This is the multi-page printable view of this section. Click here to print.

Return to the regular view of this page.

Motific documentation

Welcome to Motific.ai!

Motific is a SaaS platform that helps organizations leverage Generative-AI powered capabilities with enterprise-grade controls for sensitive data, security, responsible AI, and cost.

In this Motific technical documentation, you have everything you need to start your Generative AI journey with trust security and ease! You can discover the advanced features of Motific, how to use them, the concepts behind the features and how to get started with Motific. Also, refer to the API documentation to integrate your AI app with Motific in no time.

Introduction to Motific

Learn what is Motific and how it can benefit your organization. Also glance through the features of Motific and how you can leverage the platform and provision Gen AI apps faster with security and data compliance.

Getting started

Learn how you can quickly onboard and get started with the Motific SaaS platform. Also, explore how you can test out the policy enabled Motifs that you create in our sandbox environment.

User guide

The Motific user guide provides you with everything you need, to start provisioning the Generative AI apps such as AI assistants, chat interfaces etc.

API documentation

Learn about connecting your own Generative AI application to a Motif and interacting with the LLM you configured via Motific API endpoints.

Settings

Learn about how you can add users to your Motific tenant, and the different roles authourized to interact with certain features of Motific.

Known issues

Running into an issue? Before trying support, check our known issues page to see if it is documented as a known issue and if a work-around is noted.

1 - What's new

Motific key features

Security and compliance

Personally Identifiable Information (PII) protection

Motific detects, classifies, controls and produces analytics reports on PII entities flagged going in and out of LLMs. The following PII entities can be protected through redaction or blocking: Social Security Number (SSN), Credit card number, Phone number, Physical address, Person name, Email address.

LLM (Large Language Model) security

Prompt injection detection and malicious URLs can be detected in prompts and defined actions are taken to mitigate the security risks.

Toxic content prevention

Motific detects, classifies, controls and produces analytics reports on inappropriate and harmful content going in and out of LLMs that violates company’s harmful content policies. The following toxic content categories are prevented from inputs and responses: hate, self-harm, violence, and sexual. Enterprises can identify content prohibitied by their usage policies, and Motific’s inbuilt controls can take appropriate action to warn or block that content.

Off-topic prevention

Off-topic detection policy in Motific, helps keep conversations focused and relevant, preventing misuse of chatbots for unintended purposes. The prompts and LLM responses are scanned to determine if the topic of conversation with an AI assistant is not from the off-topic list. The off-topic list can be configured through the Motific admin console.

Cost metrics

Options for optimization of LLM usage budgets are provided. The cost trends based on token usage and prompt categories can be viewed for each Motif’s usage.

Shadow GenAI detection

Monitor and detect usage of unauthorized LLMs and GenAI services using integrations such as Cisco Umbrella solutions.

LLM (Large Language Model) gateway

Motific provides hassle free integration with industry leading Large Language Models (LLM) like Azure OpenAI, Mistral and AWS (Amazon Web Services) Bedrock. Motific adds additional LLM providers on an ongoing basis.

Common LLM adapter

Standardized interfaces are provided to integrate all supported LLM models by unifying their APIs and request protocols to enable simplified consumption by enterprise business clients.

Currently, LLMs from the following providers are supported:

  • Azure OpenAI models
  • Mistral
  • Amazon Bedrock models

Prompt intelligence

Motific has a distinctive approach to gathering and presenting information and patterns related to the prompts sent via a Motif to the LLMs, which we call prompt-intelligence. Prompt-intelligence encompasses various details of the prompts that are passed to an AI assistant. For example, what kind of tasks does each prompt request, which tasks are most often requested by the user, what are the token usage trends for each requested task, what is the time saved by using an AI assistant for a particular task, and trends and comparisons with different tasks. All this data can help you make the necessary decisions, and you can use this data to optimize your AI assistant usage.

With Motific, you can now measure the time saved by users when they utilize the Gen AI assistant for a task. Time-saving is demonstrated through various tasks that users typically engage in, such as reading, writing, searching, or reviewing for specific details. These metrics allow you to discover how Gen AI assistants enable productivity within your organization.

Moreover, Motific offers model optimization options, allowing you to compare the performance of your chosen AI model with other providers based on parameters like delay, cost, and quality of replies.

Motific provides detailed information in easily understandable graphical representations of prompt trends for tasks that users may perform with the Gen AI assistant, such as brainstorming, content generation, Q&A, and data analysis.

Some of the graphs include:

  • Tasks most frequently requested by users via your Gen AI assistant.
  • Token usage patterns for various tasks, indicating the tasks that consume the most tokens.
  • Gen AI cost trends per task.
  • Time savings per task.

Motif access control

Definition and enforcement of policies to ensure that only authorized individuals, groups or user roles can interact with provisioned APIs and assistants.

Configuration & Monitoring

  • Policy configurator: Policy definition templates for individual system control plugins to enable definition and review of policies for individual security and compliance controls.

  • Abstracted API console: Abstracted API enable teams to connect their GenAI apps with Motific.

  • Testing console: Testing tools are provided to evaluate the performance of policies applied to newly vended APIs and assistant.

  • Observability dashboard: IT admin dashboard to view real time activity, configure alerts and notifications, generate reports and capture logs. For each of the above visibility and insights mechanisms, Motific provides the following details to the IT administrators: Violations of defined policies,input and output tokens used, most utilized LLMs, most active Motifs.

RAG system

RAG (Retrieval-Augmented Generation) integration in AI assistants and abstracted API for adding organizational private data sources such as:

  • SharePoint
  • Websites

What can you do in Motific?

  • With just a few clicks, central-IT and security teams in organizations can provision GenAI assistants and abstracted APIs for Large Language Models, that are customized with Retrieval-Augmented-Generation (RAG) on organizational data sources, for out-of-the-box use or for building GenAI applications.
  • Sign up and get started with Motific.
  • Create a Motif, create LLM connections, and enable policies and actions that can be applied to the AI assistant that you provision via a Motif.
  • You can also connect knowledge bases customized with Retrieval-Augmented-Generation (RAG) on organizational data sources.
  • Learn about how you can add users to your Motific tenant. There are different roles authourized to interact with certain features of Motific.
  • Integrate your application with the Motif created by your organization. Connect to the Motific API endpoints that enable you to interact with the LLM provider and knowledge bases configured while adhering to the policies defined in the Motif.
  • Test out the Motifs you create in our chat console environment.

2 - Introduction

What is Motific?

Motific is a Saas platform empowering IT, security, and compliance teams to deliver Generative AI capabilities to their organization’s internal teams with control over sensitive data, security, responsible AI (Artificial Intelligence), and cost. Motific also enables teams to monitor the usage of the GenAI applications in their organization.

With just a few clicks, central-IT and security teams in organizations can provision GenAI assistants and abstracted APIs for Large Language Models, that are customized with Retrieval-Augmented-Generation (RAG) on organizational data sources, for out-of-the-box use or for GenAI applications. Motific includes built-in policy controls:

  • For sensitive data such as personally identifiable information (PII), corporate data and intellectual property.
  • For protection against security risk such as prompt injection, adversarial content and access control.
  • For responsible AI such as content safety.
  • Cost controls such as token budgets to all user inputs and ML model responses flowing through the assistant or abstracted API.

Motific also provides audit trail and key metrics tracking of all user requests for consolidated monitoring.

Who can benefit from Motific?

Motific can benefit any organization wishing to harness the power of Generative AI applications for productivity enhancement, in a secure, trusted and controlled environment.

Key sectors identified as high-imapct beneficiaries of Generative AI are Financial, Healthcare, Marketing and highly data-focused organizations.

Policy and decision makers

The personas like CXOs, VPs and Directors in IT, Security, Compliance, and Data teams who are responsible for business compliance, defining strategic direction to AI deployment, reviewing evaluations of GenAI deployment approaches and approval of GenAI policy controls for the organization can benefit from the PII, content, data loss prevention and security policies that can be defined and applied to apps via Motific.

Administrative users

Central IT, Security and Data Operations teams are responsible for providing employees within the company with access to GenAI services. Their objective is to ensure policy control effectiveness and compliance, fast and frictionless GenAI end-user experience, visibility & analytics of business & operational metrics and security of GenAI connected systems and data, all of which is embedded in Motific.

End-users

Knowledge workers and managers in business functions such as Marketing, HR, Sales, Customer Support, Finance etc., can benefit from Motific’s ability to add data-sources and knowledge bases. These functions can leverage Gen AI assistants to boost productivity in a compliant manner with the backing of their business data.

Application developers

Application developers can use the provisioned abstracted APIs in their business applications to interact with LLMs or RAG configs in a consistent and compliant manner.

Key features

Security and compliance

Personally Identifiable Information (PII) protection

Motific detects, classifies, controls and produces analytics reports on PII entities flagged going in and out of LLMs. The following PII entities can be protected through redaction and blocking: Social Security Number (SSN), Credit card number, Phone number, Physical address, Person name, Email address.

LLM (Large Language Model) security

Prompt injection detection, Data Loss Prevention (DLP), and RAG data poisoning can be detected in prompts and defined actions are taken to mitigate the security risks.

Toxic content prevention

Motific detects, classifies, controls and produces analytics reports on inappropriate and harmful content going in and out of LLMs that violates company’s harmful content policies. The following toxic content categories are prevented from inputs and responses: hate, self-harm, violence, and sexual. Enterprises can identify content prohibitied by their usage policies, and Motific’s inbuilt controls can take appropriate action to warn or block that content.

Cost compliance

Enable cost control mechanisms for individual applications or teams, to enable optimization of LLM usage budgets. The following control mechanisms are implemented in Motific: threshold warnings, suspension of usage, prompt rerouting to cheaper LLMs.

Shadow GenAI detection

Monitor, detect and control usage of unauthorized LLMs and GenAI services using integrations such as Cisco Umbrella solutions.

LLM (Large Language Model) gateway

Motific supports the closed-sourced and open-sourced models. Motific provides hassle free integration with industry leading Large Language Models (LLM) like Azure OpenAI, Mistral and AWS (Amazon Web Services) Bedrock.

Motific is continously adding support for more industry leading models.

Common LLM adapter

Standardized interfaces are provided that integrate all approved LLM models by unifying their APIs and request protocols to enable simplified consumption by enterprise business clients. The LLM Adapter also facilitates the consumption of RAG resources.

LLMs from the following services are supported:

  • Azure OpenAI models
  • Mistral
  • Amazon Bedrock models

LLM access control

Granular definition and access management for each enterprise connected LLM (i.e., third-party or self-hosted) is possible with Motific. It provides access control configurations to control approved integrations and communications between LLMs and other GenAI services by applying rules of connection and communication to approved APIs, IP addresses, data systems, third-party plugins and connectors.

App access control

Definition and enforcement of policies to ensure that only authorized individuals, groups/roles or applications can interact with provisioned vended APIs and assistants.

From an end-user perspective, Motific enables RBAC where the same RAG infrastructure is shared across different individuals and teams.

Prompt intelligence

Motific has a distinctive approach to gathering and presenting information and patterns related to the prompts sent via a Motif to the LLMs. With Motific, you can now accurately measure the time saved by users when they utilize the Gen AI assistant for a task. Time-saving is demonstrated through various tasks that users typically engage in, such as reading, writing, searching, or reviewing for specific details. These metrics allow you to discover how Gen AI assistants enable productivity within your organization.

Moreover, Motific offers model optimization options, allowing you to compare the performance of your chosen AI model with other providers based on parameters like delay, cost, and quality of replies.

Motific provides detailed information in easily understandable graphical representations of prompt trends for tasks that users may perform with the Gen AI assistant, such as brainstorming, content generation, Q&A, and data analysis.

Some of the graphs include:

  • Tasks most frequently requested by users via your Gen AI assistant.
  • Token usage patterns for various tasks, indicating the tasks that consume the most tokens.
  • Gen AI cost trends per task.
  • Time savings per task.

Configuration & Monitoring

  • Policy configurator: Policy definition templates for individual system control plugins to enable definition and review of policies for individual security and compliance controls.

  • Abstracted API console: Abstracted APIs enable teams to connect their GenAI apps with Motific.

  • Testing console: Testing tools (prompt library and accuracy benchmarks), to check the performance of policies applied to newly vended APIs and assistants.

  • Observability dashboard: IT admin dashboard to view real time activity, configure alerts and notifications, generate reports and capture logs. For each of the above visibility and insights mechanisms, Motific provides the following details to the IT administrators with different mixes and matches across some of the following items: Violations of defined policies, input and output tokens used, most utilized LLMs, most active applications.

RAG system

RAG integration in AI assistants and abstracted API for data connectors such as:

  • SharePoint
  • Websites

How does Motific work?

There are two main course of actions that can be taken with Motific by two personas:

  1. Provisioning an assistant or abstracted APIs with LLMs of choice, knowledge bases attached, policy, usage and cost controls by IT-Admin. Provision

  2. Using the assistant by an end user in a compliant manner. user-flow

2.1 - Getting started

Sign up to Motific

  1. To get started with Motific, navigate to the login page
  1. To sign up, click the Sign up link.

  2. Enter a first name, last name, these fields are mandatory.

  3. Enter a valid email address. A verification email will be sent to the entered email address with a verification link.

  4. Enter a password for your account. The password should adhere to the guidelines as shown in the figure below.

  5. To sign up, accept the terms and conditions, then click Sign Up.

Next steps

Once you have signed up with Motific and activated your account, you can login and get started with creating:

2.2 - Chat console

Motific chat console is an AI assistant where you can test and see the Motifs (apps/assistants) that you provisioned with various policy controls in action. The Motifs that you created are displayed here.

To navigate to the Motific chat console environment, go to the right-hand side corner, click on your account, then click the Go to chat menu. chat

When you click on the assistant that you provisioned, you are directed to a chat interface where you can test any prompts, and see live how the policies that you applied work. The knowledge bases that you attached can be seen in action and you can send inputs to fetch the responses from the knowledge bases. chat

chat

2.3 - Search Results

3 - Motific user guide

At a glance

This Motific user guide provides you everything you need to start using the Motific Saas platform to provision your Generative AI apps such as AI assistants, chat interfaces etc.

In this guide we cover some of the concepts like:

  • Adding an LLM connection to your chosen LLM provider
  • Adding a knowledge base to a Motif
  • Creating a policy
  • Creating a Motif
  • Fundamental API documentation for the abstracted API
  • API reference that developers can follow to integrate apps with Motifc
  • Monitoring options

3.1 - Dashboard

In the Motific dashboard, you get an overview of your organization’s Motific usage. Initially on the dashboard you see the steps that you need to take to get started with building your first Motif, and create a custom, trustworthy AI assistant.

When you start creating Motifs and experiencing how Motific helps you in your journey of provisioning secure and compliant GenAI deployments, you can get a glance at the metrics. The metrics and graphs presented on the dashboard highlight important information about the policy violation flags, input token count from all the prompts sent via Motific and output token count from models after inferences. It also shows the upwards and downwards trends of the metrics in real-time.

Get started

Dashboard

Dashboard provides four steps to get started:

  • Connect your model provider and select the models that will power your Motifs.
  • Connect your data sources and create knowledge bases to provide contextual data to your Motifs.
  • Create a policy from our templates to improve trust, safety, security, and cost compliance.
  • Create a Motif with your model, knowledge base, and policies to deliver a trustworthy GenAI assistant.

System at aglance

The metrics here show the trends in comparison to the previous month to the current month.

  • Total Motifs: The total count of the number of Motifs that your organization has created with Motific.
  • Total policy flags: The number of policy flags that were detected during the Gen AI app usage with the policies that you set up.
  • Total input token: The total number of input tokens that were used for prompts within your organization.
  • Total output token: The total number of output tokens that were used for inference responses by the models used by your organization.

Dashboard

Usage insights

Dashboard

  • Number of prompts for the top 5 tasks: In this section, you are provided with an easy-to-read graphical representation of the top 5 requested task category out of the total prompts requested by the users across all the Motifs that you have created.

  • Task usage by prompts and time saved: Task usage by prompts and time saved graph represents what percentage of tasks were requested by the prompts/inputs requested by the users of all the Motifs and how much of the user’s time was saved by using the Gen AI assistant for the tasks.

Graphical representation

Motific dashboard also has graphical representation for easy understanding of your app usage and to view the number of policy violation flags per Motif. These graphs also facilitate to easily identify trends, patterns, and outliers in the data.

If you hover over the graph, the total number of times the particular policy was flagged is displayed.

Dashboard

3.2 - Model connections

Motific Model connections page enables you to configure model connections.

What is a model connection?

Large language models are the backbone of any Generative AI project. Motific provides standardized interfaces that integrate with our approved LLM provider.

Initially, models from the following providers are supported:

  • Azure OpenAI models
  • Mistral
  • Amazon Bedrock models

In the model connections page you can:

Create a new model conection

  1. Navigate to the Model connections page to create a new model connection.

  2. To add a new model connection, click on the Create a new model connection button.

    add model

  3. To start creating a model connection, provide a connection name.

  4. Select which LLM provider to connect to and provide the necessary credentials for Motific to connect to it from the drop-down.

    add llm

  5. Provide the respective model provider API key. This is necessary for motific to connect to the provider.

    • AWS Bedrock- To connect to a AWS bedrock model, Provide Access key and Secret key. Both the credentials are required to connect to the AWS Bedrock provider.The access key and secret key can be found on your AWS IAM portal.

      add model

      • Follow the steps below to choose models for this AWS Bedrock connection will have access to:

      • To add a model, click the Add model button.

        add model

      • Select a model from the drop-down list.

        add model

      • Enter the Access URL. The Access URL of your Bedrock model. This can be found on your Bedrock dashboard. For example: https://bedrock.us-east-1.amazonaws.com

      • To add the model, click Add model.

    • Mistral- Provide API key to connect to the Mistral models.

      • For Mistral model the models are displayed automatically
      • Here you can select the model that you want the connection to have access to.
    • Azure OpenAI- Provide API key to connect to the Azure OpenAI models.

      • Follow the steps below to add the deployments and choose that models will have access to the Azure OpenAI connection:

      • To add a deployments, click the Add deployment button.

        alt text

      • Select a model from the drop-down list.

        alt text

      • Enter the deployment URL for your Azure OpenAI model.

      • To add the deployment, click Add deployment.

  6. To test the connection, click the Test connection button.

  7. Once you are done adding the details, click the Add connection button.

Update model connections

The model connections that you created can be viewed in the model connections page, you can see the existing model connections alreadt listed (if any).

  1. To update a model connection, click on the overflow menu (three dots) at the end of the model connection card view or in the Actions column.
  2. Select the Edit option. Or alternatively click the model connection name.
  3. Here you can view the previously selected model provider details. You cannot change the previously configured LLM provider, but you can choose different models from the Models list.
  4. Here you can view the previously configured connection name.
    • Edit connection name- Click on the Edit button to edit a connection name.
    • Once you are done editing the name, click the Save button.
  5. The models or deployments are displayed based on the LLM provider selected.
    • Edit model/deployment- Click on the Edit button to edit the models/deployments.
    • You can select or unselect the models/deployments from the list or add other deployments. Click the Save button to save the updates.

Delete a model connection

  1. To delete model connection, click on the overflow menu (three dots) at the end of the model connection card view or in the Actions column.

  2. Select the Delete option.

  3. A confirmation screen opens up cautioning you that deleting an model connection is irreversible, and asks you to type in the word DELETE in the field provided for confirmation. The word should match the word presented for the delete button to be active.

  4. To delete an model connection, click Delete. Once you delete a model connection you cannot view it in the list or grid view.

3.3 - Knowledge bases

What is a knowledge base?

A knowledge base is a collection of data sources. Knowledge bases are used to empower your Motifs with contextual knowledge of the data that may not be part of the model’s original training data. Each knowledge base may consists of multiple data sources. Each data source can be configured to synchronize on a set schedule. This ensures that a Motif’s contextual data is kept up to date with the changing data.

Motific enables you to create KB configurations to connect with your data sources so that the model’s response gets relevant contextual information from the data sources.

The knowledge base data sources can be one of the following:

  • SharePoint - SharePoint-Online sites where you have different files and folders that can be the data source for the knowledge base.
  • Public websites - Public websites are the websites available on the general internet. These websites can be added as data sources to a KB.

On KB page you can:

Create a new knowledge base

  1. To navigate to the knowledge base connections page, go to Connections menu » Knowledge bases tab. add KB

  2. To create a new knowledge base (KB) connection, click on the Create new knowledge base button. A page with fields to create new KB opens. add KB

  3. Next, add data sources for your KB. You have the option of adding one or multiple of the following two data source types:

Add SharePoint

Motific supports SharePoint-Online to be added a data source. Also, Motific Azure AD App-Only authentication. Procedure to grant access via Azure AD App-only is available in the Microsoft documentation

  • To add a SharePoint as a data source, click Add SharePoint option.

  • Provide the URL for the SharePoint site that you want to add.

  • Enter the source path- the path to the folder or document library you want to index.

  • Provide a Data source name for your SharePoint data source.

    add KB

  • Enter the Azure app ID for the site.

  • Enter the Azure tenant ID for the site.

  • Enter the Certificate thumbprint for the SharePoint site.

  • Provide Certificate PEM (Privacy Enhanced Mail). Drag and drop or click on the drop area to upload the PEM certificate.

    add KB

  • Define a schedule of how often the data source should be updated by selecting an option from the drop-down. You have the following options:

    • One-time - The data source sync begins as soon the KB is created and is a one-time sync.
    • Weekly- Specify the day of the week and time of the day when the data source should be updated with any new content.
    • Monthly- Specify day of the month and time of the day when the data source should be updated with any new content.
    • Daily- Specify the time of the day when the data source should be updated with any new content.
  • Verify all the details, then to add the data source , click the Add SharePoint button.

Add public website

Public websites are the websites available on the general Internet. These websites can be added as data sources to a KB.

  • To add a website as a data source, click Add Website button. add ws

  • Provide the source URL for the website that you want to add.

  • Provide a name and description for your website.

  • Define a schedule of how often the data source should be updated by selecting an option from the drop-down. You have the following options:

    • One-time - The data source sync begins as soon the KB is created and is a one-time sync.
    • Weekly- Specify the day of the week and time of the day when the data source should be updated with any new content.
    • Monthly- Specify day of the month and time of the day when the data source should be updated with any new content.
    • Daily- Specify the time of the day when the data source should be updated with any new content.
  • Verify all the details, then to add the data source, click the Add public website source button.

  • When you are done adding the data source(s), you can view the details of the data source added in the knowledge base summary section. You can also delete the data sources in the summary section by clicking the red delete icon.

  • Click the Create Knowledge base button to add the data sources to the knowledge base.

View KB details

When you navigate to the Knowledge base menu the existing KBs are displayed.

You can view the KB name, number of data sources added to the KB when it was created. The data source names can be viewed by hovering over the data sources. Also, created by and the last modified time.

To view each KB detail, follow the steps below:

  1. To view KB details, click on the KB name.

  2. The page with the KB metrics opens.

  3. Here, you can view the following:

    • Knowledge base details: The sync status depicts what is the status of the ingestion of the KB. You can also view the KB name, KB creation date, last updated date. The sync status can be in one of the following states- Syncing, Complete, N/A, and None.

    • Data source details: In the data source details following items can be viewed:

      • name of the data source and the link to the URL can be viewed.
      • the data source’s last updated date
      • number of records- shows the number of files processed, passed, or failed during the ingestion of the data source.

    If you see any record that has failed ingestion, then you can view the syncs. Click the View syncs button.

  4. In the data source details if you see any failure of ingestion of a data source, then you can click the Resync data source button to resync the data source.

Resync data sources

The knowledge base that you created can be viewed on the Knowledge base page. The existing KB (if any) are in the list view.

  1. To view the sync status of each data source in a KB, click on the KB whose data source sync status you want to check.

  2. In the Data source details section, when some of the records have failed ingestion, you can resync the data source.

  3. To resync the data sources, click the Resync data source button. The resync starts and the details of the sync can be seen by clicking View syncs.

View syncs

In the data source details section, to see the sync details of a data source, then follow the steps below:

  1. To view the sync details of a particular data source, click the respective data source’s View syncs button.

  2. The data source syncs section is displayed. Here, you can see all the resyncs that you have performed.

  3. The following data is displayed for each sync:

    • Sync status- The status of the sync started.
    • Sync ID- Unique ID for each re-sync request
    • Started at- The time period when the sync was started
    • Sync duration- The duration taken to ingest and sync the data source.
    • Expected- The number of expected files that need to be ingested for the data source.
    • Processed- The number of files that are already processed and ingested.
    • Failed- The number of files that have failed the ingestion process.
    • View logs- View the logs for the particular sync run.
    • View details- View the details of each and every sync that was started.

View logs

To view the logs of each sync and to find out of what kind or alert level or message is recorded, follow the steps below.

  1. Navigate to the knowledge base details of the KB.

  2. To view the sync details of a particular data source, click the respective data source’s View syncs button.

  3. To view the logs of a data source during a particular sync, click the corresponding sync’s View logs link. A small modal with the log opens.

  4. The following details can be viewed in the logs:

    • Alert level: Alert level shows the level of the alert in the log details.
      • Info: Info alert includes the information about the operations of the data source sync.
      • Error: Error alert is assigned to event logs that contain a data source sync error message.
    • Message: Here, the message of the log is displayed depending on the alert level.
    • Origin: Origin signifies what is the origin of the particular log.
    • Step: Step signifies at which step of the sync process the event was logged.
    • Reported at: The time when the alert or log was reported.

View sync details

To view the details of each sync and the status of the records/files within the data source, follow the steps below:

  1. Navigate to the knowledge base details of the KB.

  2. To view the sync details of a particular data source, click the respective data source’s View syncs button.

  3. To view the details of each record within the data source during a particular sync, click the corresponding sync’s View details link.

  4. The page with the details of each record within the data source for that sync cycle opens.

    If there are errors while syncing the data source then the errors would be diaplyes ad shown below:

  5. The list of latest ingestion with summary and list of documents opens.

  6. The summary section shows the number of documents that are in the following state:

    • Ingestion- The total number of records to be ingested.
    • Processing- The total number of records that are being processed.
    • Indexing- The total number of records that are being indexed.
    • Completed- The total number of records that have completed ingestion and are now in completed state.
    • Error- The total number of records that were errored out during ingestion.
  7. In the documents section, the following details are indicated about each document/record present in the data source.

    • Path- The path of the document.
    • Ingestion status- The ingestion status of the file. If there is a green check mark that means the ingestion is completed, otherwise it is still in progress.
    • Processing status- The processing status of the file. If there is a green check mark that means the processing is completed, otherwise it is still in progress.
    • Indexing status- The indexing status of the file. If there is a green check mark that means the indexing is completed, otherwise it is still in progress.
    • Error- This data shows if the document/record failed one of the above steps and was not synced with Motific.

3.3.1 - Delete knowledge bases

Delete KB connections

In the Knowledge base page, the existing knowledge bases are listed. You can delete the KB (Knowledge Base) by following the steps below.

  1. To delete a knowledge base, click on the overflow menu (three dots) at the end of a knowledge base card view or in the Actions column in list view.
  2. Select Delete option.
  3. A confirmation screen opens.

Delete a KB with Motif associated

  1. If you have the knowledge base associated with a Motif, then the alert that opens asks to disconnect the KB from the Motif.
  2. Disconnect the KB from the Motif before deleting the KB.
  3. Now the alert that opens asks to delete the data source associatedwith the KB first.
  4. Delete the data sources from the Knowledge base before deleting the KB.
  5. You are asked to type in the word DELETE in the field provided for confirmation. The word should match the word presented to activate the delete button.
  6. To delete a knowledge base, click Delete. Once you delete a knowledge base you cannot view it in the list view.

Delete a KB not associated to a Motif

  1. If you do not have the KB associated with a Motif, then the alert that opens asks to delete the data source associated with the KB first.
  2. Delete the data sources from the Knowledge base before deleting the KB.
  3. You are asked to type in the word DELETE in the field provided for confirmation. The word should match the word presented to activate the delete button.
  4. To delete a knowledge base, click Delete. Once you delete a knowledge base you cannot view it in the list view.

3.3.2 - SharePoint details

Overview

In the Motific knowledge base page, SharePoint online can be added as a data source for a knowledge base. For Motific to connect to your organization’s SharePoint it needs information about your SharePoint. And in this document, we have provided you with official Microsoft Azure documentation links, and we have documented the steps for you. You can refer any of the docs to get the following essential information:

  • URL: The URL of your SharePoint site. Example: https://testing.sharepoint.com/sites/mysite
  • Source path: The path to the document library or folder to index. Example: ‘Documents’ or ‘Documents/folder’
  • Data source name: Give your SharePoint data source a distinct name
  • Azure app ID: It is the ID that you receive when you register your SharePoint app online.
  • Azure tenant ID: The tenant ID is created when you create an Azure Active Directory B2C (Azure AD B2C) for your organization, it’s assigned a default domain name (name) and a directory (tenant) ID. The tenant ID is same as the organization ID.
  • Certificate thumbprint: A certificate thumbprint, often referred to as a fingerprint, represents a cryptographic hash value computed from the entirety of the certificate’s data, including its signature.
  • Certificate PEM: A PEM (Privacy Enhanced Mail) certificate is a base64 encoded certificate used in various digital security protocols, such as SSL/TLS for securing web communications. It includes the certificate itself, the certificate chain (if any), and possibly the private key, all in a standardized text-based format that is delimited by specific header and footer lines.

Below you can see the steps to follow to get the above information.

Create a self-signed certificate

For demonstration purposes here we are creating a self-signed certificate via Mac terminal:

  • Open a terminal on you Mac
  • Enter the following code into Cloud Shell to create a self-signed certificate:
openssl req -x509 -sha256 -nodes -days 365 -newkey rsa:2048 -keyout privateKey.key -out selfsigncert.crt
  • Export the certificate private key by running the following command:
cat selfsigncert.crt privateKey.key > selfsigncert.pem

  • Export the certificate pem file by running the following command:
cat  selfsigncert.pem >> fullchain.pem
  • Submit the CSR (Certificate Signing Request) to Azure. You can associate the certificate-based credential with the client application in Azure AD from the Azure portal.

Register an application in Microsoft Azure portal

  1. Click on the Azure Active Directory link under the Admin centers group on the left-side. A new browser’s tab opens to Microsoft Azure portal.

  2. In the App registrations tab the list of Azure AD applications registered in your tenant is displayed.

  3. Click the New registration button in the upper-left.

  4. Register the app by entering the details.

  5. Fill the app registration details and and click the Register button at the bottom.

  6. After App Registration, AppID is displayed.

Connect the certificate to the application

  1. Click on Certificates & secrets in the left menu bar.

  2. Click on the Upload certificate button, select the fullchain.pem file you generated earlier and click on Add to upload it.

  3. Once the certificate is uploaded, note down the Cert Thumbprint from the Certificates & secrets UI. This Cert Thumbprint is needed as an input to Motific while creating a KB with SharePoint as a data source.

Grant Azure Graph API Permissions permissions

  • You’ll need to add API permissions to use SharePoint API. Choose Add a permission and under Microsoft APIs.

  • Grant API Permissions.

  • Select Graph API Permissions

Note down the URL to the SharePoint site and the folder where all the files and folders are present, this information should be added while creating a SahrePoint data source. Within each of the steps above you can collate all the information needed to add the SharePoint site as a data source. For any other SharePoint site follow the same procedure to get the necessary information.

3.4 - Policies

What is a Policy?

In Motific, a policy refers to a set of guidelines defined for the usage of a Gen AI application associated with a Motif. These policies cover a wide aray of usecases like sensitive data protection, blocking unsafe and harmful content from going to the LLM. Or stopping assistants from engaging with hateful content, or protecting users from potential phishing scams or prompt hacking attempts via the LLM. A policy also outlines a course of action to be taken when Motific identifies an application usage does not conform to the defined policies. When Motific detects app usage violating any of the policies, it takes pre-configured actions.

The policies are created and used when a Motif is created. These policies assist organizations in ensuring security and compliance for Gen AI apps.

Available policies

Motific provides the following policy templates to be defined with a Motif:

Features

In the Motifs menu, you have the following actions that you can perform:

3.4.1 - Create a policy

Create a new policy

  1. To create a new policy, click on the Create a new policy button.

  2. Provide a policy name as a unique identifier for the policy.

  3. Next, choose the policy properties from the policy template displayed:

Adversarial content

An adversarial content policy can block attempts to exploit AI models through prompt injections, SQL query injection, and security threats, ensuring safe interactions.

You can define the policy with an action that Motific should perform when it detects that a prompt is injected with adversarial or harmful content. This policy also examines and blocks the output from a model that contains any adversarial or harmful content. The actions that Motific can take are Pass the prompt and out or Block it.

  • To define adversarial content policy, select the Adversarial content template.

  • The following categories are available for you to define policies over:

    • Adversarial- The adversarial category is triggered when the content of a prompt tries to deceive a LLM with harmful input. Select a policy action for Motific to perform when it identifies that the prompt or a model output contains adversarial content.

    • Harmful- This category is triggered when a prompt contains hate speech, profanity, or self-harm content. Select a policy action for Motific to perform when it detects that the prompt passed contains harmful content.

    • SQL injection- This category is triggered when an input contains SQL code intended to manipulate data. Select a policy action for Motific to perform when it detects that the prompt or a model output is injected with SQL content i.e., SQL queries.

    • XSS injection- The XSS injection, also known as a cross-site scripting attack, it is triggered when an input contains malicious scripts. Select a policy action for Motific to perform when it detects that the prompt or a model output is injected with XSS content i.e., malicious scripts.

    • Context switch- This category is triggered when a prompt contains content that signals a LLM to change the topic or type of content that it is generating. Select a policy action for Motific to perform when it detects that the prompt or a model output is injected with context switching content.

    • DAN (Do anything now)- The DAN category is triggered when the input contains open-ended master instructions that could potentially lead the LLM to generate outputs without clear ethical or safety boundaries. Select a policy action for Motific to perform when it identifies that the prompt or a model output is injected with a master prompt.

Toxic content

Toxic content policy helps you enforce guidelines for toxic (umbrella term for rude, offensive content) and unsafe content. It ensures interactions with any LLMs are free from racism, sexism, and other harmful behaviors.

Here, you can set actions for when Motific identifies that a prompt contains ethically wrong and unsafe content, such as hate, violence, self-harm, or sexual etc. The actions that Motific can take are Pass or Block the PII.

  • To set toxic content actions, select the Toxic content option.

  • The following categories are available for you to define policies over:

    • Violence- Select a policy action such as Pass or Block that Motific can perform when it detects that the prompt contains content describing violence.

    • Self-harm- Select a policy action such as Pass or Block that Motific can perform when it detects that the prompt contains content that describes or is related to self-harm.

    • Hate- Select a policy action such as Pass or Block that Motific can perform when it detects that the prompt contains hateful or fairness-related harmful content.

    • Sexual- Select a policy action to perform for when Motific detects that the prompt or inference response contains sexually explicit content.

Malicious URL

Malicious URL and data protection policy prohibits the injection of harmful URLs, protecting the chat interface from cybersecurity risks.

Here, you can select the action that Motific should perform when it detects that a prompt contains deliberately malicious, sensitive data theft, or data poisoning content. The actions that Motific can take are Pass or Block the malicious content from reaching the model.

  • To define malicious URL policy, select the Malicious URL option.
  • The following malicious URL and data protection content categories are available for you to define policies over:
    • Malicious URL- Malicious URLs are unsafe URLs that, if undetected, can cause phishing attacks, etc. Select a policy action for Motific to perform when it detects that a prompt is injected with malicious URL(s).

Off-topic content

Off-topic content policy, when set, helps keep conversations focused and relevant, preventing misuse of chatbots for unintended purposes.

Here, you can set actions for when Motific identifies that a prompt or a model output contains content from the restricted/uintended topics that you define. You are provided with fields where you can define the topic names that are considered to be off-topic or restricted while interacting with a LLM. For example, topics like dating, vacation, travel, and gaming etc., are topics that an organaization may regard as irrelevant to the users to be productive.

When Motific detects these topics in a prompt, that prompt can either be blocked from getting an inference from an LLM or can be passed to an LLM. Off-topic detection when set also examines the output of a model i.e., an LLM response for any restricted topic content. And depending on the action set, Motifc takes next course of action. This helps ensure that the interaction with the Gen AI apps is within the organization’s values and ethics.

The actions that Motific can take are Warn or Block the off-topic content.

  • To set off-topic detection policy actions, select the Off-topic detection template.
  • You can see the pre-populated fields available to enter the topics you want to restrict.
  • Enter the off-topic names on which the action needs to be taken.

PII content

PII content policy prevents the sharing of sensitive personal information with LLMs to safeguard user privacy.

Here, you define an action that Motific should perform, when it detects that a prompt contains any or all the PII(Personally identifiable information) entities. This helps safeguard user’s privacy from unauthorized access and breaches. The actions that Motific can take are Pass, Block, or Redact the PII. By default, the action is set to Pass for each category.

  • To define PII content, select the PII content option.

  • The following PII categories are available for you to define policies over:

    • Credit cards- Select a policy action to perform when Motific detects that the prompt or inference response contains credit card numbers.
    • Email address- Select a policy action to perform when Motific detects that the prompt or inference response contains email addresses.
    • Person- Select a policy action to perform when Motific detects that the prompt or inference response contains a person’s details like first name, last name.
    • Phone numbers- Select a policy action to perform when Motific detects that the prompt contains a US phone number.
    • Location- Select a policy action to perform when Motific detects that the prompt or inference response contains a locations details like address, country, etc.
    • US social security numbers- Select a policy action to perform when Motific detects that the prompt contains US social security number(s).

Code presence

Code presence policy ensures the prompt is scanned for any vulnerable code.

Here you can set actions for when Motific identifies that a prompt sent to a model, or and inference output form a model contains code in coding languages such as Python, Java, or JavaScript. The actions that Motific can take are Pass or Block the insecure code.

When you are done configuring the policies, click Save policy button. And the policy is saved and displayed on the Policies page.

3.4.2 - Update a policy

Update a policy

The policy that you created can be viewed in the policies page. The existing policies (if any) are in the list view.

  1. To update a policy, click on the overflow menu (three dots) at the end of the policy card view or in the Actions column of the list view.

  2. Select the Edit option, or alternatively click on the policy name.

  3. Edit policy name.

    • Edit policy name- Click on the Edit button to edit a policy name.
    • Once you are done editing the name, click the Save button.
  4. Next, you can edit the policy template. Here you can view the previously selected policy template details. You have the option to choose a new policy template and define actions or edit any of the actions for the categories available for the previously selected policy template.

    • Edit policy template- Click on the Edit button icon to edit a policy template.
    • You can edit the policy template, then click the Save button.

3.4.3 - Delete a policy

Delete a Policy

  1. To delete a policy, click on the overflow menu (three dots) at the end of a policy card view or in the Actions column in list view.

  2. Select Delete option.

  3. A confirmation screen opens up cautioning you that deleting a policy is irreversible, and asks you type in the word DELETE in the field provided for confirmation. The word should match the word presented for the delete button to be active.

  4. To delete a policy, click Delete. Once you delete a policy you cannot view it in the list or grid view.

3.5 - Motifs

What is a Motif?

A Motif is an aggregation of configurations specifically designed for Generative AI (GenAI) applications. These settings include connection details for Large Language Model (LLM) providers, knowledge base connections, policy information, and user access control details.

Organizations can create Motifs to establish comprehensive policy controls for security, trust, compliance, and cost management across its GenAI applications or abstracted APIs.

Once a Motif is created, the following features are available:

  • An API definition is provided that can be used to integrate your GenAI application with Motific. This enables the application to apply the policies set within the Motif to each inference.
  • You can also set cost controls by setting a token budget for your Motif. With this token budget you can define a percentage warning threshold for token usage by a Motif, which will provide a warning when the budget is exceeded.
  • You can also get information and patterns related to the prompts sent via a Motif to the LLMs. With Motific, you can now accurately measure the time saved by users when they utilize the Gen AI assistant for a task. Time-saving is demonstrated through various tasks that users typically engage in, such as reading, writing, searching, or reviewing for specific details. These metrics allow you to discover how Gen AI assistants enable productivity within your organization. Moreover, Motific offers model optimization options, allowing you to compare the performance of your chosen AI model with other providers based on parameters like delay, cost, and quality of replies.

Pre-requisites

  1. Sign up with Motific.

  2. Log in to your Motific account using your credentials. Or add a user with Admin credentials.

Before creating a Motif

To get started with Motific you need to provision Motifs, which enable developers to use AI models with applied policies. Before creating a Motif, it is a recommended you set up the following as per your requirement:

Features

In the Motifs menu, you can perform the following actions:

3.5.1 - Create Motifs

Create a Motif

In this section, we show you how to create a Motif and configure the advanced Motif options like KB’s, policies, and access control, that can get you one step closer to provisioning GenAI apps for your users.

To create a new Motif, follow the steps below:

Required steps

Motif options

Step 1: Motif details

  1. To create a Motif, click on the Motifs menu on the left navigation bar.

  2. Next, click on the Create a new Motif button on the upper-right side corner.

    Create Motif

  3. Enter a Motif name. This field is mandatory.


Step 2: Model connection

Select a model connection

The existing model connections can be viewed in the dropdown. If you do not have a model connection then create a new model connection. Once you have created a new model connection or have existing model connections, it’s time to select a model for your Motif.

  • To choose a model connection, click on the drop-down. The newly added and previously configured model connections are displayed in the drop-down.

  • Select the connection you want to add.

  • Next, choose a model connection from the drop-down that can connect to the Motif. Depending on the model provider, different models are listed in the drop down.

    add llm

Add a new model connection

If you do not wish to use existing model connections or want to connect to a new LLM provider then you can create a new model connection.

add model

  1. To add a new model connection, click on the Create a new model connection link.

  2. A new browser tab with the page to add a model connection opens. add model

  3. To start creating a model connection, provide a connection name.

  4. Select a LLM provider to connect to from the drop-down. Provide the necessary credentials for Motific to connect to it.

  5. Provide the respective model provider API key. This is necessary for motific to connect to the provider.

    • AWS Bedrock- To connect to a AWS bedrock model, Provide Access key and Secret key. Both the credentials are required to connect to the AWS Bedrock provider.The access key and secret key can be found on your AWS IAM portal.

      add model

      • Follow the steps below to choose models for this AWS Bedrock connection will have access to:

      • To add a model, click the Add model button.

        add model

      • Select a model from the drop-down list.

        add model

      • Enter the Access URL. The Access URL of your Bedrock model. This can be found on your Bedrock dashboard. For example: https://bedrock.us-east-1.amazonaws.com

      • To add the model, click Add model.

    • Mistral- Provide API key to connect to the Mistral models.

      • For Mistral model the models are displayed automatically
      • Here you can select the model that you want the connection to have access to.
    • Azure OpenAI- Provide API key to connect to the Azure OpenAI models.

      • Follow the steps below to add the deployments and choose that models will have access to the Azure OpenAI connection:

      • To add a deployments, click the Add deployment button.

        alt text

      • Select a model from the drop-down list.

        alt text

      • Enter the deployment URL for your Azure OpenAI model.

      • To add the deployment, click Add deployment.

  6. To test the connection, click the Test connection button.

  7. Once you are done adding the details, click the Add connection button.


Step 3: Knowledge base (KB)

Selecting a KB

The existing KB connections can be viewed in the dropdown. If you do not have a KB connection then create a new KB connection. Once you have created a new KB connection or have existing KB connections, it’s time to select a KB for your Motif.

  • To choose a knowledge base, click on the drop-down. The newly added and the existing knowledge base connections are displayed in the drop-down.

  • Select the knowledge base you want to add.

Add a new KB

If you have not already created a knowledge base, then follow the steps below to create a knowledgebase:

  1. To create a new knowledge base (KB) connection, click on the Create new knowledge base link. add KB

  2. A new browser window with the page to create a new KB opens. add KB

  3. Enter a unique name for your KB. Optionally, provide a description for your KB.

  1. Next, add data sources for your KB. You have the option of adding one or multiple of the following two data sources:
    • Add SharePoint: SharePoint sites where you have different files and folders that can be the data source for the knowledgebase.
    • Add website: Public websites are the websites available on the general internet. These websites can be added as data sources to a KB.
Add SharePoint

A SharePoint can be a website or a secure place to store, organize, share, and access information from any device. It can also consist of folders with various files.

  • To add a SharePoint as a data source , click the Add SharePoint option.

  • Provide the URL for the SharePoint site that you want to add.

  • Enter the source path- the path to the folder or document library you want to index.

  • Provide a Data source name for your SharePoint data source. add KB

  • Enter the Azure app ID for the site.

  • Enter the Azure tenant ID for the site.

  • Enter the Certificate thumbprint for the SharePoint site.

  • Provide Certificate PEM (Privacy Enhanced Mail). Drag and drop or click on the drop area to upload the PEM certificate. add KB

  • Define a schedule of how often the data source should be updated by selecting an option from the drop-down. You have the following options:

    • One-Time - The data source sync begins as soon the KB is created.
    • Weekly- Specify the day of the week and time of the day when the data source should be updated with any new content.
    • Monthly- Specify the day of the month and time of the day when the data source should be updated with any new content.
    • Daily- Specify the time of the day when the data source should be updated with any new content.
  • Verify all the details, then to add the data source , click the Add SharePoint button.

  • When you are done adding the data source(s), you can view the details of the data source added in the knowledge base summary section. You can also delete the data sources in the summary section by clicking the red delete icon.
Add public website

Public websites are the websites available on the general internet. These websites can be added as data sources to a KB.

  • To add a website as a datasource, click Add Website button. add ws
  • Provide the source URL for the website that you want to add.
  • Provide a name and description for your website.
  • Define a schedule of how often the data source should be updated by selecting an option from the drop-down. You have the following options:
    • One-Time - The data source sync begins as soon the KB is created.
    • Weekly- Specify the day of the week and time of the day when the data source should be updated with any new content.
    • Monthly- Specify day of the month and time of the day when the data source should be updated with any new content.
    • Daily- Specify the time of the day when the data source should be updated with any new content.
  • Verify all the details, then to add the data source , click the Add public website button.
  • When you are done adding the data source(s), you can view the details of the data source added in the knowledge base summary section. You can also delete the data sources in the summary section by clicking the red delete icon.

  • Click Create Knowledgebase button to add the data sources to the knowledge base.


Step 4: Policies

Choose policies

The existing policy can be viewed in the dropdown. If you do not have a KB connection then create a new KB connection Once you have created a new policy or have existing policy, it’s time to select a policy for your Motif.

  • To choose a policy, click on the drop-down. The newly added and existing policies are displayed in the drop-down.

  • Select the policy base you want to add. add policy

Add a policy

  1. To define a policy, click on the Create a new policy link. add ws

  2. A new browser tab with the page to create a new policy opens. add ws

  3. Provide identification information for the new policy.

  4. Next, choose from the policy template displayed:

Adversarial content

An adversarial content policy can block attempts to exploit AI models through prompt injections, SQL query injection, and security threats, ensuring safe interactions.

You can define the policy with an action that Motific should perform when it detects that a prompt is injected with adversarial or harmful content. This policy also examines and blocks the output from a model that contains any adversarial or harmful content. The actions that Motific can take are Pass the prompt and out or Block it.

  • To define adversarial content policy, select the Adversarial content template.

  • The following categories are available for you to define policies over:

    • Adversarial- The adversarial category is triggered when the content of a prompt tries to deceive a LLM with harmful input. Select a policy action for Motific to perform when it identifies that the prompt or a model output contains adversarial content.

    • Harmful- This category is triggered when a prompt contains hate speech, profanity, or self-harm content. Select a policy action for Motific to perform when it detects that the prompt passed contains harmful content.

    • SQL injection- This category is triggered when an input contains SQL code intended to manipulate data. Select a policy action for Motific to perform when it detects that the prompt or a model output is injected with SQL content i.e., SQL queries.

    • XSS injection- The XSS injection, also known as a cross-site scripting attack, it is triggered when an input contains malicious scripts. Select a policy action for Motific to perform when it detects that the prompt or a model output is injected with XSS content i.e., malicious scripts.

    • Context switch- This category is triggered when a prompt contains content that signals a LLM to change the topic or type of content that it is generating. Select a policy action for Motific to perform when it detects that the prompt or a model output is injected with context switching content.

    • DAN (Do anything now)- The DAN category is triggered when the input contains open-ended master instructions that could potentially lead LLM to generate outputs without clear ethical or safety boundaries. Select a policy action for Motific to perform when it identifies that the prompt or a model output is injected with a master prompt.

Toxic content

Toxic content policy helps you enforce guidelines for toxic (umbrella term for rude, offensive content) and unsafe content. It ensures interactions with any LLMs are free from racism, sexism, and other harmful behaviors.

Here, you can set actions for when Motific identifies that a prompt contains ethically wrong and unsafe content, such as hate, violence, self-harm, or sexual etc. The actions that Motific can take are Pass or Block the PII.

  • To set toxic content actions, select the Toxic content option.

  • The following categories are available for you to define policies over:

    • Violence- Select a policy action such as Pass or Block that Motific can perform when it detects that the prompt contains content describing violence.

    • Self-harm- Select a policy action such as Pass or Block that Motific can perform when it detects that the prompt contains content that describes or is related to self-harm.

    • Hate- Select a policy action such as Pass or Block that Motific can perform when it detects that the prompt contains hateful or fairness-related harmful content.

    • Sexual- Select a policy action to perform for when Motific detects that the prompt or inference response contains contains sexually explicit content.

Malicious URL

Malicious URL and data protection policy prohibits the injection of harmful URLs, protecting the chat interface from cybersecurity risks.

Here, you can select the action that Motific should perform when it detects that a prompt contains deliberately malicious, sensitive data theft, or data poisoning content. The actions that Motific can take are Pass or Block the malicious content from reaching the model.

  • To define malicious URL policy, select the Malicious URL option.
  • The following malicious URL and data protection content categories are available for you to define policies over:
    • Malicious URL- Malicious URLs are unsafe URLs that, if undetected, can cause phishing attacks, etc. Select a policy action for Motific to perform when it detects that a prompt is injected with malicious URL(s).

Off-topic content

Off-topic content policy, when set, helps keep conversations focused and relevant, preventing misuse of chatbots for unintended purposes.

Here, you can set actions for when Motific identifies that a prompt or a model output contains content from the restricted/uintended topics that you define. You are provided with fields where you can define the topic names that are considered to be off-topic or restricted while interacting with a LLM. For example, topics like dating, vacation, travel, and gaming etc., are topics that an organaization may regard as irrelevant to the users to be productive.

When Motific detects these topics in a prompt, that prompt can either be blocked from getting an inference from an LLM or can be passed to an LLM. Off-topic detection when set also examines the output of a model i.e., an LLM response for any restricted topic content. And depending on the action set, Motifc takes next course of action. This helps ensure that the interaction with the Gen AI apps is within the organization’s values and ethics.

The actions that Motific can take are Warn or Block the off-topic content.

  • To set off-topic detection policy actions, select the Off-topic content template.
  • You can see the pre-populated fields available to enter the topics you want to restrict.
  • Enter the off-topic names on which the action needs to be taken.

PII content

PII content policy prevents the sharing of sensitive personal information with LLMs to safeguard user privacy.

Here, you define an action that Motific should perform, when it detects that a prompt contains any or all the PII(Personally identifiable information) entities. This helps safeguard user’s privacy from unauthorized access and breaches. The actions that Motific can take are Pass, Block, or Redact the PII. By default, the action is set to Pass for each category.

  • To define PII content, select the PII content option.

  • The following PII categories are available for you to define policies over:

    • Credit cards- Select a policy action to perform when Motific detects that the prompt or inference response contains credit card numbers.
    • Email address- Select a policy action to perform when Motific detects that the prompt or inference response contains email addresses.
    • Person- Select a policy action to perform when Motific detects that the prompt or inference response contains a person’s details like first name, last name.
    • Phone numbers- Select a policy action to perform when Motific detects that the prompt contains a US phone number.
    • Location- Select a policy action to perform when Motific detects that the prompt or inference response contains a locations details like address, country, etc.
    • US social security numbers- Select a policy action to perform when Motific detects that the prompt contains US social security number(s).

Code presence

Code presence policy ensures the prompt is scanned for any vulnerable code.

Here you can set actions for when Motific identifies that a prompt sent to a model, or and inference output form a model contains code in coding languages such as Python, Java, or JavaScript. The actions that Motific can take are Pass or Block the insecure code.

Once you are done adding the details and selecting the policies, click Save policy button.


Step 5: Access control

As a part of the access control for a Motif, Motific provides an option to add users or user groups to a Motif. Provisioning access allows you to share the Motif with specific users or groups.

Add users

  1. To add new individual users to provision access to a Motif, click on the Users dropdown. The list of users added to your organization’s tenant are displayed.

  2. Select the users, or alternatively you can search for the users, click on the user you want to provision access to. You can add multiple users.

  3. Once you add new users to provision access to the Motif, or if you have previously added users, then you can see them in the field below.

  4. Alternatively, you can remove the users, click on the cross button on the users and they no longer have access to the Motif.

Add groups

  1. To add new user groups to provision access to a Motif, click on the Users dropdown. The list of user groups added to your organization’s tenant are displayed.

  2. Select the user groups, or alternatively you can search for the groups, click on the group you want to provision access to. You can add multiple groups.

  3. Once you add new user groups to provision access to the Motif, or if you have previously added user groups, then you can see them in the field below.

  4. Alternatively, you can remove the user groups, click on the cross button on the user group and they no longer have access to the Motif.

Step 6: Add Motif

  1. Verify that you have added all the configurations to the Motif you want to create in the Motif summary section.

  2. Once you are done verifying, to create the Motif, click Add Motif button.

3.5.2 - Update Motifs

Update Motifs

The Motif that you created can be viewed in the Motifs page. The Motifs are in the list view.

  1. To update a Motif, click on Motif name or alternatively click on the overflow menu (three dots) at the end of the Motif card view or in the Actions column of the list view.

  2. Select the Edit option.You are taken to the Motif details tab.

  3. Here you can view the previously configured Motif details.

    • Edit Motif name- Click on the Edit button to edit a Motif name.
    • Once you are done editing the name, click the Save button.
  4. Next, you can edit the model connections.

    • Edit model connection- Click on the Edit button to edit a model connection.
    • You can edit the model connection, model, or create a new model connection. Click the Save button to save the update.
  5. Next, you can edit knowledge base.

    • Edit knowledge base- Click on the Edit button to edit a knowledge base.
    • You can choose a different knowledge base or create a new knowledge base. Click the Save button to save the update.
  6. Next, you can edit policies.

    • Edit policies- Click on the Edit button to edit a policies.
    • You can choose different policies or create new policies to choose. Click the Save button to save the update.
  7. Next, you can edit users or user groups.

    • Edit users or user groups- Click on the Edit button icon to edit a users or user groups.
    • You can choose different users or user groups. You can also remove the current users and groups from having the access to the Motif. Click the Save button to save the update.

3.5.3 - Delete Motifs

Delete Motifs

  1. To delete a Motif, click on the overflow menu (three dots) at the end of a Motif card view or in the Actions column in list view.

  2. Select Delete option.

  3. A confirmation screen opens up cautioning you that deleting a Motif is irreversible, and asks you type in the word DELETE in the field provided for confirmation. The word should match the word presented for the delete button to be active.

  4. To delete a Motif, click Delete. Once you delete a Motif you cannot view it in the list or grid view.

3.5.4 - Manage Motifs

Manage Motif details

When a Motif is created, it is displayed on the Motifs page. Here, at a glance you can view the Motif’s name, model connection, the policies attached to the Motif and the user who added to the Motif.

Motif details

After creating a Motif, you are brought to the Motif details screen. This screen includes variety of metrics which are associated with that Motif.

Also, you can manage the Motif from here as well. You can set token budget for your Motif, such that once the budget is exceeded the users a warning is displayed.

Alternatively, you can follow the steps below to view any Motif details:

  1. To view a Motif’s details, click on the Motif name, or alternatively overflow menu (three dots) at the end of the Motif’s card view, or in the Actions column of the list view.
  2. Select the Edit option.
  3. Here you can view the existing Motif details. In Motif details tab, you can update the Motif.
  4. The Motif summary section gives a quick look at the all the configurations that have been made, you can view which model connection, policies, data soureces for KB, and users/user groups you have added.

Motif summary

Motif summary is a section that holds the summary of each configuration you made while creating a Motif. At a glance you can observe which model connection, knowledge base, policies, users/user groups you have added to a Motif.

KB summary

The knowledge base (KB) summary section is present while creating a Motif and in the Motif details tab when a Motif is created. To view the knowledge base summary follow thes steps below:

  1. To view the knowledge base summary added to a Motif, click the KB name in the Motif summary section.

  2. Here, you can see the Name and URL of the KB added.

  3. When you are done viewing the details, you can close the pop-up window.

Policies summary

The policies summary section is present while creating a Motif and in the Motif details tab for a existing Motif.

To view the policies summary follow thes steps below:

  1. To view the policies summary added to a Motif, click the policy name in the Motif summary section.

  2. Here, you can see the policy that is added as well as what are actions defined in the policy at one glance.

  3. When you are done viewing the details, you can close the pop-up window.

Other Motif details

There are also following tabs containing details associated with a Motif:

Token budget

In the Token budget tab, you can set the Motif token budget, and see the graphical representation of the token usage for the previous month. Also, you can check the token-budget for each task category that the users are performing, like text summarization, code generation etc., using the Gen AI application. You can also view the trends for token-budget change for different categories.

When the Motif has exceeded its total token budget, and the user sends a prompt via that Motif, then the prompts get blocked and are not processsed until next budget period when token budget is refreshed.

Set Motif token budget

When you create a Motif you can set the Motif token budget for a period of time, for example, monthly, weekly, yearly, or quarterly. The token budget will be applicable for the set time period.

To set Motific token budget follow the steps below:

  1. Navigate to the Motif menu, click on the Motif for which you want to set the token budget. If you have not created a Motif, then create a new Motif.
  2. Go to the Cost tab of the Motif details.
  3. Click on the Set budget button.
  4. Enter the total token budget that you would like to set for the Motif. The total token budget encompasses both input tokens for user input, and the output tokens from the model after an inference.
  5. To set the percentage for the warning token threshold, adjust the slider.
  6. Select a time period for which this budget is enforced from the dropdown. For example, monthly, weekly, yearly, or quarterly .
  7. You can also view the token used.
  8. Click Update budget. You can see the graph for the token usage forthe previous month is populated with the real time data.

Update Motif token budget

To update Motific token budget follow the steps below:

  1. Navigate to the Motif menu, click on the Motif for which you want to set the token budget. If you have not created a Motif, then create a new Motif.
  2. Go to the Cost tab of the Motif details. You can view the budget that has already been set.
  3. Click on the pencil icon.
  4. Now you can edit the total token budget, warning threshold and the time period for the budget.
  5. Click Update budget. You can see the graph for the token usage for previous month is populated with the real time data.

API definition

In the API definition tab, you can see the essential elements that are necessary for connecting your Gen AI app with the Motific API endpoint.

The following details are displayed:

  • API base URL: This is the initial part of the API URL. The API endpoint requests should use this as the base URL for all the calls.
  • Token: This is the authentication token (Bearer token) that should be passed with each API request. The request will not be authorized to access the Motif and hence the LLMs if this token is not passed.
  • Sample cURL: This is a sample cURL command that you can use to test the Motif.

3.5.5 - User groups

The User group tab allows you to add user groups where you can add individual users. The user groups created here can be used in the access provision section while creating a Motif. When you add a user group to a Motif all the users within the group get access to that Motif.

Create a new user group

To create a user group, follow the steps below:

  1. Navigate to the Motif menu, go to the User group tab.
  2. Enter a group name.
  3. Click Create group.
  4. A page with an option to add users opens.
  5. To add users to the group, click Add users.
  6. A pop-up with the list of all the users that you have added to your organization’s tenant are displayed.
  7. Select the users that you want to add and click Add user

Update a user group

A previously created user group can be edited, users can be added and deleted from the group and the group can be updated.

To update a user group, follow the steps below:

  1. Navigate to the Motif menu, go to the User group tab.
  2. The user groups you have created are displayed in the list view or grid view, and you can toggle between the two views.
  3. To update a user group, click on user group name or alternatively click on the overflow menu (three dots) at the end of the group card view or in the Actions column of the list view.
  4. Next, you can view the details of the user group. You can view the name and email and other details associated with the group.
  5. Here you can add users or delete users from the group.

3.6 - Intelligence

Overview

The Intelligence page provides insights to enterprises and organizations on how your GenAI users benefit from the AI assistants by looking at the actual work being conducted via the users prompts/ inputs. On this page, you can access an aggregate view of the intelligence related to the tasks that users are performing via the AI assistants that you have provisioned through Motifs.

Now the question arises what is “Intelligence” about? The intelligence is related to prompts, can also be refered as prompt-intelligence and it encompasses various details of the prompts/user inputs that are passed to an AI assistant. It aims at providing business analysts with credible base parameters to help them better understand GenAI usage such as:

  • Intelligence regarding information about each Motif that you have created and how it is being used by the users.
  • It comprises of the information about the tasks categories requested by each prompt/user input.
  • The task categories that are most often requested by the user, the token usage trends for each requested task category.
  • The time saved by using an AI assistant for a particular task category, and comparisons with different tasks categories.

All this data and trends can help you make necessary decisions and optimize your AI assistant usage. And also supports the understanding of how the user inputs evolve and give visibility to the actual prompts form users.

On this page you can also view the aggregate data of the prompt intelligence for all the Motifs that you have created. The various graphs displayed on this page are:

  • Trends on requested categories across all Motifs: This section showcases the tasks that were requested across all the Motifs by the users the most and the least. Based on this information you can optimize the model usage.

    • Least prompted category- This metric shows the prompt trend and the number of prompts for the task category that were least used by the user across all the Motifs.
    • Most prompted category- This metric shows the prompt trend and the number of prompts for the task category that were most used by the user across all the Motifs.
  • Trends on token usage across all Motifs: This section highlights the token usage trends for requested task across all the Motifs. You can determine which tasks consume the highest or least number of tokens and optimize the cost and LLM usage. The following token usage trends are displayed:

    • Lowest token consumption category- The task category with lowest token consumption across all the Motifs is displayed here. Also, the tokens trends of the task with the lowest percent change in the number of tokens is displayed.
    • Highest token consumption category- The task category with highest token consumption across all the Motifs is displayed here. Also, the tokens trends of the task with the highest percent change in the number of tokens is displayed.
  • Number of prompts for the top 5 categories: In this section, you are provided with an easy-to-read graphical representation of the top 5 requested task category out of the total prompts requested by the users across all the Motifs that you have created.

  • Categories usage by prompts and time saved: Categories usage by prompts and time saved graph represents what percentage of task categories were requested by the prompts/inputs requested by the users of all the Motifs and how much of the user’s time was saved by using the Gen AI assistant for the tasks.

Motifs

The Motifs you have created are listed and you can view the individual Motif’s prompt intelligence details by clicking on the Motif. The data associated with the Motif such as Least prompted category, Most prompted category, and Total time saved while using the particular Motif are displayed.

Here, you can filter the Motifs based on intelligence data, i.e., if a GenAI assistant provisioned via a Motif has been utilized by the users for different tasks and prompts have been provided, then there will be prompt intelligence data associated with the Motif and such Motifs can be viewed with Contains intelligence data filter. Whereas if prompts have not been passed for a Motif, then such Motifs can be filtered with No intelligence data.

When you click on the Motif of your choice, you get the following tabs with easy-to-read graphs and metrics about the prompts/inputs from the user that users request for a particular Motif.

Overview

The overview section provides you with the information about the prompts passed to the model via a Motif.

Latest prompts

The latest prompts section the most recent prompts sent by the users of the Gen AI assistant you provisioned via Motific. A prompt classification is also provided. The following details can be viewed:

  • Date: The time the prompt was passed to the model.
  • Prompt ID: The ID of the prompt.
  • Prompt: The user input that is passed by the user to get an inference from the model.
  • Requested task: The requested task column represents the task category that the prompt belongs to. The tasks category can one of the following:
    • Content Processing
    • Coding support
    • Brainstorming
    • Greetings
    • Text translation
    • Unclassified

Number of prompts for the top 5 categories

In this section, you are provided with an easy-to-read graphical representation of the top 5 requested task category out of the total prompts requested by the users of a Motif over the selected period.

Categories usage by prompts and time saved

Categories usage by prompts and time saved graph represents what percentage of tasks were requested by the prompts/inputs requested by the users of a Motif and how much of the user’s estimated time was saved by using the Gen AI assistant for the task.

The benefits of these graphs are instant understanding about the type of tasks requested by the Motif users, and the identification of which task categories are saving more time to the Motif users.

This section showcases the tasks that were requested by the Motif users the most and the least. Based on this information you can optimize the model usage.

  • Least prompted category- This metric shows the prompt trend and the number of prompts for the task that were least used by the user while using the Motif.
  • Most prompted category- This metric shows the prompt trend and the number of prompts for the task that were most used by the user while using the Motif.

This section highlights the token usage trends for requested tasks. You can determine which tasks consume the highest or least number of tokens and optimize the cost and LLM usage. The following token usage trends are displayed:

  • Lowest token consumption category- The task with lowest token consumption is displayed here. Also, the tokens trends of the task with the lowest percent change in the number of tokens is displayed.
  • Highest token consumption category- The task with highest token consumption is displayed here. Also, the tokens trends of the task with the highest percent change in the number of tokens is displayed.

Category usage

The category usage tab represents how such each task category is being requested over time. The user can select which task to display information for.

Based on the various tasks identified by Motific the graphs for prompts per task, token usage per task, Gen AI cost per task, and trends and comparison with other tasks are provided. Each task has its own screen with all the above mentioned graphs and metrics calculated when the data for the specific task is available.

The benefits of these graphs are detailed understanding of the usage, and the understanding of the token consumption and LLM costs for a given task category.

The tasks present in the Motific are as follows:

  • Coding Support
  • Content Creation
  • Content Processing
  • Conversational
  • Data Analysis
  • Greetings
  • Question & Answer
  • Text Translation
  • Unclassified

Let’s dive in and look at each of these graphs for a task. Every task has the same graphs presented with the data for the respective requested task category via the Motif. The graphs can be empty if there is no corresponding data available in the prompts requested by the user.

Prompts per category

The prompts per task graph displays the data of how many prompts were requested for the particular task over a period of time. The task is determined by which tab you are on. The legend on the graph explains that the number of prompts is signified by a particular color.

Token usage per category

The token usage per task graph displays the data of how many tokens were consumed when a prompt for a particular category was requested by the users of a Motif over a period of time. The task is determined by which tab you are on.

Gen AI cost per category

The Gen AI cost per task graph displays the data for the cost incurred over a period of time for the particular task, depending on which task tab you are on.

The trends and comparison with other tasks graph provides information about the prompts, input and output tokens, and cost. It compares these entities for the current and past periods of time to show the trends for a particular task. The task is determined by the tab you are on. This graph has two sections, Trends and comparisons. You can select from within the graph what you like to view.

Trends- In the trends graph, you can see the total number of prompts, input and output tokens, and cost of the current and previous periods for a particular task, providing the trends.

Comparison- In the comparison graph, you can see the comparison of the total prompts, total tokens, and total costs of a particular task with other tasks.

Time savings

Time savings builds on usage insights. It adds estimations of time savings based on per transaction or prompts input from the various users for a particular application.

In the time savings tab, based on the various tasks identified by Motific the graphs for time savings per task, estimated time saved per task, Gen AI cost per task are provided.

The tasks present in the Motific are as follows:

  • Coding Support
  • Content Creation
  • Content Processing
  • Conversational
  • Data Analysis
  • Greetings
  • Question & Answer
  • Text Translation
  • Unclassified

Let’s dive in and look at each of these graphs for a task. Every task has the same graphs presented with the data for the respective time savings.

Time savings per category

The time savings per task table provides the breakdown of the estimated time saved for the selected task category with a comparison to the previous period and the corresponding trend (up or down). It displays data on the estimated time saved and Gen AI costs for a specific task requested by a user through a motif. It shows the data for both the current and previous time periods. Additionally, it calculates and presents the time saved for reading, writing, reviewing, and searching for the task in either the current or previous time period. This data assists in estimating future trends. For each different tasks the same graphs with the particular task’s data are displayed.

The benefit of these metrics is that the estimated time savings comparison with previous period allow you to detect if users are becoming more efficient with the way they prompt LLMs.

Time savings breakdown

The time savings breakdown graph illustrates the total and average estimated time saved for a specific category of tasks, such as coding support, content creation, and content processing. The steps are categorized as thinking, reading, writing, and testing. Motific then estimates the time these steps would take for a particular task category, both without using GenAI and with using GenAI. This provides valuable insight to organizations on how GenAI tools can enhance user productivity.

Total

Average

Estimated time saved per category

The estimated time saved per task graph provides information about the time saved in reading, writing, testing, and searching by using the Gen AI assistant for a particular task via a Motif over a period of time.

Optimization

In the optimization tab, you can compare the performance, delay, and cost between the current model that you have selected for a Motif versus any other model of your choice. Based on the calculations, Motific recommends which model best suits your use case.

Also, you can see the results between the current model that you have selected for a Motif versus any other model based on any one of the optimization options such as performance, delay, or cost.

The optimization of the models allows you to verify if the best model was selected for a given Motif according to the type of tasks users leverage. After looking at the optimization results in comparison with other models, you can choose to reconfigure the Motif to use another model and observe the results and metrics.

To get the optimization recommendations, follow the steps below:

  1. Navigate to the Motif menu, click on the Motif for which you want to check the optimization recommendations. If you have not created a Motif, then create a new Motif.
  2. Go to the Optimization tab of the Motif details.
  3. Here, you can see that the current model for the Motif has been selected and cannot be changed.
  4. Next, select if you want optimization recommendation based on model or other options.

Optimization by model

  1. For optimization details by model select By model.
  2. Next, select a LLM provider and a model against which you want to check the optimization details.
  3. Click Submit.
  4. You can see the results on the graph in the Absolute tab. The graph displayed shows the comparison between the cost, performance and delays of the two models selected. Also, you can view the recommended model
  5. In the Normalized result tab, you can view the normalized graph of the optimization between cost delay and performance.

Optimization by options

  1. For optimization details by different options select By options.
  2. Next, select one or more options for which you want to check the optimization details. The options available are cost, delay, and quality.
  3. Click Submit.
  4. You can see the results on the graph in the Absolute tab. The graph displayed shows the comparison between the cost, quality and delays of the current model and the best model for the option you selected. Also, you can view the recommended model
  5. In the Normalized result tab, you can view the normalized graph of the optimization between cost, delay and quality.

3.7 - Monitoring

Overview

Monitoring involves observing and tracking the performance of the policies that you have created. You can also track the usage of your Motif with reports and logs.

Motific also monitors if there are unapproved or disallowed LLMs (Shadow GenAI) being used in your organization using Cisco Umbrella.

Policy flags

In the policy flags tab, track out of compliance GenAI usage.

  • To see the policy performance page, navigate to Monitoring » Policy flags tab.
  • To see the policy performance graphs for a particular time period, select the date range from the drop down.
  • Based on the date the graphs are populated with the data for that period.
    • Motifs with policy flags- Motifs with policy flags section displays the graph for the Motifs whose usage has violated the policies set for the given date range.
    • Policy flags over time- This graph shows the number of policies violated within the given data range.
    • Flags triggered by user- This graph displays the number of policy flags violated by a user.

Token usage

The token usage tab, displays the details about your Motif’s token usage. You can view the details of token usage by user and token usage by Motif.

Shadow GenAI

You can integrate your Cisco Umbrella account with Motific and discover the Generative AI usage in your organization with Motific.

  • To see shadow GenAI page, navigate to the Monitoring » Shadow GenAI tab.

  • Click on the Connect Motific to Cisco Umbrella.

  • You are taken to the integration tab in the Settings menu.

    • Enter the Client ID and Client secret.
    • Click Save to save the Cisco Umbrella details.
  • Once you integrate Cisco umbrella with Motific, navigate to the Monitoring » Shadow GenAI.

  • When you are done configuring Cisco Umbrella, you can see the activity in the Motific Shadow GenAI tab.

Prompt history

You can observe usage patterns in the prompt history section.

  1. To see the Reports and logs page, navigate to Monitoring » Prompt history tab.

  2. To view details of a particular log, click the View details link in the Actions column.

Prompt history details

Here, you can view the details of the prompt that you selected. You can find the following information about the prompt:

  • General information- This section provides general information about the prompt like prompt execution time, input and output token count for the prompt.
  • Profiler- The profiler shows the time elapsed at each step of the process right from the prompt submission to checking for policy violations to LLM response.

General information

alt text This section provides general information about the prompt.

  • Prompt ID- The id of the prompt sent via the Motif.
  • Motif- The Motif name is displayed here via which the user input was sent.
  • Execution timestamp- The time stamp when the prompt was executed i.e., sent to the LLM to fetch a response.
  • Response tokens- The number of response tokens consumed by the LLM while providing the response to the prompt.
  • Input tokens- The number of input tokens consumed by the LLM when the prompt was sent for the inference.

Model input and output

  • User quesry- The original prompt from the user sent for get an inference from the LLM.
  • Knowledge base context- The content of the knowledgbase onwhich the Motif response is based on.
  • Model response- The out from the LLM in response to the prompt. If there is no response, then check if there was any policy violation that caused Motific to enforce the policy action of blocking the prompt from going to LLM.

Policy actions

The actions that were taken by the Motific according to the policy actions set for the Motif this prompt belongs to.

  • Step- The step of the execution, the step can be while checking for the policy violations by the prompt or during fetching response form the model, etc.
  • Policies action- What action were taken based on the policies applied to the Motif via which the user input was sent.
  • Details- When you click on the View link, the JSON details about the response from Motific are displayed.

Execution profiler

alt text The profiler shows the sequence of the prompt execution and the time elapsed at each step of the process right from the prompt submission to checking for policy violations to LLM response. When you hover over the graph you can view the details of the execution like response at each step, time taken to execute the step, and the policy action.

3.8 - Testing

The testing page allows you to evaluate the effectiveness of your applied policy in a controlled, simulated LLM environment.

Test a Motif

To test a Motif that you created, follow the steps below:

  1. Navigate to the Testing menu.
  2. Next, select a Motif from the drop down.
  3. Next, provide a prompt to test the Motif.
  4. Click Test.
  5. The prompt is sent to the LLM that was configured while creating the Motif. You get a response based on the prompt and the policies you applied to the Motif.

4 - API docs

Fundamentals

Welcome to Motific’s API docs!

Motific API documentation guides you to integrate your application with the Motif created by your organization. Here, you can also find the information about how to obtain an access token include in a request to Motific.

The API reference gives you details of the endpoints and enables you to interact with the LLM provider and knowledge bases configured to recieve responses for the prompts, all the while adhering to the policies defined in the Motif.

Obtaining access token

Each enterprise application must have a Motif created and obtain the access token. Each application is tied to a Motif and to access the API endpoints should pass the unique access token that the API will validate before returning a response.

Follow the steps below to obtain the access token:

  1. Log in to your Motific account.

  2. Navigate to Motifs menu.

  3. To view the Motif details, click on the three dots in right corner of the Motif.

  4. To obtain the access token, go to API definition tab.

  5. In the API definition tab, you can get the following information:

  • The access token is displayed in the Token field. You can use this token for the Motific that is required by your enterprize application.

  • The Motif-Id is displayed in the API base URL required to send the post request to Motific. The Motif-id can be found at the end of the API base URL. For example: {BASE_URL}/api/v1/apps/{MOTIF_ID}

API structure

Basics

The root url for the API is-https://api.motific.ai..

Response Format

The response format for all requests is a JSON object.

Whether a request succeeded is indicated by the HTTP status code. A 2xx status code indicates success, whereas a 4xx status code indicates failure. When a request fails, the response body is still JSON, but always contains the fields error which you can inspect to use for debugging.

Authentication

Authenticate requests using OAuth access token. This token enables you to access the Motific API in order to integrate your enterprise Gen AI application with Motific and to seamlesslessly interact with LLMs. All requests to connect to the API for an inference via Motific, require userless access in which you use HTTP Bearer Authentication for every request. You can find the token in the Motific API definition screen.

Bearer Authentication


curl -X POST https://<BASE_URL>/api/v1/apps/<MOTIF_ID>/prompts 

-H "Content-Type: application/json" -H "Authorization: Bearer <YOUR_TOKEN_HERE>

Status codes

Status code Reason
200 Request successful

Error codes

Error code Reason
401 Unauthorized access error

Quick API reference

You can quickly look up the API endpoint.

Prompts enpoints

URL HTTP method Functionality
/api/v1/apps/{motific-id}/prompts POST Fetching inference response

4.1 - API reference

Prompts

/prompts endpoint enables you to connect with the LLM provider configured via Motific. You can send a prompt from your Gen AI application to this endpoint and receive the inference response with all the policies applied. The policies that are applied to the prompt and the inference are configured by your organization’s admin.

POST /api/v1/apps/{motif-id}/prompts

Authentication

A bearer token must be added to your API endpoint call. For more details see how to obtain an access token.

Base URL

https://api.motific.ai- This is the base URL to access API endpoints for this release.

Request URL

/api/v1/apps/{motif-id}/

Parameter Required Description
{motif-id} True When you create a Motif in the Admin console, you get a Motif ID that should be used in the request URL. For more details see how to obtain the Motif-Id.

Content-Type: application/json

Request parameters

Parameter Type Required Default value Description
messages object True The object to hold the input.
content string True The prompt that is entered by a user.
role True The roles that guide the LLM response. The usual roles used are `system`- provides high level instructions, `user`- provides prompts, or `assistant`- can be model's response.
Sample request body


{
    "messages": [
        {
            "content": "Give me 5 slogans for social media campaign of our product.",
            "role": "user"

        }
    ]
}

cURL sample request

Sample request body

curl -X POST https://https://<BASE_URL>/api/v1/apps/<MOTIF_ID>/prompts
-H "Content-Type: application/json" -H "Authorization: Bearer {YOUR_AUTH_TOKEN}" -H "x-vowel-tenant-id: {YOUR_TENANT_ID}" 
'-d {"messages":[{"role":"user","content":"Hello Motific chat assistant."}]}'

Response parameters

Parameter Type Description
blocked boolean Indicates whether a prompt was blocked by the Motific as per the policy defined by your organaization. If a prompt is blocked then that signals it has violated the org policy and there was no inference response. The user must be notified of this in anyway org's see fit.
finish_reason string Indicates the reason why the execution of the API stopped.
prompt_id string The id of the prompt that was passed in the request
response string The response object that contains the details about the inference response.
Usage object{} Usage tokens for user's prompt and the model's response.
completion_tokens integer Indicates the number of tokens returned by the LLM configured.
prompt_tokens integer Indicates the number of prompt tokens sent to the LLM.
total_tokens integer Indicates the total number of prompt and inference tokens.
Sample response body


{
	"blocked": false,
	"finish_reason": "stop",
	"prompt_id": "<<YOUR_PROMPT_ID>>",
	"response": "Hello! I'm here to help answer any questions you might have or provide information on a variety of topics. 
    I see you've written \"hello world,\" which is a common phrase used in programming to test that a program is running correctly. 
    Is there a specific programming question or topic you'd like to know more about? Let me know and I'll do my best to help you out!",
	"usage": {
		"completion_tokens": 313,
		"prompt_tokens": 18,
		"total_tokens": 331
	}
} 

5 - Settings

Settings page enables you to manage the settings of your account with Motific.

To navigate to settings page, click on the Settings menu on the lower-left corner of the navigation bar.

settings

Add a user

  1. To add a user, click the Add User button.

  2. Enter the name and email of the user you want to add.

settings

  1. Select the role that you want to assign the user from the drop down:

    • User: A user with this role only has access to Motific Chat assistant UI.
    • Admin: An admin role gives full access to Motific. This role has the permission to create, update, or delete a Motif, Policies, model connections and add other users etc.
  2. Review the details.

  3. To the add user click the Add User button.

Delete a user

  1. To delete a user click the settings button situated at the end of the user’s name.

  2. A confirmation screen opens up cautioning you that deleting an user is irreversible, and asks you type in the word DELETE in the field provided for confirmation. The word should match the word presented for the delete button to be active.

  3. To delete the user, click Delete. Once you delete the user you cannot see the user on the list.

5.1 - Configuration

Enabling non-English prompts

In the Configurations tab, you have the option to enable non-English prompts to be passed to a LLM. By default, Motific will block any prompts that are not in English. This feature can be useful in preventing plugins from generating false positives with non-English prompts.

To enable the non-English prompts select the check box and you are all set.

Testing the non-English prompts

Once you have enabled the configuration for non-English prompts, you can test Motifs by sending non-English prompts in the Motific Chat UI or testing page.

When the non-English prompts setting is checked, you get responses similar to as shown below.

  • When non-English prompt setting is unchecked you get responses similar to as shown below.

6 - Resources

In this section we have documented some resources that may be essential in your journey with Motific.

Following resources are available for you to make use of:

6.1 - Prompt collection

Prompt collection

On this page, you can see a list of prompts that you can use while using the Motific sandbox environment.

Sample prompts for Sales

Below you can find some sample prompts for a sales usecase. These prompts can get you started in your journey using Motific.

  1. Write a cold email to a prospect in the finance industry with the job title Finance manager and Banking relationship manager. Find problems related to finance software and generate solutions based on our product features such as ease of managing the finance data. Keep the email within 100 words. Greet and address the client Wiktor Hoffmann in the email.

  2. Write a LinkedIn message to George Thorne about why he should subscribe to our company page and newsletter. Explain in less than 50 words about our company motto and list our suite of products. Finally provide this email address xyz.aiprodutcs@xyz.com for any further queries.

  3. Develop four unique concepts for Google ad campaigns for our new smart phone handset that will target customers in the following locations: San Jose, Seattle, Raleigh.

Sample prompts for Marketing

  1. Analyze the sentiment of recent online reviews for our brand and provide a summary report.
  2. Create a weekly social media content calendar with post ideas that align with our brand’s summer campaign.
  3. Draft an email for our upcoming flash sale that will go out to our VIP customer segment.
  4. Compile a report on our competitors’ social media ad performance over the past month.
  5. Suggest improvements to our current landing page copy to increase conversion rates.
  6. Provide a list of SEO keywords we should target for our new line of athletic wear.
  7. Develop a press release outline for the launch of our innovative fitness app.
  8. Create a series of blog post titles that would appeal to our target demographic interested in sustainable living.
  9. Generate a list of catchy taglines for our new eco-friendly water bottle product line.

Sample prompts for Banking

These prompts cover banking AI chatbot scenarios and contain the some PII details that can be tested with Motifc.

  1. How can I request a new credit card pin for my credit card 436 5572 5767 6673?
  2. Can I withdraw cash using my card 436 5572 5767 6673 at ATM center?
  3. How do I change the address linked to my credit card to Desrosiers, Avenida Noruega 42, Villa Real, 5000-047, Portugal

7 - Known issues

In this documentation the known issues and possible work around for the issues are documented. The document is ordered based on each Motific release.

Motific release note known issues

Issue 1

Pending status shown when a user is added

When a user is added via the Settings menu, the status of the user is shown pending. But the user can immediately login to the Motific app.

Work-around

The app takes approximately 5 to 10 minutes to sync the status. The user you added can immediately login to the tenant they are invited to.

Issue 2

Data sources with folders having many level deep files or folders may be skipped during ingestion

While knowledge base creation, a site that has many level deep files or folders may be skipped during the ingestion of the data sources.

Issue 3

.xls file not supported

In Motific, when you create a knowledge base using a SharePoint as data source and that SharePoint contains .xls files, then these files will fail during the KB ingestion as the are not supported in Motific.

Work-around

Here is a list of supported file types-

Issue 4

The schedule defined for how often the data source be updated is always in UTC time zone.

When you add a data source and define a schedule to update thet data source while creating a knowledge base the time is always defined in UTC.

This is essential information as this defines your data source update schedule and defining the right time is very essential.

Issue 5

The unsynced public websites added as data sources cannot be deleted

When you add a public website as data source, depending on the number of files in the website the KB sync time can vary from six hours to ten hours.

If you try to delete a data source that is still ingesting the files and is in synching state, then you cannot delete it.

Work-around

Ignore the KB that has been stuck in the synching state and recreate the knowledge base with the same public website and wait for the sync to complete before testing it with a Motif.

Issue 6

Motific sandbox is not supported on the Safari browser

Motific does not support opening sandbox on the Safati browser.

Work-around

If you need to access the sandbox from the Safari browser, then you have to follow these steps:

  1. Go to the Settings» Privacy of the Safari browser.

  2. Uncheck the Website tracking check box.

Now if you return to the sandbox you can access the Motific sandbox on Safari browser.

Issue 6

During the creation of knowledge bases with public website as data sources, the website crawl depth is set to 2 i.e crawlDepth=2. This helps in ingesting the websites faster. Even if the website has more than 2 level deeper files, only 2 levels are crawled and ingested in Motific.

Work-around

If you want to increase the crawl depth for your website ingestions, then you can contact the Motific support @ motific-support@cisco.com.

Issue 7

The ingestion of a large website with many files may take multiple hours. You should check the sync status to view the ingestion progress and be aware of any failures during the website ingestion. This may happen because of the larger website sizes.

Work-around

If a KB with public website is not ingested or the status has not changed to Complete, then you may have to create a new KB with the same data source.