Hey,
it is exactly the same functionality index/search analyzers serve when
indexing/querying any other fields.. It defines the analysis process chain
(consisting of tokenizer, token filters and optionally char filters), when
a field is either indexed or queried, to make sure the terms are in the
same format. No special handling for the completion suggester here.
--Alex
On Wed, Apr 23, 2014 at 2:52 AM, Sviatoslav Abakumov <
abakumov.sviatos...@progforce.com> wrote:
> What about fields `index_analyzer` and `search_analyzer`? I had hope
> that `index_analyzer` would split string `message_suggest.input` into
> array, but it doesn't seem to do it. What are they for then?
>
> On Mon, Apr 7, 2014 at 11:20 AM, Alexander Reelsen <a...@spinscale.de>
> wrote:
> > Hey,
> >
> > there is no automation for this. The main reason why your solution might
> > work in your specific use-case is, that you do not have billions of
> > documents. Otherwise there would be a lot of documents, which contain for
> > example piece (pi suggestions) or winner (wi), and you would get a lot of
> > results. However the goal of a good suggestions is not to get a lot of
> > results but few very good ones, which are likely to be chosen by the user
> > for further queries. Just adding arbitrary suggestions without
> > scoring/weighting them does not make help the user a lot from my
> experience.
> >
> > Hope it makes sense..
> >
> >
> > --Alex
> >
> >
> > On Wed, Apr 2, 2014 at 10:26 AM, Sviatoslav Abakumov
> > <abakumov.sviatos...@progforce.com> wrote:
> >>
> >> Hello,
> >>
> >> I have an index `facebook` with type `post`. I need to provide users
> with
> >> autocompletions using terms that appear in `post.message`. Also the
> list of
> >> completions should be sorted by score.
> >>
> >> The mapping is as follows:
> >> {
> >> "post": {
> >> "properties": {
> >> "created_time": {
> >> "type": "date",
> >> "format": "dateOptionalTime"
> >> },
> >> "link": {
> >> "type": "string"
> >> },
> >> "message": {
> >> "type": "string"
> >> },
> >> "object_id": {
> >> "type": "string"
> >> },
> >> "picture": {
> >> "type": "string"
> >> },
> >> "shares_count": {
> >> "type": "long"
> >> },
> >> "type": {
> >> "type": "string"
> >> },
> >> "update_time": {
> >> "type": "date",
> >> "format": "dateOptionalTime"
> >> },
> >> "user": {
> >> "type": "long"
> >> }
> >> }
> >> }
> >> }
> >>
> >> An example document:
> >> {
> >> "picture": "...",
> >> "update_time": "2014-03-19T23:16:59",
> >> "message": "The
> >> day has finally arrived - the first piece of the 1,000,000 Swag Bucks
> >> pie has been served!! Check to see if you're our first winner!",
> >> "object_id": "",
> >> "shares_count": 0,
> >> "link": "...",
> >> "user": ...,
> >> "created_time": "2014-03-17T21:02:32",
> >> "type": "link"
> >> }
> >>
> >> To achieve the goal I've added one more field to the mapping:
> >> "message_suggest": {
> >> "type": "completion"
> >> }
> >>
> >> Every time I write a document, I query ES to tokenize the string
> >> `message`:
> >> POST _analyze?_tokenizer=standard
> >> The day has finally arrived - the first piece of the 1,000,000 Swag
> Bucks
> >> pie has been served!! Check to see if you're our first winner!
> >>
> >> Then I get the list of tokens from the response and add it to
> >> `post.message_suggest.input`.
> >>
> >> When I do the following request, I get what I wanted:
> >> POST facebook/_suggest
> >> {
> >> "messages": {
> >> "text": "pi",
> >> "completion": {
> >> "field": "message_suggest"
> >> }
> >> }
> >> }
> >>
> >> I sense that this approach is not right or at least not optimal. I am
> new
> >> to Elasticsearch and I would appreciate any input.
> >>
> >> Best,
> >> Sviatoslav.
> >>
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