Hatter

\n\n\n\n`;\n}\n\n// --- HONO APPLICATION SETUP ---\nconst app = new Hono();\n\n// --- API ROUTES ---\n\napp.get(\"/\", (c: Context) => c.html(generateHtml(c.req.url)));\n\napp.post(\"/api/co-pilot/plan\", async (c: Context) => {\n const { goal, company_context } = await c.req.json();\n if (!goal || !company_context) {\n return c.json({ error: \"Goal and company context are required\" }, 400);\n }\n\n try {\n // Step 1: Fetch all available industries (leverages existing logic)\n const industriesRes = await services.callOpenAI(\n config.prompts.INDUSTRY_GENERATOR,\n `Company Context: ${JSON.stringify(company_context)}`,\n { model: config.models.minor, isJson: true },\n );\n const industries =\n JSON.parse(industriesRes.choices[0].message.content).industries;\n\n // Step 2: Fetch occupations for each industry (leverages existing logic)\n let available_specialists = {};\n for (const industry of industries.slice(0, 5)) { // Limit to top 5 for speed\n const occupationsRes = await services.callOpenAI(\n config.prompts.DYNAMIC_LIST_GENERATOR,\n { generation_type: \"occupations\", company_context, industry },\n { model: config.models.major, isJson: true },\n );\n const occupations =\n JSON.parse(occupationsRes.choices[0].message.content).generated_list;\n available_specialists[industry] = occupations.map((o) => o.name);\n }\n\n // Step 3: Call the new Planner Agent\n const plannerPrompt = config.prompts.CO_PILOT_PLANNER\n .replace(\"{{user_goal}}\", goal)\n .replace(\n \"{{available_specialists}}\",\n JSON.stringify(available_specialists, null, 2),\n );\n\n return services.callOpenAI(\n \"You are a helpful assistant following strict JSON output rules.\",\n plannerPrompt,\n { c, isJson: true, model: config.models.major },\n );\n } catch (e) {\n console.error(\"Co-Pilot planning failed:\", e);\n return c.json(\n { error: `Co-Pilot failed to generate a plan. ${e.message}` },\n 500,\n );\n }\n});\n\napp.post(\"/api/industries\", async (c: Context) => {\n const { company_context } = await c.req.json();\n if (!company_context) {\n return c.json({ error: \"Company context is required\" }, 400);\n }\n const userContent = `Company Context: ${JSON.stringify(company_context)}`;\n return services.callOpenAI(\n config.prompts.INDUSTRY_GENERATOR,\n userContent,\n {\n c,\n isJson: true,\n model: config.models.minor,\n },\n );\n});\n\napp.post(\"/api/generate/:type\", async (c: Context) => {\n const { type } = c.req.param();\n if (type !== \"occupations\" && type !== \"tasks\") {\n return c.json({ error: \"Invalid generation type\" }, 400);\n }\n const body = await c.req.json();\n const payload = {\n generation_type: type,\n company_context: body.company_context,\n ...(type === \"occupations\" && { industry: body.industry }),\n ...(type === \"tasks\" && { occupation: body.occupation }),\n };\n\n return services.callOpenAI(\n config.prompts.DYNAMIC_LIST_GENERATOR,\n payload,\n { c, isJson: true },\n );\n});\n\napp.post(\"/api/prompt/dynamic\", async (c: Context) => {\n const { occupation_title, task, company_context } = await c.req.json<\n DynamicPromptBody\n >();\n const userContent = `Company Context: ${\n JSON.stringify(\n company_context,\n )\n }\\n\\nOccupation: ${occupation_title}\\n\\nTask: ${task}`;\n return services.callOpenAI(config.prompts.PROMPT_REFINER, userContent, {\n c,\n isJson: true,\n });\n});\n\napp.post(\"/api/assume-variables\", async (c: Context) => {\n const { refined_prompt, task, company_context } = await c.req.json();\n if (!refined_prompt || !task || !company_context) {\n return c.json({ error: \"Required fields missing for assumption\" }, 400);\n }\n\n const prompt = config.prompts.VARIABLE_ASSUMER\n .replace(\"{{company_context}}\", JSON.stringify(company_context))\n .replace(\"{{task}}\", task)\n .replace(\"{{refined_prompt}}\", refined_prompt);\n\n return services.callOpenAI(\n \"You are a helpful assistant following strict JSON output rules.\",\n prompt,\n { c, isJson: true, model: config.models.minor },\n );\n});\n\napp.post(\"/api/execute-stream\", async (c: Context) => {\n const {\n refined_prompt,\n user_inputs,\n company_context,\n category,\n } = await c.req.json();\n if (!company_context || !category || !refined_prompt || !user_inputs) {\n return c.json({ error: \"Required fields missing\" }, 400);\n }\n\n let promptWithVariables = refined_prompt;\n for (const [key, value] of Object.entries(user_inputs)) {\n promptWithVariables = promptWithVariables.replace(\n new RegExp(`\\\\{\\\\{\\\\s*${key}\\\\s*\\\\}\\\\}`, \"g\"),\n String(value),\n );\n }\n promptWithVariables = promptWithVariables.replace(\n /\\{\\{.*?\\}\\}/g,\n \"[Information not provided]\",\n );\n\n let systemPrompt = \"\";\n let userPrompt = \"\";\n\n switch (category) {\n case \"thinking\":\n case \"document_creation\":\n systemPrompt =\n `# YOUR CONTEXT\\n- Company Name: ${company_context.company_name}\\n- Company Description: ${company_context.company_description}\\n- Primary Goal: ${company_context.use_case}\\n- Language for Output: ${company_context.language}\\n---\\n# YOUR TASK BRIEFING\\n${promptWithVariables}\\nYou MUST write your entire response in the requested language: ${company_context.language}.`;\n userPrompt = \"Proceed with the task.\";\n break;\n\n case \"prompt_building\":\n systemPrompt = config.prompts.MASTER_PROMPT_ENGINEER;\n userPrompt =\n `Generate a master prompt for the following task, considering the company's context.\\n\\nTask: \"${promptWithVariables}\"\\n\\nCompany Context: ${\n JSON.stringify(\n company_context,\n )\n }`;\n break;\n\n default:\n return c.json({ error: \"Invalid execution category\" }, 400);\n }\n\n return services.callOpenAI(systemPrompt, userPrompt, { c, stream: true });\n});\n\napp.post(\"/api/evaluate\", async (c: Context) => {\n const { briefing, first_draft, company_context } = await c.req.json();\n if (!briefing || !first_draft || !company_context) {\n return c.json({\n error: \"Briefing, first_draft, and company_context are required\",\n }, 400);\n }\n\n try {\n const criteriaPrompt = config.prompts.SUCCESS_CRITERIA_GENERATOR.replace(\n \"{{task_briefing}}\",\n briefing,\n );\n const criteriaCompletion = await services.callOpenAI(\n \"You are a helpful assistant following strict output rules.\",\n criteriaPrompt,\n { model: config.models.minor, isJson: true },\n );\n const success_criteria =\n JSON.parse(criteriaCompletion.choices[0].message.content).criteria;\n\n const userContent = {\n task_briefing: briefing,\n success_criteria: success_criteria,\n raw_output: first_draft,\n };\n\n return services.callOpenAI(config.prompts.EVALUATOR_AGENT, userContent, {\n c,\n isJson: true,\n model: config.models.minor,\n });\n } catch (e) {\n console.error(\"Evaluation failed:\", e);\n return c.json(\n { error: `Failed to evaluate the document. ${e.message}` },\n 500,\n );\n }\n});\n\napp.post(\"/api/refine\", async (c: Context) => {\n const { briefing, first_draft, critique, language } = await c.req.json();\n const userContent = `# YOUR TASK\nRewrite your first draft to fully address every point in the critique, elevating the work to a 10/10 standard. Return only the improved final deliverable. Do not write an apology, explanation, or any meta-commentary. Produce the new, superior output in the requested language: ${language}.\n\n## Original Briefing\n${briefing}\n\n## Your First Draft\n${first_draft}\n\n## QA Critique\n${critique}`;\n\n return services.callOpenAI(\n config.prompts.REFINER_AGENT,\n userContent,\n { c, stream: true },\n );\n});\n\n// --- EXPORT THE VAL ---\nexport default app.fetch;","deleted","label",[99],"permalink","https://www.val.town/x/volt/hatter/code/main.ts","extension","actionData","errors"]

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