# QnABot on AWS

## Overview

QnABot on AWS is a multi-channel, multi-language conversational interface (chatbot) that responds to your customer’s questions, answers, and feedback. It allows you to deploy a fully functional chatbot across multiple channels including chat, voice, SMS, and Amazon Alexa. The solution’s content management environment, and contact center integration wizard allow you to set up and customize an environment that provides the following benefits:

-   Enhance your customer’s experience by providing personalized tutorials and question and answer support with intelligent multi-part interaction

-   Reduce call center wait times by automating customer support workflows

-   Implement the latest machine learning technology to create engaging, human-like interactions for chatbots

## Architecture Overview

Deploying this solution with the default parameters deploys the following components in your AWS account (bordered components are optional).

![Architecture](docs/architecture.png)

Figure 1: QnABot on AWS architecture

The high-level process flow for the solution components deployed with the AWS CloudFormation template is as follows:

1.	The admin deploys the solution into their AWS account, opens the Content Designer UI or [Amazon Lex](https://aws.amazon.com/lex/) web client, and uses [Amazon Cognito](https://aws.amazon.com/cognito/) to authenticate.

2. After authentication, [Amazon API Gateway](http://aws.amazon.com/api-gateway/) and [Amazon S3](http://aws.amazon.com/s3/) deliver the contents of the Content Designer UI.

3. The admin configures questions and answers in the Content Designer and the UI sends requests to Amazon API Gateway to save the questions and answers.

4. The `Content Designer` [AWS Lambda](http://aws.amazon.com/lambda/) function saves the input in [Amazon OpenSearch Service](http://aws.amazon.com/opensearch-service/) in a questions bank index. If using [text embeddings](docs/semantic_matching_using_LLM_embeddings/README.md), these requests will first pass through a ML model hosted on [Amazon SageMaker](https://aws.amazon.com/sagemaker/) to generate embeddings before being saved into the question bank on OpenSearch.

5. Users of the chatbot interact with Amazon Lex via the web client UI or [Amazon Connect](https://aws.amazon.com/connect/).

6. Amazon Lex forwards requests to the `Bot Fulfillment` AWS Lambda function. Users can also send requests to this Lambda function via [Amazon Alexa](https://developer.amazon.com/en-US/alexa) devices.

7. The `Bot Fulfillment` AWS Lambda function takes the users input and uses [Amazon Comprehend](https://aws.amazon.com/comprehend/) and [Amazon Translate](https://aws.amazon.com/translate/) (if necessary) to translate non-English requests to English and then looks up the answer in in Amazon OpenSearch Service. If using LLM features such as [text generation](docs/LLM_Retrieval_and_generative_question_answering/README.md) and  [text embeddings](docs/semantic_matching_using_LLM_embeddings/README.md), these requests will first pass through various ML models hosted on Amazon SageMaker to generate the search query and embeddings to compare with those saved in the question bank on OpenSearch.

8.	If an [Amazon Kendra](https://aws.amazon.com/kendra/) index is [configured for fallback](docs/Kendra_Fallback_README.md), the `Bot Fulfillment` AWS Lambda function forwards the request to Kendra if no matches were returned from the OpenSearch question bank. The text generation LLM can optionally be used to create the search query and to synthesize a response given the returned document excerpts.

9. User interactions with the `Bot Fulfillment` function generate logs and metrics data, which is sent to [Amazon Kinesis Data Firehose](http://aws.amazon.com/kinesis/data-firehose/) then to Amazon S3 for later data analysis.

Refer to the [implementation guide](https://docs.aws.amazon.com/solutions/latest/qnabot-on-aws) for detailed instructions on deploying QnABot in your AWS account.

Alternatively, if you want to custom deploy QnABot on AWS, refer to the details below.

## Custom deployment of QnABot on AWS

### Environment Prerequisites

-   Run Linux. (tested on Amazon Linux)
-   Install npm >7.10.0 and node >16.X.X ([instructions](https://nodejs.org/en/download/))
-   Install and configure git lfs ([instructions](https://git-lfs.com/))
-   Clone this repo.
-   Set up an AWS account. ([instructions](https://AWS.amazon.com/free/))
-   Configure AWS CLI and a local credentials file. ([instructions](https://docs.AWS.amazon.com/cli/latest/userguide/cli-chap-welcome.html))

### Build a version

Navigate to the root directory of QnABot (directory will be created once you have cloned this repo).

Install node.js moodules of QnABot:

```shell
npm install
```

Next, set up your configuration file:

```shell
npm run config
```

now edit `config.json` for the following parameters:

| param              | description                                                                 |
| ------------------ | --------------------------------------------------------------------------- |
| region             | the AWS region to launch stacks in                                          |
| profile            | the AWS credential profile to use                                           |
| namespace          | a logical name space to run your templates in such as dev, test and/or prod |
| devEmail(required) | the email to use when creating admin users in automated stack launches      |

Next, use the following command to launch a CloudFormation template to create the S3 bucket to be used for Lambda code and CloudFormation templates. Wait for this template to complete (you can watch progress from the command line or [AWS CloudFormation console](https://console.AWS.amazon.com/cloudformation/home))

```shell
npm run bootstrap
```

Finally, use the following command to launch template to deploy the QnABot in your AWS account. When the stack has completed you will be able to log into the Designer UI (The URL is an output of the template). A temporary password to the email in your config.json:

```shell
npm run up
```

If you have an existing stack you can run the following to update your stack:

```shell
npm run update
```

## Designer UI Compatibility

Currently the only browsers supported are:

-   Chrome
-   Firefox
    We are currently working on adding Microsoft Edge support.

## Built With

-   [Vue](https://vuejs.org/)
-   [Webpack](https://webpack.github.io/)

## License

Refer to [LICENSE.txt](LICENSE.txt) file for details.

## New features

Refer to [CHANGELOG.md](CHANGELOG.md) file for details of new features in each version.

A [workshop](https://qnabot.workshop.aws) is also available
that walks you through QnABot features.

---

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you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
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