feat: wire Azure AI Foundry knowledge base (shipbrain-knowledgebase740) into chat

- Add foundry-kb.ts: calls the Foundry Project endpoint with data_sources
  pointing to the shipbrain-knowledgebase740 index so the ShipBrain
  Action Handbook grounding file is actually retrieved on every plain Q&A
- Route all no-tool-call chat responses through the knowledge base first;
  fall back to probe response if the KB call fails (graceful degradation)
- Add AZURE_AI_FOUNDRY_PROJECT_ENDPOINT, AZURE_OPENAI_ENDPOINT, and
  AZURE_AI_FOUNDRY_KNOWLEDGE_BASE env vars to .env.local

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

Author: JeevantheDev

lib/ai/foundry-kb.ts

@@ -0,0 +1,73 @@
+/**
+ * Calls Azure AI Foundry chat completions through the Project endpoint
+ * with the configured knowledge base attached as a data_source.
+ * Returns null if knowledge base is not configured or the call fails,
+ * so callers can fall back to the probe response.
+ */
+export async function callWithFoundryKnowledgeBase(
+  messages: Array<{ role: "system" | "user" | "assistant"; content: string }>
+): Promise<string | null> {
+  const projectEndpoint = process.env.AZURE_AI_FOUNDRY_PROJECT_ENDPOINT?.trim();
+  const knowledgeBase = process.env.AZURE_AI_FOUNDRY_KNOWLEDGE_BASE?.trim();
+  const apiKey = process.env.AZURE_AI_FOUNDRY_API_KEY?.trim();
+  const deployment = process.env.AZURE_AI_FOUNDRY_DEPLOYMENT_NAME?.trim();
+  // Use the Azure OpenAI endpoint for the data_source search endpoint
+  const openAiEndpoint = process.env.AZURE_OPENAI_ENDPOINT?.trim()
+    ?? process.env.AZURE_AI_FOUNDRY_ENDPOINT?.trim();
+
+  if (!projectEndpoint || !knowledgeBase || !apiKey || !deployment) return null;
+
+  const url =
+    `${projectEndpoint}/openai/deployments/${deployment}/chat/completions` +
+    `?api-version=2025-01-01-preview`;
+
+  try {
+    const res = await fetch(url, {
+      method: "POST",
+      headers: {
+        "content-type": "application/json",
+        "api-key": apiKey
+      },
+      body: JSON.stringify({
+        messages,
+        data_sources: [
+          {
+            type: "azure_search",
+            parameters: {
+              endpoint: openAiEndpoint,
+              index_name: knowledgeBase,
+              authentication: {
+                type: "api_key",
+                key: apiKey
+              },
+              query_type: "simple",
+              top_n_documents: 5
+            }
+          }
+        ]
+      }),
+      signal: AbortSignal.timeout(15_000)
+    });
+
+    if (!res.ok) {
+      const errBody = await res.text().catch(() => "");
+      console.warn("[foundry-kb] data_sources call failed:", res.status, errBody);
+      return null;
+    }
+
+    const json = await res.json();
+    return (json.choices?.[0]?.message?.content as string) ?? null;
+  } catch (err) {
+    console.warn("[foundry-kb] data_sources call error:", err);
+    return null;
+  }
+}
+
+export function isFoundryKbConfigured(): boolean {
+  return Boolean(
+    process.env.AZURE_AI_FOUNDRY_PROJECT_ENDPOINT &&
+    process.env.AZURE_AI_FOUNDRY_KNOWLEDGE_BASE &&
+    process.env.AZURE_AI_FOUNDRY_API_KEY &&
+    process.env.AZURE_AI_FOUNDRY_DEPLOYMENT_NAME
+  );
+}

lib/ai/shipbrain-chat.ts

@@ -12,6 +12,7 @@
 
 import { AIMessage, HumanMessage, SystemMessage } from "@langchain/core/messages";
 import { getModel } from "@/lib/ai/model";
+import { callWithFoundryKnowledgeBase } from "@/lib/ai/foundry-kb";
 import { getShipBrainAgentContext } from "@/lib/agent/context";
 import { listChatMessages, type StoredChatMessage } from "@/lib/ai/chat-store";
 import {
@@ -1293,10 +1294,14 @@ export async function streamShipBrainQuestion(input: {
   const toolCalls = (probeResponse as any).tool_calls ?? (probeResponse as any).additional_kwargs?.tool_calls ?? [];
 
   if (!toolCalls.length) {
-    // Reuse probe response directly — no second LLM call needed
-    const content = typeof probeResponse.content === "string"
-      ? probeResponse.content
-      : JSON.stringify(probeResponse.content);
+    // Try Foundry IQ knowledge base first; fall back to probe response text
+    const kbMessages = messages.map((m) => ({
+      role: (m._getType?.() === "system" ? "system" : m._getType?.() === "human" ? "user" : "assistant") as "system" | "user" | "assistant",
+      content: typeof m.content === "string" ? m.content : JSON.stringify(m.content)
+    }));
+    const kbAnswer = await callWithFoundryKnowledgeBase(kbMessages).catch(() => null);
+    const content = kbAnswer
+      ?? (typeof probeResponse.content === "string" ? probeResponse.content : JSON.stringify(probeResponse.content));
     return { ...base, action: null, stream: textStream(content), responseSource: "foundry_iq" };
   }