import type { ImageBlockParam, TextBlockParam, ToolResultBlockParam, } from '@anthropic-ai/sdk/resources/index.mjs' import { BASH_TOOL_NAME } from '../tools/BashTool/toolName.js' import { formatOutput } from '../tools/BashTool/utils.js' import type { NotebookCell, NotebookCellOutput, NotebookCellSource, NotebookCellSourceOutput, NotebookContent, NotebookOutputImage, } from '../types/notebook.js' import { getFsImplementation } from './fsOperations.js' import { expandPath } from './path.js' import { jsonParse } from './slowOperations.js' const LARGE_OUTPUT_THRESHOLD = 10000 function isLargeOutputs( outputs: (NotebookCellSourceOutput | undefined)[], ): boolean { let size = 0 for (const o of outputs) { if (!o) continue size += (o.text?.length ?? 0) + (o.image?.image_data.length ?? 0) if (size > LARGE_OUTPUT_THRESHOLD) return true } return false } function processOutputText(text: string | string[] | undefined): string { if (!text) return '' const rawText = Array.isArray(text) ? text.join('') : text const { truncatedContent } = formatOutput(rawText) return truncatedContent } function extractImage( data: Record, ): NotebookOutputImage | undefined { if (typeof data['image/png'] === 'string') { return { image_data: data['image/png'].replace(/\s/g, ''), media_type: 'image/png', } } if (typeof data['image/jpeg'] === 'string') { return { image_data: data['image/jpeg'].replace(/\s/g, ''), media_type: 'image/jpeg', } } return undefined } function processOutput(output: NotebookCellOutput) { switch (output.output_type) { case 'stream': return { output_type: output.output_type, text: processOutputText(output.text), } case 'execute_result': case 'display_data': return { output_type: output.output_type, text: processOutputText(output.data?.['text/plain']), image: output.data && extractImage(output.data), } case 'error': return { output_type: output.output_type, text: processOutputText( `${output.ename}: ${output.evalue}\n${output.traceback.join('\n')}`, ), } } } function processCell( cell: NotebookCell, index: number, codeLanguage: string, includeLargeOutputs: boolean, ): NotebookCellSource { const cellId = cell.id ?? `cell-${index}` const cellData: NotebookCellSource = { cellType: cell.cell_type, source: Array.isArray(cell.source) ? cell.source.join('') : cell.source, execution_count: cell.cell_type === 'code' ? cell.execution_count || undefined : undefined, cell_id: cellId, } // Avoid giving text cells the code language. if (cell.cell_type === 'code') { cellData.language = codeLanguage } if (cell.cell_type === 'code' && cell.outputs?.length) { const outputs = cell.outputs.map(processOutput) if (!includeLargeOutputs && isLargeOutputs(outputs)) { cellData.outputs = [ { output_type: 'stream', text: `Outputs are too large to include. Use ${BASH_TOOL_NAME} with: cat | jq '.cells[${index}].outputs'`, }, ] } else { cellData.outputs = outputs } } return cellData } function cellContentToToolResult(cell: NotebookCellSource): TextBlockParam { const metadata = [] if (cell.cellType !== 'code') { metadata.push(`${cell.cellType}`) } if (cell.language !== 'python' && cell.cellType === 'code') { metadata.push(`${cell.language}`) } const cellContent = `${metadata.join('')}${cell.source}` return { text: cellContent, type: 'text', } } function cellOutputToToolResult(output: NotebookCellSourceOutput) { const outputs: (TextBlockParam | ImageBlockParam)[] = [] if (output.text) { outputs.push({ text: `\n${output.text}`, type: 'text', }) } if (output.image) { outputs.push({ type: 'image', source: { data: output.image.image_data, media_type: output.image.media_type, type: 'base64', }, }) } return outputs } function getToolResultFromCell(cell: NotebookCellSource) { const contentResult = cellContentToToolResult(cell) const outputResults = cell.outputs?.flatMap(cellOutputToToolResult) return [contentResult, ...(outputResults ?? [])] } /** * Reads and parses a Jupyter notebook file into processed cell data */ export async function readNotebook( notebookPath: string, cellId?: string, ): Promise { const fullPath = expandPath(notebookPath) const buffer = await getFsImplementation().readFileBytes(fullPath) const content = buffer.toString('utf-8') const notebook = jsonParse(content) as NotebookContent const language = notebook.metadata.language_info?.name ?? 'python' if (cellId) { const cell = notebook.cells.find(c => c.id === cellId) if (!cell) { throw new Error(`Cell with ID "${cellId}" not found in notebook`) } return [processCell(cell, notebook.cells.indexOf(cell), language, true)] } return notebook.cells.map((cell, index) => processCell(cell, index, language, false), ) } /** * Maps notebook cell data to tool result block parameters with sophisticated text block merging */ export function mapNotebookCellsToToolResult( data: NotebookCellSource[], toolUseID: string, ): ToolResultBlockParam { const allResults = data.flatMap(getToolResultFromCell) // Merge adjacent text blocks return { tool_use_id: toolUseID, type: 'tool_result' as const, content: allResults.reduce<(TextBlockParam | ImageBlockParam)[]>( (acc, curr) => { if (acc.length === 0) return [curr] const prev = acc[acc.length - 1] if (prev && prev.type === 'text' && curr.type === 'text') { // Merge the text blocks prev.text += '\n' + curr.text return acc } acc.push(curr) return acc }, [], ), } } export function parseCellId(cellId: string): number | undefined { const match = cellId.match(/^cell-(\d+)$/) if (match && match[1]) { const index = parseInt(match[1], 10) return isNaN(index) ? undefined : index } return undefined }