I am building a database in Neo4J. I am trying to build a match query within the fulltext search. The search query has to be quite robust as it will take queries from users which are not familiar with the search term and return the node which best matches the term. I am aware of a few ways of doing this, but all require that the search term is fuzzied and not the return term. My current rules rely on contains / does not contain and loops, without building a new database, is there a way to fuzzy the search term so that essentially the nodes will search through the term provided and not the other way round?
I am aware that this may not make sense. It is only my 3rd day on Neo4J. Please let me know if you need any more clarification.
Edit: I figure that I can combine the does/does not contain search terms and fuzzy the search term, by increasing the does contain score and decreasing the does not contain score.
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I'm trying to understand what is the purpose of configuring a different analyzer for searching and indexing in Azure Search. See: https://learn.microsoft.com/en-us/rest/api/searchservice/create-index#-field-definitions-
According to my understanding, the job of the indexing analyzer is to breakup the input document into individual tokens. Through this process, it might apply multiple transformations like lower-casing the content, removing punctuation and white-spaces, and even removing entire words.
If the tokens are already processed, what is the use of the search analyzer?
Initially, I thought it would apply a similar process on the search query itself, but wouldn't setting a different analyzer than the one used to index the document at this stage completely breaks the search results? If the indexing analyzer lower-cased everything, but the search analyzer doesn't lower-case the query, wouldn't that means you'll never get matches for queries with upper case characters? What if the search analyzer doesn't split tokens on white-spaces? Won't you ever get a match the moment the query includes a space?
Assuming that this is indeed how the two analyzers works together, then why would you ever want to set two different ones?
Your understanding of the difference between index and search analyzer is correct. An example scenario where that's valuable is using ngrams for indexing but not for search terms. So this would allow a document with "cat" to produce "c", "ca", "cat" but you wouldn't necessarily want to apply ngrams on the search term as that would make the query less performant and isn't necessary since the documents already produced the ngrams. Hopefully that makes sense!
Can somebody explain in details on difference between :
Search and filter keyword
I have already gone through https://www.arangodb.com/learn/search/tutorial/ -> SEARCH vs FILTER
Do anybody has any other experience on the difference?
Thanks,
Nilotpal
FILTER corresponds to the WHERE clause in SQL. It does, what the name says. It uses all sorts of arithmetic and AQL operators to filter the search result. It can make use of regular indexes. There is no ranking of filtered results. Filters operate on single collection result sets.
SEARCH offers a full fledged search engine very much like what you would get from regular search engines like Google's page ranking based on a grammar that you could formulate on your own and can operate on multiple collection contents. Its most natural functionality would be a full text search and ranking. In that use it would be a much more powerful version of the full-text index. But it can do much more: normalisation, tokenisation based on language ...
The list goes on and on. Please refer to the documentation of search here:
https://www.arangodb.com/docs/stable/arangosearch.html
Our search service uses Azure Cognitve Search in the following way:
Search non-fuzzy (i.e. with full match of query string).
Search fuzzy (i.e. it's allowed to change 1-2 letters in a query string)
Join results by certain rule.
This way we want to achieve that full match results will always be on the top.
But now we want to introduce a pagination. And to do it with two separate queries is a difficult and not effective task.
An alternative would be to somehow create a single query which will combine in itself both fuzzy and non-fuzzy search but with different scoring profiles, one with higher weights for full-match search and another with lower weights for fuzzy search.
Like
search=rabbit&scoringProfile=highWeightsProfile | seacrh=rabbit~&scoringProfile=lowWeightsProfile
Is there any way to do this, either in API or in SDK?
Is there any other alternative solutions to the problem of fuzzy search but with higher scores for full-match?
Boosting individual subqueries with Lucene query syntax worked for me as a good solution. Maybe not that flexible as separate search profiles for fuzzy and non-fuzzy parts, but still good.
Lets say I have my list of ingredients:
{'potato','rice','carrot','corn'}
and I want to return lists from a database that are most similar to mine:
{'beans','potato','oranges','lettuce'},
{'carrot','rice','corn','apple'}
{'onion','garlic','radish','eggs'}
My query would return this first:
{'carrot','rice','corn','apple'}
I've used Solr, and have looked at CloudSearch, ElasticSearch, Algolia, Searchify and Swiftype. These engines only seem to let me put in one query string and then filter by other facets.
In a real scenario my search list will be about 200 items long and will be matching against about a million lists in my database.
What technology should I use to accomplish what I want to do?
Should I look away from search indexers and more towards database-esque things like mongo, map reduce, hadoop... All I know are the names of other technologies and I just need someone to point me in the right direction on what technology path I should be exploring for this.
With so much data I can't really loop through it, I need to query everything at once.
I wonder what keeps you from trying it with Solr, as Solr provides much of what you need. You can declare the field as type="string" multiValued="true and save each list item as a value. Then, when querying, you specify each of the items in the list to look for as a search term for that field, and Solr will – by default – return the closest match.
If you need exact control over what will be regarded as a match (e.g. at least 40% of the terms from the search list have to be in a matching list) you can use the mm EDisMax parameter, cf. Solr Wiki
Having said that, I must add that I’ve never searched for 200 query terms (do I unerstand correctly that the list whose contents should be searched will contain about 200 items?) and do not know how well that performs. But I guess that setting up a test core and filling it with random lists using a script should not take more than a few hours, so it should be possible to evaluate the performance of this approach without investing too much time.
Not sure if the tittle is correct for the purpose, but what i want is to be able to search only few documents (and not all) from a lucene index.
Think about it as the following context:
The user wants to search inside a book, which is indexed on lucene, chapter by chapter (every chapter corresponds to a document). The user needs to be able to select the chapters he wants to search in, avoiding irrelevant occurences for his study.
Is that possible to restrict the search to only some documents? or do i have to search ALL index and then filter the results?
Thank you!
Lucene allows you to apply Query Filters, so that you can restrict the results only for those which match the filter criteria.
So basically you can search for chapter:chapter1 and the search will be limited only for chapter one documents
Look at the QueryWrapperFilter. It will let you easily do this kind of thing.
Note however that this is more for ease of coding. This won't really help performance, because in the background, it's effectively searching the entire index, but it makes it easier to code "search within a search." Searching the entire index is not a problem because that's the whole purpose of an index--to make indexed searching extremely fast. This assumes that you have a book ID that is indexed, incidentally. If that is the case, then including the book ID in your search allows for very fast searches of the entire index for that particular book.