I want to create search suggestions based on the tokens (and not full documents) that are present in my index.
For example:
I have a simple index for movies in which I have these two documents:
{"name":"Captain America"}
{"name":"American Made"}
If I type "ame" then I should get two suggestions (as tokens)
america
american
Similarly if I type "cap" then I should get "captain" and not "Captain America"
I am having exact same problem as this post:
https://discuss.elastic.co/t/elasticsearch-autocomplete-suggest-by-token/18392
I have gone through all types of suggesters and seems like they are focused on returning the whole documents rather than the tokens.
Apache Solr serves this requirement through its autosuggest functionality:
For example, if I type “kni“ then Solr would return knives, knife and knit as suggestions (based on the tokens coming from the indexed documents)
{
"responseHeader":{
"status":0,
"QTime":19},
"spellcheck":{
"suggestions":[
"kni",{
"numFound":3,
"startOffset":0,
"endOffset":3,
"suggestion":["knives",
"knife",
"knit"]}],
"collations":[
"collation","knives"]}}
One of the probable solution is mentioned in this StackOverflow thread:
Elasticsearch autocomplete or autosuggest by token
But it relies on explicitly adding all the suggestions in every document. This seems to be a tedious approach.
Please let me know if this can be achieved somehow in a better way.
Thanks in advance.
It wont return the part like America when you search as "ame" because its stored as "Captain America". You get the original text which is stored
You need to store it as only America.
In your case you the the field name has value "Captain America".
If you are applying the text field type for it, it may be creating tokens for you like Captain, America etc.
These are the token created at the time of indexing and created to help you in search/auto suggest.
As a response of search or autosuggest you will get the original text.
Although the alternative way is to highlight the matching term or part of the term from the response of original text of the autosuggest.
Related
I am exploring deepset haystack and found it very interesting for multiple use cases like a chatbot, search engine, document search, etc
But have not found any reference where I can create multiple indexes for different documents and search based on indexes. I thought of using meta tags for conditional search(on a particular area) by tagging the documents first and then using the params parameter of query API but the same doesn't seem to work and throws an error(I used its vanilla docker-compose based setup)
You can use multiple indices in the same document store if you want to support multiple use cases, indeed. The write_documents method of the document store has a parameter index so that you can store documents for your different use cases in different indices. In the same way, you can pass an index parameter to the query method.
As you expected, there is an alternative solution that uses the meta field of documents. However, the format needs to be slightly different. Your query needs to have the following format:
{"query": "What's the capital town?", "params": {"filters": {"name": "75_Algeria75.txt"}}}
and your documents need to have the following format:
{'text': 'Algeria is...', 'meta':{'name': "75_Algeria75.txt"}}
I want to make dependent search like when user type country and select country then on next dropdown/text search result would be from that particular Countries state, after selecting state on next text search would only based on that selected state. can anyone help to achieve this kind thing via elastic search.
i am new to elastic Search and i had basic idea of it, but didn't get idea how to do this kind of stuff where i need to search from child and feel data like map
Fist, it is important to understand how Elasticsearch store its data. You can find this kind of info here: https://www.elastic.co/guide/en/elasticsearch/reference/master/documents-indices.html
So, basically what you need is build a query with two must terms.
One for the object type (Country, State, etc).
Other for the name ("Los Angeles", "Massachussets", etc). If you want a autocomplete feature you could add a wildcard query in your list. https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-wildcard-query.html
Obs: When you store your State object do not forget to store the Country name together. Since Elasticsearch is non relational you have to have Country name indexed in the State document.
Hope that it helps
I'm trying to refine the search results received by my application by including the sort parameter in my HTTP requests. I've combed through the documentation here, but I can't find exactly what I'm looking for.
I'm searching for DOC filetypes, and I am able to sort by date or sort by metadata, as in alphabetizing by title, author, etc. I can also filter by whether or not the title contains certain keywords. What I want to do is to sort by whether or not the title contains certain keywords (these documents appearing first in the results), but to still keep the other results.
For example, with keywords [winter, Christmas, holiday] I could do a descending sort by the sum of inmeta:title~winter, inmeta:title~Christmas, inmeta:title~holiday and the top result might be
Winter holidays other than Christmas
followed by documents with one or two of the keywords, followed by documents that meet the other search parameters but contain no keywords.
Is this possible in GSA?
I finally achieved what I was trying to do, so figured I'd post in case it helps anyone else.
As far as I know, it is impossible to create a query with this capability, but with Google's Custom Search API, you can create a search engine with the desired keywords in the context file (by editing the XML file directly or by adding keywords through the CSE console). Then you can formulate the query as usual, but perform the search on your personalized engine.
https://developers.google.com/custom-search/docs/ranking
I am try to build a full text search engine using elasticsearch. We have a application which has conferences running across the globe. We have the future and past conferences data. For a POC we have already loaded the conferences details into elasticsearch and it contains fields like title,date,venue,geo_location of the venue as document.
I am able to do simple search using match all query. And also by using function_score I can get the current on going conferences and also using user geo location i can get nearby conferences to users location.
But there are some uses cases where i got stuck and could not proceed. Use cases are.
1) If user try to search with "title + location" then I should not use the user current geo location rather whatever user has provided the city_name use that place geo location and retrieve those doc. Here I know some programming is also required.
2) User search with "title + year", for ex. cardio 2014. User interested to see all the caridology conf of 2014 and it should retrieve that year documents only. But using function score it is retrieving the current years documents.
First of all let me know that above two use cases can be handled in single query. I am thinking to handle it one request, but got stuck.
A proper solution would require you to write your own query parser in your application (outside of elasticsearch) that will parse the query and extract dates, locations, etc. Once all features are extracted, the parser should generate a bool query where each feature would become an appropriate must clause. So, the date would became a range query, the location - geo_location query and everything else would go into a match query for full text matching. Then this query can be sent to elasticsearch.
I'm integrating elasticsearch into an asset tracking application. When I setup the mapping initially, I envisioned the 'brand' field being a single-term field like 'Hitachi', or 'Ford'. Instead, I'm finding that the brand field in the actual data contains multiple terms like: "MB 7 A/B", "B-7" or even "Brush Bull BB72X".
I have an autocomplete component setup now that I configured to do autocomplete against an edgeNGram field, and perform the actual search against an nGram field. It's completely useless the way I set it up because users expect the search results to be restricted to what the autocomplete matches.
Any suggestions on the best way to setup my mapping to support autocomplete and subsequent searches against a multiple term field like this? I'm considering a terms query against a keyword field, or possibly a match query with 'and' as the operator? I also have to deal with hyphens like "B-7".
you can use phrase suggest, the guide is here:
http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/search-suggesters.html
the phrase suggest guide is here:
http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/search-suggesters-phrase.html