lunr.js | A bit like Solr , but much smaller and not as bright | Search Engine library

Here I get the results, then pass them to a func which handles the DOM manipulation.

I have followed the Lunr guides to create an index. I put an input box on my webpage that gets the search value typed by the user, then runs that value against the index.

When I run console.log(results[0]) - I happily get the right result in my console:

The problem comes when I try to get "legal and legislation compliance" to show up on my webpage. Two things happen:

When I try to output console.log(results[0].ref) to the console, the program freezes and I can't type anymore.

When I try to output document.getElementById("demo").innerHTML= results[0] to the webpage I get [object object] - which is surprising because the console line gives the actual data associated to the object.

My web project with following bower.json was successfully compiled 4 months back with no modifications till date using gitlab ci pipeline (there is no change in code since then). Today, when i re-executed pipeline, bower started throwing ENOTFOUND errors as mentioned later in this question.

Errors in various run are ENOTFOUND Package mkdirp not found or ENOTFOUND Package minimist not found. How to rectify or trace source of this error maybe in terms of 3rd party package problems considering no change in bower.json and source code and also log file does not mention from which 3rd party mkdirp or minimist is demanded. As such, This is wired as Why would bower remove pre-existing packages from repository.

bower.json snippet

There are several thousand documents and tags but at most 100 tags per document.

I am looking on a lightweight and performant solution where the search should be fully on the client side using JavaScript but some preprocessing of the index with node.js is possible.

My idea is to create an inverse index of tags to documents using a multiset, and a fuzzy index that that finds the correct spelling of a misspelled tag, which are created in a preprocessing step in node.js and serialized as JSON files. In the search step, I want to consult for each item of the query set first the fuzzy index to get the most likely correct tag, and, if one exists to consult the inverse index and add the result set to a bag (numbered set). After doing this for all input tags, the contents of the bag, sorted in descending order, should provide the best matching documents.

My Questions

1. This seems like a common problem, is there already an implementation for it that I can reuse? I looked at lunr.js and fuse.js but they seem to have a different focus.
   1. Is this a sensible approach to the problem? Do you see any obvious improvements?
   2. Is it better to keep the fuzzy step separate from the inverted index or is there a way to combine them?

The thing is, I'm struggling in building then saving the index because I have several asynchronous calls.

I edited their example source code