feat(ai): persist raw tool execution results in postgres to enable fine-tuning dataset extraction

vibn-frontend/app/api/chat/route.ts

@@ -723,14 +723,38 @@ export async function POST(request: Request) {
         // segmentation it shows during streaming. Older messages
         // (pre-this-fix) won't have textSegments and fall back to
         // single-bubble content rendering.
-        const finalMsg: ChatMessage & { textSegments?: string[] } = {
+        const finalMsg: ChatMessage & {
+          textSegments?: string[];
+          _rawToolResults?: Array<{ name: string; args: any; result: string }>;
+        } = {
           role: "assistant",
           content: assistantText,
           toolCalls: assistantToolCalls.length ? assistantToolCalls : undefined,
           textSegments: assistantTextSegments.length
             ? assistantTextSegments
             : undefined,
+          _rawToolResults: assistantToolCalls.length ? [] : undefined,
         };
+
+        // Option 1 implemented: Save the raw tool results directly into the database row
+        // alongside the assistant message so it can be extracted later for fine-tuning.
+        if (finalMsg._rawToolResults) {
+          // We slice out the tool messages from the internal messages array we just built
+          // during the loop and attach them to the final row payload.
+          const toolResults = messages.filter((m) => m.role === "tool");
+          finalMsg._rawToolResults = assistantToolCalls.map((tc) => {
+            const tr = toolResults.find((m) => m.toolCallId === tc.id);
+            return {
+              name: tc.name,
+              args: tc.args,
+              result:
+                typeof tr?.content === "string"
+                  ? tr.content
+                  : JSON.stringify(tr?.content || ""),
+            };
+          });
+        }
+
         await query(
           `INSERT INTO fs_chat_messages (thread_id, user_id, data) VALUES ($1, $2, $3)`,
           [thread_id, email, JSON.stringify(finalMsg)],