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// Can be more specific if profile structure is known\n saveFeedback?: any | null; // Can be more specific\n userResources?: { [key: string]: string } | null;\n}\n\ninterface HistoryItem {\n step: number;\n worker: string;\n prompt: string;\n}\n\ninterface ImprovementResult {\n originalPrompt: string;\n improvedPrompt: string;\n analysis: any;\n workerTrace: HistoryItem[];\n usage: {\n tokens: number;\n }\n}\n\n// A map of worker names to their functions\nconst workerFunctions: { [key: string]: (prompt: string, outputFormat: string) => Promise } = {\n Clarifier: clarifyPrompt,\n Condenser: condensePrompt,\n Includer: includeDetails,\n Structurer: structurePrompt,\n Technicalizer: technicalizePrompt,\n Creativizer: creativizePrompt,\n Contextualizer: contextualizePrompt,\n Exemplifier: exemplifyPrompt,\n Constrainer: constrainPrompt,\n Validator: validatePrompt,\n Optimizer: optimizePrompt,\n Formatter: formatPrompt,\n};\n\nexport async function improvePromptBackend(prompt: string, options: ImprovePromptOptions = {}): Promise {\n const {\n outputFormat = 'text',\n maxSteps = null,\n workers = null,\n useIntelligentRouting = true,\n userId = null,\n userProfile = null,\n saveFeedback = null,\n userResources = null\n } = options;\n\n let currentPrompt = prompt;\n let selectedWorkers: string[] | null = workers;\n let actualMaxSteps = maxSteps;\n let routingInfo: any = null;\n\n if (userId) {\n if (userProfile) {\n preferences.setUserProfile(userId, userProfile);\n }\n if (saveFeedback) {\n preferences.saveFeedback(userId, saveFeedback);\n }\n if (userResources) {\n resources.setUserResources(userId, userResources);\n }\n currentPrompt = resources.enrichPromptWithResources(prompt, userId);\n const analysis = router.analyzePrompt(currentPrompt);\n const userStrategy = preferences.generateStrategy(userId, currentPrompt, analysis);\n if (!maxSteps) {\n actualMaxSteps = userStrategy?.estimatedSteps || router.getOptimalSteps(prompt);\n }\n }\n\n if (useIntelligentRouting && !workers) {\n const selection = router.selectWorkers(prompt, actualMaxSteps || undefined);\n selectedWorkers = selection.workers.map(w => router.workers[w].name);\n if (userId) {\n selectedWorkers = preferences.getPersonalizedWorkers(userId, prompt, selectedWorkers);\n }\n routingInfo = {\n analysis: selection.analysis,\n reasoning: selection.reasoning,\n selectedWorkers: selectedWorkers,\n };\n console.log(`Intelligent routing selected ${selectedWorkers.length} workers`);\n } else {\n selectedWorkers = workers || [\"Clarifier\", \"Condenser\", \"Includer\"];\n actualMaxSteps = maxSteps || 3;\n }\n\n let step = 0;\n const history: HistoryItem[] = [];\n\n for (const workerName of selectedWorkers) {\n if (actualMaxSteps && step >= actualMaxSteps) break;\n step++;\n\n const workerFn = workerFunctions[workerName];\n if (!workerFn) {\n console.log(`Unknown worker: ${workerName}, skipping`);\n continue;\n }\n\n try {\n const result = await workerFn(currentPrompt, outputFormat);\n history.push({ step, worker: workerName, prompt: result });\n\n if (result === currentPrompt || (result.length / currentPrompt.length > 0.95 && result.length / currentPrompt.length < 1.05)) {\n console.log(`Worker ${workerName} produced minimal changes, stopping early`);\n break;\n }\n currentPrompt = result;\n } catch (error) {\n console.error(`Error in worker ${workerName}:`, error);\n }\n }\n\n return {\n originalPrompt: prompt,\n improvedPrompt: currentPrompt,\n analysis: routingInfo?.analysis,\n workerTrace: history,\n usage: {\n tokens: 0 // Mocked for now\n }\n };\n}\n\nasync function callOpenAI(systemContent: string, userContent: string, temperature: number, max_tokens: number): Promise {\n const response = await openai.chat.completions.create({\n model: \"gpt-4o\",\n max_tokens,\n temperature,\n messages: [\n { role: \"system\", content: systemContent },\n { role: \"user\", content: userContent },\n ],\n });\n return response.choices[0].message.content || \"\";\n}\n\nasync function clarifyPrompt(prompt: string, outputFormat: string): Promise {\n return callOpenAI(`You are a prompt clarification specialist. Format as ${outputFormat}.`, `Clarify: ${prompt}`, 0.3, 2000);\n}\n\nasync function condensePrompt(prompt: string, outputFormat: string): Promise {\n return callOpenAI(`You are a prompt condensation specialist. Format as ${outputFormat}.`, `Condense: ${prompt}`, 0.2, 1500);\n}\n\nasync function includeDetails(prompt: string, outputFormat: string): Promise {\n return callOpenAI(`You are a prompt enhancement specialist. Format as ${outputFormat}.`, `Enhance: ${prompt}`, 0.4, 2500);\n}\n\nasync function structurePrompt(prompt: string, outputFormat: string): Promise {\n return callOpenAI(`You are a prompt structuring specialist. Format as ${outputFormat}.`, `Structure: ${prompt}`, 0.3, 2000);\n}\n\nasync function technicalizePrompt(prompt: string, outputFormat: string): Promise {\n return callOpenAI(`You are a technical specification specialist. Format as ${outputFormat}.`, `Add technical specs to: ${prompt}`, 0.2, 3000);\n}\n\nasync function creativizePrompt(prompt: string, outputFormat: string): Promise {\n return callOpenAI(`You are a creative enhancement specialist. Format as ${outputFormat}.`, `Enhance creatively: ${prompt}`, 0.7, 2500);\n}\n\nasync function contextualizePrompt(prompt: string, outputFormat: string): Promise {\n return callOpenAI(`You are a context specialist. Format as ${outputFormat}.`, `Add context to: ${prompt}`, 0.3, 2500);\n}\n\nasync function exemplifyPrompt(prompt: string, outputFormat: string): Promise {\n return callOpenAI(`You are an examples specialist. Format as ${outputFormat}.`, `Add examples to: ${prompt}`, 0.4, 2000);\n}\n\nasync function constrainPrompt(prompt: string, outputFormat: string): Promise {\n return callOpenAI(`You are a constraints specialist. Format as ${outputFormat}.`, `Add constraints to: ${prompt}`, 0.2, 1500);\n}\n\nasync function validatePrompt(prompt: string, outputFormat: string): Promise {\n return callOpenAI(`You are a validation specialist. Format as ${outputFormat}.`, `Add validation criteria to: ${prompt}`, 0.2, 2000);\n}\n\nasync function optimizePrompt(prompt: string, outputFormat: string): Promise {\n return callOpenAI(`You are an optimization specialist. Format as ${outputFormat}.`, `Optimize: ${prompt}`, 0.3, 2000);\n}\n\nasync function formatPrompt(prompt: string, outputFormat: string): Promise {\n return callOpenAI(`You are a formatting specialist. Format as ${outputFormat}.`, `Format: ${prompt}`, 0.1, 1500);\n}\n\nexport {\n clarifyPrompt,\n condensePrompt,\n includeDetails,\n structurePrompt,\n technicalizePrompt,\n creativizePrompt,\n contextualizePrompt,\n exemplifyPrompt,\n constrainPrompt,\n validatePrompt,\n optimizePrompt,\n formatPrompt\n};","deleted","label",[170,152],"backend","permalink","https://www.val.town/x/toowired/Prompt_Improver/code/backend/backend_logic.ts","extension","actionData","errors"]