put_bot async ¶

async def put_bot(
    self,
    name: "capo_lex_model_building_service.types.bot_name.BotName",
    locale: "capo_lex_model_building_service.types.locale.Locale",
    child_directed: "capo_lex_model_building_service.types.boolean.Boolean",
    *,
    config_overrides: Optional[AsyncLexModelBuildingServiceClientConfig] = None,
    description: Optional[
        "capo_lex_model_building_service.types.description.Description"
    ] = None,
    intents: Optional[
        "capo_lex_model_building_service.types.intent_list.IntentList"
    ] = None,
    enable_model_improvements: Optional[
        "capo_lex_model_building_service.types.boolean.Boolean"
    ] = None,
    nlu_intent_confidence_threshold: Optional[
        "capo_lex_model_building_service.types.confidence_threshold.ConfidenceThreshold"
    ] = None,
    clarification_prompt: Optional[
        "capo_lex_model_building_service.types.prompt.Prompt"
    ] = None,
    abort_statement: Optional[
        "capo_lex_model_building_service.types.statement.Statement"
    ] = None,
    idle_session_ttl_in_seconds: Optional[
        "capo_lex_model_building_service.types.session_ttl.SessionTTL"
    ] = None,
    voice_id: Optional[
        "capo_lex_model_building_service.types.string.String"
    ] = None,
    checksum: Optional[
        "capo_lex_model_building_service.types.string.String"
    ] = None,
    process_behavior: Optional[
        "capo_lex_model_building_service.types.process_behavior.ProcessBehavior"
    ] = None,
    detect_sentiment: Optional[
        "capo_lex_model_building_service.types.boolean.Boolean"
    ] = None,
    create_version: Optional[
        "capo_lex_model_building_service.types.boolean.Boolean"
    ] = None,
    tags: Optional["capo_lex_model_building_service.types.tag_list.TagList"] = None,
) -> "capo_lex_model_building_service.types.put_bot_response.PutBotResponse":
    """<p>Creates an Amazon Lex conversational bot or replaces an existing bot. When you create or update a bot you are only required to specify a name, a locale, and whether the bot is directed toward children under age 13. You can use this to add intents later, or to remove intents from an existing bot. When you create a bot with the minimum information, the bot is created or updated but Amazon Lex returns the <code></code> response <code>FAILED</code>. You can build the bot after you add one or more intents. For more information about Amazon Lex bots, see <a>how-it-works</a>. </p> <p>If you specify the name of an existing bot, the fields in the request replace the existing values in the <code>$LATEST</code> version of the bot. Amazon Lex removes any fields that you don't provide values for in the request, except for the <code>idleTTLInSeconds</code> and <code>privacySettings</code> fields, which are set to their default values. If you don't specify values for required fields, Amazon Lex throws an exception.</p> <p>This operation requires permissions for the <code>lex:PutBot</code> action. For more information, see <a>security-iam</a>.</p>

    Args:
        name: <p>The name of the bot. The name is <i>not</i> case sensitive. </p>
        description: <p>A description of the bot.</p>
        intents: <p>An array of <code>Intent</code> objects. Each intent represents a command that a user can express. For example, a pizza ordering bot might support an OrderPizza intent. For more information, see <a>how-it-works</a>.</p>
        enable_model_improvements: <p>Set to <code>true</code> to enable access to natural language understanding improvements. </p> <p>When you set the <code>enableModelImprovements</code> parameter to <code>true</code> you can use the <code>nluIntentConfidenceThreshold</code> parameter to configure confidence scores. For more information, see <a href="https://docs.aws.amazon.com/lex/latest/dg/confidence-scores.html">Confidence Scores</a>.</p> <p>You can only set the <code>enableModelImprovements</code> parameter in certain Regions. If you set the parameter to <code>true</code>, your bot has access to accuracy improvements.</p> <p>The Regions where you can set the <code>enableModelImprovements</code> parameter to <code>true</code> are:</p> <ul> <li> <p>US East (N. Virginia) (us-east-1)</p> </li> <li> <p>US West (Oregon) (us-west-2)</p> </li> <li> <p>Asia Pacific (Sydney) (ap-southeast-2)</p> </li> <li> <p>EU (Ireland) (eu-west-1)</p> </li> </ul> <p>In other Regions, the <code>enableModelImprovements</code> parameter is set to <code>true</code> by default. In these Regions setting the parameter to <code>false</code> throws a <code>ValidationException</code> exception.</p>
        nlu_intent_confidence_threshold: <p>Determines the threshold where Amazon Lex will insert the <code>AMAZON.FallbackIntent</code>, <code>AMAZON.KendraSearchIntent</code>, or both when returning alternative intents in a <a href="https://docs.aws.amazon.com/lex/latest/dg/API_runtime_PostContent.html">PostContent</a> or <a href="https://docs.aws.amazon.com/lex/latest/dg/API_runtime_PostText.html">PostText</a> response. <code>AMAZON.FallbackIntent</code> and <code>AMAZON.KendraSearchIntent</code> are only inserted if they are configured for the bot.</p> <p>You must set the <code>enableModelImprovements</code> parameter to <code>true</code> to use confidence scores in the following regions.</p> <ul> <li> <p>US East (N. Virginia) (us-east-1)</p> </li> <li> <p>US West (Oregon) (us-west-2)</p> </li> <li> <p>Asia Pacific (Sydney) (ap-southeast-2)</p> </li> <li> <p>EU (Ireland) (eu-west-1)</p> </li> </ul> <p>In other Regions, the <code>enableModelImprovements</code> parameter is set to <code>true</code> by default.</p> <p>For example, suppose a bot is configured with the confidence threshold of 0.80 and the <code>AMAZON.FallbackIntent</code>. Amazon Lex returns three alternative intents with the following confidence scores: IntentA (0.70), IntentB (0.60), IntentC (0.50). The response from the <code>PostText</code> operation would be:</p> <ul> <li> <p>AMAZON.FallbackIntent</p> </li> <li> <p>IntentA</p> </li> <li> <p>IntentB</p> </li> <li> <p>IntentC</p> </li> </ul>
        clarification_prompt: <p>When Amazon Lex doesn't understand the user's intent, it uses this message to get clarification. To specify how many times Amazon Lex should repeat the clarification prompt, use the <code>maxAttempts</code> field. If Amazon Lex still doesn't understand, it sends the message in the <code>abortStatement</code> field. </p> <p>When you create a clarification prompt, make sure that it suggests the correct response from the user. for example, for a bot that orders pizza and drinks, you might create this clarification prompt: "What would you like to do? You can say 'Order a pizza' or 'Order a drink.'"</p> <p>If you have defined a fallback intent, it will be invoked if the clarification prompt is repeated the number of times defined in the <code>maxAttempts</code> field. For more information, see <a href="https://docs.aws.amazon.com/lex/latest/dg/built-in-intent-fallback.html"> AMAZON.FallbackIntent</a>.</p> <p>If you don't define a clarification prompt, at runtime Amazon Lex will return a 400 Bad Request exception in three cases: </p> <ul> <li> <p>Follow-up prompt - When the user responds to a follow-up prompt but does not provide an intent. For example, in response to a follow-up prompt that says "Would you like anything else today?" the user says "Yes." Amazon Lex will return a 400 Bad Request exception because it does not have a clarification prompt to send to the user to get an intent.</p> </li> <li> <p>Lambda function - When using a Lambda function, you return an <code>ElicitIntent</code> dialog type. Since Amazon Lex does not have a clarification prompt to get an intent from the user, it returns a 400 Bad Request exception.</p> </li> <li> <p>PutSession operation - When using the <code>PutSession</code> operation, you send an <code>ElicitIntent</code> dialog type. Since Amazon Lex does not have a clarification prompt to get an intent from the user, it returns a 400 Bad Request exception.</p> </li> </ul>
        abort_statement: <p>When Amazon Lex can't understand the user's input in context, it tries to elicit the information a few times. After that, Amazon Lex sends the message defined in <code>abortStatement</code> to the user, and then cancels the conversation. To set the number of retries, use the <code>valueElicitationPrompt</code> field for the slot type. </p> <p>For example, in a pizza ordering bot, Amazon Lex might ask a user "What type of crust would you like?" If the user's response is not one of the expected responses (for example, "thin crust, "deep dish," etc.), Amazon Lex tries to elicit a correct response a few more times. </p> <p>For example, in a pizza ordering application, <code>OrderPizza</code> might be one of the intents. This intent might require the <code>CrustType</code> slot. You specify the <code>valueElicitationPrompt</code> field when you create the <code>CrustType</code> slot.</p> <p>If you have defined a fallback intent the cancel statement will not be sent to the user, the fallback intent is used instead. For more information, see <a href="https://docs.aws.amazon.com/lex/latest/dg/built-in-intent-fallback.html"> AMAZON.FallbackIntent</a>.</p>
        idle_session_ttl_in_seconds: <p>The maximum time in seconds that Amazon Lex retains the data gathered in a conversation.</p> <p>A user interaction session remains active for the amount of time specified. If no conversation occurs during this time, the session expires and Amazon Lex deletes any data provided before the timeout.</p> <p>For example, suppose that a user chooses the OrderPizza intent, but gets sidetracked halfway through placing an order. If the user doesn't complete the order within the specified time, Amazon Lex discards the slot information that it gathered, and the user must start over.</p> <p>If you don't include the <code>idleSessionTTLInSeconds</code> element in a <code>PutBot</code> operation request, Amazon Lex uses the default value. This is also true if the request replaces an existing bot.</p> <p>The default is 300 seconds (5 minutes).</p>
        voice_id: <p>The Amazon Polly voice ID that you want Amazon Lex to use for voice interactions with the user. The locale configured for the voice must match the locale of the bot. For more information, see <a href="https://docs.aws.amazon.com/polly/latest/dg/voicelist.html">Voices in Amazon Polly</a> in the <i>Amazon Polly Developer Guide</i>.</p>
        checksum: <p>Identifies a specific revision of the <code>$LATEST</code> version.</p> <p>When you create a new bot, leave the <code>checksum</code> field blank. If you specify a checksum you get a <code>BadRequestException</code> exception.</p> <p>When you want to update a bot, set the <code>checksum</code> field to the checksum of the most recent revision of the <code>$LATEST</code> version. If you don't specify the <code> checksum</code> field, or if the checksum does not match the <code>$LATEST</code> version, you get a <code>PreconditionFailedException</code> exception.</p>
        process_behavior: <p>If you set the <code>processBehavior</code> element to <code>BUILD</code>, Amazon Lex builds the bot so that it can be run. If you set the element to <code>SAVE</code> Amazon Lex saves the bot, but doesn't build it. </p> <p>If you don't specify this value, the default value is <code>BUILD</code>.</p>
        locale: <p> Specifies the target locale for the bot. Any intent used in the bot must be compatible with the locale of the bot. </p> <p>The default is <code>en-US</code>.</p>
        child_directed: <p>For each Amazon Lex bot created with the Amazon Lex Model Building Service, you must specify whether your use of Amazon Lex is related to a website, program, or other application that is directed or targeted, in whole or in part, to children under age 13 and subject to the Children's Online Privacy Protection Act (COPPA) by specifying <code>true</code> or <code>false</code> in the <code>childDirected</code> field. By specifying <code>true</code> in the <code>childDirected</code> field, you confirm that your use of Amazon Lex <b>is</b> related to a website, program, or other application that is directed or targeted, in whole or in part, to children under age 13 and subject to COPPA. By specifying <code>false</code> in the <code>childDirected</code> field, you confirm that your use of Amazon Lex <b>is not</b> related to a website, program, or other application that is directed or targeted, in whole or in part, to children under age 13 and subject to COPPA. You may not specify a default value for the <code>childDirected</code> field that does not accurately reflect whether your use of Amazon Lex is related to a website, program, or other application that is directed or targeted, in whole or in part, to children under age 13 and subject to COPPA.</p> <p>If your use of Amazon Lex relates to a website, program, or other application that is directed in whole or in part, to children under age 13, you must obtain any required verifiable parental consent under COPPA. For information regarding the use of Amazon Lex in connection with websites, programs, or other applications that are directed or targeted, in whole or in part, to children under age 13, see the <a href="https://aws.amazon.com/lex/faqs#data-security">Amazon Lex FAQ.</a> </p>
        detect_sentiment: <p>When set to <code>true</code> user utterances are sent to Amazon Comprehend for sentiment analysis. If you don't specify <code>detectSentiment</code>, the default is <code>false</code>.</p>
        create_version: <p>When set to <code>true</code> a new numbered version of the bot is created. This is the same as calling the <code>CreateBotVersion</code> operation. If you don't specify <code>createVersion</code>, the default is <code>false</code>.</p>
        tags: <p>A list of tags to add to the bot. You can only add tags when you create a bot, you can't use the <code>PutBot</code> operation to update the tags on a bot. To update tags, use the <code>TagResource</code> operation.</p>

    Raises:
        capo_lex_model_building_service.errors.bad_request_exception.BadRequestException: <p>The request is not well formed. For example, a value is invalid or a required field is missing. Check the field values, and try again.</p>
        capo_lex_model_building_service.errors.conflict_exception.ConflictException: <p> There was a conflict processing the request. Try your request again. </p>
        capo_lex_model_building_service.errors.internal_failure_exception.InternalFailureException: <p>An internal Amazon Lex error occurred. Try your request again.</p>
        capo_lex_model_building_service.errors.limit_exceeded_exception.LimitExceededException: <p>The request exceeded a limit. Try your request again.</p>
        capo_lex_model_building_service.errors.precondition_failed_exception.PreconditionFailedException: <p> The checksum of the resource that you are trying to change does not match the checksum in the request. Check the resource's checksum and try again.</p>
        capo_lex_model_building_service.errors.UnknownServiceError: The service returned an error code this client does not model.

    Examples:
        To create a bot
        This example shows how to create a bot for ordering pizzas.

        >>> await client.put_bot(name='DocOrderPizzaBot', description='Orders a pizza from a local pizzeria.', intents=[{'intentName': 'DocOrderPizza', 'intentVersion': '$LATEST'}], clarification_prompt={'messages': [{'contentType': 'PlainText', 'content': "I'm sorry, I didn't hear that. Can you repeat what you just said?"}, {'contentType': 'PlainText', 'content': 'Can you say that again?'}], 'maxAttempts': 1}, abort_statement={'messages': [{'contentType': 'PlainText', 'content': "I don't understand. Can you try again?"}, {'contentType': 'PlainText', 'content': "I'm sorry, I don't understand."}]}, idle_session_ttl_in_seconds=300, process_behavior='SAVE', locale='en-US', child_directed=True)
    """

    async def _handler(
        req: "AsyncOperationRequest[capo_lex_model_building_service.types.put_bot_request.PutBotRequest]",
    ) -> AsyncOperationResponse[
        "capo_lex_model_building_service.types.put_bot_response.PutBotResponse"
    ]:
        import capo_lex_model_building_service._operations.aws_deep_sense_model_building_service.put_bot

        (
            output,
            http_response,
        ) = await capo_lex_model_building_service._operations.aws_deep_sense_model_building_service.put_bot.async_put_bot(
            req.options, req.input
        )
        return AsyncOperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("PutBot")
    )
    input_: capo_lex_model_building_service.types.put_bot_request.PutBotRequest = {
        "name": name,
        "locale": locale,
        "child_directed": child_directed,
    }
    if description is not None:
        input_["description"] = description
    if intents is not None:
        input_["intents"] = intents
    if enable_model_improvements is not None:
        input_["enable_model_improvements"] = enable_model_improvements
    if nlu_intent_confidence_threshold is not None:
        input_["nlu_intent_confidence_threshold"] = nlu_intent_confidence_threshold
    if clarification_prompt is not None:
        input_["clarification_prompt"] = clarification_prompt
    if abort_statement is not None:
        input_["abort_statement"] = abort_statement
    if idle_session_ttl_in_seconds is not None:
        input_["idle_session_ttl_in_seconds"] = idle_session_ttl_in_seconds
    if voice_id is not None:
        input_["voice_id"] = voice_id
    if checksum is not None:
        input_["checksum"] = checksum
    if process_behavior is not None:
        input_["process_behavior"] = process_behavior
    if detect_sentiment is not None:
        input_["detect_sentiment"] = detect_sentiment
    if create_version is not None:
        input_["create_version"] = create_version
    if tags is not None:
        input_["tags"] = tags

    response = await aexecute_pipeline(
        AsyncOperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    await response.response.aclose()
    return response.output