7.15.15. language_model_knn — Groonga v16.0.5 documentation

Skip to main content
`Ctrl`+`K`

# 7.15.15. `language_model_knn`

Added in version 15.1.8.

Note

This is an experimental feature. Currently, this feature is still not stable.

## 7.15.15.1. Summary

`language_model_knn` is a function for semantic search.

Semantic search uses the k-Nearest Neighbors (k-NN) algorithm.

You must use it with TokenLanguageModelKNN.

It can be used as a condition for `--filter` and as a sort key for `--sort_keys`.

To enable this function, register `language_model/knn` plugin by the following command:

    plugin_register language_model/knn

## 7.15.15.2. Syntax

`language_model_knn` requires two parameters:

    language_model_knn(column, query)

`column` is the search target column. It must be a column with an index.

`query` is a search query.

## 7.15.15.3. Requirements

You need Faiss enabled Groonga. The official packages enable it.

## 7.15.15.4. Usage

You need to register `language_model/knn` plugin at first:

Execution example:

    plugin_register language_model/knn
    # [[0,1337566253.89858,0.000355720520019531],true]

Here is a schema definition and sample data.

Sample schema:

Execution example:

    table_create --name Memos --flags TABLE_NO_KEY
    # [[0,1337566253.89858,0.000355720520019531],true]
    column_create \
      --table Memos \
      --name content \
      --flags COLUMN_SCALAR \
      --type ShortText
    # [[0,1337566253.89858,0.000355720520019531],true]

Sample data:

Execution example:

    load --table Memos
    [
    {"content": "I am a boy."},
    {"content": "This is an apple."},
    {"content": "Groonga is a full text search engine."}
    ]
    # [[0,1337566253.89858,0.000355720520019531],3]

You need to store embedding information for each record. Here is how to create that column.

Execution example:

    column_create Memos embedding_code COLUMN_SCALAR ShortBinary
    # [[0,1337566253.89858,0.000355720520019531],true]

Create an index for semantic search.

Specify TokenLanguageModelKNN as the tokenizer. The tokenizer’s arguments are `model` and `code_column`. Specify the model to use for `model`, and specify the column to store the generated embedding information for `code_column`.

Execution example:

    table_create Centroids TABLE_HASH_KEY ShortBinary \
      --default_tokenizer \
        'TokenLanguageModelKNN("model", "hf:///groonga/all-MiniLM-L6-v2-Q4_K_M-GGUF", \
                               "code_column", "embedding_code")'
    # [[0,1337566253.89858,0.000355720520019531],true]
    column_create Centroids data_content COLUMN_INDEX Memos content
    # [[0,1337566253.89858,0.000355720520019531],true]

This enables semantic search. When you `load` data into `Memos.content`, Groonga automatically generates embeddings. Users do not need to generate embeddings.

Here is an example of semantic search:

Execution example:

    select Memos \
      --filter 'language_model_knn(content, "male child")' \
      --output_columns content
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         3
    #       ],
    #       [
    #         [
    #           "content",
    #           "ShortText"
    #         ]
    #       ],
    #       [
    #         "I am a boy."
    #       ],
    #       [
    #         "This is an apple."
    #       ],
    #       [
    #         "Groonga is a full text search engine."
    #       ]
    #     ]
    #   ]
    # ]

`language_model_knn` function can also be used as a sort key. Specify `language_model_knn` for `--sort_keys`. Since you likely need to fetch results in descending order of similarity, you add a `-` prefix to fetch them in descending order.

Here is an example of filtering by `_id` and then sorting by similarity:

Execution example:

    select Memos \
      --filter '_id < 3' \
      --sort_keys '-language_model_knn(content, "male child")' \
      --output_columns content
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         2
    #       ],
    #       [
    #         [
    #           "content",
    #           "ShortText"
    #         ]
    #       ],
    #       [
    #         "I am a boy."
    #       ],
    #       [
    #         "This is an apple."
    #       ]
    #     ]
    #   ]
    # ]

## 7.15.15.5. Parameters

There are two required parameters.

### 7.15.15.5.1. `column`

`column` is the search target column. It must be a column with an index.

### 7.15.15.5.2. `query`

`query` is a search query.

### 7.15.15.5.3. `k`

Added in version 15.2.1.

Specify the number of records to return. If you don’t set this option, the upper limit of the number of records to return is `10`.

You can specify a negative value. It means that the number of matched records + `k` + 1. For example, `"{ "k" : -1 }"` outputs all records. It’s a very useful value to show all records.

Here is a simple negative `k` value usage example.

Sample data:

Execution example:

    load --table Memos
    [
    {"content": "I am a boy."},
    {"content": "This is an apple."},
    {"content": "Groonga is a full text search engine."},
    {"content": "This is an orange."},
    {"content": "This is a banana."},
    {"content": "This is a tomato."},
    {"content": "This is a carrot."},
    {"content": "This is a cucumber."},
    {"content": "This is a pepper."},
    {"content": "This is a potato."},
    {"content": "This is an onion."}
    ]
    # [[0,1337566253.89858,0.000355720520019531],11]

Execution example:

    select Memos \
      --filter 'language_model_knn(text, "male child", { "k" : -1 })' \
      --output_columns content
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         11
    #       ],
    #       [
    #         [
    #           "content",
    #           "ShortText"
    #         ]
    #       ],
    #       [
    #         "I am a boy."
    #       ],
    #       [
    #         "This is a pepper."
    #       ],
    #       [
    #         "This is a cucumber."
    #       ],
    #       [
    #         "This is a carrot."
    #       ],
    #       [
    #         "This is a potato."
    #       ],
    #       [
    #         "This is an apple."
    #       ],
    #       [
    #         "This is a banana."
    #       ],
    #       [
    #         "This is a tomato."
    #       ],
    #       [
    #         "This is an orange."
    #       ],
    #       [
    #         "This is an onion."
    #       ]
    #     ]
    #   ]
    # ]

## 7.15.15.6. Return value

This function works as a selector. It means that this function executes effectively.

## 7.15.15.7. See also

- TokenLanguageModelKNN