feat: add lighthouse caching

ui/app/(prowler)/lighthouse/page.tsx

@@ -2,16 +2,55 @@ import { getLighthouseConfig } from "@/actions/lighthouse/lighthouse";
 import { LighthouseIcon } from "@/components/icons/Icons";
 import { Chat } from "@/components/lighthouse";
 import { ContentLayout } from "@/components/ui";
+import { CacheService } from "@/lib/lighthouse/cache";
+import { suggestedActions } from "@/lib/lighthouse/suggested-actions";
 
-export default async function AIChatbot() {
+interface LighthousePageProps {
+  searchParams: { cachedMessage?: string };
+}
+
+export default async function AIChatbot({ searchParams }: LighthousePageProps) {
   const config = await getLighthouseConfig();
 
   const hasConfig = !!config;
   const isActive = config?.attributes?.is_active ?? false;
 
+  // Fetch cached content if a cached message type is specified
+  let cachedContent = null;
+  if (searchParams.cachedMessage) {
+    const cached = await CacheService.getCachedMessage(
+      searchParams.cachedMessage,
+    );
+    cachedContent = cached.success ? cached.data : null;
+  }
+
+  // Pre-fetch all question answers and processing status
+  const isProcessing = await CacheService.isRecommendationProcessing();
+  const questionAnswers: Record<string, string> = {};
+
+  if (!isProcessing) {
+    for (const action of suggestedActions) {
+      if (action.questionRef) {
+        const cached = await CacheService.getCachedMessage(
+          `question_${action.questionRef}`,
+        );
+        if (cached.success && cached.data) {
+          questionAnswers[action.questionRef] = cached.data;
+        }
+      }
+    }
+  }
+
   return (
     <ContentLayout title="Lighthouse AI" icon={<LighthouseIcon />}>
-      <Chat hasConfig={hasConfig} isActive={isActive} />
+      <Chat
+        hasConfig={hasConfig}
+        isActive={isActive}
+        cachedContent={cachedContent}
+        messageType={searchParams.cachedMessage}
+        isProcessing={isProcessing}
+        questionAnswers={questionAnswers}
+      />
     </ContentLayout>
   );
 }

ui/components/lighthouse/banner.tsx

@@ -50,7 +50,7 @@ export const LighthouseBanner = async () => {
     ) {
       return renderBanner({
         message: cachedRecommendations.data,
-        href: "/lighthouse",
+        href: "/lighthouse?cachedMessage=recommendation",
         gradient:
           "bg-gradient-to-r from-blue-500 to-purple-600 hover:from-blue-600 hover:to-purple-700 focus:ring-blue-500/50 dark:from-blue-600 dark:to-purple-700 dark:hover:from-blue-700 dark:hover:to-purple-800 dark:focus:ring-blue-400/50",
       });
@@ -62,7 +62,7 @@ export const LighthouseBanner = async () => {
     if (isProcessing) {
       return renderBanner({
         message: "Lighthouse is reviewing your findings for insights",
-        href: "/lighthouse",
+        href: "",
         gradient:
           "bg-gradient-to-r from-orange-500 to-yellow-500 hover:from-orange-600 hover:to-yellow-600 focus:ring-orange-500/50 dark:from-orange-600 dark:to-yellow-600 dark:hover:from-orange-700 dark:hover:to-yellow-700 dark:focus:ring-orange-400/50",
       });

ui/components/lighthouse/chat.tsx

@@ -1,30 +1,40 @@
 "use client";
 
 import { useChat } from "@ai-sdk/react";
-import { useEffect, useRef, useState } from "react";
+import { useCallback, useEffect, useRef, useState } from "react";
+
 import { useForm } from "react-hook-form";
 
 import { MemoizedMarkdown } from "@/components/lighthouse/memoized-markdown";
 import { CustomButton, CustomTextarea } from "@/components/ui/custom";
 import { CustomLink } from "@/components/ui/custom/custom-link";
 import { Form } from "@/components/ui/form";
-
-interface SuggestedAction {
-  title: string;
-  label: string;
-  action: string;
-}
+import {
+  SuggestedAction,
+  suggestedActions,
+} from "@/lib/lighthouse/suggested-actions";
 
 interface ChatProps {
   hasConfig: boolean;
   isActive: boolean;
+  cachedContent?: string | null;
+  messageType?: string;
+  isProcessing: boolean;
+  questionAnswers: Record<string, string>;
 }
 
 interface ChatFormData {
   message: string;
 }
 
-export const Chat = ({ hasConfig, isActive }: ChatProps) => {
+export const Chat = ({
+  hasConfig,
+  isActive,
+  cachedContent,
+  messageType,
+  isProcessing,
+  questionAnswers,
+}: ChatProps) => {
   const [errorMessage, setErrorMessage] = useState<string | null>(null);
 
   const {
@@ -74,6 +84,13 @@ export const Chat = ({ hasConfig, isActive }: ChatProps) => {
     },
   });
 
+  // State for cached response streaming simulation
+  const [isStreamingCached, setIsStreamingCached] = useState(false);
+  const [streamingMessageId, setStreamingMessageId] = useState<string | null>(
+    null,
+  );
+  const [currentStreamText, setCurrentStreamText] = useState("");
+
   const form = useForm<ChatFormData>({
     defaultValues: {
       message: "",
@@ -108,6 +125,149 @@ export const Chat = ({ hasConfig, isActive }: ChatProps) => {
     }
   }, [errorMessage, form, setMessages]);
 
+  // Function to simulate streaming text
+  const simulateStreaming = useCallback(
+    async (text: string, messageId: string) => {
+      setIsStreamingCached(true);
+      setStreamingMessageId(messageId);
+      setCurrentStreamText("");
+
+      // Stream word by word with realistic delays
+      const words = text.split(" ");
+      let currentText = "";
+
+      for (let i = 0; i < words.length; i++) {
+        currentText += (i > 0 ? " " : "") + words[i];
+        setCurrentStreamText(currentText);
+
+        // Shorter delay between words for faster streaming
+        const delay = Math.random() * 80 + 40; // 40-120ms delay per word
+        await new Promise((resolve) => setTimeout(resolve, delay));
+      }
+
+      setIsStreamingCached(false);
+      setStreamingMessageId(null);
+      setCurrentStreamText("");
+    },
+    [],
+  );
+
+  // Function to handle cached response for suggested actions
+  const handleCachedResponse = useCallback(
+    async (action: SuggestedAction) => {
+      if (!action.questionRef) {
+        // No question ref, use normal flow
+        append({
+          role: "user",
+          content: action.action,
+        });
+        return;
+      }
+
+      try {
+        if (isProcessing) {
+          // Processing in progress, fallback to real-time LLM
+          append({
+            role: "user",
+            content: action.action,
+          });
+          return;
+        }
+
+        // Check if we have cached answer
+        const cachedAnswer = questionAnswers[action.questionRef];
+
+        if (cachedAnswer) {
+          // Cache hit - use cached content with streaming simulation
+          const userMessageId = `user-cached-${Date.now()}`;
+          const assistantMessageId = `assistant-cached-${Date.now()}`;
+
+          const userMessage = {
+            id: userMessageId,
+            role: "user" as const,
+            content: action.action,
+          };
+
+          const assistantMessage = {
+            id: assistantMessageId,
+            role: "assistant" as const,
+            content: "",
+          };
+
+          const updatedMessages = [...messages, userMessage, assistantMessage];
+          setMessages(updatedMessages);
+
+          // Start streaming simulation
+          setTimeout(() => {
+            simulateStreaming(cachedAnswer, assistantMessageId);
+          }, 300);
+        } else {
+          // Cache miss/expired/error - fallback to real-time LLM
+          append({
+            role: "user",
+            content: action.action,
+          });
+        }
+      } catch (error) {
+        console.error("Error handling cached response:", error);
+        // Fall back to normal API flow
+        append({
+          role: "user",
+          content: action.action,
+        });
+      }
+    },
+    [
+      messages,
+      setMessages,
+      append,
+      simulateStreaming,
+      isProcessing,
+      questionAnswers,
+    ],
+  );
+
+  // Load cached message on mount if cachedContent is provided
+  useEffect(() => {
+    const loadCachedMessage = () => {
+      if (cachedContent && messages.length === 0) {
+        // Create different user questions based on message type
+        let userQuestion = "Tell me more about this";
+
+        if (messageType === "recommendation") {
+          userQuestion =
+            "Tell me more about the security issues Lighthouse found";
+        }
+        // Future: handle other message types
+        // else if (messageType === "question_1") {
+        //   userQuestion = "Previously cached question here";
+        // }
+
+        // Create message IDs
+        const userMessageId = `user-cached-${messageType}-${Date.now()}`;
+        const assistantMessageId = `assistant-cached-${messageType}-${Date.now()}`;
+
+        // Add user message
+        const userMessage = {
+          id: userMessageId,
+          role: "user" as const,
+          content: userQuestion,
+        };
+
+        // Add assistant message with the cached content
+        const assistantMessage = {
+          id: assistantMessageId,
+          role: "assistant" as const,
+          content: cachedContent,
+        };
+
+        setMessages([userMessage, assistantMessage]);
+      }
+    };
+
+    loadCachedMessage();
+  }, [cachedContent, messageType, messages.length, setMessages]);
+
   // Sync form value with chat input
   useEffect(() => {
     const syntheticEvent = {
@@ -146,6 +306,19 @@ export const Chat = ({ hasConfig, isActive }: ChatProps) => {
     return () => document.removeEventListener("keydown", handleKeyDown);
   }, [messageValue, onFormSubmit]);
 
+  // Update assistant message content during streaming simulation
+  useEffect(() => {
+    if (isStreamingCached && streamingMessageId && currentStreamText) {
+      setMessages((prevMessages) =>
+        prevMessages.map((msg) =>
+          msg.id === streamingMessageId
+            ? { ...msg, content: currentStreamText }
+            : msg,
+        ),
+      );
+    }
+  }, [currentStreamText, isStreamingCached, streamingMessageId, setMessages]);
+
   useEffect(() => {
     if (messagesContainerRef.current && latestUserMsgRef.current) {
       const container = messagesContainerRef.current;
@@ -156,30 +329,6 @@ export const Chat = ({ hasConfig, isActive }: ChatProps) => {
     }
   }, [messages]);
 
-  const suggestedActions: SuggestedAction[] = [
-    {
-      title: "Are there any exposed S3",
-      label: "buckets in my AWS accounts?",
-      action: "List exposed S3 buckets in my AWS accounts",
-    },
-    {
-      title: "What is the risk of having",
-      label: "RDS databases unencrypted?",
-      action: "What is the risk of having RDS databases unencrypted?",
-    },
-    {
-      title: "What is the CIS 1.10 compliance status",
-      label: "of my Kubernetes cluster?",
-      action:
-        "What is the CIS 1.10 compliance status of my Kubernetes cluster?",
-    },
-    {
-      title: "List my highest privileged",
-      label: "AWS IAM users with full admin access?",
-      action: "List my highest privileged AWS IAM users with full admin access",
-    },
-  ];
-
   // Determine if chat should be disabled
   const shouldDisableChat = !hasConfig || !isActive;
 
@@ -267,10 +416,7 @@ export const Chat = ({ hasConfig, isActive }: ChatProps) => {
                   key={`suggested-action-${index}`}
                   ariaLabel={`Send message: ${action.action}`}
                   onPress={() => {
-                    append({
-                      role: "user",
-                      content: action.action,
-                    });
+                    handleCachedResponse(action); // Use cached response handler
                   }}
                   className="hover:bg-muted flex h-auto w-full flex-col items-start justify-start rounded-xl border bg-gray-50 px-4 py-3.5 text-left font-sans text-sm dark:bg-gray-900"
                 >
@@ -320,10 +466,12 @@ export const Chat = ({ hasConfig, isActive }: ChatProps) => {
               </div>
             );
           })}
-          {status === "submitted" && (
+          {(status === "submitted" || isStreamingCached) && (
             <div className="flex justify-start">
               <div className="bg-muted max-w-[80%] rounded-lg px-4 py-2">
-                <div className="animate-pulse">Thinking...</div>
+                <div className="animate-pulse">
+                  {isStreamingCached ? "" : "Thinking..."}
+                </div>
               </div>
             </div>
           )}
@@ -358,10 +506,18 @@ export const Chat = ({ hasConfig, isActive }: ChatProps) => {
               ariaLabel={
                 status === "submitted" ? "Stop generation" : "Send message"
               }
-              isDisabled={status === "submitted" || !messageValue?.trim()}
+              isDisabled={
+                status === "submitted" ||
+                isStreamingCached ||
+                !messageValue?.trim()
+              }
               className="flex h-10 w-10 flex-shrink-0 items-center justify-center rounded-lg bg-primary p-2 text-primary-foreground hover:bg-primary/90 disabled:opacity-50 dark:bg-primary/90"
             >
-              {status === "submitted" ? <span>■</span> : <span>➤</span>}
+              {status === "submitted" || isStreamingCached ? (
+                <span>■</span>
+              ) : (
+                <span>➤</span>
+              )}
             </CustomButton>
           </div>
         </form>

ui/lib/lighthouse/cache.ts

@@ -2,11 +2,16 @@ import Valkey from "iovalkey";
 
 import { auth } from "@/auth.config";
 
-import { generateRecommendation } from "./recommendations";
 import {
+  generateBannerFromDetailed,
+  generateDetailedRecommendation,
+  generateQuestionAnswers,
+} from "./recommendations";
+import { suggestedActions } from "./suggested-actions";
+import {
+  compareProcessedScanIds,
   generateSecurityScanSummary,
   getCompletedScansLast24h,
-  compareProcessedScanIds,
 } from "./summary";
 
 let valkeyClient: Valkey | null = null;
@@ -136,18 +141,12 @@ export class CacheService {
           await this.setProcessedScanIds(scanIds);
 
           // Generate and cache recommendations asynchronously
-          this.generateAndCacheRecommendations(scanSummary)
-            .then((result) => {
-              if (result.success && result.data) {
-                console.log("Background recommendation generated successfully");
-              }
-            })
-            .catch((error) => {
-              console.error(
-                "Background recommendation generation failed:",
-                error,
-              );
-            });
+          this.generateAndCacheRecommendations(scanSummary).catch((error) => {
+            console.error(
+              "Background recommendation generation failed:",
+              error,
+            );
+          });
 
           return {
             success: true,
@@ -207,10 +206,6 @@ export class CacheService {
     }
   }
 
-  static async getCachedMessage(): Promise<string | null> {
-    return await this.get("scan-summary");
-  }
-
   static async getRecommendations(): Promise<{
     success: boolean;
     data?: string;
@@ -245,6 +240,7 @@ export class CacheService {
 
     const lockKey = "recommendations-processing";
     const dataKey = `_lighthouse:${tenantId}:recommendations`;
+    const detailedDataKey = `_lighthouse:${tenantId}:cached-messages:recommendation`;
 
     try {
       const client = await getValkeyClient();
@@ -280,17 +276,40 @@ export class CacheService {
           };
         }
 
-        // Generate recommendation using LLM
-        const recommendation = await generateRecommendation(scanSummary);
+        // Generate detailed recommendation first
+        const detailedRecommendation =
+          await generateDetailedRecommendation(scanSummary);
 
-        // Only cache non-empty recommendations
-        if (recommendation.trim()) {
-          await client.set(dataKey, recommendation);
+        if (!detailedRecommendation.trim()) {
+          return { success: true, data: "" };
+        }
+
+        // Generate banner from detailed content
+        const bannerRecommendation = await generateBannerFromDetailed(
+          detailedRecommendation,
+        );
+
+        // Both must succeed - no point in detailed without banner
+        if (!bannerRecommendation.trim()) {
+          return { success: true, data: "" };
+        }
+
+        // Generate question answers
+        const questionAnswers = await generateQuestionAnswers(suggestedActions);
+
+        // Cache both versions
+        await client.set(dataKey, bannerRecommendation);
+        await client.set(detailedDataKey, detailedRecommendation);
+
+        // Cache question answers with 24h TTL
+        for (const [questionRef, answer] of Object.entries(questionAnswers)) {
+          const questionKey = `_lighthouse:${tenantId}:cached-messages:question_${questionRef}`;
+          await client.set(questionKey, answer, "EX", 86400); // 24 hours
         }
 
         return {
           success: true,
-          data: recommendation,
+          data: bannerRecommendation,
         };
       } finally {
         await this.releaseProcessingLock(tenantId, lockKey);
@@ -315,6 +334,52 @@ export class CacheService {
       return false;
     }
   }
+
+  // New method to get cached message by type
+  static async getCachedMessage(messageType: string): Promise<{
+    success: boolean;
+    data?: string;
+  }> {
+    const tenantId = await this.getTenantId();
+    if (!tenantId) return { success: false };
+
+    try {
+      const client = await getValkeyClient();
+      const dataKey = `_lighthouse:${tenantId}:cached-messages:${messageType}`;
+
+      const cachedData = await client.get(dataKey);
+      if (cachedData) {
+        return {
+          success: true,
+          data: cachedData.toString(),
+        };
+      }
+
+      return { success: true, data: undefined };
+    } catch (error) {
+      console.error(`Error getting cached message ${messageType}:`, error);
+      return { success: false };
+    }
+  }
+
+  // New method to set cached message by type
+  static async setCachedMessage(
+    messageType: string,
+    content: string,
+  ): Promise<boolean> {
+    const tenantId = await this.getTenantId();
+    if (!tenantId) return false;
+
+    try {
+      const client = await getValkeyClient();
+      const dataKey = `_lighthouse:${tenantId}:cached-messages:${messageType}`;
+      await client.set(dataKey, content);
+      return true;
+    } catch (error) {
+      console.error(`Error caching message type ${messageType}:`, error);
+      return false;
+    }
+  }
 }
 
 export async function initializeTenantCache(): Promise<{

ui/lib/lighthouse/recommendations.ts

@@ -2,7 +2,10 @@ import { ChatOpenAI } from "@langchain/openai";
 
 import { getAIKey, getLighthouseConfig } from "@/actions/lighthouse/lighthouse";
 
-export const generateRecommendation = async (
+import { type SuggestedAction } from "./suggested-actions";
+import { initLighthouseWorkflow } from "./workflow";
+
+export const generateDetailedRecommendation = async (
   scanSummary: string,
 ): Promise<string> => {
   try {
@@ -11,52 +14,56 @@ export const generateRecommendation = async (
       return "";
     }
 
-    // Get lighthouse configuration
     const lighthouseConfig = await getLighthouseConfig();
     if (!lighthouseConfig?.attributes) {
       return "";
     }
 
     const config = lighthouseConfig.attributes;
-    const finalBusinessContext = config.business_context || "";
+    const businessContext = config.business_context || "";
 
     const llm = new ChatOpenAI({
       model: config.model || "gpt-4o",
       temperature: config.temperature || 0,
-      maxTokens: 150,
+      maxTokens: 1500,
       apiKey: apiKey,
     });
 
-    // Build the prompt with business context awareness
-    let systemPrompt = `You are a cloud security analyst creating concise business recommendations for a banner notification.
+    let systemPrompt = `You are a cloud security analyst providing focused, actionable recommendations.
 
-IMPORTANT: Your response must be a single, short sentence (max 80 characters) that would make a user want to click on a banner to learn more.
+IMPORTANT: Focus on ONE of these high-impact opportunities:
+1. The most CRITICAL finding that needs immediate attention
+2. A pattern where fixing one check ID resolves many findings (e.g., "Fix aws_s3_bucket_public_access_block to resolve 15 findings")
+3. The issue with highest business impact
 
-GUIDELINES:
-- Frame recommendations in business terms, not technical jargon
-- Focus on actionable insights
-- Make it clickable and engaging
-- Don't use phrases like "Lighthouse says" or "Lighthouse recommends"
-- Be specific about the type of improvement when possible
-- Use only information from the security scan summary to generate the recommendation
-- Add words like "Lighthouse" to the recommendation
-- Don't end with a question mark or full stop
-- Don't use words like "urges" or "requires"
-- Don't wrap the message in double quotes or single quotes
-- Use words like "detected" or "found" to describe the issue
+Your response should be a comprehensive analysis of this ONE focus area including:
 
-EXAMPLES OF GOOD RESPONSES:
-- Lighthouse detected critical issues in authentication services
-- Lighthouse found a new exposed S3 bucket in recent scan
-- Lighthouse identified fixing one check could resolve 30 open findings
+**Issue Description:**
+- What exactly is the problem
+- Why it's critical or high-impact
+- How many findings it affects
 
-Based on the below security scan summary, generate ONE short business recommendation:`;
+**Affected Resources:**
+- Specific resources, services, or configurations involved
+- Number of affected resources
 
-    if (finalBusinessContext) {
-      systemPrompt += `\n\nBUSINESS CONTEXT: ${finalBusinessContext}`;
+**Business Impact:**
+- Security risks and potential consequences
+- Compliance violations (mention specific frameworks if applicable)
+- Operational impact
+
+**Remediation Steps:**
+- Clear, step-by-step instructions
+- Specific commands or configuration changes where applicable
+- Expected outcome after fix
+
+Be specific with numbers (e.g., "affects 12 S3 buckets", "resolves 15 findings"). Focus on actionable guidance that will have the biggest security improvement.`;
+
+    if (businessContext) {
+      systemPrompt += `\n\nBUSINESS CONTEXT: ${businessContext}`;
     }
 
-    systemPrompt += `\n\nBased on this security scan summary, generate 1 engaging banner message:\n\n${scanSummary}`;
+    systemPrompt += `\n\nSecurity Scan Summary:\n${scanSummary}`;
 
     const response = await llm.invoke([
       {
@@ -65,11 +72,128 @@ Based on the below security scan summary, generate ONE short business recommenda
       },
     ]);
 
-    const recommendation = response.content.toString().trim();
-
-    return recommendation.length > 0 ? recommendation : "";
+    return response.content.toString().trim();
   } catch (error) {
-    console.error("Error generating recommendation:", error);
+    console.error("Error generating detailed recommendation:", error);
     return "";
   }
 };
+
+export const generateBannerFromDetailed = async (
+  detailedRecommendation: string,
+): Promise<string> => {
+  try {
+    const apiKey = await getAIKey();
+    if (!apiKey) {
+      return "";
+    }
+
+    const lighthouseConfig = await getLighthouseConfig();
+    if (!lighthouseConfig?.attributes) {
+      return "";
+    }
+
+    const config = lighthouseConfig.attributes;
+
+    const llm = new ChatOpenAI({
+      model: config.model || "gpt-4o",
+      temperature: config.temperature || 0,
+      maxTokens: 100,
+      apiKey: apiKey,
+    });
+
+    const systemPrompt = `Create a short, engaging banner message from this detailed security analysis.
+
+REQUIREMENTS:
+- Maximum 80 characters
+- Include "Lighthouse" in the message
+- Focus on the key insight or opportunity
+- Make it clickable and business-focused
+- Use action words like "detected", "found", "identified"
+- Don't end with punctuation
+
+EXAMPLES:
+- Lighthouse found fixing 1 S3 check resolves 15 findings
+- Lighthouse detected critical RDS encryption gaps
+- Lighthouse identified 3 exposed databases needing attention
+
+Based on this detailed analysis, create one engaging banner message:
+
+${detailedRecommendation}`;
+
+    const response = await llm.invoke([
+      {
+        role: "system",
+        content: systemPrompt,
+      },
+    ]);
+
+    return response.content.toString().trim();
+  } catch (error) {
+    console.error(
+      "Error generating banner from detailed recommendation:",
+      error,
+    );
+    return "";
+  }
+};
+
+// Legacy function for backward compatibility
+export const generateRecommendation = async (
+  scanSummary: string,
+): Promise<string> => {
+  const detailed = await generateDetailedRecommendation(scanSummary);
+  if (!detailed) return "";
+
+  return await generateBannerFromDetailed(detailed);
+};
+
+export const generateQuestionAnswers = async (
+  questions: SuggestedAction[],
+): Promise<Record<string, string>> => {
+  const answers: Record<string, string> = {};
+
+  try {
+    const apiKey = await getAIKey();
+    if (!apiKey) {
+      return answers;
+    }
+
+    // Initialize the workflow system
+    const workflow = await initLighthouseWorkflow();
+
+    for (const question of questions) {
+      if (!question.questionRef) continue;
+
+      try {
+        // Use the existing workflow to answer the question
+        const result = await workflow.invoke({
+          messages: [
+            {
+              role: "user",
+              content: question.action,
+            },
+          ],
+        });
+
+        // Extract the final message content
+        const finalMessage = result.messages[result.messages.length - 1];
+        if (finalMessage?.content) {
+          answers[question.questionRef] = finalMessage.content
+            .toString()
+            .trim();
+        }
+      } catch (error) {
+        console.error(
+          `Error generating answer for question ${question.questionRef}:`,
+          error,
+        );
+        continue;
+      }
+    }
+  } catch (error) {
+    console.error("Error generating question answers:", error);
+  }
+
+  return answers;
+};