first-update

easy-dataset-main/app/api/projects/[projectId]/playground/chat/route.js

@@ -0,0 +1,99 @@
+import { NextResponse } from 'next/server';
+import LLMClient from '@/lib/llm/core/index';
+import { getModelConfigById } from '@/lib/db/model-config';
+
+async function resolveLatestModelConfig(projectId, incomingModel = {}) {
+  const modelId = incomingModel?.id;
+  if (!modelId) {
+    return incomingModel;
+  }
+
+  try {
+    const latestModelConfig = await getModelConfigById(modelId);
+    if (!latestModelConfig) {
+      return incomingModel;
+    }
+    if (String(latestModelConfig.projectId) !== String(projectId)) {
+      return incomingModel;
+    }
+
+    // Keep transient client-only fields, but force endpoint/auth/model fields to latest DB values.
+    return {
+      ...incomingModel,
+      ...latestModelConfig
+    };
+  } catch (error) {
+    console.error('Failed to resolve latest model config:', String(error));
+    return incomingModel;
+  }
+}
+
+export async function POST(request, { params }) {
+  try {
+    const { projectId } = params;
+
+    // Validate project ID.
+    if (!projectId) {
+      return NextResponse.json({ error: 'The project ID cannot be empty' }, { status: 400 });
+    }
+
+    // Read request payload.
+    const { model, messages } = await request.json();
+    const resolvedModel = await resolveLatestModelConfig(projectId, model);
+
+    // Validate request parameters.
+    if (!resolvedModel) {
+      return NextResponse.json({ error: 'The model parameters cannot be empty' }, { status: 400 });
+    }
+
+    if (!Array.isArray(messages) || messages.length === 0) {
+      return NextResponse.json({ error: 'The message list cannot be empty' }, { status: 400 });
+    }
+
+    // Use custom LLM client.
+    const llmClient = new LLMClient(resolvedModel);
+
+    // Normalize message payload for text + vision models.
+    const formattedMessages = messages.map(msg => {
+      // Plain text message.
+      if (typeof msg.content === 'string') {
+        return {
+          role: msg.role,
+          content: msg.content
+        };
+      }
+      // Multimodal message (e.g. image parts).
+      if (Array.isArray(msg.content)) {
+        return {
+          role: msg.role,
+          content: msg.content
+        };
+      }
+      // Fallback.
+      return {
+        role: msg.role,
+        content: msg.content
+      };
+    });
+
+    // Call LLM API.
+    let response = '';
+    try {
+      const { answer, cot } = await llmClient.getResponseWithCOT(formattedMessages.filter(f => f.role !== 'error'));
+      response = `<think>${cot}</think>${answer}`;
+    } catch (error) {
+      console.error('Failed to call LLM API:', String(error));
+      return NextResponse.json(
+        {
+          error: `Failed to call ${resolvedModel.modelId || resolvedModel.modelName || 'unknown'} model: ${error.message}`
+        },
+        { status: 500 }
+      );
+    }
+
+    return NextResponse.json({ response });
+  } catch (error) {
+    console.error('Failed to process chat request:', String(error));
+    return NextResponse.json({ error: `Failed to process chat request: ${error.message}` }, { status: 500 });
+  }
+}