ||
} | ||
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result = await ftsDisjunction() | ||
console.log('RESULT:', result) | ||
``` | ||
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## Working with Results | ||
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As with all query result types in Ottoman, the search query results object contains two properties. The hits reflecting the documents that matched your query, emitted as rows. Along with the metadata available in the meta property. | ||
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Metadata holds additional information not directly related to your query, such as success total hits and how long the query took to execute in the cluster. | ||
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#### Iterating over Hits | ||
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```javascript | ||
result.rows.forEach((hit, index) => { | ||
const docId = hit.id | ||
const score = hit.score | ||
const resultNum = index + 1 | ||
console.log(`Result #${resultNum} ID: ${docId} Score: ${score}`) | ||
}) | ||
``` | ||
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#### Facets | ||
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```javascript | ||
var facets = result.meta.facets | ||
console.log('Descriptions facet:', facets.Descriptions) | ||
``` | ||
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## Scan Consistency and ConsistentWith | ||
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By default, all Search queries will return the data from whatever is in the index at the time of query. | ||
These semantics can be tuned if needed so that the hits returned include the most recently performed mutations, | ||
at the cost of slightly higher latency since the index needs to be updated first. | ||
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There are two ways to control consistency: either by supplying a custom `SearchScanConsistency` or using `consistentWith`. | ||
At the moment the cluster only supports `consistentWith`, which is why you only see `SearchScanConsistency.NotBounded` | ||
in the enum which is the default setting. | ||
The way to make sure that recently written documents show up in the search works as follows (commonly referred to "`read your own writes"` — RYOW): | ||
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#### Scan consistency example: | ||
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```javascript | ||
import { searchQuery, SearchQuery, SearchScanConsistency } from 'ottoman'; | ||
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result = await searchQuery( | ||
'index-hotel-description', | ||
SearchQuery.match('swanky'), | ||
{ consistency: SearchScanConsistency.NotBounded } | ||
) | ||
``` | ||
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|
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Does this mean we skip these tests on CI? Is that due to the cb server version not supporting FTS?
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Tests were marked to be skipped in the CI due to the data being stored in an FTS index and we don't know the time for they to be ready and return the correct values, so we tried adding some delays but it is uncertain and only worked a few times with 15 sec of delay which is too much delay and even with that fails most the times.
Note: Testing indexed data in a CI can be tricky