Agentive

\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)));\napp.get(\"/api/industries\", (c: Context) => c.json(industries));\n\n// Refactored API endpoints to use the centralized service\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 userContent = {\n type,\n ...body,\n };\n return services.callOpenAI(\n config.prompts.DYNAMIC_LIST_GENERATOR,\n userContent,\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(company_context)\n }\\n\\nOccupation: ${occupation_title}\\n\\nTask: ${task}`;\n return services.callOpenAI(config.prompts.PROMPT_REFINER, userContent, {\n c,\n isJson: true,\n });\n});\n\napp.post(\"/api/inputs\", async (c: Context) => {\n const { refined_prompt } = await c.req.json();\n return services.callOpenAI(config.prompts.INPUT_EXTRACTOR, refined_prompt, {\n c,\n isJson: true,\n });\n});\n\napp.post(\"/api/clarify\", async (c: Context) => {\n const { refined_prompt } = await c.req.json();\n return services.callOpenAI(\n config.prompts.CLARIFICATION_AGENT,\n refined_prompt,\n { c, isJson: true },\n );\n});\n\n// --- REFACTORED EXECUTION ENDPOINT WITH TOOL SUPPORT ---\napp.post(\"/api/execute-stream\", async (c: Context) => {\n const { refined_prompt, user_inputs, clarifications, company_context } =\n await c.req.json();\n if (!refined_prompt || !user_inputs || !company_context) {\n return c.json({ error: \"Required fields missing\" }, 400);\n }\n\n // 1. Prepare the initial prompt by filling placeholders\n let finalUserPrompt = refined_prompt;\n for (const [key, value] of Object.entries(user_inputs)) {\n finalUserPrompt = finalUserPrompt.replace(\n new RegExp(`\\\\{\\\\{\\\\s*${key}\\\\s*\\\\}\\\\}`, \"g\"),\n String(value),\n );\n }\n\n let strategicGuidance =\n clarifications && Object.keys(clarifications).length > 0\n ? `# USER'S STRATEGIC GUIDANCE\\n${\n Object.entries(clarifications).map(([q, a]) => `- Q: ${q}\\n - A: ${a}`)\n .join(\"\\n\")\n }\\n---\\n`\n : \"\";\n\n const systemPrompt = `# 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: ${company_context.language}\n---\n${strategicGuidance}# YOUR TASK BRIEFING\n${finalUserPrompt}\nYou MUST write your entire response in the requested language: ${company_context.language}.`;\n\n const messages: any[] = [\n { role: \"system\", content: systemPrompt },\n {\n role: \"user\",\n content:\n \"Proceed with the task. If you need to research current information, use the web_search tool.\",\n },\n ];\n\n return streamText(c, async (stream) => {\n try {\n const openai = new OpenAI();\n\n // 2. First call to the AI to see if it wants to use a tool\n const initialResponse = await openai.chat.completions.create({\n model: config.models.major,\n messages,\n tools: config.toolDefinitions,\n tool_choice: \"auto\",\n });\n\n const responseMessage = initialResponse.choices[0].message;\n\n // 3. Check for tool calls\n if (responseMessage.tool_calls) {\n messages.push(responseMessage); // Add AI's decision to use tools to history\n\n // 4. Execute all tool calls\n for (const toolCall of responseMessage.tool_calls) {\n const toolResult = await services.executeTool(toolCall);\n messages.push({\n tool_call_id: toolCall.id,\n role: \"tool\",\n name: toolCall.function.name,\n content: toolResult,\n });\n }\n\n // 5. Make the final call with tool results included, and stream the response\n const finalStream = await openai.chat.completions.create({\n model: config.models.major,\n messages,\n stream: true,\n });\n\n for await (const part of finalStream) {\n await stream.write(part.choices[0]?.delta?.content || \"\");\n }\n } else {\n // If no tool is called, stream the content from the first response\n if (responseMessage.content) {\n await stream.write(responseMessage.content);\n }\n }\n } catch (e) {\n console.error(\"Execution failed:\", e);\n await stream.write(\"Error: AI execution failed.\");\n }\n });\n});\n\n// --- AGENTIVE LOOP ROUTES (REFACTORED) ---\napp.post(\"/api/generate-criteria\", async (c: Context) => {\n const { refined_prompt, company_context } = await c.req.json<\n GenerateCriteriaBody\n >();\n const userContent = `Company Context:\\n${\n JSON.stringify(company_context)\n }\\n\\nTask Briefing:\\n${refined_prompt}`;\n return services.callOpenAI(\n config.prompts.EVALUATION_CRITERIA_GENERATOR,\n userContent,\n { c, model: config.models.minor, isJson: true },\n );\n});\n\napp.post(\"/api/evaluate-output\", async (c: Context) => {\n const { refined_prompt, criteria, raw_output, language } = await c.req.json<\n EvaluateBody\n >();\n const userContent =\n `Task Briefing:\\n${refined_prompt}\\n\\nSuccess Criteria:\\n${\n JSON.stringify(criteria)\n }\\n\\nOutput to Evaluate:\\n${raw_output}`;\n const systemPrompt =\n `${config.prompts.EVALUATOR_AGENT}\\n\\nOutput Language: ${language}`;\n return services.callOpenAI(systemPrompt, userContent, { c, stream: true });\n});\n\n// Re-evaluation endpoint is identical to evaluation, just called at a different stage\napp.post(\"/api/reevaluate-output\", async (c: Context) => {\n const { refined_prompt, criteria, raw_output, language } = await c.req.json<\n EvaluateBody\n >();\n const userContent =\n `Task Briefing:\\n${refined_prompt}\\n\\nSuccess Criteria:\\n${\n JSON.stringify(criteria)\n }\\n\\nOutput to Evaluate:\\n${raw_output}`;\n const systemPrompt =\n `${config.prompts.EVALUATOR_AGENT}\\n\\nOutput Language: ${language}`;\n return services.callOpenAI(systemPrompt, userContent, { c, stream: true });\n});\n\napp.post(\"/api/refine-output\", async (c: Context) => {\n const { refined_prompt, raw_output, evaluation, language } = await c.req.json<\n RefineBody\n >();\n const userContent =\n `Original Task Briefing:\\n${refined_prompt}\\n\\nMy First Draft:\\n${raw_output}\\n\\nQA Critique:\\n${\n JSON.stringify(evaluation)\n }`;\n const systemPrompt =\n `${config.prompts.REFINER_AGENT}\\n\\nOutput Language: ${language}`;\n return services.callOpenAI(systemPrompt, userContent, { c, stream: true });\n});\n\n// --- EXPORT THE VAL ---\nexport default app.fetch;","deleted","label","children",[],[99],"files",[150],"counts",{"_173":32,"_174":175,"_176":32,"_177":32},"pulls","branches",1,"forks","envVars","contents",[],"file",{"_104":151,"_95":99,"_152":153,"_154":-5,"_155":138,"_156":101,"_157":22,"_106":182,"_100":159,"_160":161,"_162":163,"_164":22,"_165":96,"_98":183},["D",1755576089582],[99],"upstreamProject","upstreamBranch","emptyEnvVars","isNew","flags",[],"routes/_app.x.$handle.$name.branch.$branch.version.$version.code.$",{"_93":94,"_95":96,"_97":43,"_100":101,"_98":99,"_102":192,"_148":197,"_169":201,"_180":202,"_178":204,"_205":-5,"_206":22,"_207":-5,"_208":-5,"_209":-5,"_210":211},{"_104":105,"_95":96,"_106":193,"_108":-5,"_109":22,"_110":111,"_112":-5,"_113":-5,"_14":114,"_115":-5,"_116":22,"_117":-5,"_118":32,"_16":119,"_120":121,"_122":121,"_123":124,"_125":124,"_126":124,"_127":121,"_128":121,"_23":194,"_132":195,"_135":196,"_147":22},["D",1755490429474],{"_93":94,"_130":131},{"_93":94,"_134":131},{"_100":137,"_95":43,"_104":138,"_139":105,"_140":-5,"_141":-5,"_142":-5,"_143":-5,"_144":-5,"_145":96,"_146":94},[198],{"_104":151,"_95":99,"_152":153,"_154":-5,"_155":138,"_156":101,"_157":22,"_106":158,"_100":159,"_160":161,"_162":163,"_164":22,"_165":96,"_166":199,"_98":200},[],[99],[198],{"_104":151,"_95":99,"_152":153,"_154":-5,"_155":138,"_156":101,"_157":22,"_106":182,"_100":159,"_160":161,"_162":163,"_164":22,"_165":96,"_98":203},[99],[],"upstream","showCompareCallout","pull","ahead","behind","templates",[],"actionData","errors"]

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