Add Hotel Booking Agent

samples/hotel-booking-agent/.gitignore

@@ -0,0 +1,16 @@
+# Python
+.venv/
+__pycache__/
+*.pyc
+
+# Node
+node_modules/
+
+# Env
+.env
+
+# OS
+.DS_Store
+
+# Booking API
+backend/booking_api/data/bookings.json

samples/hotel-booking-agent/README.md

@@ -0,0 +1,82 @@
+# Hotel Booking Agent
+
+Minimal Python stack for the travel planner agent, plus optional frontend.
+
+- **AI Agent**: `samples/hotel-booking-agent/agent/`
+- **Hotel API**: `samples/hotel-booking-agent/services/hotel_api/`
+- **Frontend (optional)**: `samples/hotel-booking-agent/frontend/`
+- **Policy ingest**: `samples/hotel-booking-agent/services/hotel_api/resources/ingest/`
+- **Sample policy PDFs**: `samples/hotel-booking-agent/services/hotel_api/resources/policy_pdfs/`
+
+## Quick Start
+
+### Agent Manager deployment
+Deploy the agent in your Agent Manager environment (details to be added). The flow below covers the required supporting services:
+
+**Agent Manager**
+- Repo URL: `https://github.com/wso2/agent-manager/tree/amp/v0/samples/travel_planner_agent`
+- Language/runtime: Python 3.11
+- Run command: `uvicorn app:app --host 0.0.0.0 --port 9090`
+- Agent type: Chat API Agent
+- Schema path: `openapi.yaml`
+- Port: `9090`
+
+**Agent environment variables**
+Required:
+- `OPENAI_API_KEY`
+- `ASGARDEO_BASE_URL`
+- `ASGARDEO_CLIENT_ID`
+- `PINECONE_API_KEY`
+- `PINECONE_SERVICE_URL`
+
+Optional (defaults are applied if unset):
+- `OPENAI_MODEL` (default: `gpt-4o-mini`)
+- `OPENAI_EMBEDDING_MODEL` (default: `text-embedding-3-small`)
+- `WEATHER_API_KEY`
+- `WEATHER_API_BASE_URL` (default: `http://api.weatherapi.com/v1`)
+- `BOOKING_API_BASE_URL` (default: `http://localhost:9091`)
+
+**Expose the agent endpoint after deploy**
+Run this inside the WSO2-AMP dev container to expose the agent on `localhost:9090`:
+
+```bash
+kubectl -n dp-default-default-default-ccb66d74 port-forward svc/travel-planner-agent-is 9090:80
+```
+
+**Hotel API**
+- Runs locally on `http://localhost:9091` when started via `uvicorn`.
+- You can also deploy it to a cloud host; just point the agent configuration at the deployed base URL.
+
+**Pinecone policies**
+- Create a Pinecone index using your preferred embedding model.
+- Set the Pinecone and embedding configurations when deploying or locally running the hotel api 
+- Run the ingest to populate the index.
+
+### Local services (Agent + Hotel API)
+#### 1) Start the agent (local)
+```bash
+cd samples/hotel-booking-agent/agent
+python -m venv .venv
+source .venv/bin/activate
+pip install -r requirements.txt
+uvicorn app:app --host 0.0.0.0 --port 9090
+```
+
+#### 2) Start the Hotel API (local)
+```bash
+cd samples/hotel-booking-agent/services/hotel_api
+python -m venv .venv
+source .venv/bin/activate
+pip install -r requirements.txt
+uvicorn service:app --host 0.0.0.0 --port 9091
+```
+
+### Sample chat request
+```bash
+curl -s http://localhost:9090/chat \
+  -H "Content-Type: application/json" \
+  -d '{"message":"Plan a 3-day trip to Tokyo","sessionId":"session_abc123","userId":"user_123","userName":"Traveler"}'
+```
+
+## Notes
+- The agent serves chat at `http://localhost:9090/chat`.

samples/hotel-booking-agent/agent/.env.example

@@ -0,0 +1,14 @@
+OPENAI_API_KEY=
+OPENAI_MODEL=
+
+BOOKING_API_BASE_URL=
+
+ASGARDEO_BASE_URL=
+ASGARDEO_CLIENT_ID=
+
+PINECONE_API_KEY=
+PINECONE_SERVICE_URL=
+PINECONE_INDEX_NAME=
+
+WEATHER_API_KEY=
+WEATHER_API_BASE_URL=

samples/hotel-booking-agent/agent/app.py

@@ -0,0 +1,84 @@
+from __future__ import annotations
+
+from datetime import datetime, timezone
+import logging
+
+from fastapi import FastAPI, HTTPException, Request, status
+from fastapi.middleware.cors import CORSMiddleware
+from langchain_core.messages import HumanMessage
+from pydantic import BaseModel
+
+from config import Settings
+from graph import build_graph
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s %(levelname)s %(name)s: %(message)s",
+)
+
+configs = Settings.from_env()
+agent_graph = build_graph(configs)
+
+class ChatRequest(BaseModel):
+    message: str
+    sessionId: str 
+    userId: str
+    userName: str | None = None
+
+
+class ChatResponse(BaseModel):
+    message: str
+
+app = FastAPI(title="Hotel Booking Agent")
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=False,
+    allow_methods=["GET", "POST", "PUT", "DELETE", "OPTIONS"],
+    allow_headers=["Content-Type", "Authorization", "Accept", "x-user-id"],
+    max_age=84900,
+)
+
+
+def _wrap_user_message(user_message: str, user_id: str, user_name: str | None) -> str:
+    now = datetime.now(timezone.utc).isoformat()
+    resolved_user_id = user_id
+    resolved_user_name = user_name or "Traveler"
+    return (
+        f"User Name: {resolved_user_name}\n"
+        f"User Context (non-hotel identifiers): {resolved_user_name} ({resolved_user_id})\n"
+        f"UTC Time now:\n{now}\n\n"
+        f"User Query:\n{user_message}"
+    )
+
+
+def _extract_user_from_payload(request: ChatRequest) -> tuple[str, str | None]:
+    user_id = request.userId
+    if not user_id:
+        raise HTTPException(
+            status_code=status.HTTP_422_UNPROCESSABLE_ENTITY,
+            detail="Missing userId in request payload.",
+        )
+    return user_id, request.userName
+
+
+@app.post("/chat", response_model=ChatResponse)
+def chat(request: ChatRequest, http_request: Request) -> ChatResponse:
+    session_id = request.sessionId
+    user_id, user_name = _extract_user_from_payload(request)
+    wrapped_message = _wrap_user_message(
+        request.message,
+        user_id,
+        user_name,
+    )
+    thread_id = f"{user_id}:{session_id}"
+    result = agent_graph.invoke(
+        {"messages": [HumanMessage(content=wrapped_message)]},
+        config={
+            "recursion_limit": 50,
+            "configurable": {"thread_id": thread_id},
+        },
+    )
+
+    last_message = result["messages"][-1]
+    return ChatResponse(message=last_message.content)

samples/hotel-booking-agent/agent/config.py

@@ -0,0 +1,56 @@
+import os
+from dataclasses import dataclass
+from dotenv import load_dotenv
+
+load_dotenv()
+
+def _split_csv(value: str | None, default: list[str]) -> list[str]:
+    if value is None:
+        return default
+    stripped = [item.strip() for item in value.split(",")]
+    return [item for item in stripped if item]
+
+
+@dataclass
+class Settings:
+    openai_api_key: str
+    openai_model: str
+    openai_embedding_model: str
+    asgardeo_base_url: str
+    asgardeo_client_id: str
+    pinecone_api_key: str
+    pinecone_service_url: str
+    pinecone_index_name: str
+    weather_api_key: str | None
+    weather_api_base_url: str
+    booking_api_base_url: str
+    cors_allow_origins: list[str]
+    cors_allow_credentials: bool
+
+    @classmethod
+    def from_env(cls) -> "Settings":
+        def required(name: str) -> str:
+            value = os.getenv(name)
+            if not value:
+                raise ValueError(f"Missing required env var: {name}")
+            return value
+        asgardeo_base_url = required("ASGARDEO_BASE_URL")
+        asgardeo_client_id = required("ASGARDEO_CLIENT_ID")
+        return cls(
+            openai_api_key=required("OPENAI_API_KEY"),
+            openai_model=os.getenv("OPENAI_MODEL", "gpt-4o-mini"),
+            openai_embedding_model=os.getenv("OPENAI_EMBEDDING_MODEL", "text-embedding-3-small"),
+            asgardeo_base_url=asgardeo_base_url,
+            asgardeo_client_id=asgardeo_client_id,
+            pinecone_api_key=required("PINECONE_API_KEY"),
+            pinecone_service_url=required("PINECONE_SERVICE_URL"),
+            pinecone_index_name=os.getenv("PINECONE_INDEX_NAME", "hotel-policies"),
+            weather_api_key=os.getenv("WEATHER_API_KEY"),
+            weather_api_base_url=os.getenv("WEATHER_API_BASE_URL", "http://api.weatherapi.com/v1"),
+            booking_api_base_url=os.getenv("BOOKING_API_BASE_URL", "http://localhost:9091"),
+            cors_allow_origins=_split_csv(
+                os.getenv("CORS_ALLOW_ORIGINS"),
+                ["http://localhost:3001"],
+            ),
+            cors_allow_credentials=os.getenv("CORS_ALLOW_CREDENTIALS", "true").lower() == "true",
+        )

samples/hotel-booking-agent/agent/graph.py

@@ -0,0 +1,80 @@
+from __future__ import annotations
+
+import logging
+from typing import Annotated, TypedDict
+
+from langchain_core.messages import BaseMessage, SystemMessage
+from langchain_openai import ChatOpenAI
+from langgraph.graph import StateGraph, END
+from langgraph.graph.message import add_messages
+from langgraph.prebuilt import ToolNode, tools_condition
+from langgraph.checkpoint.memory import InMemorySaver
+
+
+from config import Settings
+from tools import build_tools
+
+logger = logging.getLogger(__name__)
+
+SYSTEM_PROMPT = """You are an assistant for planning trip itineraries of a hotel listing company.
+Help users plan their perfect trip, considering preferences and available hotels.
+
+Instructions:
+- Match hotels near attractions with user interests when prioritizing hotels.
+- You may plan itineraries with multiple hotels based on user interests and attractions.
+- Include the hotel and things to do for each day in the itinerary.
+- Use markdown formatting in non-hotel-search answers. Include hotel photos if available.
+- Always call get_user_profile_tool first to retrieve personalization data.
+- If the user explicitly asks to book, call create_booking_tool using available hotel/room data.
+- When calling create_booking_tool, include pricePerNight for each room from availability results.
+- If the user asks to edit or modify a booking, call edit_booking_tool with the bookingId.
+- If the user asks to cancel a booking, call cancel_booking_tool with the bookingId.
+- If the user asks to list or view bookings, call list_bookings_tool with the userId from context. Filter by status when asked (available/my bookings => CONFIRMED, cancelled => CANCELLED, all => ALL).
+- If booking details are missing (hotelId, roomId, dates, guests, or primary guest contact info), ask a concise follow-up question instead of making up data. Use bullet points for the missing fields and list available room options as bullets when asking for a room selection.
+- Do not claim a booking failed unless the booking tool returns an error.
+- If a booking attempt fails, ask a concise follow-up to retry with corrected details or an alternative hotel.
+- After a successful booking tool response, provide the final user response and do not call more tools.
+- When listing past bookings, use hotelName when available; otherwise fall back to hotelId.
+- For hotel policy questions, always call query_hotel_policy_tool with the hotel name or id.
+- Do not answer policy questions from hotel search/details responses or dataset fields.
+- Use resolve_relative_dates_tool to resolve phrases like tomorrow, this weekend, next Friday into ISO dates. If ambiguity remains, ask a clarifying question and do not guess.
+- For availability responses, format each room with: Room Type, Price per night, Max Occupancy.
+- Prefer this discovery flow for hotels: call search_hotels_tool even if dates are missing, rank/summarize, ask for dates if missing.
+- When the user asks about a specific hotel, resolve hotelId then call get_hotel_info_tool.
+- For hotel search results or single-hotel details, return only HOTEL_RESULTS_JSON followed by valid JSON.
+- Do not output raw tool traces, internal reasoning, markdown headings, or code fences."""
+
+
+class AgentState(TypedDict):
+    messages: Annotated[list[BaseMessage], add_messages]
+
+
+def build_graph(configs: Settings):
+    tools = build_tools(configs)
+    llm = ChatOpenAI(
+        model=configs.openai_model,
+        api_key=configs.openai_api_key,
+    ).bind_tools(tools)
+
+    def agent_node(state: AgentState) -> AgentState:
+        messages = [SystemMessage(content=SYSTEM_PROMPT)] + state["messages"]
+        response = llm.invoke(messages)
+        tool_calls = getattr(response, "tool_calls", None) or []
+        if tool_calls:
+            tool_names = [call.get("name") for call in tool_calls if isinstance(call, dict)]
+            logger.debug("agent_node decided to call tools: %s", tool_names)
+        else:
+            logger.debug("agent_node returned a final response (no tool calls).")
+        return {"messages": [response]}
+
+    graph = StateGraph(AgentState) #add in memory server
+    graph.add_node("agent", agent_node)
+    graph.add_node("tools", ToolNode(tools)) 
+
+    # Remove the mapping - tools_condition returns "tools" or END automatically
+    graph.add_conditional_edges("agent", tools_condition)
+    graph.add_edge("tools", "agent")
+    graph.set_entry_point("agent")
+
+    checkpointer = InMemorySaver()
+    return graph.compile(checkpointer=checkpointer)