AI Assistant

• 2026-03-19
• Morten Lønskov

Applies to:

• ❌ Tabular Editor 2
• Tabular Editor 3 (Available since 3.26.0)
  ✔ Desktop
  ✔ Business
  ✔ Enterprise

The AI Assistant is a chat-based interface for AI-assisted semantic model development designed to help you create semantic models faster. With an enterprise-ready design, full control of what is sent to the AI, and built-in consent management, you can use the AI Assistant with confidence. The AI Assistant has undergone independent security penetration testing. For details, visit the Tabular Editor Trust Center. It can explore your model metadata, write and execute DAX queries, generate C# scripts, run Best Practice Analyzer checks, query VertiPaq Analyzer statistics and search the Tabular Editor knowledge base.

The AI Assistant uses a bring-your-own-key model. You provide an API key from one of the supported providers and the assistant runs directly against that provider's API.

Note

The AI Assistant is in public preview starting with Tabular Editor 3.26.0. We welcome feedback on the experience as we continue to refine it.

Getting Started

1. Open Tools > Preferences > AI Assistant
2. Select your AI provider and enter your API key
3. Open the AI Assistant panel from View > AI Assistant
4. Type a message and press Enter to start a conversation

Tip

Use our interactive demo of the AI Assistant to see how to set up and use it.

Note

API keys are stored encrypted on your local machine.

Supported Providers

Configure your AI provider under Tools > Preferences > AI Assistant > AI Provider. Select a provider from the dropdown, enter your API key and optionally override the default model name.

Provider	Default Model	Configuration Required
OpenAI	gpt-4o	API key. Optional Organization ID and Project ID
Anthropic	claude-sonnet-4-6	API key
Azure OpenAI	Deployment-dependent	API key, endpoint URL and deployment name
Custom (OpenAI-compatible)	User-specified	API key and custom endpoint URL

OpenAI

Select OpenAI as the provider and enter your API key. You can optionally specify an Organization ID and Project ID if your OpenAI account uses these. The default model is gpt-4o, but you can change it to any model available on your account.

Anthropic

Select Anthropic as the provider and enter your API key. The default model is claude-sonnet-4-6. You can change the model name to any Anthropic model available on your account.

Important

Anthropic enforces input token per minute (ITPM) rate limits based on your account tier. A new API key starts at Tier 1 with 30,000 ITPM for Claude Sonnet 4.x. A single request against a large model can exceed this limit. Purchase $40 or more in API credits to reach Tier 2 (450,000 ITPM). See the Anthropic rate limits documentation for full tier details.

Azure OpenAI

Select Azure OpenAI as the provider. Enter your API key and the service endpoint URL for your Azure OpenAI resource. Set the model name to match your deployment name.

Custom (OpenAI-compatible)

The Custom provider option supports local or organizational LLMs that expose an OpenAI-compatible API endpoint. Enter your API key and the custom endpoint URL. This allows you to keep all data within your own infrastructure for data privacy or compliance requirements.

Using a local or organizational LLM

You can run the AI Assistant against a self-hosted LLM by using the Custom provider. This keeps all data within your own infrastructure — whether that is a model running on your local machine or a centrally hosted LLM within your organization's network. Either way, no data is sent to a third-party cloud provider.

Several tools can host models with an OpenAI-compatible API:

• Ollama — lightweight CLI for downloading and running models locally
• LM Studio — desktop application with a graphical interface for managing and running local models
• LocalAI — self-hosted, community-driven alternative with broad model support

These tools can run on a developer's workstation for individual use, or be deployed on a shared server within your organization to provide a centrally managed LLM endpoint for your team.

Example: Ollama

1. Download and install Ollama
2. Pull a model, for example: ollama pull llama3.1
3. Start the Ollama server (it runs automatically after installation, by default on port 11434)
4. In Tabular Editor, go to Tools > Preferences > AI Assistant > AI Provider
5. Set Choose provider to Custom (OpenAI-compatible)
6. Set Service Endpoint to http://localhost:11434/v1
7. Set Model name to the model you pulled (e.g. llama3.1)
8. The API Key field can be set to any non-empty value (e.g. ollama) — Ollama does not require authentication, but the field cannot be left blank

Example: LM Studio

1. Download and LM Studio
2. Pull a model. Either through the model search page on the left panel or the CLI. For example: lms get lmstudio-community/Meta-Llama-3.1-8B-Instruct-GGUF
3. Start the LM Studio server. Either through the developer page on the left panel or through the CLI. for example lms server start Note, you will have to configure it to use OpenAI compatible mode. Additionally, you may have to change the default context size to be over 100,000 tokens.
4. In Tabular Editor, go to Tools > Preferences > AI Assistant > AI Provider
5. Set Choose provider to Custom (OpenAI-compatible)
6. Set Service Endpoint to http://localhost:1234/v1
7. Set Model name to the model you pulled (e.g. lmstudio-community/Meta-Llama-3.1-8B-Instruct-GGUF)
8. The API Key field can be set to any non-empty value (e.g. lms) — LM Studio does not require authentication, but the field cannot be left blank

Note

Response quality with local models depends on the model size and your hardware. Larger models generally produce better results but require more RAM and a capable GPU. The AI Assistant's tool-calling capabilities require a model that supports function calling in the OpenAI-compatible format.

Tip

We recommend a model with a minimum of 30 billion parameters but ideally at least 100 billion parameters. For example, the Qwen3.5-122B-A10B model performed well in our internal testing.

Capabilities

The AI Assistant has access to your model context and can perform the following actions:

• Model exploration: Query model metadata including tables, columns, measures, relationships and their properties
• DAX query writing: Generate DAX queries and execute them against your connected model, returning result sets directly in the chat
• C# script generation: Create C# scripts for model modifications that open in a new editor window. When you click Execute in the chat, the preview changes dialog is shown by default, letting you review all model metadata changes before accepting them. You can also open the script in the editor and run it from the script toolbar, with or without the preview. Model metadata changes can be undone with Ctrl+Z
• Best Practice Analyzer: Run BPA analysis, view rule violations and create or modify BPA rules
• VertiPaq Analyzer: Query memory usage statistics and column cardinality
• Document access: Read and modify open documents such as DAX scripts and DAX queries
• Knowledge base search: Search the embedded Tabular Editor documentation for answers
• UI navigation: Generate te3:// action links that open specific Tabular Editor dialogs and features

Conversations

The AI Assistant supports multiple simultaneous conversations. Each conversation maintains its own message history and context.

• Conversations persist across sessions, stored locally in %LocalAppData%\TabularEditor3\AI\Conversations\
• Titles are generated automatically after the first exchange. You can rename conversations manually
• Auto-compaction: When the conversation approaches the context window limit (~80%), older messages are automatically summarized to free up space. A snapshot of the full conversation is archived before compaction

Artifacts

When the AI Assistant generates code, it creates artifacts that open directly in editor windows:

• C# Scripts: Open in a new C# script editor with syntax highlighting, compilation and execution support
• DAX Queries: Open in a new DAX query editor with syntax highlighting and execution support

Artifacts stream in real-time as the AI generates them. C# script artifacts include safety analysis that flags potentially unsafe code (e.g. file system access or network operations).

When you execute a C# script from the chat, the Script Preview dialog shows a side-by-side diff of all model metadata changes made by the script. You can accept the changes or revert them. See Running scripts with preview for details.

Custom Instructions

Custom Instructions are instruction sets that guide the AI Assistant's behavior for specific tasks. They are activated automatically based on intent detection or invoked explicitly.

Built-in Custom Instructions

The AI Assistant includes the following built-in Custom Instructions:

Custom Instruction	Triggers On
DAX Querying	DAX, query, evaluate, measure
Model Modification	Modify, change, add, update, create
Model Design	Design, architecture, pattern, best practice
Organize Model	Organize, clean up, folder, rename
Optimize Model	Optimize, performance, slow, speed
Macros	Macro, automate, record
UDFs	UDF, function, user-defined
BPA	BPA, best practice, rule, violation

Custom Instructions are shown as indicators above assistant responses, indicating which instructions influenced the response. You can toggle this display in Preferences > AI Assistant > Preferences > Show custom instructions indicator.

Invoking a Custom Instruction

Type / to browse available custom instructions, or type the full /instruction-id at the start of your message to explicitly invoke a specific instruction. For example, /dax-querying forces the DAX querying instruction regardless of message content.

Add your own Custom Instructions

You can create custom instructions by placing .md files in %LocalAppData%\TabularEditor3\AI\CustomInstructions\. Each file requires YAML frontmatter defining the instruction metadata:

---
id: my-custom-skill
name: My Custom Skill
description: A brief description shown in the autocomplete popup.
priority: 100
always_inject: false
hidden: false
triggers:
  keywords:
    - keyword1
    - keyword2
  patterns:
    - "\\bregex pattern\\b"
  context_required:
    - model_loaded
---

Your instruction content goes here. This is the text that will be
injected into the AI's system prompt when the instruction is activated.

Field	Required	Default	Description
id	No	Filename without .md	Unique identifier, also used as /id for explicit invocation
name	No	Title-cased id	Display name in autocomplete
description	No	-	Short description shown below the name
priority	No	100	Higher values are injected first when multiple Custom Instructions match
always_inject	No	false	If true, always included in the system prompt
hidden	No	false	If true, not shown in /command autocomplete
triggers.keywords	No	[]	Words that activate the instruction (case-insensitive)
triggers.patterns	No	[]	Regex patterns for complex matching
triggers.context_required	No	[]	Conditions that must be met (e.g. model_loaded)

Custom Instructions with an id matching a built-in instruction will override the built-in version.

Consent

The AI Assistant requests permission before sending data to the AI provider. Consent is scoped to specific data types:

Consent Category	Description
Query data	DAX query results and data samples
Read documents	Reading content from open documents such as DAX scripts and DAX queries
Modify documents	Making changes to open documents
Model metadata	Table and column schemas, measure definitions and other model metadata
Edit BPA rules	Creating or modifying Best Practice Analyzer rules
Read macros	Reading macro definitions

When the AI Assistant needs access to a data type for the first time, a consent dialog appears. You can choose the duration of your consent:

Option	Scope
This time	Single request only
This session	Until Tabular Editor is restarted
For this model	Persisted in the model's user options (.tmuo) file
Always	Global preference, persisted across all models and sessions

Managing Consents

You can review and reset your consent choices under Tools > Preferences > AI Assistant > AI Consents. Each consent category shows its current state. Click Reset to revoke an "Always allowed" consent and return it to "Ask when needed".

Preferences

Configure AI Assistant display and behavior options under Tools > Preferences > AI Assistant > Preferences.

Chat Display

Preference	Default	Description
Show selection context indicator	true	Display the currently selected model object in the chat
Show custom instructions indicator	true	Show Custom Instruction indicators above assistant responses
Show knowledge base search indicator	true	Display progress when searching the knowledge base

Context Compaction

Preference	Default	Description
Auto compact	true	Automatically summarize old messages when approaching the context limit
Auto compact threshold %	80	Token usage percentage that triggers auto-compaction

Knowledge Base

Preference	Default	Description
Check for knowledge base updates on startup	true	Automatically check for knowledge base updates when Tabular Editor starts

C# Script

Preference	Default	Description
Preview changes	true	Show the preview changes dialog when executing AI-generated C# scripts from the chat

Token Usage

Each message to the AI Assistant consumes input tokens. The token cost of a single message depends on what context is included:

• System prompt and custom instructions: Sent with every message. Typically 5,000 to 15,000 tokens depending on which custom instructions are active.
• Model metadata: When the assistant needs to understand your model, it retrieves metadata through tool calls. A compact summary includes table names, column names, measure names, relationships and descriptions. A full metadata retrieval includes the complete model definition. For large models this can consume tens of thousands of tokens.

Reducing Token Usage

Select specific objects in the TOM Explorer before asking your question. When objects are selected, the assistant scopes its context to those objects instead of retrieving metadata for the entire model. This is the most effective way to reduce both token usage and API cost.

Other ways to reduce token usage:

• Ask focused questions about specific tables, measures or columns rather than broad questions about the entire model. A vague prompt such as "Set display folders on all measures" forces the assistant to retrieve metadata for the entire model. A specific prompt such as "Set display folders on the measures I have selected" limits the context to the current selection and uses far fewer tokens
• Start new conversations when switching topics to avoid accumulating long conversation histories
• Use a smaller or less expensive model for exploratory questions

Limitations

• Requires a user-provided API key. No built-in API key is included
• AI responses depend on the selected model and provider capabilities
• Maximum context window is 200,000 tokens
• The AI Assistant is not a replacement for understanding DAX and semantic model design fundamentals
• Response quality varies by provider and model selection
• The AI Assistant cannot connect to external files, services or search the web
• The AI Assistant cannot add or act as an MCP server
• The AI Assistant cannot connect to a different model from within the chat. Use the Tabular Editor user interface to change model connections
• The AI Assistant cannot manage preferences

Disabling the AI Assistant

The AI Assistant is an optional component. While the feature is in public preview, it will be excluded by default during installation, but users have the option to include it. You can modify an existing Tabular Editor 3 installation, to include or exclude the AI Assistant component, by running the Tabular Editor 3 installer again. If using the portable build of Tabular Editor 3, you can remove the AI Assistant component by deleting the file named TabularEditor3.AI.dll from the installation directory.

Note

Regardless of whether the AI Assistant component is installed or not, a system admin can disable all AI functionality in Tabular Editor 3 by specifying the DisableAi policy.