wehub-resource-sync
84 lines
2.8 KiB
TypeScript
84 lines
2.8 KiB
TypeScript
/**
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* PBL Runtime Chat API
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*
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* Handles @mention routing during PBL runtime.
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* Students @question or @judge an agent, and this endpoint generates a response.
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*/
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import { NextRequest } from 'next/server';
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import { callLLM } from '@/lib/ai/llm';
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import type { PBLAgent, PBLIssue } from '@/lib/pbl/types';
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import { createLogger } from '@/lib/logger';
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import { apiError, apiSuccess } from '@/lib/server/api-response';
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import { resolveModelFromRequest } from '@/lib/server/resolve-model';
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const log = createLogger('PBL Chat');
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interface PBLChatRequest {
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message: string;
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agent: PBLAgent;
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currentIssue: PBLIssue | null;
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recentMessages: { agent_name: string; message: string }[];
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userRole: string;
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agentType?: 'question' | 'judge';
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}
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export async function POST(req: NextRequest) {
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let agentName: string | undefined;
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let resolvedAgentType: string | undefined;
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try {
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const body = (await req.json()) as PBLChatRequest;
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const { message, agent, currentIssue, recentMessages, userRole, agentType } = body;
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agentName = agent?.name;
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resolvedAgentType = agentType;
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if (!message || !agent) {
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return apiError('MISSING_REQUIRED_FIELD', 400, 'Message and agent are required');
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}
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// Get model config from request headers/body
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const { model, thinkingConfig } = await resolveModelFromRequest(req, body, 'pbl-chat');
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// Build context for the agent, differentiating question vs judge
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let issueContext = '';
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if (currentIssue) {
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issueContext = `\n\n## Current Issue\nTitle: ${currentIssue.title}\nDescription: ${currentIssue.description}\nPerson in Charge: ${currentIssue.person_in_charge}`;
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if (currentIssue.generated_questions) {
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if (agentType === 'judge') {
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issueContext += `\n\nQuestions to Evaluate Against:\n${currentIssue.generated_questions}`;
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} else {
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issueContext += `\n\nGenerated Questions:\n${currentIssue.generated_questions}`;
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}
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}
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}
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const recentContext =
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recentMessages.length > 0
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? `\n\n## Recent Conversation\n${recentMessages
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.slice(-5)
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.map((m) => `${m.agent_name}: ${m.message}`)
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.join('\n')}`
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: '';
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const systemPrompt = `${agent.system_prompt}${issueContext}${recentContext}${userRole ? `\n\nThe student's role is: ${userRole}` : ''}`;
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const result = await callLLM(
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{
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model,
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system: systemPrompt,
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prompt: message,
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},
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'pbl-chat',
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undefined,
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thinkingConfig,
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);
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return apiSuccess({ message: result.text, agentName: agent.name });
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} catch (error) {
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log.error(
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`PBL chat failed [agent="${agentName ?? 'unknown'}", type=${resolvedAgentType ?? 'question'}]:`,
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error,
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);
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return apiError('INTERNAL_ERROR', 500, error instanceof Error ? error.message : String(error));
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}
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}
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