<requestHandler name="/rf" class="org.dice.solrenhancements.relevancyfeedback.RelevancyFeedbackHandler">
        <lst name="defaults">
            <str name="omitHeader">true</str>
            <str name="wt">json</str>
            <str name="indent">true</str>			
	
            <!-- Regular query configuration - query parser used when parsing the rf query-->
            <str  name="defType">lucene</str>
            
            <!-- fields returned -->
            <str  name="fl">jobTitle,skill,company</str>
            <!-- fields to match on-->
            <str  name="rf.fl">skillFromSkill,extractTitles</str>
            
            <!-- field weights. Note that term weights are normalized so that each field is weighted exactly in this ratio
            as different fields can get different numbers of matching terms-->
            <str  name="rf.qf">skillFromSkill^3 extractTitles^4.5</str>
            
            <int  name="rows">10</int>
            
            <!-- How many terms to extract per field (max) -->
            <int  name="rf.maxflqt">10</int>
            <bool name="rf.boost">true</bool>
            
            <!-- normalize the weights for terms in each field (custom to dice rf, not present in solr MLT) -->
            <bool name="rf.normflboosts">true</bool>
            
            <!-- Take the raw term frequencies (false) or log of the term frequenies (true) -->
            <bool name="rf.logtf">true</bool>
            
            <!-- Minimum should match settings for the rf query - determines what proportion of the terms have to match -->
            <!-- See Solr edismax mm parameter for specifics --> 
            <bool name="rf.mm">25%</bool>            
            
            <!-- Returns the top k terms (see regular solr MLT handler) -->
            <str  name="rf.interestingTerms">details</str>
            
            <!-- Turns the rf query into a boost query using a multiplicative boost, allowing for boosting -->
            <str  name="rf.boostfn"></str>
            
            <!-- q parameter -  If you want to execute one query, and use the rf query to boost the results (e.g. for personalizing search), 
            	 pass the user's query into this parameter. Can take regular query syntax e.g.rf.q={!edismax df=title qf=.... v=$qq}&qq=Java
            	 The regular q parameter is reserved for the rf query (see abpve)
            -->
            <str name="q"></str>
            <!-- Query parser to use for the q query, if passed -->
            <str name="defType"></str>

            <!-- rf.q parameter - Note that the regular q parameter is used only for personalized search scenarios, where you have a main query
            	 and you want to use the rf query generated to boost the main queries documents. Typically the rf.q query is a query that identifies
            	 one or more documents, e.g. rf.q=(id:686867 id:98928980 id:999923). But it cam be any query. Note that it will process ALL documents
            	 matched by the q query (as it's intended mainly for looking up docs by id), so use cautiously.
            -->
            <str name="rf.q"></str>
            <!-- query parser to use for the rf.q query -->
            <str name="rf.defType"></str>
            
            <!-- Settings for personalized search - use the regular parameter names for the query parser defined by defType parameter -->
            <str name="df">title</str>
            <str name="qf"> company_text^0.01 title^12 skill^4 description^0.3</str>
            <str name="pf2">company_text^0.01 title^12 skill^4 description^0.6</str> 
            
            <!-- Content based recommendations settings (post a document to the endpoint in a POST request). The stream.body and stream.head are form parameters
                 You can send them in a GET request, but a POST handles larger data. If you have really large documents, you will need to change the buffer settings
                 so that the request doesn't blow the buffer limits in Solr or your web server.
             -->
            
            <!-- Fields used for processing documents posted to the stream.body and stream.head parameters in a POST call -->
            <str  name="stream.head.fl">title,title_syn</str>
            <str  name="stream.body.fl">extractSkills,extractTitles</str>
            
            <!-- pass a url in this parameter for Solr to download the webpage, and process the Html using the fields configured in the stream.qf parameters -->
            <str  name="stream.url"></str>
            
            <!-- Note that we have two different content stream fields to pass over in the POST request. This allows different analyzers to be appkied to each. 
            For instance, we pass the job title into the stream.head field and parse out job titles, while we pass the job description to the stream.head parameter 
            to parse out skills -->
            <!-- Pass the document body in this parameter as a form parameter. Analysed using the stream.body.fl fields-->
            <str  name="stream.body"></str>
            <!-- Pass the second document field in this parameter. Analysed using the stream.head.fl fields-->
            <str  name="stream.head"></str>
            
            <!-- Specifies a separate set of field weights to apply when procesing a document posted to the request handler via the 
                 stream.body and stream.head parameters -->
            <str  name="stream.qf">extractSkills^4.5 extractTitles^2.25 title^3.0 title_syn^3.0</str>           
        </lst>
</requestHandler>

{
  "match":{
      "numFound":2,
      "start":0,
      "docs":[
          {
            "id":"a2fd2f2e34667d61fadcdcabfd359cf4",        
            "title":"Console AAA Sports Video Game Programmer.",
            "skills":["Sports Game Experience a plus.",
              "2-10 years plus Console AAA Video Game Programming Experience"],
            "geocode":"38.124447,-122.55051",
            "city":"Novato",
            "state":"CA"
          },
          {
            "id":"11f407d319d6cc707437fad874a097c0",
            "title":"Game Engineer - Creative and Flexible Work Environment!",
            "skills":["3D Math",
              "Unity3d",
              "C#",
              "3D Math - game programming",
              "game programming",
              "C++",
              "Java"],
            "geocode":"33.97331,-118.243614",
            "city":"Los Angeles",
            "state":"CA"
          }
      ]
  },
  "response":{
      "numFound":5333,
      "start":0,
      "docs":[
          {
            "title":"Software Design Engineer 3 (Game Developer)",
            "skills":["C#",
              "C++",
              "Unity"],
            "geocode":"47.683647,-122.12183",
            "city":"Redmond",
            "state":"WA"
          },
          {          
            "title":"Game Server Engineer - MMO Mobile Gaming Start-Up!",
            "skills":["AWS",
              "Node.JS",
              "pubnub",
              "Websockets",
              "pubnub - Node.JS",
              "Vagrant",
              "Linux",
              "Git",
              "MongoDB",
              "Jenkins",
              "Docker"],
            "geocode":"37.777115,-122.41733",
            "city":"San Francisco",
            "state":"CA"
          },...
      ]
   }
}

 <requestHandler name="/ufselect" class="org.dice.solrenhancements.unsupervisedfeedback.UnsupervisedFeedbackHandler">
        <lst name="defaults">
            <str name="omitHeader">true</str>
            <str name="wt">json</str>
            <str name="indent">true</str>

            <!-- Regular query configuration -->
            <str  name="defType">edismax</str>
            <str  name="df">title</str>
            <str  name="qf">title^1.5   skills^1.25 description^1.1</str>
            <str  name="pf2">title^3.0  skills^2.5  description^1.5</str>
            <str  name="mm">1</str>
            <str  name="q.op">OR</str>

            <str  name="fl">jobTitle,skills,company</str>
            <int  name="rows">30</int>
                        
            <!-- Unsupervised Feedback (Blind Feedback) query configuration-->
            <str  name="uf.fl">skillsFromskills,titleFromJobTitle</str>
            <!-- How many docs to extract the top terms from -->
            <str  name="uf.maxdocs">50</str>
            <!-- How many terms to extract per field (max) -->
            <int  name="uf.maxflqt">10</int>
            <bool name="uf.boost">true</bool>
            <!-- Relative per-field boosts on the extracted terms (similar to edismax qf parameter -->
            <!-- NOTE: with  uf.normflboosts=true, all terms are normalized so that the total importance of each
            	field on the query is the same, then these relative boosts are applied per field-->
            
            <str  name="uf.qf">skillsFromskills^4.5 titleFromJobTitle^6.0</str>
            
            <!-- Returns the top k terms (see regular solr MLT handler) -->
            <str  name="uf.interestingTerms">details</str>
			
            <!-- unit-length norm all term boosts within a field (recommended) - see talk for details -->
            <bool name="uf.normflboosts">true</bool>
            <!-- use raw term clounts or log term counts? -->
            <bool name="uf.logtf">false</bool>
        </lst>
</requestHandler>

{
  "match":
  {
    "numFound":7729,
    "start":0,
    "docs":[
      {
        "title":"NLP/Machine Learning Engineer",
        "skills":["Linux",
          "NLP (Natural Language Processing)",
          "SQL",
          "Bash",
          "Python",
          "ML (Machine Learning)",
          "JavaScript",
          "Java"],
        "geocode":"42.35819,-71.050674",
        "city":"Boston",
        "state":"MA"
      },
      {
        "title":"Machine Learning Engineer",
        "skills":["machine learning",
          "java",
          "scala"],
        "geocode":"47.60473,-122.32594",
        "city":"Seattle",
        "state":"WA"
      },
      {
        "title":"Machine Learning Engineer - REMOTE!",
        "skills":["Neo4j",
          "Hadoop",
          "gensim",
          "gensim - C++",
          "Java",
          "R",
          "MongoDB",
          "elastic search",
          "sci-kit learn",
          "Python",
          "C++"],
        "geocode":"37.777115,-122.41733",
        "city":"San Francisco",
        "state":"CA"
        },...
    ]
}