Merge pull request #1770 from oracle-devrel/lsa5-agent

improved mock_api, changed README

Author: anshoracle

ai/gen-ai-agents/travel_agent/README.md

@@ -1,16 +1,54 @@
 # Travel Agent
-This repository contains all the code for a demo of a Travel Agent.
+This repository contains all the code for a demo of a **Travel Agent**.
 The AI Agent enables a customer to get information about available destinations and to organize a trip, book flight, hotel...
 
-The agent has been developed using OCI Generative AI and LangGraph.
+The agent has been developed using **OCI Generative AI** and **LangGraph**.
 
-## List of packages
+## Configuration
+You only need to create a file, named config_private.py, with the value for **COMPARTMENT_OCID**.
+The compartment must be a compartment where you have setup the right policies to access OCI Generative AI.
+
+In config.py AUTH_TYPE is set to API_KEY, therefore you need to have in $HOME/.oci the key pair to access OCI.
+
+## List of libraries used
 * oci
 * langchain-community
 * langgraph
 * streamlit
 * fastapi
 * black
+* pydeck
 * uvicorn
 
+see also: requirements.txt
+
+## Demo data
+Demo data are contained in mock_data.py
+
+If you want some realistic results, you should ask to plan a trip from **Rome** to one
+of the following cities:
+* Amsterdam
+* Barcelona
+* Florence
+* Madrid
+* Valencia
+
+or, simply add other records to the JSON in mock_data.py.
+
+If you want to diplsay the positio of the Hotel in a map, you need to provide in the file
+correct values for latitude and longitude.
+
+## Supported languages
+As of now, the demo supports:
+* English
+* Italian
+
+to add other languages, you need to add the translations in translations.py and change, accordingly, some
+code in streamlit_app.py.
+
+
+
+
+
+
 

ai/gen-ai-agents/travel_agent/base_node.py

@@ -61,3 +61,17 @@ def log_error(self, message: str):
             message (str): The error message to log.
         """
         self.logger.error("[%s] %s", self.name, message)
+
+    def invoke(self, state: dict, config=None, **kwargs) -> dict:
+        """
+        Abstract method to be implemented by subclasses.
+
+        Args:
+            state (dict): The current state of the workflow.
+            config (optional): Configuration options for the node.
+            **kwargs: Additional keyword arguments.
+
+        Returns:
+            dict: Updated state after processing.
+        """
+        raise NotImplementedError("Subclasses must implement this method.")

ai/gen-ai-agents/travel_agent/config.py

@@ -19,10 +19,14 @@
 ]
 
 # OCI GenAI services configuration
+
+# can be also INSTANCE_PRINCIPAL
+AUTH_TYPE = "API_KEY"
+
 REGION = "eu-frankfurt-1"
 SERVICE_ENDPOINT = f"https://inference.generativeai.{REGION}.oci.oraclecloud.com"
 
-# seems to work with both models
+# seems to work fine with both models
 MODEL_ID = "meta.llama-3.3-70b-instruct"
 # MODEL_ID = "cohere.command-a-03-2025"
 

ai/gen-ai-agents/travel_agent/config_private_template.py

@@ -0,0 +1,8 @@
+"""
+Config private template
+
+use it to create your config_private.py file
+This file contains sensitive information such as compartment OCIDs, and other
+"""
+
+COMPARTMENT_OCID = "ocid1.compartment.xxxx"

ai/gen-ai-agents/travel_agent/mock_api.py

@@ -4,10 +4,13 @@
 A simplified mock FastAPI server with two endpoints:
 - /search/transport
 - /search/hotels
+
+mock data in mock_data.py
 """
 
 from fastapi import FastAPI, Query
 from fastapi.responses import JSONResponse
+from mock_data import hotels_by_city, transport_data
 
 app = FastAPI()
 
@@ -19,24 +22,33 @@ def search_transport(
     transport_type: str = Query(...),
 ):
     """
-    Mock endpoint to simulate transport search.
+    Mock endpoint to simulate transport search from Rome.
     Args:
         destination (str): Destination city.
         start_date (str): Start date of the trip in 'YYYY-MM-DD' format.
-        transport_type (str): Type of transport (e.g., "airplane", "train").
+        transport_type (str): Type of transport ("airplane" or "train").
     Returns:
         JSONResponse: Mocked transport options.
     """
+    key = destination.strip().lower()
+    option = transport_data.get(key, {}).get(transport_type.lower())
+
+    if not option:
+        return JSONResponse(content={"options": []}, status_code=404)
+
+    departure_time = f"{start_date}T08:00"
+    duration = option["duration_hours"]
+    arrival_hour = 8 + int(duration)
+    arrival_time = f"{start_date}T{arrival_hour:02}:00"
+
     return JSONResponse(
         content={
             "options": [
                 {
-                    "provider": (
-                        "TrainItalia" if transport_type == "train" else "Ryanair"
-                    ),
-                    "price": 45.50,
-                    "departure": f"{start_date}T09:00",
-                    "arrival": f"{start_date}T13:00",
+                    "provider": option["provider"],
+                    "price": option["price"],
+                    "departure": departure_time,
+                    "arrival": arrival_time,
                     "type": transport_type,
                 }
             ]
@@ -45,7 +57,12 @@ def search_transport(
 
 
 @app.get("/search/hotels")
-def search_hotels(destination: str = Query(...), stars: int = Query(3)):
+def search_hotels(
+    destination: str = Query(...),
+    start_date: str = Query(...),
+    num_days: int = Query(1),
+    stars: int = Query(3),
+):
     """
     Mock endpoint to simulate hotel search.
     Args:
@@ -54,58 +71,11 @@ def search_hotels(destination: str = Query(...), stars: int = Query(3)):
     Returns:
         JSONResponse: Mocked hotel options.
     """
-    hotels_by_city = {
-        "valencia": {
-            "name": "Hotel Vincci Lys",
-            "price": 135.0,
-            "stars": stars,
-            "location": "Central district",
-            "amenities": ["WiFi", "Breakfast"],
-            "latitude": 39.4702,
-            "longitude": -0.3750,
-        },
-        "barcelona": {
-            "name": "Hotel Jazz",
-            "price": 160.0,
-            "stars": stars,
-            "location": "Eixample",
-            "amenities": ["WiFi", "Rooftop pool"],
-            "latitude": 41.3849,
-            "longitude": 2.1675,
-        },
-        "madrid": {
-            "name": "Only YOU Hotel Atocha",
-            "price": 170.0,
-            "stars": stars,
-            "location": "Retiro",
-            "amenities": ["WiFi", "Gym", "Restaurant"],
-            "latitude": 40.4093,
-            "longitude": -3.6828,
-        },
-        "florence": {
-            "name": "Hotel L'Orologio Firenze",
-            "price": 185.0,
-            "stars": stars,
-            "location": "Santa Maria Novella",
-            "amenities": ["WiFi", "Spa", "Bar"],
-            "latitude": 43.7760,
-            "longitude": 11.2486,
-        },
-        "amsterdam": {
-            "name": "INK Hotel Amsterdam",
-            "price": 190.0,
-            "stars": stars,
-            "location": "City Center",
-            "amenities": ["WiFi", "Breakfast", "Bar"],
-            "latitude": 52.3745,
-            "longitude": 4.8901,
-        },
-    }
-
     hotel_key = destination.strip().lower()
     hotel = hotels_by_city.get(hotel_key)
 
     if not hotel:
         return JSONResponse(content={"hotels": []}, status_code=404)
 
+    hotel["stars"] = stars
     return JSONResponse(content={"hotels": [hotel]})

ai/gen-ai-agents/travel_agent/mock_data.py

@@ -0,0 +1,76 @@
+"""
+Mock data for API
+"""
+
+# Hotel data by city
+hotels_by_city = {
+    "valencia": {
+        "name": "Hotel Vincci Lys",
+        "price": 135.0,
+        "stars": None,  # placeholder, updated dynamically
+        "location": "Central district",
+        "amenities": ["WiFi", "Breakfast"],
+        "latitude": 39.4702,
+        "longitude": -0.3750,
+    },
+    "barcelona": {
+        "name": "Hotel Jazz",
+        "price": 160.0,
+        "stars": None,
+        "location": "Eixample",
+        "amenities": ["WiFi", "Rooftop pool"],
+        "latitude": 41.3849,
+        "longitude": 2.1675,
+    },
+    "madrid": {
+        "name": "Only YOU Hotel Atocha",
+        "price": 170.0,
+        "stars": None,
+        "location": "Retiro",
+        "amenities": ["WiFi", "Gym", "Restaurant"],
+        "latitude": 40.4093,
+        "longitude": -3.6828,
+    },
+    "florence": {
+        "name": "Hotel L'Orologio Firenze",
+        "price": 185.0,
+        "stars": None,
+        "location": "Santa Maria Novella",
+        "amenities": ["WiFi", "Spa", "Bar"],
+        "latitude": 43.7760,
+        "longitude": 11.2486,
+    },
+    "amsterdam": {
+        "name": "INK Hotel Amsterdam",
+        "price": 190.0,
+        "stars": None,
+        "location": "City Center",
+        "amenities": ["WiFi", "Breakfast", "Bar"],
+        "latitude": 52.3745,
+        "longitude": 4.8901,
+    },
+}
+
+# Transport data from Rome
+transport_data = {
+    "valencia": {
+        "train": {"provider": "TrainItalia", "duration_hours": 15, "price": 120.0},
+        "airplane": {"provider": "Ryanair", "duration_hours": 2.5, "price": 160.0},
+    },
+    "barcelona": {
+        "train": {"provider": "TrainItalia", "duration_hours": 13, "price": 110.0},
+        "airplane": {"provider": "Vueling", "duration_hours": 2.0, "price": 155.0},
+    },
+    "madrid": {
+        "train": {"provider": "TrainItalia", "duration_hours": 17, "price": 130.0},
+        "airplane": {"provider": "Iberia", "duration_hours": 2.5, "price": 165.0},
+    },
+    "amsterdam": {
+        "train": {"provider": "Thalys", "duration_hours": 20, "price": 150.0},
+        "airplane": {"provider": "KLM", "duration_hours": 2.5, "price": 175.0},
+    },
+    "florence": {
+        "train": {"provider": "Frecciarossa", "duration_hours": 1.5, "price": 30.0},
+        "airplane": {"provider": "ITA Airways", "duration_hours": 1.0, "price": 190.0},
+    },
+}

ai/gen-ai-agents/travel_agent/model_factory.py

@@ -1,10 +1,18 @@
 """
 Factory for Chat models
+
+This module contains a factory function to create and return a ChatOCIGenAI model instance.
+It is designed to be used in the context of an application that interacts with Oracle Cloud
+Infrastructure (OCI) Generative AI services.
+
+Author: L. Saetta
+Date: 21/05/2025
+
 """
 
 from langchain_community.chat_models import ChatOCIGenAI
 
-from config import MODEL_ID, SERVICE_ENDPOINT
+from config import MODEL_ID, SERVICE_ENDPOINT, AUTH_TYPE
 from config_private import COMPARTMENT_OCID
 
 
@@ -22,6 +30,7 @@ def get_chat_model(
     """
     # Create and return the chat model
     return ChatOCIGenAI(
+        auth_type=AUTH_TYPE,
         model_id=model_id,
         service_endpoint=service_endpoint,
         model_kwargs={"temperature": temperature, "max_tokens": max_tokens},

ai/gen-ai-agents/travel_agent/nodes/__pycache__/generate_itinerary_node.cpython-311.pyc

@@ -11,14 +11,15 @@
 """
 
 from base_node import BaseNode
-from langchain_core.runnables import Runnable
-from langchain_core.output_parsers import StrOutputParser
 from model_factory import get_chat_model
 from config import MODEL_ID, SERVICE_ENDPOINT, MAX_TOKENS, DEBUG
 from translations import TRANSLATIONS
 
 
 class GenerateItineraryNode(BaseNode):
+    """
+    Node in the LangGraph workflow responsible for generating a personalized travel itinerary.
+    """
     def __init__(self):
         super().__init__("GenerateItineraryNode")
 
@@ -47,9 +48,9 @@ def invoke(self, state: dict, config=None, **kwargs) -> dict:
 
         if DEBUG:
             self.log_info("Generating itinerary...")
-        
+
         response = self.llm.invoke(itinerary_prompt).content
 
         state["final_plan"] += f"\n\n{t['suggested_itinerary_title']}\n{response}"
-        
+
         return state

ai/gen-ai-agents/travel_agent/nodes/__pycache__/hotel_node.cpython-311.pyc

@@ -26,13 +26,17 @@ def __init__(self):
     def invoke(self, state: dict, config=None, **kwargs) -> dict:
         self.log_info("Searching for hotels")
         try:
+            num_days = state.get("num_days", 1)
+            start_date = state.get("start_date")
             prefs = state.get("hotel_preferences", {})
 
             # here we call the API
             response = requests.get(
                 HOTEL_API_URL,
                 params={
                     "destination": state.get("destination"),
+                    "start_date": start_date,
+                    "num_days": num_days,
                     # default 3 stars
                     "stars": prefs.get("stars", 3),
                 },

ai/gen-ai-agents/travel_agent/nodes/__pycache__/router_node.cpython-311.pyc

@@ -15,7 +15,7 @@
 from base_node import BaseNode
 from model_factory import get_chat_model
 from prompt_template import router_prompt
-from config import MODEL_ID, SERVICE_ENDPOINT, DEBUG
+from config import MODEL_ID, SERVICE_ENDPOINT
 
 
 class RouterNode(BaseNode):