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reduced chunk size for better granularity\nconst DEFAULT_MAX_TOKENS_PER_CHUNK = 300;\nconst DEFAULT_MAX_TOKENS_PER_RESPONSE = 800;\n\n// Serve static files\napp.get(\"/frontend/*\", c => serveFile(c.req.path, import.meta.url));\napp.get(\"/shared/*\", c => serveFile(c.req.path, import.meta.url));\n\n// Serve main page\napp.get(\"/\", async c => {\n const html = await readFile(\"/frontend/index.html\", import.meta.url);\n return c.html(html);\n});\n\n// Simple token-based text chunking\nfunction chunkTextByTokens(text: string, maxTokensPerChunk: number): { content: string; tokenCount: number }[] {\n const totalTokens = encode(text).length;\n \n if (totalTokens <= maxTokensPerChunk) {\n return [{ content: text, tokenCount: totalTokens }];\n }\n\n const chunks: { content: string; tokenCount: number }[] = [];\n const words = text.split(/\\s+/);\n let currentChunk = \"\";\n \n for (const word of words) {\n const testChunk = currentChunk + (currentChunk ? \" \" : \"\") + word;\n const testTokens = encode(testChunk).length;\n \n if (testTokens <= maxTokensPerChunk) {\n currentChunk = testChunk;\n } else {\n // Save current chunk if it has content\n if (currentChunk.trim()) {\n chunks.push({\n content: currentChunk.trim(),\n tokenCount: encode(currentChunk.trim()).length\n });\n }\n currentChunk = word;\n }\n }\n \n // Add remaining content\n if (currentChunk.trim()) {\n chunks.push({\n content: currentChunk.trim(),\n tokenCount: encode(currentChunk.trim()).length\n });\n }\n \n return chunks;\n}\n\n// Analyze individual chunk with context from adjacent chunks\nasync function analyzeChunk(\n chunk: { content: string; tokenCount: number }, \n chunkIndex: number, \n maxTokensPerResponse: number,\n organizeChunks: boolean = false,\n precedentChunk?: { content: string; tokenCount: number },\n subsequentChunk?: { content: string; tokenCount: number }\n): Promise {\n const startTime = Date.now();\n console.log(`Starting analysis of chunk ${chunkIndex} at ${new Date().toISOString()}`);\n \n const baseSystemPrompt = `You are analyzing a SPECIFIC CHUNK of a GitHub PR conversation. IMPORTANT: You are analyzing only a PART of the conversation, not the entire conversation.\n\n**CONTEXT EXPLANATION:**\n- You will receive the chunk you need to analyze marked with \n- If available, you'll also receive (previous chunk) and (next chunk)\n- If no precedent context is provided, this is likely the FIRST chunk of the conversation\n- If no subsequent context is provided, this is likely the LAST chunk of the conversation\n- Use the context to better understand the flow, but ONLY analyze the content in \n\n**YOUR ANALYSIS SHOULD FOCUS ON:**\n\n1. **Specific Issues, Concerns, or Problems Mentioned**: Extract ALL issues from YOUR chunk only, but use context to understand them better.\n\n2. **Why Issues Matter & Agreement with Solutions**: For each issue in YOUR chunk, explain WHY it matters and whether you AGREE with proposed solutions. Be opinionated and suggest better alternatives if needed.\n\n3. **Code Changes with Suggestions**: Include original code and corrected code from YOUR chunk. Add YOUR additional improvements with clear reasoning.\n\n4. **What's Missing & Deviation Recommendations**: Identify what's missing from YOUR chunk's analysis and provide clear observations on why the coder should consider deviating from the original suggestions.`;\n\n const organizedFormatAddition = organizeChunks ? `\n\n**IMPORTANT FORMATTING NOTE:**\nThis analysis will be formatted as a task item in an organized workflow. Focus on actionable insights and clear recommendations that can be tracked as tasks. Structure your response to be practical and implementable.` : \"\";\n\n const systemPrompt = baseSystemPrompt + organizedFormatAddition + `\n\nProvide comprehensive, opinionated analysis in markdown format. Focus ONLY on your assigned chunk but use context for better understanding.`;\n\n // Build the content with context tags\n let contentWithContext = \"\";\n \n if (precedentChunk) {\n contentWithContext += `\\n${precedentChunk.content}\\n\\n\\n`;\n }\n \n contentWithContext += `\\n${chunk.content}\\n`;\n \n if (subsequentChunk) {\n contentWithContext += `\\n\\n\\n${subsequentChunk.content}\\n`;\n }\n\n try {\n const completion = await openai.chat.completions.create({\n messages: [\n { role: \"system\", content: systemPrompt },\n { role: \"user\", content: `Analyze this PR conversation chunk (${chunk.tokenCount} tokens) with context:\\n\\n${contentWithContext}` }\n ],\n model: \"gpt-4o-mini\",\n max_tokens: maxTokensPerResponse,\n temperature: 0.1\n });\n\n const analysis = completion.choices[0]?.message?.content || \"No analysis generated\";\n const endTime = Date.now();\n console.log(`Completed analysis of chunk ${chunkIndex} in ${endTime - startTime}ms at ${new Date().toISOString()}`);\n\n return {\n chunkIndex,\n content: chunk.content,\n tokenCount: chunk.tokenCount,\n precedentContext: precedentChunk?.content,\n subsequentContext: subsequentChunk?.content,\n analysis\n };\n } catch (error) {\n const endTime = Date.now();\n console.error(`Error analyzing chunk ${chunkIndex} after ${endTime - startTime}ms:`, error);\n return {\n chunkIndex,\n content: chunk.content,\n tokenCount: chunk.tokenCount,\n precedentContext: precedentChunk?.content,\n subsequentContext: subsequentChunk?.content,\n analysis: `Error analyzing chunk: ${error instanceof Error ? error.message : 'Unknown error'}`\n };\n }\n}\n\n// Generate summary from all chunk responses\nasync function generateSummary(chunkResponses: ChunkResponse[]): Promise {\n const systemPrompt = `You are creating a comprehensive summary of a GitHub PR analysis. The PR was split into ${chunkResponses.length} chunks and each was analyzed with opinionated technical insights.\n\nCreate a high-level summary that:\n1. Provides an overall technical assessment of the PR\n2. Highlights the most critical issues and WHY they matter\n3. Evaluates whether the proposed solutions are adequate or need improvement\n4. Lists key code changes and improvements suggested across all chunks\n5. Identifies patterns of missing analysis or areas where developers should deviate from original suggestions\n6. Provides clear, opinionated next steps with technical reasoning\n\nBe thorough, opinionated, and technically focused. Use markdown formatting with clear sections.`;\n\n const allAnalyses = chunkResponses\n .map(chunk => `## Chunk ${chunk.chunkIndex + 1} Analysis (${chunk.tokenCount} tokens)\\n\\n${chunk.analysis}`)\n .join('\\n\\n---\\n\\n');\n\n try {\n const completion = await openai.chat.completions.create({\n messages: [\n { role: \"system\", content: systemPrompt },\n { role: \"user\", content: `Generate a comprehensive, opinionated summary based on these chunk analyses:\\n\\n${allAnalyses}` }\n ],\n model: \"gpt-4o-mini\",\n max_tokens: 1000,\n temperature: 0.2\n });\n\n return completion.choices[0]?.message?.content || \"Unable to generate summary\";\n } catch (error) {\n console.error(\"Error generating summary:\", error);\n return `Error generating summary: ${error instanceof Error ? error.message : 'Unknown error'}`;\n }\n}\n\n// Generate full markdown report\nfunction generateMarkdownReport(\n summary: string, \n chunkResponses: ChunkResponse[], \n totalTokens: number, \n maxTokensPerChunk: number,\n organizeChunks: boolean = false\n): string {\n if (organizeChunks) {\n // Generate organized format with task structure\n let markdown = \"# pr-analysis.md\\n\\n\";\n markdown += \"---\\n\\n\";\n \n chunkResponses.forEach((chunk, index) => {\n const taskNumber = index + 1;\n markdown += `## TASK ${taskNumber}\\n`;\n markdown += \"**Progress:** Pending\\n\\n\";\n markdown += \"- ☑ Pending\\n\";\n markdown += \"- ☐ Started\\n\";\n markdown += \"- ☐ Completed\\n\\n\";\n markdown += chunk.analysis + \"\\n\\n\";\n markdown += \"---\\n\\n\";\n });\n \n return markdown;\n }\n\n // Original format\n let markdown = \"# PR Analysis Report\\n\\n\";\n markdown += `**Generated on:** ${new Date().toLocaleString()}\\n\\n`;\n markdown += `**Total Tokens:** ${totalTokens}\\n\\n`;\n markdown += `**Total Chunks:** ${chunkResponses.length}\\n\\n`;\n markdown += `**Max Tokens Per Chunk:** ${maxTokensPerChunk}\\n\\n`;\n \n // Summary section\n markdown += \"## Executive Summary\\n\\n\";\n markdown += summary + \"\\n\\n\";\n \n // Individual chunk analyses\n markdown += \"## Detailed Analysis by Chunk\\n\\n\";\n \n chunkResponses.forEach((chunk, index) => {\n markdown += `### Chunk ${chunk.chunkIndex + 1} (${chunk.tokenCount} tokens)\\n\\n`;\n markdown += chunk.analysis + \"\\n\\n\";\n markdown += \"---\\n\\n\";\n });\n \n return markdown;\n}\n\n// API endpoint to analyze PR messages\napp.post(\"/api/analyze\", async c => {\n try {\n const body: AnalysisRequest = await c.req.json();\n \n if (!body.prText || body.prText.trim().length === 0) {\n return c.json({\n success: false,\n error: \"Please provide PR text to analyze\"\n });\n }\n\n // Validate and set defaults for token limits\n const maxTokensPerChunk = Math.max(10, Math.min(4000, body.maxTokensPerChunk || DEFAULT_MAX_TOKENS_PER_CHUNK));\n const maxTokensPerResponse = Math.max(100, Math.min(2000, body.maxTokensPerResponse || DEFAULT_MAX_TOKENS_PER_RESPONSE));\n const organizeChunks = body.organizeChunks ?? false;\n\n // Step 1: Measure total tokens\n const totalTokens = encode(body.prText).length;\n console.log(`Total tokens in input: ${totalTokens}`);\n\n // Step 2: Split into chunks based on max tokens per chunk\n const chunks = chunkTextByTokens(body.prText, maxTokensPerChunk);\n console.log(`Split into ${chunks.length} chunks with max ${maxTokensPerChunk} tokens each`);\n console.log(`Chunk sizes: ${chunks.map(c => c.tokenCount).join(', ')} tokens`);\n\n // Step 3: Analyze all chunks concurrently with context\n console.log(`Starting concurrent analysis of ${chunks.length} chunks with context at ${new Date().toISOString()}...`);\n const concurrentStartTime = Date.now();\n \n const chunkPromises = chunks.map((chunk, index) => {\n const precedentChunk = index > 0 ? chunks[index - 1] : undefined;\n const subsequentChunk = index < chunks.length - 1 ? chunks[index + 1] : undefined;\n \n return analyzeChunk(chunk, index, maxTokensPerResponse, organizeChunks, precedentChunk, subsequentChunk);\n });\n \n const chunkResponses = await Promise.all(chunkPromises);\n const concurrentEndTime = Date.now();\n console.log(`Completed concurrent analysis of all ${chunks.length} chunks in ${concurrentEndTime - concurrentStartTime}ms at ${new Date().toISOString()}`);\n\n // Step 4: Generate summary from all responses\n console.log(\"Generating summary...\");\n const summary = await generateSummary(chunkResponses);\n\n // Step 5: Generate full markdown report\n const fullMarkdownReport = generateMarkdownReport(summary, chunkResponses, totalTokens, maxTokensPerChunk, organizeChunks);\n\n console.log(`Analysis complete: ${chunks.length} chunks, ${totalTokens} total tokens`);\n\n return c.json({\n success: true,\n totalTokens,\n totalChunks: chunks.length,\n maxTokensPerChunk,\n summary,\n chunkResponses,\n fullMarkdownReport\n });\n\n } catch (error) {\n console.error(\"Analysis error:\", error);\n return c.json({\n success: false,\n error: error instanceof Error ? error.message : \"An unexpected error occurred\"\n });\n }\n});\n\nexport default app.fetch;","deleted","label",[168,149],"backend","permalink","https://www.val.town/x/arthrod/ai_comments_to_tasks/code/backend/index.ts","extension","actionData","errors"]