FuzzySearch | : mag : Fast autocomplete suggestion engine | Autocomplete library
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:mag: Fast autocomplete suggestion engine using approximate string matching
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QUESTION
Given the following QueryParser with a FuzzySearch term in the query string:
...ANSWER
Answered 2021-Apr-14 at 20:41This may cross the border into "not an answer" - but it is too long for a comment (or a few comments):
Why is this?
That was a design decision, it would seem. It's mentioned in the documentation here.
"The value is between 0 and 2"
There is an old article here which gives an explanation:
"Larger differences are far more expensive to compute efficiently and are not processed by Lucene.".
I don't know how official that is, however.
More officially, from the JavaDoc for the FuzzyQuery
class, it states:
"At most, this query will match terms up to 2 edits. Higher distances (especially with transpositions enabled), are generally not useful and will match a significant amount of the term dictionary."
How can I correctly get the fuzzy edit distance I want into the query parser?
You cannot, unless you customize the source code.
The best (least worst?) alternative, I think, is probably the one mentioned in the above referenced FuzzyQuery
Javadoc:
"If you really want this, consider using an n-gram indexing technique (such as the SpellChecker in the suggest module) instead."
In this case, one price to be paid will be a potentially much larger index - and even then, n-grams are not really equivalent to edit distances. I don't know if this would meet your needs.
QUESTION
I have a number of enterprise datasets that I must find missing links between, and one of the ways I use for finding potential matches is joining on first and last name. The complication is that we have a significant number of people who use their legal name in one dataset (employee records), but they use either a nickname or (worse yet) their middle name in others (i.e., EAD, training, PIV card, etc.). I am looking for a way to match up these potentially disparate names across the various datasets.
Simplified ExampleHere is an overly simplified example of what I am trying to do, but I think it conveys my thought process. I begin with the employee table:
Employees table employee_id first_name last_name 052451 Robert Armsden 442896 Jacob Craxford 054149 Grant Keeting 025747 Gabrielle Renton 071238 Margaret Seifenmacherand try to find the matching data from the PIV card dataset:
Cards table card_id first_name last_name 1008571527 Bobbie Armsden 1009599982 Jake Craxford 1004786477 Gabi Renton 1000628540 Maggy Seifenmacher Desired ResultAfter trying to match these datasets on first name and last name, I would like to end up with the following:
Employees_Cards table emp_employee_id emp_first_name emp_last_name crd_card_id crd_first_name crd_last_name 052451 Robert Armsden 1008571527 Bobbie Armsden 442896 Jacob Craxford 1009599982 Jake Craxford 054149 Grant Keeting NULL NULL NULL 025747 Gabrielle Renton 1004786477 Gabi Renton 071238 Margaret Seifenmacher 1000628540 Maggy SeifenmacherAs you can see, I would like to make the following matches:
Gabrielle -> Gabi
Jacob -> Jacob
Margaret -> Maggy
Robert -> Bobbie
My initial thought was to find a common names dataset along the lines of:
Name_Aliases table name1 name2 name3 name4 Gabrielle Gabi NULL NULL Jacob Jake NULL NULL Margaret Maggy Maggie Meg Michael Mike Mikey Mick Robert Bobbie Bob Roband use something like this for the JOIN:
...ANSWER
Answered 2021-Mar-20 at 01:10How to structure and query and the aliases table is an interesting question. I'd suggest organizing it in pairs rather than wider rows, because you don't know in advance how many variations may eventually be needed in a group of connected names, and a two column structure gives you the ability to add to a given group indefinitely:
name1 name2 Jacob Jake Margaret Maggy Margaret Maggie Margaret Meg Maggy Maggie Maggy Meg Maggie MegThen you just check both columns in each JOIN in the query, something like this:
QUESTION
I have a dnd selection tab with search functionality to filter the items available. Once an item is selected from draggable section it should be removed from that section and dropped to the droppable section and vice versa. The link to my code is https://codesandbox.io/s/dnd-search-select-sort-xfdtn When an item say "Apple" is selected it is going to the droppable section but when I search for "Apple" again in the draggable section search bar it reappears again and I can again move that to the droppable section which should not be the case. Once it is selected it should not appear again on the list. Below is the corresponding code.
...ANSWER
Answered 2020-May-21 at 02:55In your state you create Fuse
instance once with full list of items
. In your handleItemSearch
you always search against full list of items
. Hence the issue.
To solve the issue, create fresh instance of Fuse
in your handleItemSearch
QUESTION
I'm building a help page with Gatsby and have a search bar (Searchbar.js) where I'm trying to pass the user's input in the field (search bar is always present within the page--think like Evernote's help page) to a component that conducts the search (search.js), which then passes that output to the actual results page (SearchResults.js).
When I do gatsby develop
everything works as it should, but when I do a gatsby build
I get an error where it says it cant read the property "query" because its undefined (line 63 of search.js: var search = location.state.query.trim()
). Why is this failing on build?
Searchbar.js
...ANSWER
Answered 2020-Apr-28 at 17:03location
is short for window.location
, but at build-time your code is running in Node.js which does not have a window
. Instead consider testing for the existence of window
(typeof window !== "undefined"
) before running your location.state.query.trim
call, and fall back to a default value in the case that window
does not exist.
QUESTION
I have couple of questions here.
I want to search a term jumps
With Fuzzy search, I can do jump~
With wild card search, I can do jump*
With stemmer I can do, jump
My understanding is that, fuzzy search gives pump
. Wildcard search gives jumping
as well. Stemmer gives "jumper" also.
I totally agree with the results.
What is the performance of thes three?
Wild card is not recommended if it is at the beginning of the term - my understanding as it has to match with all the tokens in the index - But in this case, it would be all the tokens which starts jump
Fuzzy search gives me unpredicted results - It has to do something kind of spellcheck I assume.
Stemmer suits only particular scenarios like it can;t match pumps.
How should I use these things which can give more relevant results?
I probably more confused about all these because of this section. Any suggestions please?
...ANSWER
Answered 2020-Apr-10 at 08:08For question 2 you can go strict to permissive.
Option one: Only give strict search result. If no result found give stemmer results. Continue with fuzzy or wildcard search if no result found previously.
Option two: Give all results but rank them by level (ie. first exact match, then stemmer result, ...)
QUESTION
I have 2 tables (projects and tasks) that both contain a name field. I want users to be able to search both tables at the same time when entering a new item. I want to rank results based on all the terms entered. A user should be able to enter text in any order he/she chooses.
For example, searching on:
...ANSWER
Answered 2019-Nov-01 at 01:43In your case, to_tsquery()
need to indicate that all words are required, you can use to_tsquery('english', 'off & bmt') and indicates a particular dictionary containing the 'off'
word, listed in the link 4, below.
Some tips to use tsvector:
Create a field on your table that contains all fields with terms that you want to search, this field should be the type
tsvector
Your search should use
tsquery
as you mentioned in your answer. In search, you can make some good tricks, like as follow:2.a. Create a rank, with
ts_rank()
, indicating the search priority, this indicates the priority and how much thetsquery
approximates with original terms2.b. If you have specific words (like my case, search of chemical terms), you can create a dictionary with the commonly words used, this words can be used to extract radical or parts to compare the similarity.
2.c. About the performance: The tsquery works very well with
gin
andgist
indexes. I have used full text search in a table with +200k registers and the search returns in < 0.4secs.
If you need more fuzzy search in words, you can also use the fuzzy match. I used with tsquery, the levenshtein_less_equal
search, using a distance of 3. The function searches words with 3 or minus letters differing from the search, for unique words is a good way to search.
- tsquery and tsvector: https://www.postgresql.org/docs/10/datatype-textsearch.html
- text search: https://www.postgresql.org/docs/10/textsearch-controls.html#TEXTSEARCH-RANKING
- Fuzzy: https://www.postgresql.org/docs/11/fuzzystrmatch.html#id-1.11.7.24.6
- Lexize: https://www.postgresql.org/docs/10/textsearch-dictionaries.html#TEXTSEARCH-SIMPLE-DICTIONARY
QUESTION
I have the following methods in a component that implements OnInit, AfterViewInit. The trackFuzzySearchChanges is in the AfterViewInit(). No matter how much debouncetime is set, the method get executed immediately and executes loadData() that executes a WEB API getScreeningSummaries3() . Please help. Thanks.
...ANSWER
Answered 2019-Jun-16 at 19:53you are calling debounceTime(2000)
and distinctUntilChanged()
within tap
operators callback. they should be piped in order to have actual effect. i also don't see any point of using merge
opeartor as it is used for turn multiple observables into a single onbservable try this way;
QUESTION
So, I'm making my own home assistant and I'm trying to make a multi-intent classification system. However, I cannot find a way to split the query said by the user into the multiple different intents in the query.
For example:
...ANSWER
Answered 2019-May-13 at 06:40It seems that you are mixing two problems in your questions:
- Multiple independent intents within a single query (e.g.
shut down the music and play White Collar
) - Multiple slots (using the form-filling framework) within a single intent (e.g.
turn the lights off in the living room bedroom and kitchen
).
These problems are quite different. Both, however, can be formulated as word tagging problem (similar to POS-tagging) and solved with machine learning (e.g. CRF or bi-LSTM over pretrained word embeddings, predicting label for each word).
The intent labels for each word can be created using BIO notation, e.g.
QUESTION
This question may be better suited for superuser -- if that's the case let me know and I'll shift it.
I use zsh and frequently run pytest from the command line. A very common situation is that I need to run a specific test (or a subtest of a class).
The former looks something like
pytest test/test_foo_file.py::test_foo_function
and the latter something like
pytest test/test_foo_file.py::FooClassTest::test_specific_functionality
It's kind of a pain to write out the entire exact class and test name, and this seems like something ripe for autocompletion or fuzzysearching of some kind. I've been unable to achieve this with what I've found researching -- does anyone have any recommendations?
Let me know if I can be more specific in any way.
...ANSWER
Answered 2019-Jan-05 at 01:20Disclaimer: I am not a zsh
user, but the approach is pretty much similar to the customizing of bash completions:
Create a custom completion file, e.g.
QUESTION
So I'm trying to use Fuzzy for my fuzzy search thing I have going on but it is very slow and doesn't seem like my states are updating on time. Here is a CodeSandbox with everything, and here is a snippet of my App.js:
...ANSWER
Answered 2018-Aug-09 at 19:22There were a number of things wrong with your initial solution.
- as I said in my comment,
this.setState
is asynchronous/batched
following,
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