Initial commit: RA-H Open Source Edition
Local-first knowledge management system with BYO API keys. Features: - 3-panel UI (Nodes | Focus | Helpers) - SQLite + sqlite-vec for vector search - Agent system (Easy/Hard mode orchestrators) - Content extraction (YouTube, PDF, web) - Integrate workflow for connection discovery - Dimension system with auto-assignment Tech stack: - Next.js 15 + TypeScript + Tailwind CSS - Anthropic (Claude) + OpenAI (GPT) via Vercel AI SDK Setup: npm install && npm rebuild better-sqlite3 scripts/dev/bootstrap-local.sh npm run dev MIT License
This commit is contained in:
“BeeRad”
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import { tool } from 'ai';
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import { z } from 'zod';
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import { openai } from '@ai-sdk/openai';
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import { generateText } from 'ai';
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import { extractPaper } from '@/services/typescript/extractors/paper';
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import { formatNodeForChat } from '../infrastructure/nodeFormatter';
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// AI-powered content analysis
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async function analyzeContentWithAI(title: string, description: string, contentType: string) {
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try {
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const prompt = `Analyze this ${contentType} content and provide classification:
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Title: "${title}"
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Description: "${description}"
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Respond with ONLY valid JSON (no markdown, no code blocks):
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{
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"enhancedDescription": "Improved 2-3 sentence description explaining what this content is about",
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"tags": ["relevant", "semantic", "tags", "like", "ai", "economics", "research"],
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"reasoning": "Brief explanation of why you chose these categories"
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}
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Guidelines:
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- Include 3-8 relevant semantic tags (not just generic ones)
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- Make description informative and contextual
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- For academic papers, include tags like: research, academic, paper, plus domain-specific tags
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- For AI/ML papers, include: ai, machine-learning, artificial-intelligence, deep-learning
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- For economics papers, include: economics, finance, markets, policy
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- Be specific and insightful
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- Return ONLY the JSON object, no other text`;
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const response = await generateText({
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model: openai('gpt-5-mini'),
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prompt,
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maxOutputTokens: 500
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});
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let content = response.text || '{}';
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// Clean up the response - remove markdown code blocks if present
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content = content.replace(/```json\s*/g, '').replace(/```\s*/g, '').trim();
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const result = JSON.parse(content);
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return {
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enhancedDescription: result.enhancedDescription || description,
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tags: Array.isArray(result.tags) ? result.tags : [],
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reasoning: result.reasoning || 'AI analysis completed'
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};
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} catch (error) {
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const message = error instanceof Error ? error.message : 'unknown error';
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console.warn('Paper analysis fallback (using default description):', message);
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return {
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enhancedDescription: description,
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tags: [],
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reasoning: 'Fallback description used'
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};
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}
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}
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export const paperExtractTool = tool({
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description: 'Extract a PDF or research paper into a node with summary, metadata, and full-text chunk',
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inputSchema: z.object({
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url: z.string().describe('The PDF URL to add to inbox'),
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title: z.string().optional().describe('Custom title (auto-generated if not provided)'),
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dimensions: z.array(z.string()).min(1).max(5).optional().describe('Dimension tags to apply to the created node (locked dimensions first)')
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}),
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execute: async ({ url, title, dimensions }) => {
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try {
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// Validate PDF URL
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if (!url.startsWith('http://') && !url.startsWith('https://')) {
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return {
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success: false,
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error: 'Invalid URL format - must start with http:// or https://',
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data: null
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};
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}
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// Check if URL likely points to a PDF
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if (!url.toLowerCase().includes('.pdf') && !url.includes('arxiv.org')) {
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return {
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success: false,
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error: 'URL does not appear to point to a PDF file',
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data: null
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};
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}
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let result: { success: boolean; content?: string; chunk?: string; metadata?: any; error?: string };
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try {
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const extractionResult = await extractPaper(url);
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result = {
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success: true,
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content: extractionResult.content,
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chunk: extractionResult.chunk,
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metadata: {
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title: extractionResult.metadata.title,
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pages: extractionResult.metadata.pages,
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info: extractionResult.metadata.info,
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text_length: extractionResult.metadata.text_length,
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filename: extractionResult.metadata.filename,
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extraction_method: 'typescript'
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}
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};
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} catch (error: any) {
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result = {
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success: false,
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error: error.message || 'TypeScript extraction failed'
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};
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}
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if (!result.success || (!result.content && !result.chunk)) {
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return {
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success: false,
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error: result.error || 'Failed to extract PDF content',
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data: null
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};
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}
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console.log('🎯 PDF extraction successful, analyzing with AI...');
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// Step 2: AI Analysis for enhanced metadata
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const aiAnalysis = await analyzeContentWithAI(
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result.metadata?.title || `PDF: ${new URL(url).pathname.split('/').pop()?.replace('.pdf', '')}`,
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result.content?.substring(0, 1000) || 'PDF document content',
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'pdf'
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);
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// Step 3: Create node with extracted content and AI analysis
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const nodeTitle = title || result.metadata?.title || `PDF: ${new URL(url).pathname.split('/').pop()?.replace('.pdf', '')}`;
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const enhancedDescription = aiAnalysis?.enhancedDescription || `PDF document from ${new URL(url).hostname}`;
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const suppliedDimensions = Array.isArray(dimensions) ? dimensions : [];
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let trimmedDimensions = suppliedDimensions
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.map(dim => (typeof dim === 'string' ? dim.trim() : ''))
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.filter(Boolean);
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trimmedDimensions = trimmedDimensions.slice(0, 5);
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const createResponse = await fetch(`${process.env.NEXT_PUBLIC_BASE_URL || 'http://localhost:3000'}/api/nodes`, {
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method: 'POST',
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headers: { 'Content-Type': 'application/json' },
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body: JSON.stringify({
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title: nodeTitle,
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content: enhancedDescription,
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link: url,
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dimensions: trimmedDimensions,
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chunk: result.chunk || result.content,
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metadata: {
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source: 'pdf',
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hostname: new URL(url).hostname,
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author: result.metadata?.author || result.metadata?.info?.Author,
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pages: result.metadata?.pages,
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file_size: result.metadata?.file_size,
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content_length: (result.chunk || result.content)?.length,
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extraction_method: result.metadata?.extraction_method || 'python_pdfplumber',
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ai_analysis: aiAnalysis?.reasoning,
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enhanced_description: enhancedDescription,
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refined_at: new Date().toISOString()
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}
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})
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});
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const createResult = await createResponse.json();
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if (!createResponse.ok) {
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return {
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success: false,
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error: createResult.error || 'Failed to create node',
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data: null
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};
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}
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console.log('🎯 PaperExtract completed successfully');
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const formattedNode = createResult.data?.id
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? formatNodeForChat({ id: createResult.data.id, title: nodeTitle, dimensions: trimmedDimensions || [] })
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: nodeTitle;
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const dimsDisplay = trimmedDimensions.length > 0 ? trimmedDimensions.join(', ') : 'none';
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return {
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success: true,
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message: `Added ${formattedNode} with dimensions: ${dimsDisplay}`,
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data: {
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nodeId: createResult.data?.id,
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title: nodeTitle,
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contentLength: (result.chunk || result.content || '').length,
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url: url,
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dimensions: trimmedDimensions
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}
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};
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} catch (error) {
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return {
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success: false,
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error: error instanceof Error ? error.message : 'Failed to extract PDF content',
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data: null
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};
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}
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}
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});
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