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We set out to change that. Our goal was to make a service to not only streamline the construction of great search but also the analytics and data workflows to create an ongoing flywheel of continual improvement. We wanted to put that power into the hands of any developer.

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Now let’s see how it’s done. Here are the steps we’re taking.

Getting started 

There are few initial steps involved to get Sajari search.io search off the ground:

1. Import some data to help define a schema

2. Configure pipelines 

3. Sync data

4. Generate a user interface (UI)

5. Connect data feedback to improve ranking and performance automatically

We will walk through each of these below.

Defining a schema

Schemas are all about performance. If you aren’t convinced we wrote an article on schema vs schemaless just to convince you 😉 

You can do this a) manually in our admin console b) via our API or c) via uploading a sample record during your collection setup, or d) using a connector. This example used option b.

Note: If you’re using our website crawler, Shopify, or another connector this will happen automatically.

Configure pipelines

The configuration of an intelligent search algorithm can be extremely complicated. So, we re-imagined how engineers can build search by creating pipelines. Pipelines break down search configuration into smaller pieces that can be easily mixed, matched, and combined to create an incredibly powerful search experience. Pipelines are highly composable and extendable. You can read a quick pipeline overview here and more specific details related to this example below.

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• Record pipelines define how your product data is processed on ingest 

• Query pipelines define how your queries are constructed 

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Sajari search.io automatically generates initial pipelines for you that you can modify or append later on via our built-in pipeline editor. When you first set up Sajarisearch.io, the auto-detecting onboarding flow allows you to:

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There is much more you can do with pipelines, which is somewhat expanded on below, but it’s worth noting you also don’t need much to get started.

Query pipelines

Query pipelines define the query execution and results ranking strategies used when searching the records in your collection. 

Steps in a query pipeline can be used for:

• Query understanding - query rewrites, spelling, NLP, etc.

• Filtering results - based on any attribute in the index. For example location or customer-specific results.

• Changing the relevance logic - dynamically boost different aspects based on the search query, parameters or data models

• Constructing the engine query - as opposed to the input query, the engine query is what is actually executed, it can be extremely complex but you don’t need to worry about that!

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Let’s walk through just a few of the customizations you can add to search using query pipelines. Below are some explanations of step configurations used in this example. 

Field weights

Field weights allow different fields to be worth more when searching. For example the product name is probably a more valuable match than description. 

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# Index lookups
id: set-score-mode
  params:
    mode:
      const: MAX
id: index-text-index-boost
  params:
    field:
      const: title
    score:
      const: "1"
    text:
      bind: q
id: index-text-index-boost
  params:
    field:
      const: body_html
    score:
      const: "0.06125"
    text:
      bind: q
id: index-text-index-boost
  params:
    field:
      const: vendor
    score:
      const: 0.5
    text:
      bind: q
id: index-text-index-boost
  params:
    field:
      const: product_type
    score:
      const: 0.25
    text:
      bind: q
id: index-text-index-boost
  params:
    field:
      const: tags
    score:
      const: 0.125
    text:
      bind: q
id: index-text-index-boost
  params:
    field:
      const: image_tags
    score:
      const: 0.06125
    text:
      bind: q
id: index-text-index-boost
  params:
    field:
      const: variant_titles
    score:
      const: 0.06125
    text:
      bind: q

Spelling correction

Spelling is hard! If you’ve tried a fuzzy match in other search engines you would know it’s underwhelming. It also typically slows things down a lot! 

The nice thing about Sajari search.io spelling is that it’s not only very very fast, it’s also much smarter than a standard fuzzy matching. You can add spell correction via pipelines using a few lines of YAML. 

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Below are sample queries showing how missing characters are handled seamlessly.

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The nice thing about Sajari search.io spelling is that it also learns over time. As people execute more queries the suggestions and autocomplete begin to better understand the most likely suggestions your customers want. 

Business boosts

Next part is looking at business metrics and how they impact ranking. You may want to promote items that sell more frequently, have inventory in stock, have higher user ratings, or anything else you can think of! 

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Now let’s see  how more targeted logic can be applied to specific queries.

Gmail-style filters

Want to add Gmail-style filters to your search bar? For power users these are amazing. See below where the in:cheap syntax is used to filter to lower priced items. This example is contrived, but shows how easy this is to do with pipelines:

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Below illustrates the transform:

Note: 

• in is just an example. You could look for has, from or anything you like.

• Many additional filters can be added for all the other potential values here. It could be in:sale or anything else.

Natural language parsing

We have two ways to do this, a) basic pattern matches and b) more complex models. This example uses the first option to show how some basic capabilities are easily addressed:

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Note here that the first step transformed the initial query and the second step conditionally activates the filter when appropriate. This is interesting as the price input could actually be sent in as part of the initial query (i.e. not extracted). This is a common use case for personalisation and recommendations where the gender, size or preferences may be known. 

Product segmentation

If someone searches for “tv” they probably want a tv and not a tv aerial, or a tv cabinet, or something as “seen on tv.” I say “probably” because search and discovery is all about ambiguity. This alignment of queries to some form of segmentation is very valuable. Below shows an example of a query for “tv” both before and after applying a conditional boost to make this alignment.TODO IMAGE

Before

Image Added

After

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The boost is fairly simple. The condition checks to see if the query contains (~) “tv”, if so it then activates and boosts products in the specified category.

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That’s interesting and useful, but obviously difficult to do for many variations right? Actually Sajari search.io supports uploading these in bulk in several ways:

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The API is useful if this information already exists in the business (ie, if you already have a solid understanding of what queries are generating purchases, we can import this data for you so you can avoid a “cold start” when migrating to Sajarisearch.io. Contact us for more details).  

The analytically generated conditional boosts are far more interesting though, as they can be regenerated automatically using machine learning to dynamically improve your business performance with no effort required. 

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So the step we used previously to boost the tv category would actually now be basically unnecessary, as the performance would create this relationship for all queries with any detected statistically significant relationships. It’s less specific than a hand written rule, but it’s far more accurate than any one person can determine! 

Record pipelines

The record pipeline can update and augment information as it is indexed. 

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For this example build we’re looking at the last example to do with image analysis for visual search which is explained further below. 

Visual search

You may have noticed the color palette in the facet and filters menu. This was generated using the Google Cloud Vision API, which is powered by advanced AI image analysis. The color information and image descriptions are AI generated and were not in the original data set. 

The image analysis was done as each product was loaded. Uniquely this was done by a pipeline step calling out to a cloud function. Why is that important? Because it shows you can augment product processing with anything you like. Note that these features are available even if you’re using a third-party service such as Shopify to host your store. 

Sync product data

There are a couple different ways to sync your data with Sajarisearch.io:

• During your initial onboarding, you can submit a JSON file and Sajari search.io walks you through a data indexing wizard

• You can use our API to load and update data

For this demo, we opted for the latter using our Golang API which neatly uses gRPC. 

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We have had people in the past comment that the updates were too fast to be consistent updates, but they indeed are! We achieve this via upserts to create and sync records The record wasn’t added to a buffer to merge later; instead, the differential was calculated and executed within the request-response sequence. This makes any changes instantly available as soon as they are synchronized. If you’re using ElasticSearch or one of the Lucene variants you will love this feature.

Generating a UI

So far we have ingested and categorized data intelligently, and built smart query pipelines with deep functionality. Now we need to add it to our site and make it shine for a great user experience.

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Note a few things here:

1. The search facets include several different types (numeric, category, tags and buckets). Buckets allow any filter expression to define a facet grouping so these are highly flexible.

2. The facets support filtered and non-filtered options. This is important as it allows you to retain the higher level numbers on a single query even when a facet is selected. Unlike many other search providers where this requires two requests!

3. This supports desktop and mobile layouts responsively

4. The result layout can be switched from a list to tiled

5. Multiple different pipelines can be supported (popular, best, price based). In these options the price is a “hard sort” whereas the popularity is a score based “boosted sort” pipeline
   

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In the above example the filter is active, but the other facet counts are still displayed, even though the items are not in the result set. This allows the UI to show the user what else is available, even if not selected. This is optional though.

Conclusion

In less time than it took to write this article we’ve shown how to build a blazing fast e-commerce search and discovery interface with autosuggest, sorting, some basic NLP, machine learning powered automatic result improvement, automated categorical segment alignment, AI generated visual image search, spell correction, gmail style filters, conditional business logic and much more!

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Documentation

Pipelines Overview

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