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automotive_mal_agent/agent.py

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+# Automotive Dealership Multi-Agent Example using Google ADK
+
+import os
+import json
+import asyncio
+from dotenv import load_dotenv
+import sqlite3
+from google.adk.agents import Agent
+from google.adk.code_executors import BuiltInCodeExecutor
+from google.adk.tools.retrieval.vertex_ai_rag_retrieval import VertexAiRagRetrieval
+from google.adk.tools import AgentTool
+from vertexai.preview import rag
+from google.adk.sessions import InMemorySessionService
+from google.adk.runners import Runner
+from google.genai import types
+from util.py import inventory_agent
+
+
+load_dotenv()
+
+# ------------------------------------------------------------------------------
+# Tools
+# ------------------------------------------------------------------------------
+
+# --- 1. RAG Retrieval Tool for Brochures / Vehicle Info ---
+ask_vehicle_docs = VertexAiRagRetrieval(
+    name="vehicle_rag_retriever",
+    description="Retrieves product brochures, specifications, and dealership documentation.",
+    rag_resources=[
+        rag.RagResource(
+            rag_corpus=get_rag_corpus_from_config() or os.environ.get("RAG_CORPUS")
+        )
+    ],
+    similarity_top_k=10,
+    vector_distance_threshold=0.6,
+)
+
+# ------------------------------------------------------------------------------
+# Sub-Agents (Specialist Agents)
+# ------------------------------------------------------------------------------
+
+# --- Customer Sales Agent ---
+customer_sales_agent = Agent(
+    name="customer_sales_agent",
+    model="gemini-2.5-flash",
+    description="Provides information about vehicles, trims, features, and general pricing.",
+    instruction="""
+    You are the Customer Sales Agent at an automotive dealership.
+    Your job is to answer questions about vehicle models, features, and pricing.
+    Use the RAG retrieval tool to find relevant information from brochures or documents.
+    Keep responses friendly and concise.
+    Never send empty response to the user. you have all documents at your disposal.
+    """,
+    tools=[ask_vehicle_docs],
+)
+customer_sales_agent2 = Agent(
+    name="customer_sales_agent",
+    model="gemini-2.5-flash",
+    description="Provides information about vehicles, trims, features, and general pricing.",
+    instruction="""
+    You are the Customer Sales Agent at an automotive dealership.
+    Your job is to answer questions about vehicle models, features, and pricing.
+    Use the RAG retrieval tool to find relevant information from brochures or documents.
+    Keep responses friendly and concise.
+    Never send empty response to the user. you have all documents at your disposal.
+    """,
+    tools=[ask_vehicle_docs],
+)
+
+# --- Finance Agent ---
+finance_agent = Agent(
+    name="finance_agent",
+    model="gemini-2.5-flash",
+    description="Handles all financing-related questions, including EMI and total cost calculations.",
+    instruction="""
+    You are the Finance Agent. 
+    When given information like price, down payment, interest rate, and loan term,
+    you will write and execute Python code using your code execution tool
+    to calculate EMI and total payable amount.
+    Provide responses with clear numeric results (USD), formatted neatly.
+    """,
+    tools=[BuiltInCodeExecutor()],
+    #code_executor=BuiltInCodeExecutor(),
+)
+
+# ------------------------------------------------------------------------------
+# Root Agent (Orchestrator) - Using AgentTool pattern
+# ------------------------------------------------------------------------------
+
+root_agent = Agent(
+    name="root_dealership_agent",
+    model="gemini-2.5-flash",
+    description="Root orchestrator agent that delegates customer queries to specialized subagents.",
+    instruction="""
+    You are the Root Automotive Dealership Assistant.
+    Your job is to understand user intent and delegate tasks to the appropriate specialist agents:
+    
+    - For questions about vehicle features, specifications, or brochures, delegate to the `customer_sales_agent`.
+    - For questions about financing, EMIs, or loan calculations, delegate to the `finance_agent`.
+    - For questions about stock availability and inventory, delegate to the `inventory_agent`.
+    
+    You can delegate to multiple tools if the user's query requires information from different specialists.
+    Always provide a courteous and consolidated summary of the information you receive from the specialist agents.
+    """,
+    tools=[
+        AgentTool(customer_sales_agent),
+        AgentTool(finance_agent),
+        AgentTool(inventory_agent),
+    ],
+)

automotive_mal_agent/rag_config.json

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+{
+  "corpus_name": "projects/2984842/locations/87955182"
+}

automotive_mal_agent/util.py

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+# --- Database Tool for Inventory ---
+def query_inventory_database(query_text: str) -> dict:
+    """
+    Execute SQL query on the dealership inventory database.
+
+    Args:
+        query_text (str): The SQL query to execute on the inventory database.
+
+    Returns:
+        dict: Query results with status and data or error message.
+    """
+    db_path = "data/inventory.db"
+    try:
+        conn = sqlite3.connect(db_path)
+        cursor = conn.cursor()
+        cursor.execute(query_text)
+        rows = cursor.fetchall()
+        conn.close()
+        return {
+            "status": "success",
+            "data": rows,
+            "message": f"Found {len(rows)} results",
+        }
+    except Exception as e:
+        return {"status": "error", "error_message": f"Database error: {str(e)}"}
+
+
+# --- Inventory Agent ---
+inventory_agent = Agent(
+    name="inventory_agent",
+    model="gemini-2.5-flash",
+    description="Checks current stock availability for models, trims, and colors.",
+    instruction="""
+    You are the Inventory Agent for the dealership.
+    Query the connected database to check if a vehicle model, color, or configuration is available.
+    Always return results in a structured way: Model | Trim | Color | Quantity.
+    If nothing matches, say 'Out of Stock'.
+    If you have less arguments in the query, assume model/color/trim but never send an empty response.
+    """,
+    tools=[query_inventory_database],
+)
+
+def get_rag_corpus_from_config(config_path="rag_config.json"):
+    """Reads the RAG corpus name from a JSON config file."""
+    with open(config_path, 'r') as f:
+        config = json.load(f)
+    return config.get("corpus_name")