The Sensei Python client API help developers to build a Sensei request object and execute the query.

An example to build request and do the query:

	
  # create a sample sensei request
    
  req = SenseiRequest()
    
  # add paging info;
  req.set_count(50)    \
  .set_offset(0)
    
  # add query info;
  req.set_query(SenseiQueryTerm("tags", "automatic"))
    
  # add selection info;
  range_selection = SenseiSelectionRange("year", "1995", "2000", True, False)  # [1995 TO 2000)
  req.append_selection(range_selection)
    
  # add filter info;
  req.set_filter(SenseiFilterRange("price", 7900, 11000))
    
  # add group by;
  req.set_groupby("category").set_max_per_group(4)
    
  # add sort;
  req.append_sort(SenseiSort("color", True))
    
  # add fetch_stored
  req.set_fetch_stored(False)
    
  # need explain or not
  req.set_explain(False)
    
  # add facets information
  facets = SenseiFacets().add_facet("color", False, 1, 10, "hits")  \
                         .add_facet("year")
  req.set_facets(facets)
    
  # execute and display results;
  proxy = SenseiServiceProxy()
  sensei_results = proxy.doQuery(req)
  sensei_results.display(["*"], max_col_width=40)
  

An example to get the stored source data if available:

  
  proxy = SenseiServiceProxy()
  print proxy.get([1,2])
  print proxy.get(['1','2'])
  

More examples »

Download

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