VIBN Frontend for Coolify deployment

lib/ai/chat-extractor.ts

@@ -0,0 +1,42 @@
+import type { LlmClient } from '@/lib/ai/llm-client';
+import { ChatExtractionSchema } from '@/lib/ai/chat-extraction-types';
+import type { ChatExtractionData } from '@/lib/ai/chat-extraction-types';
+import type { KnowledgeItem } from '@/lib/types/knowledge';
+
+const SYSTEM_PROMPT = `
+You are the Product Chat Signal Extractor for stalled SaaS projects.
+- Read the provided transcript carefully.
+- Extract grounded signals about the product, market, users, execution status, and unknowns.
+- Never invent data. Use "null" or empty arrays when the transcript lacks information.
+- Respond with valid JSON that matches the provided schema exactly. Do not include prose or code fences.
+`.trim();
+
+export async function runChatExtraction(
+  knowledgeItem: KnowledgeItem,
+  llm: LlmClient,
+): Promise<ChatExtractionData> {
+  const transcript = knowledgeItem.content.trim();
+
+  const userMessage = `
+You will analyze the following transcript. Use message references when listing evidence (e.g., msg_1). 
+Focus on actionable product-building insights.
+
+TRANSCRIPT_START
+${transcript}
+TRANSCRIPT_END`.trim();
+
+  return llm.structuredCall<ChatExtractionData>({
+    model: 'gemini',
+    systemPrompt: SYSTEM_PROMPT,
+    messages: [
+      {
+        role: 'user',
+        content: userMessage,
+      },
+    ],
+    schema: ChatExtractionSchema,
+    temperature: 0.2,
+  });
+}
+
+