I am writing a simple app in django that searches for records in database.
Users inputs a name in the search field and that query is used to filter records using a particular field like -
Result = Users.objects.filter(name__icontains=query_from_searchbox)
E.g. -
Database consists of names- Shiv, Shivam, Shivendra, Kashiva, Varun... etc.
A search query 'shiv' returns records in following order-
Kahiva, Shivam, Shiv and Shivendra
Ordered by primary key.
My question is how can i achieve the order -
Shiv, Shivam, Shivendra and Kashiva.
I mean the most relevant first then lesser relevant result.
It's not possible to do that with standard Django as that type of thing is outside the scope & specific to a search app.
When you're interacting with the ORM consider what you're actually doing with the database - it's all just SQL queries.
If you wanted to rearrange the results you'd have to manipulate the queryset, check exact matches, then use regular expressions to check for partial matches.
Search isn't really the kind of thing that is best suited to the ORM however, so you may which to consider looking at specific search applications. They will usually maintain an index, which avoids database hits and may also offer a percentage match ordering like you're looking for.
A good place to start may be with Haystack
Related
I'm using Elasticsearch to build search for ecommerece site.
One index will have products stored in it, in products index I'll store categories in it's other attributes along with. Categories can be multiple but the attribute will have single field value. (E.g. color)
Let's say user types in Black(color) Nike(brand) shoes(Categories)
I want to process this query so that I can extract entities (brand, attribute, etc...) and I can write Request body search.
I have tought of following option,
Applying regex on query first to extract those entities (But with this approach not sure how Fuzzyness would work, user may have typo in any of the entity)
Using OpenNLP extension (But this one only works on indexation time, in above scenario we want it on query side)
Using NER of any good NLP framework. (This is not time & cost effective because I'll have millions of products in engine also they get updated/added on frequent basis)
What's the best way to solve above issue ?
Edit:
Found couple of libraries which would allow fuzzy text matching in regex. But the entities to find will be many, so what's the best solution to optimise that ?
Still not sure about OpenNLP
NER won't work in this case because there are fixed number of entities so prediction is not right when there are no entity available in the query.
If you cannot achieve desired results with tuning of built-in ElasticSearch scoring/boosting most likely you'll need some kind of 'natural language query' processing:
Tokenize free-form query. Regex can be used for splitting lexems, however very often it is better to write custom tokenizer for that.
Perform named-entity recognition to determine possible field(s) for each keyword. At this step you will get associations like (Black -> color), (Black -> product name) etc. In fact you don't need OpenNLP for that as this should be just an index (keyword -> field(s)), and you can try to use ElasticSearch 'suggest' API for this purpose.
(optional) Recognize special phrases or combinations like "released yesterday", "price below $20"
Generate possible combinations of matches, and with help of special scoring function determine 'best' recognition result. Scoring function may be hardcoded (reflect 'common sense' heuristics) or it this may be a result of machine learning algorithm.
By recognition result (matches metadata) produce formal query to produce search results - this may be ElasticSearch query with field hints, or even SQL query.
In general, efficient NLQ processing needs significant development efforts - I don't recommend to implement it from scratch until you have enough resources & time for this feature. As alternative, you can try to find existing NLQ solution and integrate it, but most likely this will be commercial product (I don't know any good free/open-source NLQ components that really ready for production use).
I would approach this problem as NER tagging considering you already have corpus of tags. My approach for this problem will be as below:
Create a annotated dataset of queries with each word tagged to one of the tags say {color, brand, Categories}
Train a NER model (CRF/LSTMS).
This is not time & cost effective because I'll have millions of
products in engine also they get updated/added on frequent basis
To handle this situation I suggest dont use words in the query as features but rather use the attributes of the words as features. For example create an indicator function f(x',y) for word x with context x' (i.e the word along with the surrounding words and their attributes) and tag y which will return a 1 or 0. A sample indicator function will be as below
f('blue', 'y') = if 'blue' in `color attribute` column of DB and words previous to 'blue' is in `product attribute` column of DB and 'y' is `colors` then return 1 else 0.
Create lot of these indicator functions also know as features maps.
These indicator functions are then used to train a models using CRFS or LSTMS. Finially we use viterbi algorithm to find the best tagging sequence for your query. For CRFs you can use packages like CRFSuite or CRF++. Using these packages all you have go do is create indicator functions and the package will train a model for you. Once trained you can use this model to predict the best sequence for your queries. CRFs are very fast.
This way of training without using vector representation of words will generalise your model without the need of retraining. [Look at NER using CRFs].
This question already has answers here:
Django: __in query lookup doesn't maintain the order in queryset
(6 answers)
Closed 8 years ago.
I've searched online and could only find one blog that seemed like a hackish attempt to keep the order of a query list. I was hoping to query using the ORM with a list of strings, but doing it that way does not keep the order of the list.
From what I understand bulk_query only works if you have the id's of the items you want to query.
Can anybody recommend an ideal way of querying by a list of strings and making sure the objects are kept in their proper order?
So in a perfect world I would be able to query a set of objects by doing something like this...
Entry.objects.filter(id__in=['list', 'of', 'strings'])
However, they do not keep order, so string could be before list etc...
The only work around I see, and I may just be tired or this may be perfectly acceptable I'm not sure is doing this...
for i in listOfStrings:
object = Object.objects.get(title=str(i))
myIterableCorrectOrderedList.append(object)
Thank you,
The problem with your solution is that it does a separate database query for each item.
This answer gives the right solution if you're using ids: use in_bulk to create a map between ids and items, and then reorder them as you wish.
If you're not using ids, you can just create the mapping yourself:
values = ['list', 'of', 'strings']
# one database query
entries = Entry.objects.filter(field__in=values)
# one trip through the list to create the mapping
entry_map = {entry.field: entry for entry in entries}
# one more trip through the list to build the ordered entries
ordered_entries = [entry_map[value] for value in values]
(You could save yourself a line by using index, as in this example, but since index is O(n) the performance will not be good for long lists.)
Remember that ultimately this is all done to a database; these operations get translated down to SQL somewhere.
Your Django query loosely translated into SQL would be something like:
SELECT * FROM entry_table e WHERE e.title IN ("list", "of", "strings");
So, in a way, your question is equivalent to asking how to ORDER BY the order something was specified in a WHERE clause. (Needless to say, I hope, this is a confusing request to write in SQL -- NOT the way it was designed to be used.)
You can do this in a couple of ways, as documented in some other answers on StackOverflow [1] [2]. However, as you can see, both rely on adding (temporary) information to the database in order to sort the selection.
Really, this should suggest the correct answer: the information you are sorting on should be in your database. Or, back in high-level Django-land, it should be in your models. Consider revising your models to save a timestamp or an ordering when the user adds favorites, if that's what you want to preserve.
Otherwise, you're stuck with one of the solutions that either grabs the unordered data from the db then "fixes" it in Python, or constructing your own SQL query and implementing your own ugly hack from one of the solutions I linked (don't do this).
tl;dr The "right" answer is to keep the sort order in the database; the "quick fix" is to massage the unsorted data from the database to your liking in Python.
EDIT: Apparently MySQL has some weird feature that will let you do this, if that happens to be your backend.
Is there a way to use fuzzy matching in a django queryset filter?
I'm looking for something along the lines of:
Object.objects.filter(fuzzymatch(namevariable)__gt=.9)
or is there a way to use lambda functions, or something similar in django queries, and if so, how much would it affect performance time (given that I have a stable set of ~6000 objects in my database that I want to match to)
(realized I should probably put my comments in the question)
I need something stronger than contains, something along the lines of difflib. I'm basically trying to get around doing a Object.objects.all() and then a list comprehension with fuzzy matching.
(although I'm not necessarily sure that doing that would be much slower than trying to filter based on a function, so if you have thoughts on that I'm happy to listen)
also, even though it's not exactly what I want, I'd be open to some kind of tokenized opposite-contains, like:
Object.objects.filter(['Virginia', 'Tech']__in=Object.name)
Where something like "Virginia Technical Institute" would be returned. Although case insensitive, preferably.
When you're using the ORM, the thing to understand is that everything you do converts to SQL commands and it's the performance of the underlying queries on the underlying database that matter. Case in point:
SELECT COUNT (*) ...
Is that fast? Depends on whether your database stores any records to give you that information - MySQL/MyISAM does, MySQL/InnoDB does not. In English - this is one lookup in MYISAM, and n in InnoDB.
Next thing - in order to do exact match lookups efficiently in SQL you have to tell it when you create the table - you can't just expect it to understand. For this purpose SQL has the INDEX statement - in django, use db_index=True in the field options of your model. Bear in mind that this has an added performance hit on writes (to create the index) and obviously extra storage is needed (for the datastructure) so you cannot "INDEX all the things". Also, I don't think it will help for fuzzy matching - but it's worth noting anyway.
Next consideration - how do we do fuzzy matching in SQL? Well apparently LIKE and CONTAINS allow a certain amount of searching and wildcard-results to be executed in SQL. These are T-SQL links - translate for your database server :) You can achieve this via Model.objects.get(fieldname__contains=value) which will produce LIKE SQL, or similar. There are a number of options available there for different lookups.
This may or may not be powerful enough for you - I'm not sure.
Now, for the big question: performance. Chances are if you're doing a contains search that the SQL server will have to hit all of the rows in the database - don't take my word on that, but it would be my bet - even with indexing on. With 6000 rows this might not take all that long; then again, if you're doing this on a per-connection-to-your-app basis it's probably going to create a slowdown.
Next thing to understand about the ORM: if you do this:
Model.objects.get(fieldname__contains=value)
Model.objects.get(fieldname__contains=value)
You will issue two queries to the database server. In other words, the ORM doesn't always cache the results - so you might just want to do an .all() and search in memory. Do read about caching and querysets.
Further on on that last page, you'll also see Q objects - useful for more complicated queries.
So in summary then:
SQL contains some basic fuzzy matching-like parameters.
Whether or not these are sufficient depends on your needs.
How they perform depends on your SQL server - definitely measure it.
Whether you can cache these results in memory depends on how likely scaling is - again might be worth measuring the memory commit as a result - if you can share between instances and if the cache will be frequently invalidated (if it will be, don't do it).
Ultimately, I'd start by getting your fuzzy matching working, then measure, then tweak, then measure until you work out how to improve performance. 99% of this I learnt doing exactly that :)
with postgres as database, you can use TrigramSimilarity to do fuzzy search and rank your results on different weight as well. Here is the link to documentation :
https://docs.djangoproject.com/en/2.0/ref/contrib/postgres/search/#trigram-similarity
For full text search you can refer to https://czep.net/17/full-text-search.html
If you need something stronger than contains lookup, have a look at regex lookups: https://docs.djangoproject.com/en/1.0/ref/models/querysets/#regex
I'm having an issue with querying an index where a common search term also happens to be part of a company name interspersed throughout most of the documents. How do I exclude the business name in results without effecting the ranking on a search that includes part of the business name?
example: Bobs Automotive Supply is the business name.
How can I include relevant results when someone searches automotive or supply without returning every document in the index?
I tried "-'Bobs Automotive Supply' +'search term'" but this seems to exclude any document with Bobs Automotive Supply and isn't very effective on searching 'supply' or 'automotive'
Thanks in advance.
Second answer here, based on additional clarification from first answer.
A few options.
Add the business name as StopWords in the StopWordFilter. This will stop Solr from Indexing them at all. Searches that use them will only really search for those words that aren't in the business name.
Rely on the inherent scoring that Solr will apply due to Term frequency. It sounds like these terms will be in the index frequently. Queries for them will still return the documents, but if the user queries for other, less common terms, those will get a higher score.
Apply a low query boost (not quite negative, but less than other documents) to documents that contain the business name. This is covered in the Solr Relevancy FAQ http://wiki.apache.org/solr/SolrRelevancyFAQ#How_do_I_give_a_negative_.28or_very_low.29_boost_to_documents_that_match_a_query.3F
Do you know that the article is tied to the business name or derive this? If so, you could create another field and then just exclude entities that match on the business name using a filter query. Something like
q=search_term&fq=business_name:(NOT search_term)
It may be helpful to use subqueries for this or to just boost down rather than filter out results.
EDIT: Update to question make this irrelavent. Leaving it hear for posterity. :)
This is why Solr Documents have different fields.
In this case, it sounds like there is a "Footer" field that is separate from your "Body" field in your documents. When searches are performed, they would only done against the Body, which won't include data from the Footer. You could even have a third field which is the "OriginalContent" field, which contains the original copy for display purposes. You wouldn't search that, just store it for later.
The important part is to create the two separate fields in your schema and make sure that you index those field that you want to be able to search.
I am trying to implement a search engine for a new app.
The app allows people to rate items (+1 or -1) - Giving the items a +ve or -ve score.
When people search for items, I'd like to take into account their rating and to order the results accordingly. If the item is a match, it should show up. But if it's a match with a high score it should be boosted up the results a bit.
A really good match should win over a fairly good match with a high score, so it needs to be weighted along with the rest of it (i.e. I boosted my titles a bit).
Not stuck on Solr by any means, only just started playing today.
With Solr, you can maintain a field with the document which holds the difference.
The difference can be between the total +1ve's and the -1ve's.
Solr allows you to boost on field values using function queries.
So you can query with the boost on the difference field, with documents with better difference scoring over others.
From indexing front, as this difference would change quite often, the respective document needs to be updated everytime.
Solr does not allow the updation of the single field, so you need to handle the incremental updates of the difference field.
If that would be a concern to you, can try using ExternalFileField.
This allows mapping of certain fields of documents such as ranking, popularity external to the index in a separate file.
The file can be updated and index committed to reflect the changes.
The field can also be used with function queries to boost the results as needed, however have lot of limitations.
You can order your results by a field that stores the ranking.
sqs.filter(content='blah').order_by('rating')