Merge remote-tracking branch 'origin/main' into Oracle-SQL-Change-II-May2025

Author: ppaolucc-it

.DS_Store

@@ -9,6 +9,10 @@ You only need to create a file, named config_private.py, with the value for **CO
 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.
+Another option, if you're deploying in OCI, is to setup INSTANCE_PRINCIPAL.
+
+## How-to start the demo
+Two shell scripts (.sh) are provided. We use **uvicorn** to start the API.
 
 ## List of libraries used
 * oci
@@ -35,17 +39,32 @@ of the following cities:
 
 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
+If you want to diaplay the position of the Hotel in a map, you need to provide in the file
 correct values for latitude and longitude.
 
+(26/05/2025) The demo has been updated: now you should be able to select any place of departure and destination.
+Data are generated by a LLM.
+
 ## Supported languages
-As of now, the demo supports:
+As of now, the demo fully 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.
 
+## Features
+* **Routing** (you can ask **for information** or **book** a travel)
+* **Multi-turn** conversation to get all the required information for booking a travel
+* Easy **localization** (english, italian, ...)
+* **Streaming** support in API
+
+## Streaming
+Streaming support is based on the same core agent implementation than nop-streaming.
+
+To use it, you should:
+* start the API contained in agent_api (use the script)
+* use the [Streaming Streamlit client](./streamlit_app_streaming.py) 
 
 
 

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

@@ -0,0 +1,54 @@
+"""
+FastAPI server for the travel agent example using LangChain.
+This server provides a simple chat interface to interact with the travel agent.
+"""
+
+import json
+from fastapi import FastAPI, Query
+from fastapi.responses import StreamingResponse
+from workflow import create_travel_planner_graph
+from travel_state import TravelState
+from utils import get_console_logger
+
+MEDIA_TYPE = "application/json"
+
+app = FastAPI()
+
+# here we create the graph
+travel_agent_graph = create_travel_planner_graph()
+
+logger = get_console_logger("agent_fastapi_logger", level="INFO")
+
+
+async def stream_graph_updates(user_input: str):
+    """
+    Stream the updates from the travel agent graph.
+    Args:
+        user_input (str): User input for the travel agent.
+    Yields:
+        str: JSON string of the step output.
+    """
+    # prepare the input
+    state = TravelState(user_input=user_input)
+
+    # here we call the agent and return the state
+    # update the state with the user input
+    async for step_output in travel_agent_graph.astream(state):
+        # using stream with LangGraph returns state updates
+        # for each node in the graph
+        # yield returns the state update
+        yield json.dumps(step_output) + "\n"
+
+
+@app.get("/invoke")
+async def invoke(user_input: str = Query(...)):
+    """
+    endpoint to interact with the travel agent.
+    Args:
+        user_input (str): User input for the travel agent.
+    Returns:
+        StreamingResponse: Stream of JSON responses from the travel agent.
+    """
+    logger.info("Invoked Agent API...")
+
+    return StreamingResponse(stream_graph_updates(user_input), media_type=MEDIA_TYPE)

ai/gen-ai-agents/travel_agent/agent_api/__pycache__/agent_fastapi.cpython-311.pyc

@@ -0,0 +1,61 @@
+"""
+Test client for the FastAPI agent.
+This client sends a request to the FastAPI agent and prints the response.
+"""
+
+import asyncio
+import json
+import httpx
+from utils import get_console_logger
+from config import AGENT_API_URL
+
+logger = get_console_logger("agent_fastapi_client_logger", level="INFO")
+
+
+async def stream_invoke(_user_input: str):
+    """
+    Stream the chat with the agent.
+    Args:
+        user_input (str): User input for the travel agent.
+    """
+    # Prepare the input
+    params = {"user_input": _user_input}
+
+    async with httpx.AsyncClient(timeout=None) as client:
+        print("--------------------")
+        print("Streaming response:")
+        print("--------------------")
+
+        async with client.stream("GET", AGENT_API_URL, params=params) as response:
+            async for line in response.aiter_lines():
+                if line.strip():  # skip empty lines
+                    try:
+                        data = json.loads(line)
+
+                        for key, value in data.items():
+                            # key here is the name of the node
+                            logger.info("Step: %s completed...", key)
+
+                            if key == "synthesize_plan":
+                                print("")
+                                print(value["final_plan"])
+                            if key == "generate_itinerary":
+                                print("")
+                                print(value["itinerary"])
+
+                    except json.JSONDecodeError as e:
+                        print("Failed to parse JSON:", e)
+
+
+if __name__ == "__main__":
+
+    USER_INPUT = """I want to go from Rome to Florence
+    from June 10 to June 15 with my partner.
+    I want to go by train and I need a hotel in the city center."""
+
+    print("--------------------")
+    print("User input:")
+    print(USER_INPUT)
+    print("--------------------")
+
+    asyncio.run(stream_invoke(USER_INPUT))

ai/gen-ai-agents/travel_agent/agent_api/agent_fastapi.py

@@ -0,0 +1,3 @@
+# start the Agent API
+export PYTHONPATH="..:$PYTHONPATH"
+uvicorn agent_fastapi:app --port 8080 --reload

ai/gen-ai-agents/travel_agent/agent_api/agent_fastapi_client.py

@@ -0,0 +1,2 @@
+export PYTHONPATH="..:$PYTHONPATH"
+python agent_fastapi_client.py

ai/gen-ai-agents/travel_agent/agent_api/start_agent_api.sh

@@ -5,8 +5,14 @@
 #
 # application configs
 #
+# in secs
+SLEEP_TIME = 1
 DEBUG = False
 
+# this is only for the UI
+SUPPORTED_LANGUAGES = ["EN", "IT"]
+SUPPORTED_LANGUAGES_LONG = {"EN": "English", "IT": "Italiano"}
+
 # this is the list of the mandatory fields in user input
 # if any of these fields is missing, the agent will ask for clarification
 REQUIRED_FIELDS = [
@@ -24,6 +30,7 @@
 AUTH_TYPE = "API_KEY"
 
 REGION = "eu-frankfurt-1"
+# REGION = "us-chicago-1"
 SERVICE_ENDPOINT = f"https://inference.generativeai.{REGION}.oci.oraclecloud.com"
 
 # seems to work fine with both models
@@ -32,9 +39,21 @@
 
 MAX_TOKENS = 2048
 
-# Mock API configuration
-HOTEL_API_URL = "http://localhost:8000/search/hotels"
-TRANSPORT_API_URL = "http://localhost:8000/search/transport"
-
+#
+# Map configuration
 # Hotel Map
 MAP_STYLE = "https://basemaps.cartocdn.com/gl/positron-gl-style/style.json"
+
+#
+# Mock API (Hotel and transport)
+#
+HOST_API = "localhost"
+PORT_MOCK_API = 8000
+HOTEL_API_URL = f"http://{HOST_API}:{PORT_MOCK_API}/search/hotels"
+TRANSPORT_API_URL = f"http://{HOST_API}:{PORT_MOCK_API}/search/transport"
+
+#
+# AGENT API
+#
+PORT_AGENT = 8080
+AGENT_API_URL = f"http://{HOST_API}:{PORT_AGENT}/invoke"

ai/gen-ai-agents/travel_agent/agent_api/start_streaming_client.sh

@@ -0,0 +1,66 @@
+"""
+Simulate hotel options using an LLM.
+This script uses an LLM to generate realistic hotel options
+"""
+
+from model_factory import get_chat_model
+from utils import extract_json_from_text
+from config import SERVICE_ENDPOINT, MODEL_ID
+
+
+def simulate_hotel_with_llm(destination, start_date, num_days, stars):
+    """
+    Simulate a hotel option using an LLM.
+    Args:
+        destination (str): Destination city.
+        start_date (str): Start date of the stay in YYYY-MM-DD format.
+        num_days (int): Number of nights to stay.
+        stars (int): Star rating of the hotel (1-5).
+    Returns:
+        dict: A dictionary containing the hotel option details.
+        - name (str): Name of the hotel.
+        - price (float): Price per night in EUR.
+        - stars (int): Star rating of the hotel.
+        - location (str): Location description.
+        - amenities (list): List of amenities available at the hotel.
+        - latitude (float): Approximate latitude of the hotel.
+        - longitude (float): Approximate longitude of the hotel.
+    """
+    prompt = f"""
+Simulate a realistic hotel option in {destination} starting on {start_date} for {num_days} nights.
+The hotel should be rated {stars} stars.
+
+Return a JSON object with the following fields:
+- name
+- price (EUR per night)
+- stars
+- location
+- amenities (list of 2-3 amenities)
+- latitude (approximate)
+- longitude (approximate)
+JSON must be enclosed in triple backticks
+
+Return a **JSON object** with the following format:
+```json
+{{
+  "name": "Hotel Example",
+  "price": 150.0,
+  "stars": 4,
+  "location": "City Center",
+  "amenities": ["WiFi", "Breakfast", "Gym"],
+  "latitude": 40.7128,
+  "longitude": -74.0060
+}}
+```
+"""
+    llm = get_chat_model(
+        model_id=MODEL_ID,
+        service_endpoint=SERVICE_ENDPOINT,
+        temperature=0.2,
+        max_tokens=1000,
+    )
+
+    response = llm.invoke(prompt).content
+    data = extract_json_from_text(response)
+
+    return data

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

@@ -0,0 +1,59 @@
+"""
+Simulate transport options using an LLM.
+"""
+
+from model_factory import get_chat_model
+from utils import extract_json_from_text
+from config import SERVICE_ENDPOINT, MODEL_ID
+
+
+def simulate_transport_with_llm(departure, destination, date, mode):
+    """
+    Simulate a transport option using an LLM.
+    Args:
+        departure (str): Place of departure.
+        destination (str): Destination place.
+        date (str): Date of travel in YYYY-MM-DD format.
+        mode (str): Mode of transport (e.g., "airplane", "train").
+    Returns:
+        dict: A dictionary containing the transport option details.
+        - provider (str): Name of the transport provider.
+        - price (float): Price in EUR.
+        - duration_hours (float): Duration of the trip in hours.
+    """
+    prompt = f"""
+Simulate a realistic {mode} travel option between {departure} and {destination} for the date {date}.
+Return JSON with the fields: provider, price (EUR), duration_hours.
+JSON must be enclosed in triple backticks.
+
+Return a **JSON object** with the following format:
+```json
+Example:
+{{
+  "provider": "Lufthansa",
+  "price": 155.0,
+  "departure": "2025-10-10T08:00",
+  "duration_hours": 2.5
+}}
+```
+"""
+
+    llm = get_chat_model(
+        model_id=MODEL_ID,
+        service_endpoint=SERVICE_ENDPOINT,
+        temperature=0.2,
+        max_tokens=1000,
+    )
+
+    response = llm.invoke(prompt).content
+
+    data = extract_json_from_text(response)
+
+    # transform in the expected output format
+    return {
+        "provider": data["provider"],
+        "price": data["price"],
+        "departure": f"{date}T08:00",
+        "arrival": f"{date}T{int(8 + data['duration_hours']):02}:00",
+        "type": mode,
+    }