Modifying configuration settings

The solution uses AWS Systems Manager Parameter Store to hold default and custom configuration settings. You can view and edit these settings using the Settings menu in the content designer.

Explore the available configuration settings, and override the defaults to configure the solution’s customize keyword filtering, answer field scoring, messages, redaction from logs and metrics (ENABLE_REDACTING and REDACTING_REGEX), and more. You can also enable the debug mode (ENABLE_DEBUG_RESPONSES), initiate fuzzy matching (ES_USE_FUZZY_MATCH), and experiment with score boosting for exact phrase matches (ES_PHRASE_BOOST). Follows are a set of example of settings frequently use. For more complete information on the settings, see QnABot Settings in the GitHub repository.

Note

Custom settings are kept when you upgrade the solution.

Configure keyword filters feature

1. Sign in to the content designer, select the tools menu ( ☰ ), and then choose Settings.

2. Change the value of the ES_USE_KEYWORD_FILTERS setting from true to false.

3. Scroll to the bottom and select Save. This turns off the new keyword filters feature.

You can further customize how the keyword filters feature works by changing the following settings:

• ES_KEYWORD_SYNTAX_TYPES - A list of tokens representing parts of speech identified by Amazon Comprehend.

• ES_MINIMUM_SHOULD_MATCH - A query rule used to determine how many keywords must match an item question in a valid answer.

Configure words and phrases replacement in user questions

If you want to replace words or phrases in user questions, for example, you want Thumbs up rewritten to be a direct match by question ID, you can use the

SEARCH_REPLACE_QUESTION_SUBSTRINGS setting.

1. Sign in to the content designer, select the tools menu ( ☰ ), and then choose Settings.

2. Change the value of the SEARCH_REPLACE_QUESTION_SUBSTRINGS setting to a JSON object, such as {"Thumbs Down": "QID::Feedback.001", "Thumbs Up": "QID::Feedback.002"}. You can add additional pairs separated by commas.

3. Scroll to the bottom and select Save. This will now rewrite all input matching "Thumbs Down" to "QID::Feedback.001".

Configure pre-processing and post-processing Lambda hooks

The content designer enables you to dynamically generate answers by letting you specify your own Lambda hook function for any item/question defined in the content designer (hook). In addition, a Lambda hook can be called as the first step in the fulfillment pipeline (PREPROCESS) or after processing has completed (POSTPROCESS ) and before the userInfo is saved to DynamoDB and the result has been sent back to the client.

You can add pre-processing and post-processing Lambda hooks (that run before preprocessing and after every question is run) via the Settings page.

1. Sign in to the content designer, select the tools menu ( ☰ ), and then choose Settings.

2. Find the LAMBDA_PREPROCESS_HOOK setting and set its value to your hook name. The name of the Lambda must start with qna- or QNA- to comply with the permissions of the role attached to the Fulfillment Lambda, for example, QNA-ExampleJSLambdahook.

3. Scroll to the bottom and select Save. The Lambda function specified will now be run before each question is processed.

Note

For more information on Lambda hooks, see the Extending QnABot with Lambda hook functions and the QnABot Settings sections in the GitHub repository.

Configure multi-language support

QnABot on AWS supports both voice and text interactions in multiple languages. QnABot can detect the predominant language in an interaction by using Amazon Comprehend, a NLP service that uses machine learning to find insights and relationships in text. The bot then uses Amazon Translate, a neural machine translation service to convert questions and answers across languages from a single shared set of FAQs and documents.

By default the multi-language feature is disabled. QnABot on AWS uses a property named ENABLE_MULTI_LANGUAGE_SUPPORT with a default value of false. You can change this setting using the content designer Settings page. Set it to true to enable multi-language support.

QnABot on AWS uses Amazon Translate to convert the question posed by the user to your core language that was chosen during your deployment. It performs a lookup of the answer in Amazon OpenSearch Service just as it normally does, using the native language translation of the question.

Searches are done in the language you have selected for your deployment only since QnABot on AWS documents are indexed using the language analyzer and their corresponding text analyzer for example, stemming and stop words. Once it finds the question, QnABot serves up the configured answer.

When you are in multi-language mode, consider letting the user choose their preferred language at the beginning of the chat and then have the whole conversation session from that point on to be only in the preferred language. Use Handlebars to do this. For details, see Integrating Handlebars templates.

You must enter a question into the QnA question bank with an utterance that is the name of the language that you are trying to set as preference, such as Spanish, coupled with the #setLang Handlebar in the answer of that utterance. This utterance or question must be invoked at the point where you want to set the conversation to the preferred language. For example, you can import the Language / Multiple Language Support sample or extension from the QnABot Import menu option. This adds two questions to the system: Language.000 and Language.001. The first question allows the end user to set their preferred language explicitly to a list of supported languages; the latter resets the preferred language and allows QnABot to choose the locale based on the automatically detected predominant language.

Note

Button values in the response cards are still displayed with their original value as input in the chat conversation.

When deploying the QnABot on AWS solution (version 5.5.0 and higher) CloudFormation template, there will be a Language parameter in which you have the option of selecting one of the 33 languages. This Language parameter is used as the core language for your QnABot on AWSdeployment. The Language Analyzer for your Opensearch index setting uses the language that you have specified in this parameter. In the case that your input has a low confidence rate, it defaults to English since that is the backup language.

• Custom terminology also supports your NATIVE_LANGUAGE.

• For the SageMaker LLM, Llama-2-13b-chat is supported in English. If you want to use the multi-language feature with an LLM, we encourage you to use Bedrock with a model that can support other languages. If you are using a language other than English as your core language, then make sure to change your LLM prompt settings to match your core language. If your preferred core language is not supported by any Bedrock model, then you must use your own Lambda function and LLM.

• For the embeddings, the SageMaker intfloat/e5-large-v2 JumpStart model only supports English. If you are using a non-English native language, then you should use your own embeddings model and provide the Lambda function in your deployment.

• If using the thumbs up and down feature, you should translate thumbs up and thumbs down into your native language and put that phrase in the PROTECTED_UTTERANCES setting. This is to prevent it from being treated as a question by the solution. To do this, complete the following steps:

  1. Use the AWS translate API to translate thumbs up and thumbs down to your deployment language, if it is not English.

  2. Add the translation of thumbs up and thumbs down in the website client config file inside your QnABot on AWS code and deploy.

  3. Add the translation of the thumbs up and thumbs down as a question in your QnABot deployment.

  4. Go to the content designer, navigate to the top left and select Settings.

  5. Find the PROTECTED_UTTERANCES variable and insert that phrase in by adding a comma, and then enter the translation.

• PII redaction will still be for English, since that is still accurate with other languages.

• Changing the NATIVE_LANGUAGE should always be done from the CloudFormation stack by changing the Language parameter.

When creating an Amazon Kendra web crawling data source from the QnABot UI, it will be created in the native language specified in your CloudFormation parameters. If the specified native language is not supported by Amazon Kendra, English will be used as the default language.

When querying within your Amazon Kendra data source, the following logic will be applied to determine the language used for querying:

1. The algorithm will determine the user's locale and use the `shouldUseOriginalLanguageQuery()` function to decide whether to query in the user's native language or the locale's language.

2. Based on the result from `shouldUseOriginalLanguageQuery()`, it will either:

   • Use the locale's language if it is supported by Amazon Kendra.

   • If the locale's language is not supported, it will check if the native language (the language chosen in the CloudFormation parameters) is supported by Amazon Kendra.

3. If neither the locale's language nor the native language is supported by Amazon Kendra, English will be used as the default language for querying.

In summary, the algorithm tries to use the user's preferred language (either the locale or the native language specified in the CloudFormation parameters) if it is supported by Amazon Kendra. If neither language is supported, English is used as the fallback language for querying the Amazon Kendra data source.

For more information, see the MultiLanguage Support section in the GitHub repository or the Multi Language section in the QnABot Workshop.

Using automatic translation

This solution supports automatic translation to the end user’s language using Amazon Translate.

1. Turn on multiple-language support by setting: ENABLE_MULTI_LANGUAGE_SUPPORT to TRUE.

2. In the web UI, ask: Qu’est-ce que q et a bot?  

3. The chatbot replies to you in French.

The solution also supports speech recognition and voice interaction in multiple languages. When you install or update QnABot on AWS, specify the languages using the LexV2BotLocaleIds CloudFormation parameter. The default languages are US English, US Spanish, and Canadian French, but you can customize the list to use any of the languages supported by Amazon LexV2.

Use the ENABLE_DEBUG_RESPONSES setting to see how local language questions are translated to English by QnABot on AWS. Use this translation to tune the content as needed to ensure that QnABot on AWS finds the best answer to a non-English question.

The solution also supports Amazon Translate custom terminology to provide additional control over the translation of entities and phrases. Custom terminology supports the language that you are deploying with. For more information on how to use the Import Custom Terminology tool in the content designer, see the Using Custom Terminologies with Amazon Translate section in the GitHub repository.

Configure personally identifiable information (PII) rejection and redaction

QnABot on AWS can detect and redact personally identifiable information (PII) using Amazon Comprehend and regular expressions.

If ENABLE_REDACTING is set to true, the Comprehend detected PII entities will also be redacted from Amazon CloudWatch logs and OpenSearch Service logs.

PII rejection and redaction

For more information, see QnABot Settings in the GitHub repository.