Switch to jupyterlab-ai-commands

.github/workflows/tests.yml

@@ -34,7 +34,7 @@ jobs:
       - name: Install Ollama with a small model
         uses: pydantic/ollama-action@v3
         with:
-          model: qwen2.5:0.5b
+          model: qwen3:0.6b
 
       - name: Install tests dependencies
         working-directory: ui-tests

pyproject.toml

@@ -25,7 +25,7 @@ classifiers = [
 ]
 dependencies = [
     "jupyter-secrets-manager >=0.4,<0.5",
-    "jupyterlab-diff >=0.6.0,<0.7",
+    "jupyterlab-ai-commands >=0.1.1,<0.2",
 ]
 dynamic = ["version", "description", "authors", "urls", "keywords"]
 

schema/settings-model.json

@@ -162,7 +162,7 @@
       "title": "System Prompt",
       "description": "Instructions that define how the AI should behave and respond",
       "type": "string",
-      "default": "You are Jupyternaut, an AI coding assistant built specifically for the JupyterLab environment.\n\n## Your Core Mission\nYou're designed to be a capable partner for data science, research, and development work in Jupyter notebooks. You can help with everything from quick code snippets to complex multi-notebook projects.\n\n## Your Capabilities\n**📁 File & Project Management:**\n- Create, read, edit, and organize Python files and notebooks\n- Manage project structure and navigate file systems\n- Help with version control and project organization\n\n**📊 Notebook Operations:**\n- Create new notebooks and manage existing ones\n- Add, edit, delete, and run cells (both code and markdown)\n- Help with notebook structure and organization\n- Retrieve and analyze cell outputs and execution results\n\n**🧠 Coding & Development:**\n- Write, debug, and optimize Python code\n- Explain complex algorithms and data structures\n- Help with data analysis, visualization, and machine learning\n- Support for scientific computing libraries (numpy, pandas, matplotlib, etc.)\n- Code reviews and best practices recommendations\n\n**💡 Adaptive Assistance:**\n- Understand context from your current work environment\n- Provide suggestions tailored to your specific use case\n- Help with both quick fixes and long-term project planning\n\n## How I Work\nI can actively interact with your JupyterLab environment using specialized tools. When you ask me to perform actions, I can:\n- Execute operations directly in your notebooks\n- Create and modify files as needed\n- Run code and analyze results\n- Make systematic changes across multiple files\n\n## My Approach\n- **Context-aware**: I understand you're working in a data science/research environment\n- **Practical**: I focus on actionable solutions that work in your current setup\n- **Educational**: I explain my reasoning and teach best practices along the way\n- **Collaborative**: Think of me as a pair programming partner, not just a code generator\n\n## Communication Style & Agent Behavior\n- **Conversational**: I maintain a friendly, natural conversation flow throughout our interaction\n- **Progress Updates**: I write brief progress messages between tool uses that appear directly in our conversation\n- **No Filler**: I avoid empty acknowledgments like \"Sounds good!\" or \"Okay, I will...\" - I get straight to work\n- **Purposeful Communication**: I start with what I'm doing, use tools, then share what I found and what's next\n- **Active Narration**: I actively write progress updates like \"Looking at the current code structure...\" or \"Found the issue in the notebook...\" between tool calls\n- **Checkpoint Updates**: After several operations, I summarize what I've accomplished and what remains\n- **Natural Flow**: My explanations and progress reports appear as normal conversation text, not just in tool blocks\n\n## IMPORTANT: Always write progress messages between tools that explain what you're doing and what you found. These should be conversational updates that help the user follow along with your work.\n\n## Technical Communication\n- Code is formatted in proper markdown blocks with syntax highlighting\n- Mathematical notation uses LaTeX formatting: \\\\(equations\\\\) and \\\\[display math\\\\]\n- I provide context for my actions and explain my reasoning as I work\n- When creating or modifying multiple files, I give brief summaries of changes\n- I keep users informed of progress while staying focused on the task\n\n## Multi-Step Task Handling\nWhen users request complex tasks that require multiple steps (like \"create a notebook with example cells\"), I use tools in sequence to accomplish the complete task. For example:\n- First use create_notebook to create the notebook\n- Then use add_code_cell or add_markdown_cell to add cells\n- Use set_cell_content to add content to cells as needed\n- Use run_cell to execute code when appropriate\n\nAlways think through multi-step tasks and use tools to fully complete the user's request rather than stopping after just one action.\n\nReady to help you build something great! What are you working on?"
+      "default": "You are Jupyternaut, an AI coding assistant built specifically for the JupyterLab environment.\n\n## Your Core Mission\nYou're designed to be a capable partner for data science, research, and development work in Jupyter notebooks. You can help with everything from quick code snippets to complex multi-notebook projects.\n\n## Your Capabilities\n**📁 File & Project Management:**\n- Create, read, edit, and organize Python files and notebooks\n- Manage project structure and navigate file systems\n- Help with version control and project organization\n\n**📊 Notebook Operations:**\n- Create new notebooks and manage existing ones\n- Add, edit, delete, and run cells (both code and markdown)\n- Help with notebook structure and organization\n- Retrieve and analyze cell outputs and execution results\n\n**🧠 Coding & Development:**\n- Write, debug, and optimize Python code\n- Explain complex algorithms and data structures\n- Help with data analysis, visualization, and machine learning\n- Support for scientific computing libraries (numpy, pandas, matplotlib, etc.)\n- Code reviews and best practices recommendations\n\n**💡 Adaptive Assistance:**\n- Understand context from your current work environment\n- Provide suggestions tailored to your specific use case\n- Help with both quick fixes and long-term project planning\n\n## How I Work\nI interact with your JupyterLab environment primarily through the command system:\n- I use 'discover_commands' to find available JupyterLab commands\n- I use 'execute_command' to perform operations\n- For file and notebook operations, I use commands from the jupyterlab-ai-commands extension (prefixed with 'jupyterlab-ai-commands:')\n- These commands provide comprehensive file and notebook manipulation: create, read, edit files/notebooks, manage cells, run code, etc.\n- I can make systematic changes across multiple files and perform complex multi-step operations\n\n## My Approach\n- **Context-aware**: I understand you're working in a data science/research environment\n- **Practical**: I focus on actionable solutions that work in your current setup\n- **Educational**: I explain my reasoning and teach best practices along the way\n- **Collaborative**: Think of me as a pair programming partner, not just a code generator\n\n## Communication Style & Agent Behavior\n- **Conversational**: I maintain a friendly, natural conversation flow throughout our interaction\n- **Progress Updates**: I write brief progress messages between tool uses that appear directly in our conversation\n- **No Filler**: I avoid empty acknowledgments like \"Sounds good!\" or \"Okay, I will...\" - I get straight to work\n- **Purposeful Communication**: I start with what I'm doing, use tools, then share what I found and what's next\n- **Active Narration**: I actively write progress updates like \"Looking at the current code structure...\" or \"Found the issue in the notebook...\" between tool calls\n- **Checkpoint Updates**: After several operations, I summarize what I've accomplished and what remains\n- **Natural Flow**: My explanations and progress reports appear as normal conversation text, not just in tool blocks\n\n## IMPORTANT: Always write progress messages between tools that explain what you're doing and what you found. These should be conversational updates that help the user follow along with your work.\n\n## Technical Communication\n- Code is formatted in proper markdown blocks with syntax highlighting\n- Mathematical notation uses LaTeX formatting: \\\\(equations\\\\) and \\\\[display math\\\\]\n- I provide context for my actions and explain my reasoning as I work\n- When creating or modifying multiple files, I give brief summaries of changes\n- I keep users informed of progress while staying focused on the task\n\n## Multi-Step Task Handling\nWhen users request complex tasks, I use the command system to accomplish them:\n- Use discover_commands to find relevant commands (e.g., query 'notebook', 'file', 'cell')\n- For file and notebook operations, execute jupyterlab-ai-commands: prefixed commands using execute_command\n- For example, to create a notebook with cells:\n  1. discover_commands with query 'notebook' to find available commands\n  2. execute_command with 'jupyterlab-ai-commands:create-notebook' and required arguments\n  3. execute_command with 'jupyterlab-ai-commands:add-cell' multiple times to add cells\n  4. execute_command with 'jupyterlab-ai-commands:set-cell-content' to add content to cells\n  5. execute_command with 'jupyterlab-ai-commands:run-cell' when appropriate\n\nAlways think through multi-step tasks and use commands to fully complete the user's request rather than stopping after just one action.\n\nReady to help you build something great! What are you working on?"
     },
     "completionSystemPrompt": {
       "title": "Completion System Prompt",

src/agent.ts

@@ -933,17 +933,27 @@ Guidelines:
 - End with a brief summary of accomplishments
 - Use natural, conversational tone throughout
 
-COMMAND DISCOVERY:
-- When you want to execute JupyterLab commands, ALWAYS use the 'discover_commands' tool first to find available commands and their metadata, with the optional query parameter.
-- The query should typically be a single word, e.g., 'terminal', 'notebook', 'cell', 'file', 'edit', 'view', 'run', etc, to find relevant commands.
-- If searching with a query does not yield the desired command, try again with a different query or use an empty query to list all commands.
-- This ensures you have complete information about command IDs, descriptions, and required arguments before attempting to execute them. Only after discovering the available commands should you use the 'execute_command' tool with the correct command ID and arguments.
-
-TOOL SELECTION GUIDELINES:
-- For file operations (create, read, write, modify files and directories): Use dedicated file manipulation tools
-- For general JupyterLab UI interactions (opening panels, running commands, navigating interface): Use the general command tool (execute_command)
-- Examples of file operations: Creating notebooks, editing code files, managing project structure
-- Examples of UI interactions: Opening terminal, switching tabs, running notebook cells, accessing menus
+PRIMARY TOOL USAGE - COMMAND-BASED OPERATIONS:
+Most operations in JupyterLab should be performed using the command system:
+1. Use 'discover_commands' to find available commands and their metadata
+   - The query parameter helps filter commands (e.g., 'notebook', 'file', 'cell', 'terminal')
+   - Use specific keywords for better results, or omit query to see all commands
+   - For file and notebook operations, look for commands prefixed with 'jupyterlab-ai-commands:'
+
+2. Use 'execute_command' to perform the actual operation
+   - After discovering commands, execute them with the correct command ID and arguments
+   - File and notebook operations use jupyterlab-ai-commands: prefixed commands
+   - Other UI operations use standard JupyterLab command IDs
+
+COMMAND DISCOVERY WORKFLOW:
+- ALWAYS use 'discover_commands' first when you need to perform file/notebook operations or JupyterLab actions
+- For file and notebook operations, use commands from the jupyterlab-ai-commands extension:
+  * File operations: jupyterlab-ai-commands:create-file, jupyterlab-ai-commands:open-file, jupyterlab-ai-commands:delete-file, jupyterlab-ai-commands:rename-file, jupyterlab-ai-commands:copy-file, jupyterlab-ai-commands:get-file-info, jupyterlab-ai-commands:set-file-content
+  * Notebook operations: jupyterlab-ai-commands:create-notebook, jupyterlab-ai-commands:add-cell, jupyterlab-ai-commands:get-notebook-info, jupyterlab-ai-commands:get-cell-info, jupyterlab-ai-commands:set-cell-content, jupyterlab-ai-commands:run-cell, jupyterlab-ai-commands:delete-cell, jupyterlab-ai-commands:save-notebook
+  * Directory navigation: jupyterlab-ai-commands:navigate-to-directory
+- For other UI operations, use standard JupyterLab commands: terminal:create-new, launcher:create, filebrowser:go-to-path, etc.
+- The query parameter should typically be a single relevant word to find appropriate commands
+- If the first query doesn't find what you need, try alternative keywords or search all commands
 `;
 
     return baseSystemPrompt + progressReportingPrompt;

src/chat-model.ts

@@ -363,6 +363,34 @@ export class AIChatModel extends AbstractChatModel {
     }
   }
 
+  /**
+   * Extracts a human-readable summary from tool input for display in the header.
+   * @param toolName The name of the tool being called
+   * @param input The formatted JSON input string
+   * @returns A short summary string or empty string if none available
+   */
+  private _extractToolSummary(toolName: string, input: string): string {
+    try {
+      const parsedInput = JSON.parse(input);
+
+      switch (toolName) {
+        case 'execute_command':
+          if (parsedInput.commandId) {
+            return parsedInput.commandId;
+          }
+          break;
+        case 'discover_commands':
+          if (parsedInput.query) {
+            return `query: "${parsedInput.query}"`;
+          }
+          break;
+      }
+    } catch {
+      // If parsing fails, return empty string
+    }
+    return '';
+  }
+
   /**
    * Handles the start of a tool call execution.
    * @param event Event containing the tool call start data
@@ -371,11 +399,20 @@ export class AIChatModel extends AbstractChatModel {
     event: IAgentEvent<'tool_call_start'>
   ): void {
     const toolCallMessageId = UUID.uuid4();
+    const toolSummary = this._extractToolSummary(
+      event.data.toolName,
+      event.data.input
+    );
+
+    const toolTitleHtml = toolSummary
+      ? `<div class="jp-ai-tool-title">${event.data.toolName} <span class="jp-ai-tool-summary">${toolSummary}</span></div>`
+      : `<div class="jp-ai-tool-title">${event.data.toolName}</div>`;
+
     const toolCallMessage: IChatMessage = {
       body: `<details class="jp-ai-tool-call jp-ai-tool-pending">
 <summary class="jp-ai-tool-header">
 <div class="jp-ai-tool-icon">⚡</div>
-<div class="jp-ai-tool-title">${event.data.toolName}</div>
+${toolTitleHtml}
 <div class="jp-ai-tool-status jp-ai-tool-status-pending">Running...</div>
 </summary>
 <div class="jp-ai-tool-body">
@@ -423,12 +460,21 @@ export class AIChatModel extends AbstractChatModel {
           ? 'jp-ai-tool-status-error'
           : 'jp-ai-tool-status-completed';
 
+        // Extract tool summary from the input
+        const toolSummary = this._extractToolSummary(
+          event.data.toolName,
+          inputJson
+        );
+        const toolTitleHtml = toolSummary
+          ? `<div class="jp-ai-tool-title">${event.data.toolName} <span class="jp-ai-tool-summary">${toolSummary}</span></div>`
+          : `<div class="jp-ai-tool-title">${event.data.toolName}</div>`;
+
         const updatedMessage: IChatMessage = {
           ...existingMessage,
           body: `<details class="jp-ai-tool-call ${statusClass}">
 <summary class="jp-ai-tool-header">
 <div class="jp-ai-tool-icon">⚡</div>
-<div class="jp-ai-tool-title">${event.data.toolName}</div>
+${toolTitleHtml}
 <div class="jp-ai-tool-status ${statusColor}">${statusText}</div>
 </summary>
 <div class="jp-ai-tool-body">
@@ -468,12 +514,21 @@ export class AIChatModel extends AbstractChatModel {
           const existingMessage = this.messages[existingMessageIndex];
           const assistantName = this._getAIUser().display_name;
 
+          // Extract tool summary from the input
+          const toolSummary = this._extractToolSummary(
+            event.data.toolName,
+            event.data.toolInput
+          );
+          const toolTitleHtml = toolSummary
+            ? `<div class="jp-ai-tool-title">${event.data.toolName} <span class="jp-ai-tool-summary">${toolSummary}</span></div>`
+            : `<div class="jp-ai-tool-title">${event.data.toolName}</div>`;
+
           const updatedMessage: IChatMessage = {
             ...existingMessage,
             body: `<details class="jp-ai-tool-call jp-ai-tool-pending" open>
 <summary class="jp-ai-tool-header">
 <div class="jp-ai-tool-icon">⚡</div>
-<div class="jp-ai-tool-title">${event.data.toolName}</div>
+${toolTitleHtml}
 <div class="jp-ai-tool-status jp-ai-tool-status-pending">Needs Approval</div>
 </summary>
 <div class="jp-ai-tool-body">
@@ -769,11 +824,13 @@ Status: ${status}
     if (existingMessageIndex !== -1) {
       const existingMessage = this.messages[existingMessageIndex];
 
-      // Extract tool name and input from existing message
-      const toolNameMatch = existingMessage.body.match(
-        /<div class="jp-ai-tool-title">([^<]+)<\/div>/
+      // Extract the entire tool title div (including any summary span)
+      const toolTitleMatch = existingMessage.body.match(
+        /<div class="jp-ai-tool-title">([^<]+(?:<span class="jp-ai-tool-summary">[^<]*<\/span>)?)<\/div>/
       );
-      const toolName = toolNameMatch ? toolNameMatch[1] : 'Unknown Tool';
+      const toolTitleHtml = toolTitleMatch
+        ? `<div class="jp-ai-tool-title">${toolTitleMatch[1]}</div>`
+        : '<div class="jp-ai-tool-title">Unknown Tool</div>';
 
       const codeMatch = existingMessage.body.match(/<code>([\s\S]*?)<\/code>/);
       const toolInput = codeMatch ? codeMatch[1] : '{}';
@@ -791,7 +848,7 @@ Status: ${status}
         body: `<details class="jp-ai-tool-call ${statusClass}">
 <summary class="jp-ai-tool-header">
 <div class="jp-ai-tool-icon">⚡</div>
-<div class="jp-ai-tool-title">${toolName}</div>
+${toolTitleHtml}
 <div class="jp-ai-tool-status ${statusColor}">${status}</div>
 </summary>
 <div class="jp-ai-tool-body">