Switch to jupyterlab-ai-commands

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 >=0.1.3,<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- For file and notebook operations, use discover_commands with query 'jupyterlab-ai-commands' to find the curated set of AI commands (~17 commands)\n- For other JupyterLab operations (terminal, launcher, UI), use specific keywords like 'terminal', 'launcher', etc.\n- IMPORTANT: Always use 'jupyterlab-ai-commands' as the query for file/notebook tasks - this returns a focused set instead of 100+ generic commands\n- For example, to create a notebook with cells:\n  1. discover_commands with query 'jupyterlab-ai-commands' to find available file/notebook 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\n## Kernel Preference for Notebooks and Consoles\nWhen creating notebooks or consoles for a specific programming language, use the 'kernelPreference' argument:\n- To specify by language: { \"kernelPreference\": { \"language\": \"python\" } } or { \"kernelPreference\": { \"language\": \"julia\" } }\n- To specify by kernel name: { \"kernelPreference\": { \"name\": \"python3\" } } or { \"kernelPreference\": { \"name\": \"julia-1.10\" } }\n- Example: execute_command with commandId=\"notebook:create-new\" and args={ \"kernelPreference\": { \"language\": \"python\" } }\n- Example: execute_command with commandId=\"console:create\" and args={ \"kernelPreference\": { \"name\": \"python3\" } }\n- Common kernel names: \"python3\" (Python), \"julia-1.10\" (Julia), \"ir\" (R), \"xpython\" (xeus-python)\n- If unsure of exact kernel name, prefer using \"language\" which will match any kernel supporting that language\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

@@ -916,17 +916,24 @@ 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
+2. Use 'execute_command' to perform the actual operation
+
+COMMAND DISCOVERY WORKFLOW:
+- For file and notebook operations, use query 'jupyterlab-ai-commands' to discover the curated set of AI commands (~17 commands for file/notebook/directory operations)
+- For other JupyterLab operations (terminal, launcher, UI), use specific keywords like 'terminal', 'launcher', etc.
+- IMPORTANT: Always use 'jupyterlab-ai-commands' as the query for file/notebook tasks - this returns a focused set of commands instead of 100+ generic JupyterLab commands
+
+KERNEL PREFERENCE FOR NOTEBOOKS AND CONSOLES:
+When creating notebooks or consoles for a specific programming language, use the 'kernelPreference' argument to specify the kernel:
+- To specify by language: { "kernelPreference": { "language": "python" } } or { "kernelPreference": { "language": "julia" } }
+- To specify by kernel name: { "kernelPreference": { "name": "python3" } } or { "kernelPreference": { "name": "julia-1.10" } }
+- Example: execute_command with commandId="notebook:create-new" and args={ "kernelPreference": { "language": "python" } }
+- Example: execute_command with commandId="console:create" and args={ "kernelPreference": { "name": "python3" } }
+- Common kernel names: "python3" (Python), "julia-1.10" (Julia), "ir" (R), "xpython" (xeus-python)
+- If unsure of exact kernel name, prefer using "language" which will match any kernel supporting that language
 `;
 
     return baseSystemPrompt + progressReportingPrompt;

src/chat-model.ts

@@ -73,6 +73,10 @@ interface IToolExecutionContext {
    * Current status.
    */
   status: ToolStatus;
+  /**
+   * Human-readable summary extracted from tool input for display.
+   */
+  summary?: string;
 }
 
 /**
@@ -341,6 +345,34 @@ 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
@@ -349,12 +381,17 @@ export class AIChatModel extends AbstractChatModel {
     event: IAgentEvent<'tool_call_start'>
   ): void {
     const messageId = UUID.uuid4();
+    const summary = this._extractToolSummary(
+      event.data.toolName,
+      event.data.input
+    );
     const context: IToolExecutionContext = {
       toolCallId: event.data.callId,
       messageId,
       toolName: event.data.toolName,
       input: event.data.input,
-      status: 'pending'
+      status: 'pending',
+      summary
     };
 
     this._toolContexts.set(event.data.callId, context);
@@ -364,6 +401,7 @@ export class AIChatModel extends AbstractChatModel {
         toolName: context.toolName,
         input: context.input,
         status: context.status,
+        summary: context.summary,
         trans: this._trans
       }),
       sender: this._getAIUser(),
@@ -464,6 +502,7 @@ export class AIChatModel extends AbstractChatModel {
         toolName: context.toolName,
         input: context.input,
         status: context.status,
+        summary: context.summary,
         output,
         approvalId: context.approvalId,
         trans: this._trans
@@ -845,6 +884,7 @@ namespace Private {
     toolName: string;
     input: string;
     status: ToolStatus;
+    summary?: string;
     output?: string;
     approvalId?: string;
     trans: TranslationBundle;
@@ -854,12 +894,16 @@ namespace Private {
    * Builds HTML for a tool call display.
    */
   export function buildToolCallHtml(options: IToolCallHtmlOptions): string {
-    const { toolName, input, status, output, approvalId, trans } = options;
+    const { toolName, input, status, summary, output, approvalId, trans } =
+      options;
     const config = STATUS_CONFIG[status];
     const statusText = getStatusText(status, trans);
     const escapedToolName = escapeHtml(toolName);
     const escapedInput = escapeHtml(input);
     const openAttr = config.open ? ' open' : '';
+    const summaryHtml = summary
+      ? `<span class="jp-ai-tool-summary">${escapeHtml(summary)}</span>`
+      : '';
 
     let bodyContent = `
 <div class="jp-ai-tool-section">
@@ -890,7 +934,7 @@ namespace Private {
     return `<details class="jp-ai-tool-call ${config.cssClass}"${openAttr}>
 <summary class="jp-ai-tool-header">
 <div class="jp-ai-tool-icon">⚡</div>
-<div class="jp-ai-tool-title">${escapedToolName}</div>
+<div class="jp-ai-tool-title">${escapedToolName}${summaryHtml}</div>
 <div class="jp-ai-tool-status ${config.statusClass}">${statusText}</div>
 </summary>
 <div class="jp-ai-tool-body">${bodyContent}