# Query suggestions
<a name="query-suggestions"></a>

**Note**  
Feature support varies by index type and search API being used. To see if this feature is supported for the index type and search API you’re using, see [Index types](https://docs.aws.amazon.com/kendra/latest/dg/hiw-index-types.html).

Amazon Kendra *Query suggestions* can help your users type their search queries faster and guide their search.

Amazon Kendra suggests queries relevant to your users based on one of the following:
+ Popular queries in the query history or query log
+ The contents of document fields/attributes

You can set your preference for using the query history or document fields by setting `SuggestionTypes` as either `QUERY` or `DOCUMENT_ATTRIBUTES` and calling [https://docs.aws.amazon.com/kendra/latest/APIReference/API_GetQuerySuggestions.html](https://docs.aws.amazon.com/kendra/latest/APIReference/API_GetQuerySuggestions.html). By default, Amazon Kendra uses the query history to base suggestions on. If the query history and document fields are both activated when you call [https://docs.aws.amazon.com/kendra/latest/APIReference/API_UpdateQuerySuggestionsConfig.html](https://docs.aws.amazon.com/kendra/latest/APIReference/API_UpdateQuerySuggestionsConfig.html) and you haven't set your `SuggestionTypes` preference to use document fields, then Amazon Kendra uses the query history.

If you use the console, you can base query suggestions on either the query history or document fields. You first select your index and then select **Query suggestions** under **Enrichments** in the navigation menu. Then select **Configure query suggestions**. After you configure query suggestions, you are directed to a search console where you can select either the **Query history** or **Document fields** in the right panel and enter a search query in the search bar.

By default, query suggestions using the query history and document fields are both activated at no additional cost. You can deactivate these types of query suggestions at any time by using the `UpdateQuerySuggestionsConfig` API. To deactivate query suggestions based on the query history, set `Mode` to `DISABLED` when calling `UpdateQuerySuggestionsConfig`. To deactivate query suggestions based on document fields, set `AttributeSuggestionsMode` to `INACTIVE` in the document fields configuration and then call `UpdateQuerySuggestionsConfig>`. If you use the console, you can deactivate query suggestions in the **Query suggestions settings**.

Query suggestions are case insensitive. Amazon Kendra converts the query prefix and the suggested query to lower case, ignores all single and double quotation marks, and replaces multiple white space characters with a single space. Amazon Kendra matches all other special characters as they are. Amazon Kendra does not show any suggestions if a user types fewer than two characters or more than 60 characters.

**Topics**
+ [Query suggestions using query history](#query-suggestions-history)
+ [Query suggestions using document fields](#query-suggestions-doc-fields)
+ [Block certain queries or document field content from suggestions](#query-suggestions-blocklist)

## Query suggestions using query history
<a name="query-suggestions-history"></a>

**Note**  
Feature support varies by index type and search API being used. To see if this feature is supported for the index type and search API you’re using, see [Index types](https://docs.aws.amazon.com/kendra/latest/dg/hiw-index-types.html).

**Topics**
+ [Settings for selecting queries for suggestions](#query-suggestions-history-settings)
+ [Clear suggestions while retaining query history](#query-suggestions-history-clear)
+ [No suggestions available](#query-suggestions-history-none)

You can choose to suggest queries relevant to your users based on popular queries in the query history or query log. Amazon Kendra uses all of the queries that your users search for and learns from these queries to make suggestions to your users. Amazon Kendra suggests popular queries to users when they start typing their query. Amazon Kendra suggests a query if the prefix or first few characters of the query matches what the user starts typing as their query.

For example, a user starts typing the query 'upcoming events'. Amazon Kendra has learned from the query history that many users have searched for 'upcoming events 2050' many times. The user sees 'upcoming events 2050' appear directly underneath their search bar, autocompleting their search query. The user selects this query suggestion, and the document 'New events: What's happening in 2050' is returned in the search results.

You can specify how Amazon Kendra selects eligible queries to suggest to your users. For example, you can specify that a query suggestion must have been searched by at least 10 unique users (default is three), have been searched within the last 30 days, and does not contain any words or phrases from your [block list](https://docs.aws.amazon.com/kendra/latest/dg/query-suggestions.html#query-suggestions-blocklist). Amazon Kendra requires that a query has at least one search result and contains at least one word of more than four characters.

### Settings for selecting queries for suggestions
<a name="query-suggestions-history-settings"></a>

You can configure the following settings for selecting queries for suggestions by using the [https://docs.aws.amazon.com/kendra/latest/APIReference/API_UpdateQuerySuggestionsConfig.html](https://docs.aws.amazon.com/kendra/latest/APIReference/API_UpdateQuerySuggestionsConfig.html) API:
+ **Mode**—Query suggestions using the query history are either `ENABLED` or `LEARN_ONLY`. Amazon Kendra activates query suggestions by default. `LEARN_ONLY` turns off query suggestions. If turned off, Amazon Kendra continues to learn suggestions but doesn't make query suggestions to users.
+ **Query log time window**—How recent your queries are in your query log time window. The time window is an integer value for the number of days from current day to past days.
+ **Queries without user information**—Set to `TRUE` to include all queries, or set to `FALSE` to only include queries with user information. You can use this setting if your search application includes user information, such as the user ID, when a user issues a query. By default, this setting doesn't filter out queries if there's no specific user information associated with the queries. However, you can use this setting to only make suggestions based on queries that include user information.
+ **Unique users**—The minimum number of unique users who must search a query for the query to be eligible to suggest to your users. This number is an integer value.
+ **Query count**—The minimum number of times a query must be searched for the query to be eligible to suggest to your users. This number is an integer value.

These settings affect how queries are selected as popular queries to suggest to your users. How you tune your settings will depend on your specific needs, for example:
+ If your users usually search once a month on average, then you can set the number of days in the query log time window to 30 days. By using that setting, you capture most of your users' recent queries before they become outdated in the time window.
+ If only a small number of your queries include user information, and you don't want to suggest queries based on a small sample size, then you can set queries to include all users.
+ If you define popular queries as being searched by at least 10 unique users and searched at least 100 times, then you set the unique users to 10 and the query count to 100.

**Warning**  
Your changes to settings might not take effect immediately. You can track the settings changes by using the [https://docs.aws.amazon.com/kendra/latest/APIReference/API_DescribeQuerySuggestionsConfig.html](https://docs.aws.amazon.com/kendra/latest/APIReference/API_DescribeQuerySuggestionsConfig.html) API. The time for your updated settings to take effect depends on the updates that you make and the number of search queries in your index. Amazon Kendra automatically updates suggestions every 24 hours, after you change a setting or after you apply a [block list](https://docs.aws.amazon.com/kendra/latest/dg/query-suggestions.html#query-suggestions-blocklist).

------
#### [ CLI ]

**To retrieve query suggestions**

```
aws kendra get-query-suggestions \
 --index-id index-id \
 --query-text "query-text" \
 --suggestion-types '["QUERY"]' \
 --max-suggestions-count 1 // If you want to limit the number of suggestions
```

**To update query suggestions**

For example, to change the query log time window and the minimum number of times a query must be searched:

```
aws kendra update-query-suggestions-config \
 --index-id index-id \
 --query-log-look-back-window-in-days 30 \
 --minimum-query-count 100
```

------
#### [ Python ]

**To retrieve query suggestions**

```
import boto3
from botocore.exceptions import ClientError

kendra = boto3.client("kendra")

print("Get query suggestions.")

# Provide the index ID
index_id = "index-id"

# Provide the query text
query_text = "query"

# Provide the query suggestions type
query_suggestions_type = "QUERY"


# If you want to limit the number of suggestions
num_suggestions = 1
 
try:
    query_suggestions_response = kendra.get_query_suggestions(
        IndexId = index_id,
        QueryText = query_text,
        SuggestionTypes = query_suggestions_type,
        MaxSuggestionsCount = num_suggestions
    )

    # Print out the suggestions you received
    if ("Suggestions" in query_suggestions_response.keys()) {
        for (suggestion: query_suggestions_response["Suggestions"]) {
            print(suggestion["Value"]["Text"]["Text"]);
        }
    }
   
except ClientError as e:
        print("%s" % e)

print("Program ends.")
```

**To update query suggestions**

For example, to change the query log time window and the minimum number of times a query must be searched:

```
import boto3
from botocore.exceptions import ClientError
import pprint
import time

kendra = boto3.client("kendra")

print("Updating query suggestions settings/configuration for an index.")

# Provide the index ID
index_id = "index-id"

# Configure the settings you want to update
minimum_query_count = 100
query_log_look_back_window_in_days = 30

try:
    kendra.update_query_suggestions_config(
        IndexId = index_id,
        MinimumQueryCount = minimum_query_count,
        QueryLogLookBackWindowInDays = query_log_look_back_window_in_days
    )

    print("Wait for Amazon Kendra to update the query suggestions.")

    while True:
        # Get query suggestions description of settings/configuration
        query_sugg_config_response = kendra.describe_query_suggestions_config(
            IndexId = index_id
        )
        
        # If status is not UPDATING, then quit
        status = query_sugg_config_response["Status"]
        print(" Updating query suggestions config. Status: " + status)
        if status != "UPDATING":
            break
        time.sleep(60)

except ClientError as e:
        print("%s" % e)

print("Program ends.")
```

------

### Clear suggestions while retaining query history
<a name="query-suggestions-history-clear"></a>

**Note**  
Feature support varies by index type and search API being used. To see if this feature is supported for the index type and search API you’re using, see [Index types](https://docs.aws.amazon.com/kendra/latest/dg/hiw-index-types.html).

You can clear query suggestions by using the [https://docs.aws.amazon.com/kendra/latest/APIReference/API_DescribeQuerySuggestionsConfig.html](https://docs.aws.amazon.com/kendra/latest/APIReference/API_DescribeQuerySuggestionsConfig.html) API. Clearing suggestions deletes existing query suggestions only, not the queries in the query history. When you clear suggestions, Amazon Kendra learns new suggestions based on new queries added to the query log from the time you cleared suggestions.

------
#### [ CLI ]

**To clear query suggestions**

```
aws  kendra clear-query-suggestions \
 --index-id index-id
```

------
#### [ Python ]

**To clear query suggestions**

```
import boto3
from botocore.exceptions import ClientError

kendra = boto3.client("kendra")

print("Clearing out query suggestions for an index.")

# Provide the index ID
index_id = "index-id"

try:
    kendra.clear_query_suggestions(
        IndexId = index_id
    )

    # Confirm last cleared date-time and that there are no suggestions
    query_sugg_config_response = kendra.describe_query_suggestions_config(
        IndexId = index_id
    )
    print("Query Suggestions last cleared at: " + str(query_sugg_config_response["LastClearTime"]));
    print("Number of suggestions available from the time of clearing: " + str(query_sugg_config_response["TotalSuggestionsCount"]));
        
except ClientError as e:
        print("%s" % e)

print("Program ends.")
```

------

### No suggestions available
<a name="query-suggestions-history-none"></a>

If you don't see suggestions for a query, it could be for one of the following reasons:
+ There are not enough queries in your index for Amazon Kendra to learn from.
+ Your query suggestions settings are too strict, resulting in most queries being filtered out from suggestions.
+ You recently cleared suggestions, and Amazon Kendra still needs time for new queries to accumulate in order to learn new suggestions.

You can check your current settings using the [https://docs.aws.amazon.com/kendra/latest/APIReference/API_DescribeQuerySuggestionsConfig.html](https://docs.aws.amazon.com/kendra/latest/APIReference/API_DescribeQuerySuggestionsConfig.html) API.

## Query suggestions using document fields
<a name="query-suggestions-doc-fields"></a>

**Topics**
+ [Settings for selecting fields for suggestions](#query-suggestions-doc-fields-settings)
+ [User control in document fields](#query-suggestions-doc-fields-user-control)

You can choose to suggest queries relevant to your users based on the contents of document fields. Instead of using the query history to suggest other popular relevant queries, you can use information contained within a document field that is useful to autocompleting the query. Amazon Kendra looks for relevant content in fields set to `Suggestable` and that closely aligns with your user's query. Then, Amazon Kendra suggests this content to your user when they start typing their query.

For example, if you specify the title field to base suggestions on and a user starts typing the query 'How amazon ken...', the most relevant title 'How Amazon Kendra works' could be suggested to autocomplete the search. The user sees 'How Amazon Kendra works' appear directly underneath their search bar, autocompleting their search query. The user selects this query suggestion, and the document 'How Amazon Kendra works' is returned in the search results.

You can use the contents of any document field of `String` and `StringList` type to suggest a query by setting the field to `Suggestable` as part of your fields configuration for query suggestions. You can also use a [block list](https://docs.aws.amazon.com/kendra/latest/dg/query-suggestions.html#query-suggestions-blocklist) so that suggested document fields that contain certain words or phrases are not shown to your users. You can use one block list. The block list applies whether you set query suggestions to use the query history or document fields.

### Settings for selecting fields for suggestions
<a name="query-suggestions-doc-fields-settings"></a>

You can configure the following settings for selecting document fields for suggestions using [https://docs.aws.amazon.com/kendra/latest/APIReference/API_AttributeSuggestionsConfig.html](https://docs.aws.amazon.com/kendra/latest/APIReference/API_AttributeSuggestionsConfig.html) and calling the [https://docs.aws.amazon.com/kendra/latest/APIReference/API_UpdateQuerySuggestionsConfig.html](https://docs.aws.amazon.com/kendra/latest/APIReference/API_UpdateQuerySuggestionsConfig.html) API to update the settings at the index level:
+ **Field/attribute suggestions mode**—Query suggestions using document fields are either `ACTIVE` or `INACTIVE`. Amazon Kendra activates query suggestions by default.
+ **Suggestible fields/attributes**—The field names or field keys to base suggestions on. These fields must be set to `TRUE` for `Suggestable`, as part of the fields configuration. You can override the fields configuration at the query level while maintaining the configuration at the index level. Use the [GetQuerySuggestions](https://docs.aws.amazon.com/kendra/latest/APIReference/API_GetQuerySuggestions.html) API to change `AttributeSuggestionConfig` at the query level. This configuration at the query level can be useful for quickly experimenting with using different document fields without having to update the configuration at the index level.
+ **Additional fields/attributes**—The additional fields that you want to include in the response for a query suggestion. These fields are used to provide extra information in the response; however, they are not used to base suggestions on.

**Warning**  
Your changes to settings might not take effect immediately. You can track the settings changes by using the [https://docs.aws.amazon.com/kendra/latest/APIReference/API_DescribeQuerySuggestionsConfig.html](https://docs.aws.amazon.com/kendra/latest/APIReference/API_DescribeQuerySuggestionsConfig.html) API. The time for your updated settings to take effect depends on the updates that you make. Amazon Kendra automatically updates suggestions every 24 hours, after you change a setting or after you apply a [block list](https://docs.aws.amazon.com/kendra/latest/dg/query-suggestions.html#query-suggestions-blocklist).

------
#### [ CLI ]

To retrieve query suggestions and override the document fields configuration at the query level instead of having to change the configuration at the index level.

```
aws kendra get-query-suggestions \
 --index-id index-id \
 --query-text "query-text" \
 --suggestion-types '["DOCUMENT_ATTRIBUTES"]' \
 --attribute-suggestions-config '{"SuggestionAttributes":'["field/attribute key 1", "field/attribute key 2"]', "AdditionalResponseAttributes":'["response field/attribute key 1", "response field/attribute key 2"]'}' \
 --max-suggestions-count 1 // If you want to limit the number of suggestions
```

**To update query suggestions**

For example, to change the document fields configuration at the index level:

```
aws kendra update-query-suggestions-config \
 --index-id index-id \
 --attribute-suggestions-config '{"SuggestableConfigList": '[{"SuggestableConfig": "_document_title", "Suggestable": true}]', "AttributeSuggestionsMode": "ACTIVE"}'
```

------
#### [ Python ]

To retrieve query suggestions and override the document fields configuration at the query level instead of having to change the configuration at the index level.

```
import boto3
from botocore.exceptions import ClientError

kendra = boto3.client("kendra")

print("Get query suggestions.")

# Provide the index ID
index_id = "index-id"

# Provide the query text
query_text = "query"

# Provide the query suggestions type
query_suggestions_type = "DOCUMENT_ATTRIBUTES"

# Override fields/attributes configuration at query level
configuration = {"SuggestionAttributes":
    '["field/attribute key 1", "field/attribute key 2"]', 
      "AdditionalResponseAttributes":
          '["response field/attribute key 1", "response field/attribute key 2"]'
          }

# If you want to limit the number of suggestions
num_suggestions = 1

try:
    query_suggestions_response = kendra.get_query_suggestions(
        IndexId = index_id,
        QueryText = query_text,
        SuggestionTypes = [query_suggestions_type],
        AttributeSuggestionsConfig = configuration,
        MaxSuggestionsCount = num_suggestions
    )

    # Print out the suggestions you received
    if ("Suggestions" in query_suggestions_response.keys()) {
        for (suggestion: query_suggestions_response["Suggestions"]) {
            print(suggestion["Value"]["Text"]["Text"]);
        }
    }
   
except ClientError as e:
        print("%s" % e)

print("Program ends.")
```

**To update query suggestions**

For example, to change the document fields configuration at the index level:

```
import boto3
from botocore.exceptions import ClientError
import pprint
import time

kendra = boto3.client("kendra")

print("Updating query suggestions settings/configuration for an index.")

# Provide the index ID
index_id = "index-id"

# Configure the settings you want to update at the index level
configuration = {"SuggestableConfigList": 
    '[{"SuggestableConfig": "_document_title", "Suggestable": true}]', 
       "AttributeSuggestionsMode": "ACTIVE"
       }

try:
    kendra.update_query_suggestions_config(
        IndexId = index_id,
        AttributeSuggestionsConfig = configuration
    )

    print("Wait for Amazon Kendra to update the query suggestions.")

    while True:
        # Get query suggestions description of settings/configuration
        query_sugg_config_response = kendra.describe_query_suggestions_config(
            IndexId = index_id
        )
        
        # If status is not UPDATING, then quit
        status = query_sugg_config_response["Status"]
        print(" Updating query suggestions config. Status: " + status)
        if status != "UPDATING":
            break
        time.sleep(60)

except ClientError as e:
        print("%s" % e)

print("Program ends.")
```

------

### User control in document fields
<a name="query-suggestions-doc-fields-user-control"></a>

You can apply user context filtering to the document fields that you want to base query suggestions on. This filters document field information based on the user or their group access to documents. For example, an intern searches the company's portal and doesn't have access to a top-secret company document. Therefore, suggested queries based on the top-secret document's title, or any other suggestible field, is not shown to the intern.

You can index your documents with an access control list (ACL), defining which users and groups are assigned access to which documents. Then, you can apply user context filtering to your documents fields for query suggestions. User context filtering that is currently set for your index is the same user context filtering applied to your document fields configuration for query suggestions. User context filtering is part of your document fields configuration. You use the [https://docs.aws.amazon.com/kendra/latest/APIReference/API_AttributeSuggestionsConfig.html](https://docs.aws.amazon.com/kendra/latest/APIReference/API_AttributeSuggestionsConfig.html) and call [https://docs.aws.amazon.com/kendra/latest/APIReference/API_GetQuerySuggestions.html](https://docs.aws.amazon.com/kendra/latest/APIReference/API_GetQuerySuggestions.html).

## Block certain queries or document field content from suggestions
<a name="query-suggestions-blocklist"></a>

A *block list* stops Amazon Kendra from suggesting certain queries to your users. A block list is a list of words or phrases that you want to exclude from query suggestions. Amazon Kendra excludes queries containing an exact match of the words or phrases in the block list.

You can use a block list to safeguard against offensive words or phrases that commonly appear in your query history or document fields and that Amazon Kendra could select as suggestions. A block list can also prevent Amazon Kendra from suggesting queries that contain information that is not ready to be publicly released or announced. For example, your users frequently query about an upcoming release of a potential new product. However, you don't want to suggest the product because you're not ready to release it.You can block queries that contain the product name and product information from suggestions.

You can create a block list for queries by using the [https://docs.aws.amazon.com/kendra/latest/APIReference/API_CreateQuerySuggestionsBlockList.html](https://docs.aws.amazon.com/kendra/latest/APIReference/API_CreateQuerySuggestionsBlockList.html) API. You put each block word or phrase on a separate line in a text file. Then you upload the text file to your Amazon S3 bucket and provide the path or location to the file in Amazon S3. Amazon Kendra currently supports creating only one block list.

You can replace the text file of your blocked words and phrases in your Amazon S3 bucket. To update the block list in Amazon Kendra, use the [https://docs.aws.amazon.com/kendra/latest/APIReference/API_UpdateQuerySuggestionsBlockList.html](https://docs.aws.amazon.com/kendra/latest/APIReference/API_UpdateQuerySuggestionsBlockList.html) API.

Use the [https://docs.aws.amazon.com/kendra/latest/APIReference/API_DescribeQuerySuggestionsBlockList.html](https://docs.aws.amazon.com/kendra/latest/APIReference/API_DescribeQuerySuggestionsBlockList.html) API to get the status of your block list. `DescribeQuerySuggestionsBlockList` can also provide you with other useful information, such as the following: 
+ When your block list was last updated
+ How many words or phrases are in your current block list
+ Helpful error messages when creating a block list

You can also use the [https://docs.aws.amazon.com/kendra/latest/APIReference/API_ListQuerySuggestionsBlockLists.html](https://docs.aws.amazon.com/kendra/latest/APIReference/API_ListQuerySuggestionsBlockLists.html) API to get a list of block list summaries for an index.

To delete your block list, use the [DeleteQuerySuggestionsBlockList](https://docs.aws.amazon.com/kendra/latest/APIReference/API_DeleteQuerySuggestionsBlockList.html) API.

Your updates to the block list might not take effect right away. You can track updates by using the `DescribeQuerySuggestionsBlockList` API.

------
#### [ CLI ]

**To create a block list**

```
aws kendra create-query-suggestions-block-list \
 --index-id index-id \
 --name "block-list-name" \
 --description "block-list-description" \
 --source-s3-path "Bucket=bucket-name,Key=query-suggestions/block_list.txt" \
 --role-arn role-arn
```

**To update a block list**

```
aws kendra update-query-suggestions-block-list \
 --index-id index-id \
 --name "new-block-list-name" \
 --description "new-block-list-description" \
 --source-s3-path "Bucket=bucket-name,Key=query-suggestions/new_block_list.txt" \
 --role-arn role-arn
```

**To delete a block list**

```
aws kendra delete-query-suggestions-block-list \
 --index-id index-id \
 --id block-list-id
```

------
#### [ Python ]

**To create a block list**

```
import boto3
from botocore.exceptions import ClientError
import pprint
import time

kendra = boto3.client("kendra")

print("Create a query suggestions block list.")

# Provide a name for the block list
block_list_name = "block-list-name"
# Provide an optional description for the block list
block_list_description = "block-list-description"
# Provide the IAM role ARN required for query suggestions block lists
block_list_role_arn = "role-arn"

# Provide the index ID
index_id = "index-id"

s3_bucket_name = "bucket-name"
s3_key = "query-suggestions/block_list.txt"
source_s3_path = {
    'Bucket': s3_bucket_name,
    'Key': s3_key
}

try:
    block_list_response = kendra.create_query_suggestions_block_list(
        Description = block_list_description,
        Name = block_list_name,
        RoleArn = block_list_role_arn,
        IndexId = index_id,
        SourceS3Path = source_s3_path
    )

    print(block_list_response)

    block_list_id = block_list_response["Id"]

    print("Wait for Amazon Kendra to create the block list.")

    while True:
        # Get block list description
        block_list_description = kendra.describe_query_suggestions_block_list(
            Id = block_list_id,
            IndexId = index_id
        )
        # If status is not CREATING, then quit
        status = block_list_description["Status"]
        print("Creating block list. Status: " + status)
        if status != "CREATING":
            break
        time.sleep(60)
        
except ClientError as e:
        print("%s" % e)

print("Program ends.")
```

**To update a block list**

```
import boto3
from botocore.exceptions import ClientError
import pprint
import time
                        
kendra = boto3.client("kendra")
                        
print("Update a block list for query suggestions.")

# Provide the block list name you want to update
block_list_name = "new-block-list-name"
# Provide the block list description you want to update
block_list_description = "new-block-list-description"
# Provide the IAM role ARN required for query suggestions block lists
block_list_role_arn = "role-arn"

# Provide the block list ID
block_list_id = "block-list-id"
# Provide the index ID
index_id = "index-id"
                        
s3_bucket_name = "bucket-name"
s3_key = "query-suggestions/new_block_list.txt"
source_s3_path = {
'Bucket': s3_bucket_name,
'Key': s3_key
}
                        
try:
    kendra.update_query_suggestions_block_list(
        Id = block_list_id,
        IndexId = index_id,
        Description = block_list_description,
        Name = block_list_name,
        RoleArn = block_list_role_arn,
        SourceS3Path = source_s3_path
    )
                        
    print("Wait for Amazon Kendra to update the block list.")
                        
    while True:
        # Get block list description
        block_list_description = kendra.describe_query_suggestions_block_list(
            Id = block_list_id,
            IndexId = index_id
        )
        # If status is not UPDATING, then the update has finished 
        status = block_list_description["Status"]
        print("Updating block list. Status: " + status)
        if status != "UPDATING":
            break
        time.sleep(60)
                        
except ClientError as e:
print("%s" % e)
                        
print("Program ends.")
```

**To delete a block list**

```
import boto3
from botocore.exceptions import ClientError

kendra = boto3.client("kendra")

print("Delete a block list for query suggestions.")

# provide the block list ID
query_suggestions_block_list_id = "query-suggestions-block-list-id"
# Provide the index ID
index_id = "index-id"

try:
    kendra.delete_query_suggestions_block_list(
        Id = query_suggestions_block_list_id,
        IndexId = index_id
    )

except ClientError as e:
        print("%s" % e)

print("Program ends.")
```

------