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Requirements

To follow this guide, you’ll need:
  • An understanding of Omni modeling concepts
  • Familiarity with Omni’s model IDE
  • Familiarity with basic AI terminology such as tokens, agents, etc.

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AI context in Omni

Context is information such as:
  • Descriptions of topics and fields
  • Possible field values
  • How a field might be used
In Omni, this is provided to the AI using a context window. A context window is the amount of text an AI can read and consider at one time when answering a question or completing a task.
Using context windows allows AI models to understand and incorporate relevant information. Specifically, providing context to the LLM makes it more effective, enabling it to interpret your request accurately and generate meaningful responses.
Omni uses the following for context, in priority order:
1

Context and tuning pre-built by the Omni Engineering team

2

The ai_context parameters in the model, topics and views, if provided

3

Topic's description, if provided

4

Topic's name and base_view

5

Prioritized field properties

  • fully_qualified_name (view_name.field_name) - For example, order_items.total_sale_price
  • name - For example, total_sale_price
  • aliases
  • ai_context
  • all_values - Used to specify the possible values for the field
6

Pruned field properties

These properties will be pruned in the following order, but note that fields may be truncated entirely if required.
  1. description
  2. group_label - The categorization of the field
  3. label - If defined, the value will be used. If not provided, a title-cased field name with underscores removed will be used. For example, Total Sale Price
  4. semantic_type - The dimension or aggregate type, such as dimension, sum, count, etc. This value is evaluated based on the field definition and can’t be directly modified.
  5. data_type - The field’s data type, such as number, string, etc.
  6. sample_values - Example values for the field
  7. synonyms - Other terms used to refer to the field
Context applies to:
Omni’s model can handle ~200K characters of context:
  • ~15-25K is reserved for Omni app context, which is used to ensure queries are generated properly
  • The remaining ~175K characters are allocated for context from the semantic layer before truncation, starting with field metadata
To prevent field truncation, we recommend limiting the number of fields included in the context window to 550. Let’s look at example to explain to demonstrate why.
1

Topic metadata & prioritized field properties

You have a topic that you want to use for AI querying. Including the topic’s description, name, base_view, ai_context, and the included fields’ prioritized properties (ex: name), let’s say the total characters used is around 10,000.
  175,000 characters   // total allocated for semantic layer context
-  10,000 characters   // topic metadata & prioritized field properties
  ------------------
  165,000 characters   // total remaining for additional context
2

Topic field metadata

In the topic, the included fields generally look like this:
active_camp_active_campaign_id_c:
  sql: '"ACTIVE_CAMP_ACTIVE_CAMPAIGN_ID_C"'
  label: Active Campaign Active Campaign ID
  group_label: Active Campaign
The total characters used for this field’s basic metadata equals 160 characters.
3

Field-level context

You’ll likely want to add field-level context, such as description, sample_values, and ai_context. Let’s add 140 characters to account for the AI-specific metadata, which brings the total characters per field to 300 characters:
  160 characters      // basic field metadata
+ 140 characters      // field-level context for AI
  --------------
  300 characters      // total field metadata
4

Remaining fields for inclusion

To get the total number of fields, divide the remaining characters available (165,000) by the total metadata characters per field:
  165,000 characters      // total remaining for additional context
/     300 characters      // total field metadata
  -------------------
    550(ish) fields       // total fields for inclusion
This gives you roughly 550 fields to include in the context window before Omni begins truncating fields.
Context provided to Omni’s AI is shared with AWS Bedrock. Refer to the AI data security guide for more information.

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Curate AI outputs across the model

You can use the model parameter ai_context to pass context shared across topics. This parameter may also be useful for topic selection in the AI Assistant.

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Implement chain-of-thought reasoning for AI chat

If you want to have Blobby give a more thorough explanation of what is being generated (topic selection, field selection, etc.), you can include the following context within the model’s ai_context. This can be altered and tweaked if desired, but the reference to GenerateQuery is required for proper behavior. 
ai_context: |
  Before calling the 'GenerateQuery' tool, please do the following steps:
     1. Explain your reasoning of how you picked the fields in detail (9-10 sentences), under the header "Reasoning for field selection"
     2. Another 3-4 sentences on alternative queries or fields you could have chosen under the header "Alternative approaches considered"
     3. Finally add 4 markdown links as follow up questions under the header "Follow-up questions"

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Output summaries in multiple languages

If you’d like Blobby to summarize outputs in multiple languages, you can include the following context: 
ai_context: |
  When generating a summary, always output the summary in both English and Spanish.

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Curate topics

Topics have an ai_context parameter, which is useful for providing behavioral prompts and guidance for handling certain questions specific to the topic. For example:
ai_context for e-commerce orders topic
ai_context: |-
  This topic is focuses on e-commerce orders. The main concepts are orders
  at the line item level in the order items table, users in our user table,
  inventory in our inventory items table, and products in the products table.

  Only respond with accurate answers based on the data that you're aware of.
  Don't pivot unless there is more than one dimension included in the query.

  Typical questions will be focused on order performance over time, breakdowns across users, and more. 
  - if asked about sales or performance, always use the order_items.total_sale_price field
  - if asked about which users or who, use the users.full_name and users.email fields. Never return an ID unless explicitly asked.
  - if asked about top n without a specific metric or dimension, assume it involves the order_items.total_sale_price and products.name fields
You can use the ai_chat_topics model parameter to curate the list of topics that the AI Assistant or embedded chat instances have access to.

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Limit fields included in the context window

Blobby only uses the fields that exist within the context of a topic to output a response. Fields that have a hidden: true parameter are excluded from the context window. If you’d like to specify fields you want included in the context window, you can leverage the ai_fields parameter within a topic. For fields that you want to include, you can provide additional context at the field level. While Blobby can create calculations and aggregations, we recommend periodically checking the Analytics > AI usage dashboard to see what questions your users are asking, so you can identify opportunities to promote logic to the shared model and improve the self-serve experience.

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Reuse logic and limit the context window with topic extensions

Topics that you’re already leveraging in Omni can be extended to further curate them for AI. Using the extends parameter, you can reuse the definition of an existing topic without needing to repeat the code. Consider the following Order Transactions topic, which you want to extend to create a curated version dedicated to AI usage:
Order Transactions topic
label: Order Transactions

default_filters:
  order_items.status:
    not: [ Returned, Cancelled ]

joins:
  users: {}
  user_order_facts: {}
  inventory_items:
    products:
      distribution_centers: {}
In a new topic, use extends: [ order_items ] to extend the Order Transactions topic. You can then specify what to include in the AI-specific topic, such as limiting fields, filtering the data for specific use cases, adding more AI context, and so on:
Orders for AI querying topic, extended from Order Transactions
extends: [ order_items ]

base_view_label: Order Items
label: Orders for AI Querying

# limit fields included in the context window
ai_fields: [ tag:use_for_ai ]

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Add example queries as context

Along with providing context about the topic itself, you can use the topic’s sample_queries to provide example questions. This approach is useful if you anticipate specific, recurring questions or you find that Blobby struggles with date filters. To do this, you’ll want to:
  1. Create a query in a workbook that contains the correct answer to the question.
  2. In the workbook, click Model > Save as sample query to topic.
  3. When prompted, fill in the following:
    • Label - A user-friendly name for the query
    • Description - An optional description
    • Display on topic overview - If checked, the query will display as a sample query when the topic is selected in a new query tab
    • Include in AI context - If checked, the query will be included in the AI context for the topic
      • Prompt - An optional example prompt that could be used to generate this query
      • AI context - Optional, additional context for the AI
  4. When finished, click Save.
You can also use the model-level sample_queries parameter to define example queries that could be performed using the topics contained in the model.

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Curate views and fields

Views also have an ai_context parameter which can be useful for passing context to AI that is specific to the view. Keeping fields organized and labeled can not only help you create a top-notch self-service experience, it can also make Omni’s AI more efficient. The following parameters can be used to add metadata to fields for the purposes of AI:
  • ai_context - Adds context useful for AI responses
  • all_values - All possible values for the field
    • Note: when the dbt integration is enabled, accepted_values tests will be ingested as all_values
  • sample_values - Example values for the field
  • synonyms - Other terms used to refer to the field
You can use the workbook or the IDE to add the parameters.
  1. In the field browser, click the (three dots icon) next to the field.
  2. Click Modeling > Edit to open the Edit field side panel. Workbook AI parameters panel
In the IDE, navigate to the view containing the field to add the parameters. For example:
products.view
dimensions:
  brand:
    sql: '"BRAND"'
    description: Brand name of product
    sample_values:
      [
        Calvin Klein,
        Carhartt,
        Hanes,
        Volcom,
        Levi's,
        Diesel,
        Lucky Brand,
        Quicksilver,
        Nautica,
        American Apparel,
        Columbia,
        Hurley,
        Dockers,
        Arc'teryx,
        C-IN2,
        DKNY,
        HOBO,
        NOM,
        ANS
      ]
    synonyms: [ logo ]
    ai_context: To filter this field, use contains with the brand name.