# groonga - An open-source fulltext search engine and column store.

- 日本語版はこちら

### Navigation

- index
- next |
- previous |
- groonga v3.0.0 documentation »
- 8. リファレンスマニュアル »
- 8.3. Command »

# 8.3.24. suggest

Note

The suggest feature specification isn't stable. The specification may be changed.

## 8.3.24.1. NAME

suggest - returns completion, correction and/or suggestion for a query.

## 8.3.24.2. SYNOPSIS

    suggest types table column query [sortby [output_columns [offset [limit [frequency_threshold [conditional_probability_threshold [prefix_search]]]]]]]

## 8.3.24.3. DESCRIPTION

The suggest command returns completion, correction and/or suggestion for a specified query.

See Introduction about completion, correction and suggestion.

## 8.3.24.4. OPTIONS

`types`

  It specifies what types are returned by the suggest command.

  Here are available types:

  > `complete`
  >   The suggest command does completion.
  > `correct`
  >   The suggest command does correction.
  > `suggest`
  >   The suggest command does suggestion.

  You can specify one or more types separated by `|`. Here are examples:

  > It returns correction:

  >     correct

  > It returns correction and suggestion:

  >     correct|suggest

  > It returns complete, correction and suggestion:

  >     complete|correct|suggest

`table`

  It specifies table name that has `item_${DATA_SET_NAME}` format. For example, `item_query` is a table name if you created dataset by the following command:

      groonga-suggest-create-dataset /tmp/db-path query

`column`
  It specifies a column name that has furigana in Katakana in `table` table.
`query`
  It specifies query for completion, correction and/or suggestion.
`sortby`

  It specifies sort key.

  Default:
    `-_score`
`output_columns`

  It specifies output columns.

  Default:
    `_key,_score`
`offset`

  It specifies returned records offset.

  Default:
    `0`
`limit`

  It specifies number of returned records.

  Default:
    `10`
`frequency_threshold`

  It specifies threshold for item frequency. Returned records must have `_score` that is greater than or equal to `frequency_threshold`.

  Default:
    `100`

`conditional_probability_threshold`

> It specifies threshold for conditional probability. Conditional probability is used for learned data. It is probability of query submission when `query` is occurred. Returned records must have conditional probability that is greater than or equal to `conditional_probability_threshold`.

> Default:
>   `0.2`

`prefix_search`

  It specifies whether optional prefix search is used or not in completion.

  Here are available values:

  > `yes`
  >   Prefix search is always used.
  > `no`
  >   Prefix search is never used.
  > `auto`
  >   Prefix search is used only when other search can't find any records.

  Default:
    `auto`
`similar_search`

  It specifies whether optional similar search is used or not in correction.

  Here are available values:

  > `yes`
  >   Similar search is always used.
  > `no`
  >   Similar search is never used.
  > `auto`
  >   Similar search is used only when other search can't find any records.

  Default:
    `auto`

## 8.3.24.5. RETURN VALUE

### 8.3.24.5.1. JSON format

Here is a returned JSON format:

    {"type1": [["candidate1", score of candidate1],
               ["candidate2", score of candidate2],
               ...],
     "type2": [["candidate1", score of candidate1],
               ["candidate2", score of candidate2],
               ...],
     ...}

`type`

> A type specified by `types`.

`candidate`

> A candidate for completion, correction or suggestion.

`score of candidate`

> A score of corresponding `candidate`. It means that higher score candidate is more likely candidate for completion, correction or suggestion. Returned candidates are sorted by `score of candidate` descending by default.

## 8.3.24.6. EXAMPLE

Here are learned data for completion.

Execution example:

    load --table event_query --each 'suggest_preparer(_id, type, item, sequence, time, pair_query)'
    [
    {"sequence": "1", "time": 1312950803.86057, "item": "e"},
    {"sequence": "1", "time": 1312950803.96857, "item": "en"},
    {"sequence": "1", "time": 1312950804.26057, "item": "eng"},
    {"sequence": "1", "time": 1312950804.56057, "item": "engi"},
    {"sequence": "1", "time": 1312950804.76057, "item": "engin"},
    {"sequence": "1", "time": 1312950805.86057, "item": "engine", "type": "submit"}
    ]
    # [[0, 1337566253.89858, 0.000355720520019531], 6]

Here are learned data for correction.

Execution example:

    load --table event_query --each 'suggest_preparer(_id, type, item, sequence, time, pair_query)'
    [
    {"sequence": "2", "time": 1312950803.86057, "item": "s"},
    {"sequence": "2", "time": 1312950803.96857, "item": "sa"},
    {"sequence": "2", "time": 1312950804.26057, "item": "sae"},
    {"sequence": "2", "time": 1312950804.56057, "item": "saer"},
    {"sequence": "2", "time": 1312950804.76057, "item": "saerc"},
    {"sequence": "2", "time": 1312950805.76057, "item": "saerch", "type": "submit"},
    {"sequence": "2", "time": 1312950809.76057, "item": "serch"},
    {"sequence": "2", "time": 1312950810.86057, "item": "search", "type": "submit"}
    ]
    # [[0, 1337566253.89858, 0.000355720520019531], 8]

Here are learned data for suggestion.

Execution example:

    load --table event_query --each 'suggest_preparer(_id, type, item, sequence, time, pair_query)'
    [
    {"sequence": "3", "time": 1312950803.86057, "item": "search engine", "type": "submit"},
    {"sequence": "3", "time": 1312950808.86057, "item": "web search realtime", "type": "submit"}
    ]
    # [[0, 1337566253.89858, 0.000355720520019531], 2]

Here is a completion example.

Execution example:

    suggest --table item_query --column kana --types complete --frequency_threshold 1 --query en
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   {
    #     "complete": [
    #       [
    #         1
    #       ],
    #       [
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "_score",
    #           "Int32"
    #         ]
    #       ],
    #       [
    #         "engine",
    #         1
    #       ]
    #     ]
    #   }
    # ]

Here is a correction example.

Execution example:

    suggest --table item_query --column kana --types correct --frequency_threshold 1 --query saerch
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   {
    #     "correct": [
    #       [
    #         1
    #       ],
    #       [
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "_score",
    #           "Int32"
    #         ]
    #       ],
    #       [
    #         "search",
    #         1
    #       ]
    #     ]
    #   }
    # ]

Here is a suggestion example.

Execution example:

    suggest --table item_query --column kana --types suggest --frequency_threshold 1 --query search
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   {
    #     "suggest": [
    #       [
    #         2
    #       ],
    #       [
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "_score",
    #           "Int32"
    #         ]
    #       ],
    #       [
    #         "search engine",
    #         1
    #       ],
    #       [
    #         "web search realtime",
    #         1
    #       ]
    #     ]
    #   }
    # ]

Here is a mixed example.

Execution example:

    suggest --table item_query --column kana --types complete|correct|suggest --frequency_threshold 1 --query search
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   {
    #     "suggest": [
    #       [
    #         2
    #       ],
    #       [
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "_score",
    #           "Int32"
    #         ]
    #       ],
    #       [
    #         "search engine",
    #         1
    #       ],
    #       [
    #         "web search realtime",
    #         1
    #       ]
    #     ],
    #     "complete": [
    #       [
    #         2
    #       ],
    #       [
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "_score",
    #           "Int32"
    #         ]
    #       ],
    #       [
    #         "search",
    #         2
    #       ],
    #       [
    #         "search engine",
    #         2
    #       ]
    #     ],
    #     "correct": [
    #       [
    #         1
    #       ],
    #       [
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "_score",
    #           "Int32"
    #         ]
    #       ],
    #       [
    #         "search",
    #         2
    #       ]
    #     ]
    #   }
    # ]

## 8.3.24.7. SEE ALSO

- Suggest
- groonga-suggest-create-dataset

### Navigation

- index
- next |
- previous |
- groonga v3.0.0 documentation »
- 8. リファレンスマニュアル »
- 8.3. Command »