Modified Agents and connected it with endpoints

Author: SplinterSword

backend/EduAssitant_Agents/agent.py

@@ -1,13 +1,32 @@
 from google.adk.agents import Agent, LoopAgent
 
-summarizer_agent = Agent(
-    name="Summarizer_Agent",
+quizzes_agent = Agent(
+    name="Quizzes_Agent",
     model="gemini-2.0-flash",
     description=(
-        "You are a Summarizer Agent operating under the control of a Manager Agent in a multi-agent system. Your sole purpose is to generate concise, accurate, and coherent summaries from the content provided to you."
+        "This is an AI agent which is responsible for generating MCQ questions and answers from the content provided to it."
     ),
     instruction=(
-        "You are the Summarizer Agent responsible for generating accurate, concise summaries of the content provided to you by the Manager Agent. Also you are to return this output back to the manager agent."
+        "You are the Quizzes Agent responsible for generating MCQ questions with 4 options and answers of the content provided to you by the Manager Agent. Also you are to return this output back to the manager agent."
+    )
+)
+
+summarizer_agent = Agent(
+    name="summarizer_agent",
+    model="gemini-2.0-flash-exp",
+    description=(
+        "This is an AI agent which is responsible for generating summaries of the content provided to it."
+    ),
+    instruction=(
+        """You are a Summarizer Agent in an educational multi-agent system.
+
+Your role:
+- Create concise, accurate summaries of educational content
+- Focus on key concepts, main ideas, and important details
+- Maintain academic accuracy and clarity
+- Structure summaries logically with clear headings when appropriate
+
+When given content, analyze it thoroughly and provide a well-structured summary that captures the essential information in a clear, academic style."""
     )
 )
 
@@ -18,20 +37,20 @@
         "This is an AI agent which is responsible for extracting relevant references or citations from the content provided to it."
     ),
     instruction=(
-"""You are a Reference Extraction Agent working under a Manager Agent in a multi-agent system.
-Your primary responsibility is to carefully scan the provided content and extract all relevant references or citations strictly from this content. These may include:
+"""You are a Reference Extraction Agent specialized in identifying and extracting citations, references, and source materials from educational content.
 
-   - Document titles
-   - Section or subsection headings/numbers
-   - In-text citations
-   - Footnotes
-   - Bibliographic references
-   - URLs or document links
-   - Mentioned papers, books, articles, datasets, standards, or guidelines
-   - Any internal or external reference points stated in the content
-   
-   
-   Also you are to return this output back to the manager agent."""
+Your role:
+- Scan content for all types of references including:
+  * Academic citations (APA, MLA, Chicago, etc.)
+  * URLs and web links
+  * Book titles and authors
+  * Journal articles and papers
+  * Document titles and section references
+  * Footnotes and endnotes
+- Format extracted references clearly and organized
+- Include page numbers, dates, and other citation details when available
+
+Provide a comprehensive list of all references related to the content provided to you. Make sure that you include at least 5 references."""
     )
 )
 
@@ -42,27 +61,15 @@
         "This is an AI agent which is responsible for generating flash cards from the content provided to it."
     ),
     instruction=(
-        """You are a Flashcard Generation Agent working under the Manager Agent in a multi-agent system.
-Your primary responsibility is to generate a list of question-answer flashcards designed to aid in the quick revision of the concepts, facts, and ideas present in the provided content.
-
-These flashcards are intended to cover all critical points, definitions, formulas, principles, processes, or factual data contained within the content.
-You must not use any outside knowledge, assumptions, or inference beyond what is present in the provided material.
+        """You are a Flashcard Generation Agent that creates effective study flashcards from educational content.
 
-You are to return the flashcards in a json format with the following structure:
-{
-    "flashcards": [
-        {
-            "question": "Question 1",
-            "answer": "Answer 1"
-        },
-        {
-            "question": "Question 2",
-            "answer": "Answer 2"
-        }
-    ]
-}
+Your role:
+- Analyze content to identify key facts, concepts, and definitions
+- Create clear question-answer pairs that test understanding
+- Ensure questions are specific and answers are concise but complete
+- Cover all important topics from the provided content
 
-Also you are to return this output back to the manager agent."""
+Create comprehensive flashcards that facilitate active recall and help students understand key concepts."""
     )
 )
 
@@ -73,13 +80,28 @@
         "This AI agent functions as an orchestrator and coordinator in a multi-agent system designed to answer queries based on domain-specific content provided to it. Its primary role is to manage the interactions between multiple specialized agents and ensure accurate, context-aware, and efficient responses to user queries."
     ),
     instruction=(
-        "You are the Manager Agent responsible for managing and directing a set of specialized AI sub-agents. Your purpose is to answer user queries solely based on the content provided to you, ensuring accuracy, relevance, and clarity. You are also responsible for giving the output to the user in whatever structure it desires be it json or pdf or any other format."
+       """You are the Manager Agent for the EduAssistant multi-agent system.
+
+Your role:
+- Understand user requests and determine appropriate actions
+- Coordinate with specialized agents when needed:
+  * Use summarizer_agent for summarization requests
+  * Use reference_agent for reference extraction requests  
+  * Use flashcard_agent for flashcard generation requests
+  * Use quizzes_agent for quizzes generation requests
+- Provide comprehensive educational assistance
+- Maintain academic accuracy and helpful tone
+- Format responses appropriately based on user needs
+
+Available agents:
+- summarizer_agent: Creates summaries of educational content
+- reference_agent: Extracts references and citations
+- flashcard_agent: Generates study flashcards
+- quizzes_agent: Generates quizzes from educational content
+
+When users ask for specific functions, delegate to the appropriate agent and provide the results in a clear, helpful format asked by the user."""
     ),
-    sub_agents=[summarizer_agent, reference_agent, flash_card_agent],
+    sub_agents=[quizzes_agent, reference_agent, flash_card_agent, summarizer_agent],
 )
 
-root_agent = LoopAgent(
-    name="Loop_Agent",
-    max_iterations=5,
-    sub_agents=[manager_agent],
-)
+root_agent = manager_agent

frontend/src/ai/flows/flow_utils.ts

@@ -0,0 +1,12 @@
+export async function extractJsonFromResponse(response: string): Promise<any> {
+    const match = response.match(/```json\s*([\s\S]*?)\s*```/);
+    if (match && match[1]) {
+        try {
+            return JSON.parse(match[1]);
+        } catch (e) {
+            console.error('Failed to parse JSON from response:', e);
+            return null;
+        }
+    }
+    return null;
+}

frontend/src/ai/flows/generate-flashcards.ts

@@ -8,8 +8,15 @@
  * - GenerateFlashcardsOutput - The return type for the generateFlashcards function.
  */
 
-import {ai} from '@/ai/genkit';
+import {genkit} from 'genkit';
+import {googleAI} from '@genkit-ai/googleai';
 import {z} from 'genkit';
+import { extractJsonFromResponse } from './flow_utils';
+
+const ai = genkit({
+  plugins: [googleAI()],
+  model: 'googleai/gemini-2.0-flash',
+});
 
 const GenerateFlashcardsInputSchema = z.object({
   courseMaterial: z
@@ -26,7 +33,66 @@ const GenerateFlashcardsOutputSchema = z.object({
 export type GenerateFlashcardsOutput = z.infer<typeof GenerateFlashcardsOutputSchema>;
 
 export async function generateFlashcards(input: GenerateFlashcardsInput): Promise<GenerateFlashcardsOutput> {
-  return generateFlashcardsFlow(input);
+  
+  // Use ADK /run endpoint with configurable backend URL
+  const ADK_URL = process.env.NEXT_PUBLIC_AGENT_API_URL || "http://localhost:8000";
+  try {
+    const res = await fetch(`${ADK_URL}/run`, {
+      method: "POST",
+      headers: { "Content-Type": "application/json" },
+      body: JSON.stringify({
+        input: `Create flashcards for this content: ${input.courseMaterial}`,
+        metadata: {
+          request_type: "flashcards",
+          course_material: input.courseMaterial,
+          output_schema: {
+            type: "object",
+            properties: {
+              flashcards: {
+                type: "array",
+                items: {
+                  type: "object",
+                  properties: {
+                    front: { type: "string" },
+                    back: { type: "string" }
+                  },
+                  required: ["front", "back"]
+                }
+              }
+            },
+            required: ["flashcards"]
+          }
+        }
+      })
+    });
+    if (!res.ok) throw new Error(`Failed to generate flashcards from backend agent: ${res.status}`);
+    const data = await extractJsonFromResponse(await res.text());
+
+    // ADK returns response in data.output
+    let result;
+    try {
+      result = typeof data === 'string' ? JSON.parse(data) : data;
+    } catch {
+      result = data;
+    }
+    // Ensure the response matches our expected schema
+    if (result.flashcards && Array.isArray(result.flashcards)) {
+      return result;
+    } else if (Array.isArray(result)) {
+      return { flashcards: result };
+    } else {
+      throw new Error('Invalid flashcards format received from agent');
+    }
+  } catch (error) {
+    console.error('Flashcard generation error:', error);
+    // Fallback to local Genkit flow if ADK fails
+    try {
+      return await generateFlashcardsFlow(input);
+    } catch (fallbackError) {
+      console.error("Error in fallback implementation:", fallbackError);
+      throw fallbackError;
+    }
+  }
 }
 
 const prompt = ai.definePrompt({

frontend/src/ai/flows/generate-quizzes.ts

@@ -11,6 +11,7 @@
 
 import {ai} from '@/ai/genkit';
 import {z} from 'genkit';
+import { extractJsonFromResponse } from './flow_utils';
 
 const GenerateQuizzesInputSchema = z.object({
   courseMaterial: z
@@ -37,7 +38,68 @@ export type GenerateQuizzesOutput = z.infer<typeof GenerateQuizzesOutputSchema>;
 export async function generateQuizzes(
   input: GenerateQuizzesInput
 ): Promise<GenerateQuizzesOutput> {
-  return generateQuizzesFlow(input);
+  // Use ADK /run endpoint with configurable backend URL
+  const ADK_URL = process.env.NEXT_PUBLIC_AGENT_API_URL || "http://localhost:8000";
+  try {
+    const res = await fetch(`${ADK_URL}/run`, {
+      method: "POST",
+      headers: { "Content-Type": "application/json" },
+      body: JSON.stringify({
+        input: `Generate ${input.numQuestions} quiz questions based on this content: ${input.courseMaterial}`,
+        metadata: {
+          request_type: "quizzes",
+          course_material: input.courseMaterial,
+          output_schema: {
+            type: "object",
+            properties: {
+              quiz: {
+                type: "array",
+                items: {
+                  type: "object",
+                  properties: {
+                    question: { type: "string" },
+                    options: { 
+                      type: "array",
+                      items: { type: "string" }
+                    },
+                    correctAnswer: { type: "string" }
+                  },
+                  required: ["question", "options", "correctAnswer"]
+                }
+              }
+            },
+            required: ["quiz"]
+          }
+        }
+      })
+    });
+
+    if (!res.ok) {
+      throw new Error(`Failed to generate quizzes: ${res.statusText}`);
+    }
+
+    const data = await extractJsonFromResponse(await res.text());
+    let result;
+    try {
+      result = typeof data === 'string' ? JSON.parse(data) : data;
+    } catch {
+      result = data;
+    }
+    // Ensure the response matches our expected schema
+    if (!GenerateQuizzesOutputSchema.safeParse(result).success) {
+      throw new Error("Failed to parse response as GenerateQuizzesOutput");
+    }
+    return result;
+  } catch (error) {
+    console.error("Error generating quizzes:", error);
+    try {
+      // Fall back to the local implementation if the API call fails
+      return await generateQuizzesFlow(input);
+    } catch (fallbackError) {
+      console.error("Error in fallback implementation:", fallbackError);
+      throw fallbackError;
+    }
+  }
 }
 
 const prompt = ai.definePrompt({

frontend/src/ai/flows/suggest-resources.ts

@@ -10,6 +10,7 @@
 
 import {ai} from '@/ai/genkit';
 import {z} from 'genkit';
+import { extractJsonFromResponse } from './flow_utils';
 
 const SuggestResourcesInputSchema = z.object({
   courseContent: z
@@ -28,7 +29,59 @@ const SuggestResourcesOutputSchema = z.array(SuggestedResourceSchema).describe('
 export type SuggestResourcesOutput = z.infer<typeof SuggestResourcesOutputSchema>;
 
 export async function suggestResources(input: SuggestResourcesInput): Promise<SuggestResourcesOutput> {
-  return suggestResourcesFlow(input);
+  // Use ADK /run endpoint with configurable backend URL
+  const ADK_URL = process.env.NEXT_PUBLIC_AGENT_API_URL || "http://localhost:8000";
+  try {
+    const res = await fetch(`${ADK_URL}/run`, {
+      method: "POST",
+      headers: { "Content-Type": "application/json" },
+      body: JSON.stringify({
+        input: `Take this content and give me reference links for this content: ${input.courseContent}`,
+        metadata: {
+          request_type: "suggest_resources",
+          course_content: input.courseContent,
+          output_schema: {
+            type: "array",
+            items: {
+              type: "object",
+              properties: {
+                title: { type: "string" },
+                url: { type: "string", format: "uri" },
+                reason: { type: "string" }
+              },
+              required: ["title", "url", "reason"]
+            }
+          }
+        }
+      })
+    });
+
+    if (!res.ok) {
+      throw new Error(`Failed to suggest resources: ${res.statusText}`);
+    }
+
+    const data = extractJsonFromResponse(await res.text());
+    let result;
+    try {
+      result = typeof data === 'string' ? JSON.parse(data) : data;
+    } catch {
+      result = data;
+    }
+    // Ensure the response matches our expected schema
+    if (!SuggestResourcesOutputSchema.safeParse(result).success) {
+      throw new Error("Failed to parse response as SuggestResourcesOutput");
+    }
+    return result;
+  } catch (error) {
+    console.error("Error in API call, falling back to local flow:", error);
+    try {
+      // Fall back to the local implementation if the API call fails
+      return await suggestResourcesFlow(input);
+    } catch (fallbackError) {
+      console.error("Error in fallback implementation:", fallbackError);
+      throw fallbackError;
+    }
+  }
 }
 
 const resourceSuggestionTool = ai.defineTool({