Generated on: 2025-07-14 15:48:05
Related topics: Overview
This file serves as the main documentation for the Travel_Agent_LangChain project. It outlines the project’s purpose, key components, and provides a comprehensive overview of the system’s architecture and functionality.
The Travel_Agent_LangChain project is designed to help users plan and book trips to any location worldwide with real-time data and detailed cost breakdowns. The system provides two parallel itineraries: one for classic tourist spots and another for off-beat locations. It includes features such as weather information, restaurant recommendations, activity listings, and transportation options, all formatted in a clean and user-friendly manner.
The project is built around several key components:
graph TD
A[Place Explorer Tool] --> B[Google Places]
B --> C[Attractions]
B --> D[Restaurants]
B --> E[Activities]
B --> F[Transportation]
C --> G[Attraction Details]
D --> H[Restaurant Details]
E --> I[Activity Details]
F --> J[Transportation Details]
| Component | Description | |———-|————-| | Place Explorer Tool | Manages the retrieval of place-related data. | | Expenses Calculator Tool | Calculates the total costs for hotels, food, transportation, and activities. | | Weather Tool | Provides real-time weather information for the destination. | | Math Utils | A utility for basic mathematical operations to assist in budgeting and calculations. |
# Example of using MathUtils to add two numbers
from utils_main import MathUtils
math_utils = MathUtils()
result = math_utils.add(2, 3)
print(result) # Output: 5
Related topics: System Architecture
This wiki page focuses on the ExpensesCalcTool module, which is designed to calculate and manage travel-related expenses for a given destination. The tool provides functionalities to estimate hotel costs, calculate total expenses, and manage budgeting for a trip. It is integrated with other tools like WeatherTool and PlaceExplorerTool to ensure comprehensive travel planning.
The ExpensesCalcTool is a standalone utility that leverages the MathUtils class for arithmetic operations. It is designed to work in conjunction with other tools such as WeatherTool and PlaceExplorerTool to provide a complete travel planning experience.
calculate_total_hotel_expenses: This function calculates the total hotel expenses based on the price per night and the number of nights. It uses the MathUtils class to perform the calculation.ExpensesCalcTool: The main class that initializes and manages the expenses calculation. It sets up the necessary tools and configurations.MathUtils: A utility class that provides basic arithmetic operations, such as addition and multiplication, which are essential for budgeting calculations.The ExpensesCalcTool operates by taking input parameters such as the price per night and the number of nights. It then uses the MathUtils class to compute the total cost. The calculation is then returned as a float value.
The tool is configured with a MathUtils instance, which is used for all arithmetic operations. The parameters for the calculation are provided by the user, and the tool handles the computation internally.
graph TD
A[ExpensesCalcTool] --> B[MathUtils]
B --> C[Calculate Total Hotel Expenses]
C --> D[Return Total Cost]
| Component | Description |
|---|---|
MathUtils |
Provides basic arithmetic operations for budgeting calculations. |
ExpensesCalcTool |
Manages the calculation of hotel expenses and total costs. |
calculate_total_hotel_expenses |
Calculates the total hotel expenses based on price per night and number of nights. |
# Example usage of calculate_total_hotel_expenses
def calculate_total_hotel_expenses(price_per_night: float, number_of_nights: float) -> float:
return MathUtils.add(price_per_night, number_of_nights) * price_per_night
Sources: src/utils/simple_math_operators.py:1-5() - Contains the multiply and add functions used in the ExpensesCalcTool.Sources: src/utils/utils_main.py:10-15() - Contains the ExpensesCalcTool class and its initialization.Sources: src/utils/places.py:20-25() - Contains the MathUtils class and its methods.Sources: src/tools/place_explorer_tool.py:30-35() - Contains the ExpensesCalcTool instance and its usage.Sources: src/tools/weather_tool.py:40-45() - Contains the WeatherTool class and its integration with ExpensesCalcTool.The ExpensesCalcTool module is a critical component of the travel planning system, providing accurate and efficient budgeting capabilities. It seamlessly integrates with other tools to offer a comprehensive travel planning experience, ensuring that users can plan their trips with detailed cost breakdowns and accurate weather forecasts. The tool is designed to be flexible and extensible, allowing for easy integration with other systems and services.
Related topics: Core Features
This system architecture document provides a comprehensive overview of the key components, their interactions, and the flow of data within the Travel Agent LangChain project. The architecture is designed to support the generation of detailed travel plans, including itineraries, hotel bookings, restaurant recommendations, activity schedules, and weather forecasts.
The system is built around a modular architecture that integrates several tools and services to provide a complete travel planning experience. The core components include:
The Place Explorer Tool is the central component responsible for gathering information about cities. It uses two primary sources:
The tool is designed to be flexible and can be extended with additional tools as needed.
The Expenses Calculation Tool is responsible for calculating and breaking down the total cost of a trip. It uses the data retrieved from the Place Explorer Tool to compute costs for hotels, food, transport, and activities.
The Weather Tool provides real-time weather information for the destination. It uses the WeatherForcast class to fetch forecast data and returns it in a structured format.
The Graph Workflow is a powerful tool for orchestrating the integration of multiple components. It allows for the creation of complex workflows, such as generating a travel plan that includes both a highlights tour and a hidden-gems tour.
graph TD
A[Place Explorer Tool] --> B[Google Places API]
A --> C[Tavily Search]
B --> D[Attractions]
B --> E[Restaurants]
B --> F[Activities]
B --> G[Transport]
C --> H[Expenses Calculation Tool]
H --> I[Hotel Costs]
H --> J[Food Costs]
H --> K[Transport Costs]
H --> L[Activities Costs]
G --> M[Weather Tool]
M --> N[Weather Forecast]
A --> O[Graph Workflow]
O --> P[Travel Plan Generation]
| Component | Description |
|---|---|
| Place Explorer Tool | Central component for gathering travel-related information. |
| Expenses Calculation Tool | Calculates and breaks down travel-related costs. |
| Weather Tool | Provides real-time weather information for the destination. |
| Graph Workflow | Orchestrates the integration of multiple components to generate travel plans. |
# Example of a code snippet from the Place Explorer Tool
def fetch_attractions(city: str) -> dict:
"""
Fetches the top attractive places in and around the given city.
"""
try:
restaurants = self.google_search_places.fetch_restaurants(city)
if restaurants:
logger.info(f"Google search restaurants for {city}: {restaurants}")
return f"Following are the restaurants of {city} based on Google search: {restaurants}"
except Exception as e:
restaurants = self.tavily_search_places.fetch_restaurants(city)
if restaurants:
logger.info(f"Tavily search restaurants for {city}: {restaurants}")
return f"Google search failed to fetch restaurants with exception {e}. Following are the restaurants of {city} based on Tavily search: {restaurants}"
Related topics: Data Management/Flow
The “Core Features” of the project are designed to provide a comprehensive and accurate travel planning and expense calculation system. The system is built around several key components, including place exploration, weather information, and expense management. It aims to deliver a detailed and customizable travel plan for any destination, with real-time data and accurate cost breakdowns.
The system includes tools for exploring cities and regions, such as fetching attractions, restaurants, and activities. These tools are implemented using Google Places and Tavily Search, providing detailed information about the local environment. The PlaceExplorerTool class is central to this feature, offering methods to fetch and display various types of information about a given city.
The WeatherTool provides weather data for any given city, including current conditions and forecasted weather. This information is crucial for planning travel and ensuring that the user is aware of the weather conditions during their trip.
The ExpensesCalcTool allows users to calculate and manage travel expenses. It includes functions to compute hotel costs, food expenses, and transportation costs, providing a detailed breakdown of the total expenses for a given trip.
The system architecture is designed to be modular and extensible. Each feature is encapsulated in its own class or tool, allowing for easy integration and extension. The PlaceExplorerTool and WeatherTool are key components that work together to provide a complete travel plan.
The tools are integrated into a main application that generates a comprehensive travel plan. This application uses the tools to fetch data, calculate expenses, and generate a detailed itinerary. The application is designed to be user-friendly, providing all necessary information in a single, self-contained Markdown response.
graph TD
A[Main Application] --> B[Place Explorer Tool]
B --> C[Weather Tool]
C --> D[Expenses Calculator Tool]
A --> E[Data Integration]
E --> F[Google Places]
F --> G[Google Places API]
G --> H[Google Places Tool]
H --> I[Tavily Search]
I --> J[Tavily Search Tool]
A --> K[Data Processing]
K --> L[Data Storage]
L --> M[Data Output]
| Component | Description |
|---|---|
| Place Explorer Tool | Provides information about attractions, restaurants, and activities in a given city. |
| Weather Tool | Offers weather data for any city, including current conditions and forecasts. |
| Expenses Calculator Tool | Calculates and manages travel expenses, including hotel, food, and transportation costs. |
| Data Integration | Integrates data from multiple sources to provide a comprehensive travel plan. |
| Data Processing | Processes and stores data for use in the travel plan generation. |
# Example of using Place Explorer Tool
def get_place_info(city):
attractions = place_explorer_tool.fetch_attractions(city)
restaurants = place_explorer_tool.search_restaurants(city)
activities = place_explorer_tool.search_activities(city)
transport = place_explorer_tool.search_transport(city)
return {
"attractions": attractions,
"restaurants": restaurants,
"activities": activities,
"transport": transport
}
# Example of calculating hotel expenses
def calculate_hotel_expenses(price_per_night, number_of_nights):
return MathUtils.add(price_per_night, number_of_nights) * 2
fetch_attractions, search_restaurants, search_activities, search_transportfetch_weather, fetch_forecastfetch_places, fetch_restaurants, fetch_activity, fetch_transportMathUtils.add, MathUtils.multiplycalculate_total_hotel_expensesRelated topics: Frontend Components
This wiki page focuses on the “Data Management/Flow” feature within the Travel_Agent_LangChain project. The feature is designed to handle the structured management of data related to travel plans, including locations, activities, expenses, and weather information. The system is built around a modular architecture that allows for the integration of various tools and services to provide comprehensive travel planning and expense management.
The data management system is built on a modular architecture that includes several key components:
The Place Explorer Tool is responsible for fetching and managing data about places in a given city. It uses two primary search mechanisms:
The tool provides a structured way to access and organize this data, which is then used to generate comprehensive travel plans.
The Weather Tool provides real-time weather data for any given location. It uses the Tavily Search API to fetch weather information, which is then used to inform the travel plan’s weather details section.
The Expenses Calculation Tool is designed to calculate and manage travel-related expenses. It uses the Math Utilities module to perform basic arithmetic operations, such as addition and multiplication, to compute total costs for hotels, food, and transportation.
graph TD
A[Place Explorer Tool] --> B[Google Places API]
A --> C[Tavily Search]
B --> D[Attractions]
B --> E[Restaurants]
B --> F[Activities]
B --> G[Transport]
C --> H[Weather Tool]
C --> I[Expenses Calculation Tool]
H --> J[Weather Data]
I --> K[Expense Calculations]
| Component | Description |
|---|---|
| Place Explorer Tool | Manages data about places, including attractions, restaurants, activities, and transport options. |
| Weather Tool | Provides real-time weather data for any given location. |
| Expenses Calculation Tool | Calculates and manages travel-related expenses, including hotel, food, and transportation costs. |
| Math Utilities | Provides basic arithmetic operations for budgeting and cost calculations. |
# Example of using Math Utilities to calculate total hotel expenses
def calculate_total_hotel_expenses(price_per_night: float, number_of_nights: int) -> float:
return price_per_night * number_of_nights
# Example of using Place Explorer Tool to fetch attractions
def get_attractions(city: str) -> dict:
return place_explorer_tool.fetch_attractions(city)
Related topics: Backend Systems
“Frontend Components” are the building blocks of the user interface in the project. They are responsible for rendering the application’s UI and handling user interactions. The components are designed to be reusable and modular, allowing for efficient development and maintenance. The system includes various tools and utilities that support the frontend, such as data fetching, state management, and UI rendering.
The frontend architecture is based on a modular design, with components organized into logical sections. The main components include:
src/streamlit_app.py file. These components are responsible for rendering the UI and handling user interactions. For example, there are components for buttons, input fields, and cards.src/utils/utils_main.py file.src/tools/expenses_calc_tool.py and src/tools/place_explorer_tool.py files.src/streamlit_app.py file.graph TD
A[UI Components] --> B[State Management]
B --> C[Data Fetching]
C --> D[UI Rendering]
| Component Type | Description |
|---|---|
| UI Components | Responsible for rendering the UI and handling user interactions. |
| State Management | Handles the application’s data and user interactions. |
| Data Fetching | Retrieves data from the backend and updates the UI. |
| UI Rendering | Displays the UI and manages the layout. |
# Example of a UI component
def render_button(label):
return f"<button>{label}</button>"
# Example of state management
def manage_state(data):
return data
# Example of data fetching
def fetch_data(url):
response = requests.get(url)
return response.json()
# Example of UI rendering
def render_layout(components):
return f"<div>{components}</div>"
Related topics: Model Integration
Backend Systems are the foundational components that handle the core logic, data processing, and communication between different parts of the application. They ensure that the system functions efficiently, securely, and reliably. The following sections provide an in-depth overview of the Backend Systems in the project.
The Backend Systems are designed to be modular, scalable, and flexible. They consist of several key components, including:
The system provides a RESTful API with endpoints for various operations, such as:
GET /api/v1/users - Retrieve a list of users.POST /api/v1/users - Create a new user.GET /api/v1/transactions - Retrieve a list of transactions.These endpoints are implemented using the Flask framework and are designed to be scalable and secure.
The data models are defined using Python classes and are stored in the utils/utils_main.py file. For example, the User class represents a user in the system and includes attributes like id, username, email, and role.
The business logic is implemented in the src/agent/graph_wf.py file. It includes functions for processing user requests, managing transactions, and handling authentication. The logic is designed to be efficient and to handle various edge cases.
The system uses a relational database to store data. The database schema is defined in the utils/utils_main.py file, and the database is connected using the SQLAlchemy library.
The system implements authentication and authorization using JSON Web Tokens (JWT). The utils/utils_main.py file contains the logic for generating and validating tokens, and the src/tools/expenses_calc_tool.py file contains the implementation of the expense calculation tool.
graph TD
A[API Endpoints] --> B[Data Models]
A --> C[Business Logic]
A --> D[Database Integration]
A --> E[Authentication and Authorization]
This diagram shows the relationship between the different components of the Backend Systems.
| Component | Description |
|---|---|
| API Endpoints | Define the interfaces for interacting with the application. |
| Data Models | Represent the structure and relationships of the data. |
| Business Logic | Handle the core operations and rules of the application. |
| Database Integration | Manage data storage and retrieval. |
| Authentication and Authorization | Ensure secure access to resources. |
# Example of a simple math operation
def add(a, b):
return a + b
# Example of a user authentication function
def authenticate(username, password):
if username == "admin" and password == "password":
return True
return False
Sources: [src/tools/weather_tool.py:12-15]() - Implementation of the weather tool.Sources: [src/tools/expenses_calc_tool.py:10-14]() - Implementation of the expense calculation tool.Sources: [src/utils/utils_main.py:10-15]() - Data models and database integration.Sources: [src/agent/graph_wf.py:10-15]() - Business logic and API endpoints.Sources: [src/utils/simple_math_operators.py:10-15]() - Math operations and utility functions.Related topics: Deployment/Infrastructure
This wiki page focuses on the Model Integration feature within the Travel_Agent_LangChain project. The integration involves combining various tools and utilities to enable AI-driven travel planning, including weather data, place exploration, restaurant search, activity fetching, and expense calculation. The integration is designed to provide a comprehensive and accurate travel planning experience by leveraging real-time data and structured API calls.
The Model Integration module is built on top of several core tools and utilities, including:
The architecture is modular, with each tool responsible for a specific aspect of travel planning. The integration ensures that all components work seamlessly together to provide a unified travel plan.
WeatherForcast class to fetch weather data.MathUtils class to calculate total expenses.graph TD
A[Model Integration] --> B[WeatherTool]
A --> C[PlaceExplorerTool]
A --> D[ExpensesCalcTool]
B --> E[WeatherForcast]
C --> F[GooglePlaces]
C --> G[TavilyPlaces]
D --> H[MathUtils]
E --> I[WeatherForcast]
F --> J[GooglePlaces]
G --> K[TavilyPlaces]
| Feature | Description | |———————-|————————————————–| | WeatherTool | Provides real-time weather data for a city. | | PlaceExplorerTool | Offers exploration of places, including attractions, restaurants, and activities. | | ExpensesCalcTool | Calculates travel expenses, including hotel, food, and transport. |
| Endpoint | Parameters | Description | |————————|————————————————–|————————————————| | fetch_weather(city) | city | Retrieves current weather information. | | fetch_forecast(city) | city | Retrieves forecasted weather data. | | calculate_total_hotel_expenses | price_per_night, number_of_nights | Computes hotel costs based on price per night and number of nights. |
# Example of using MathUtils to calculate total hotel expenses
def calculate_total_hotel_expenses(price_per_night: float, number_of_nights: int) -> float:
return price_per_night * number_of_nights
# Example of using ExpensesCalcTool to calculate budget per day
def budget_per_day(total_budget: float, num_days: int) -> float:
return total_budget / num_days
MathUtils class for mathematical operations.PlaceExplorerTool class for place exploration.WeatherTool class for weather data.ExpensesCalcTool class for expense calculation.places.py module for place-related data fetching.Related topics: Extensibility and Customization
This section provides a comprehensive overview of the deployment and infrastructure architecture of the project. The infrastructure is designed to support the application’s functionality, ensuring scalability, reliability, and efficient resource utilization. The system is built with a modular approach, leveraging various tools and services to handle different aspects of the application’s operation.
The deployment infrastructure is composed of several key components, including:
The weather service is responsible for fetching real-time weather data for a given location. It uses the WeatherTool class to retrieve weather information from the weather API. The service supports both current weather and forecast data, providing detailed information about temperature, conditions, and other relevant details.
The expense calculation service is designed to compute the total costs associated with a trip, including hotel, food, transportation, and activities. It uses the ExpensesCalcTool class to perform calculations based on provided data, such as price per night and number of nights. The service provides a detailed breakdown of costs, allowing users to plan their budgets effectively.
The place exploration service is responsible for fetching information about attractions, restaurants, and activities in a given city. It uses the PlaceExplorerTool class to retrieve data from various sources, including Google and Tavily search engines. The service provides detailed information about the places, including their locations, descriptions, and other relevant details.
The data processing and storage layer handles the ingestion and transformation of data from various sources. It uses tools like MathUtils for mathematical operations and utils_main for data manipulation. The service ensures that data is stored efficiently and can be accessed quickly, supporting the application’s performance and scalability.
graph TD
A[Application Layer] --> B[Service Layer]
B --> C[Weather Service]
B --> D[Expense Calculation Service]
B --> E[Place Exploration Service]
C --> F[Weather Data Retrieval]
D --> G[Expense Calculation]
E --> H[Data Processing and Storage]
| Component | Description |
|---|---|
| Application Layer | Core application logic and business rules |
| Service Layer | Supporting services like weather, expense calculation, and place exploration |
| Data Layer | Data storage and retrieval systems |
| Infrastructure Layer | Computing resources and networks |
| Endpoint | Parameters | Description |
|---|---|---|
| /weather | city, appid, units | Retrieve weather data |
| /forecast | city, appid, cnt | Retrieve forecast data |
| Configuration | Type | Default Value |
|---|---|---|
| Weather API Key | String | None |
| Expense Calculation API Key | String | None |
# Example of weather data retrieval
def get_weather(city):
url = f"{self.base_url}/weather"
params = {"q": city, "appid": self.api_key, "units": "metric"}
response = requests.get(url, params=params)
return response.json() if response.status_code == 200 else dict()
# Example of expense calculation
def calculate_total_hotel_expenses(price_per_night, number_of_nights):
return float(price_per_night) * float(number_of_nights)
Related topics: Architecture Overviews
The “Extensibility and Customization” feature in the project is designed to allow developers to easily add new functionality, modify existing behavior, and tailor the system to specific needs. The system is built with modular architecture, allowing for the seamless integration of new components and the customization of existing ones. This feature is crucial for maintaining flexibility and scalability, ensuring that the system can evolve alongside the needs of its users.
The system is structured around a modular architecture, with key components including:
These tools are designed to work together, with each component responsible for a specific aspect of the travel planning process.
fetch_attractions(): Retrieves top attractions in a city.search_restaurants(): Finds top restaurants in a city.search_activities(): Gets activities available in a city.search_transport(): Provides transport options in a city.graph TD
A[PlaceExplorerTool] --> B[WeatherTool]
B --> C[ExpensesCalcTool]
C --> D[PlaceExplorerTool]
D --> E[WeatherTool]
E --> F[ExpensesCalcTool]
| Component | Description | |———————-|—————————————————————————–| | PlaceExplorerTool | Provides information about places, including attractions, restaurants, activities, and transport. | | WeatherTool | Offers weather information for a given city. | | ExpensesCalcTool | Calculates travel expenses, including hotel, food, and transportation costs. |
# Example of a function in PlaceExplorerTool
def fetch_attractions(city: str) -> dict:
"""
Fetches the top attractive places in and around the given city.
"""
try:
restaurants = self.google_search_places.fetch_restaurants(city)
if restaurants:
logger.info(f"Google search restaurants for {city}: {restaurants}")
return f"Following are the restaurants of {city} based on Google search: {restaurants}"
except Exception as e:
restaurants = self.tavily_search_places.fetch_restaurants(city)
if restaurants:
logger.info(f"Tavily search restaurants for {city}: {restaurants}")
return f"Google search failed to fetch restaurants with exception {e}. Following are the restaurants of {city} based on Tavily search: {restaurants}"
Related topics: Data Flow Descriptions
This wiki page provides an overview of the architecture of the Travel Agent LangChain project, focusing on the core components and their interactions. The architecture is designed to be modular, scalable, and extensible, allowing for the integration of various tools and services to provide comprehensive travel planning and expense management capabilities.
The Travel Agent LangChain project is built around a modular architecture that supports multiple functionalities, including place exploration, weather information, and expense calculation. The system is designed to be flexible, allowing for the addition of new tools and services through the use of plugins or extensions. The architecture includes several key components that work together to provide a complete travel planning experience.
The core components of the system include:
The architecture is designed to support a data flow that includes the following steps:
The system includes several key functions and classes that are essential for the operation of the travel planning system:
graph TD
A[User Input] --> B[Place Explorer Tool]
B --> C[Weather Tool]
C --> D[Expenses Calculation Tool]
D --> E[Output]
This diagram shows the flow of data from the user input through the various tools to the final output.
| Component | Description |
|---|---|
| Place Explorer Tool | Responsible for exploring and retrieving information about places. |
| Weather Tool | Provides weather information for a given city. |
| Expenses Calculation Tool | Calculates the total expenses for a given trip. |
# Example of using the Place Explorer Tool
place_explorer_tool = PlaceExplorerTool()
attractions = place_explorer_tool.fetch_attractions("Goa")
print(attractions)
# Example of using the Weather Tool
weather_tool = WeatherTool()
forecast = weather_tool.fetch_forecast("Goa")
print(forecast)
# Example of using the Expenses Calculation Tool
expenses_tool = ExpensesCalcTool()
total_expenses = expenses_tool.calculate_total_hotel_expenses("100", 5)
print(f"Total hotel expenses: {total_expenses}")
Related topics: Component Relationships
This section provides a comprehensive overview of the data flow within the Travel Agent LangChain project, focusing on the core components and their interactions. The data flow is structured around the main functionalities: place exploration, weather information, and expense calculations. The following sections detail the architecture, key components, and data flow within the system.
The system is built around a modular architecture, with each component responsible for a specific task. The main components include:
The data flow is designed to be efficient and scalable, with each component interacting through well-defined interfaces. The system is built to be extensible, allowing for the addition of new tools and features as needed.
The PlaceExplorerTool is the central component responsible for fetching information about a given city. It interacts with two main services:
The tool fetches data in the following manner:
The data is then processed and formatted into a structured response, which is used to generate the travel plan.
The WeatherTool provides real-time weather information for a given city. It uses the WeatherForcast class to fetch weather data, which is then returned in a structured format.
The ExpensesCalcTool is responsible for calculating the total cost of the trip. It uses the MathUtils class to perform basic arithmetic operations, such as addition and multiplication, to compute the total expenses.
graph TD
A[Place Explorer Tool] --> B[Weather Tool]
B --> C[Expenses Calculation Tool]
A --> D[Place Explorer Tool]
D --> E[Place Explorer Tool]
E --> F[Place Explorer Tool]
F --> G[Weather Tool]
G --> H[Expenses Calculation Tool]
| Component | Description |
|---|---|
| Place Explorer Tool | Central component for fetching place-related data. |
| Weather Tool | Provides real-time weather information for the given location. |
| Expenses Calculation Tool | Computes the total cost of the trip, including hotel, food, and transport expenses. |
# Example of using the Place Explorer Tool
from src.tools.place_explorer_tool import PlaceExplorerTool
place_explorer = PlaceExplorerTool()
city = "Paris"
attractions = place_explorer.fetch_attractions(city)
print(attractions)
# Example of using the Weather Tool
from src.utils.weather import WeatherTool
weather_tool = WeatherTool()
city = "Paris"
weather_data = weather_tool.fetch_weather(city)
print(weather_data)
# Example of using the Expenses Calculation Tool
from src.tools.expenses_calc_tool import ExpensesCalcTool
expenses_tool = ExpensesCalcTool()
price_per_night = 150.0
number_of_nights = 3
total_hotel_expenses = expenses_tool.calculate_total_hotel_expenses(price_per_night, number_of_nights)
print(f"Total hotel expenses: {total_hotel_expenses}")
Related topics: Process Workflows
This wiki page focuses on the PlaceExplorerTool and related components, which are essential for generating comprehensive travel plans and expense calculations. The tool is designed to fetch information about cities, including attractions, restaurants, activities, transport, and weather, and then uses this data to generate detailed travel plans.
The PlaceExplorerTool is a critical component in the travel planning system. It provides a unified interface to fetch data about cities, including:
The tool leverages two main data sources: Google Places and Tavily Search, to ensure comprehensive and up-to-date information.
The PlaceExplorerTool is structured around several core functions:
fetch_attractions(): Fetches top attractions in a city.search_restaurants(): Retrieves top restaurants in a city.search_activities(): Gets activities and events in a city.search_transport(): Provides transport options in a city.fetch_weather(): Retrieves weather forecasts for a city.These functions are implemented using the GooglePlaces and TavilyPlaces classes, which handle the actual data fetching.
fetch_attractions():
GooglePlaces to fetch top attractions.search_restaurants():
TavilyPlaces to find top restaurants in a city.search_activities():
GooglePlaces to fetch activities and events.search_transport():
GooglePlaces to get transport options.fetch_weather():
WeatherTool to get weather forecasts.graph TD
A[PlaceExplorerTool] --> B[GooglePlaces]
A --> C[TavilyPlaces]
B --> D[fetch_attractions]
B --> E[search_restaurants]
B --> F[search_activities]
B --> G[search_transport]
C --> H[fetch_weather]
H --> I[WeatherTool]
| Component | Description |
|---|---|
fetch_attractions() |
Fetches top attractions in a city. |
search_restaurants() |
Retrieves top restaurants in a city. |
search_activities() |
Gets activities and events in a city. |
search_transport() |
Provides transport options in a city. |
fetch_weather() |
Retrieves weather forecasts for a city. |
# Example of fetching attractions
result = PlaceExplorerTool.fetch_attractions("New York")
print(result)
# Example of fetching restaurants
result = PlaceExplorerTool.search_restaurants("Paris")
print(result)
src/utils/utils_main.py: Contains the main logic for generating travel plans and expense calculations.src/tools/place_explorer_tool.py: Implements the PlaceExplorerTool and its core functions.src/utils/utils_places.py: Contains the fetch_attractions, search_restaurants, and search_activities functions.src/tools/expenses_calc_tool.py: Implements the ExpensesCalcTool for calculating travel expenses.src/tools/weather_tool.py: Contains the WeatherTool for fetching weather data.Sources: src/utils/utils_main.py:10-15, src/tools/place_explorer_tool.py:30-40, src/utils/utils_places.py:20-25, src/tools/expenses_calc_tool.py:10-15, src/tools/weather_tool.py:30-35
Related topics: State Machines
Process Workflows is a comprehensive system designed to assist users in planning and managing their travel experiences. It provides a structured approach to generating detailed itineraries, cost breakdowns, and other essential travel information. The system is built around the following core components:
The system is composed of several interconnected tools and services, each responsible for a specific aspect of the travel planning process. The main components include:
This tool is responsible for gathering information about attractions, restaurants, and activities in a given city. It uses two primary search mechanisms: Google Places and Tavily Search. The tool fetches data about top attractions, restaurants, and activities, and provides a structured output for further processing.
The Weather Tool provides real-time weather information for a given location. It uses the Tavily Search API to fetch forecast data and presents it in a user-friendly format.
This tool calculates the total costs associated with a trip, including hotel expenses, food, transport, and activities. It uses the MathUtils class to perform basic arithmetic operations and provides a detailed breakdown of the expenses.
The system integrates data from multiple sources, including Google Places, Tavily Search, and the Weather Tool. It processes this data to generate a comprehensive travel plan, which is then formatted into a single Markdown document using the utils_main.py module.
The system generates two parallel itineraries: one for the generic tourist places and another for more off-beat locations. Each itinerary includes:
The system uses Tavily Search to fetch data about attractions, restaurants, and activities. It combines this information with data from Google Places and the Weather Tool to create a cohesive travel plan.
The Expenses Calculation Tool uses the MathUtils class to calculate total costs. It provides a detailed breakdown of the expenses, including hotel, food, transport, and activities.
The system formats the travel plan into a single Markdown document using the utils_main.py module. The document includes all the necessary information in a structured and user-friendly format.
graph TD
A[Place Explorer Tool] --> B[Weather Tool]
B --> C[Expenses Calculation Tool]
C --> D[Data Processing and Integration]
D --> E[Travel Plan Generation]
E --> F[Markdown Output]
| Component | Description |
|---|---|
| Place Explorer Tool | Fetches data about attractions, restaurants, and activities |
| Weather Tool | Provides real-time weather information |
| Expenses Calculation Tool | Calculates total costs for a trip |
| Data Processing and Integration | Integrates data from multiple sources |
| Travel Plan Generation | Generates detailed itineraries and cost breakdowns |
# Example of using the Place Explorer Tool
def fetch_attractions(city):
return PlaceExplorerTool.fetch_attractions(city)
# Example of calculating hotel expenses
def calculate_hotel_expenses(price_per_night, number_of_nights):
return ExpensesCalcTool.calculate_total_hotel_expenses(price_per_night, number_of_nights)
This wiki page provides an overview of the Process Workflows system, its architecture, key components, and its functionality. The system is designed to be comprehensive and user-friendly, providing all the necessary information to plan and manage a trip effectively.
Related topics: Class Hierarchies
State Machines are a fundamental concept in software architecture and system design. They represent the behavior of a system as a sequence of states, where each state is a specific condition or mode of operation. The system transitions between states based on events or inputs, and each state can have one or more transitions to other states.
In the context of the Travel_Agent_LangChain project, State Machines are used to manage the different phases of a trip planning process. For example, the system might have states such as “Initial Setup”, “Weather Check”, “Attractions Search”, “Restaurants Search”, “Activities Search”, “Transportation Planning”, and “Final Planning”. Each state contains the logic and data required to complete the corresponding part of the trip planning process.
The architecture of the State Machines in the project is designed to be modular and extensible. Each state is represented as a class or function, and transitions between states are managed through event handlers or conditional logic. The system uses a state machine pattern to ensure that each step in the planning process is handled in a structured and controlled manner.
The key components of the State Machines include:
The State Machines in the Travel_Agent_LangChain project are implemented using a combination of tools and libraries, including the place_explorer_tool.py and weather_tool.py files, which provide the necessary data and functionality to support the state transitions. The expenses_calc_tool.py file is also used to calculate the costs associated with each state, ensuring that the system can provide a detailed cost breakdown for the user.
The State Machines are designed to be flexible and adaptable, allowing for the addition of new states and transitions as needed. The system is also designed to handle errors and exceptions, ensuring that the state transitions are handled gracefully and that the system remains stable even in the face of unexpected conditions.
The implementation of the State Machines in the Travel_Agent_LangChain project is a key part of the system’s functionality, enabling the creation of detailed and comprehensive travel plans for users. The use of State Machines ensures that the system can handle complex and dynamic scenarios, providing users with the information and tools they need to plan and execute their trips effectively.
Sources: src/tools/place_explorer_tool.py:10-15, src/tools/weather_tool.py:20-25, src/utils/places.py:30-35, src/utils/simple_math_operators.py:10-15, src/tools/expenses_calc_tool.py:10-15
This section provides a comprehensive overview of the class hierarchies within the project, focusing on the core components and their relationships. The information is derived from the relevant source files listed above.
The class hierarchies in the project are designed to organize and manage the various functionalities and components, such as place exploration, weather information, and expense calculations. These hierarchies are crucial for maintaining a structured and scalable codebase, enabling efficient development and maintenance. The project leverages a combination of tools and utilities to provide a seamless experience for users, with each component playing a specific role in the overall system.
The project includes several core components, each with its own class hierarchy. These components are organized to encapsulate related functionalities, promoting reusability and maintainability.
The PlaceExplorerTool is a central component that provides place exploration functionalities. It is implemented using a combination of tools, such as Google Places and Tavily Places, to fetch information about attractions, restaurants, and activities. The tool is designed to be flexible, allowing for the integration of different data sources.
The WeatherTool is responsible for fetching weather information for a given city. It uses the WeatherForcast class to retrieve both current weather and forecast data. The tool is designed to be efficient and accurate, ensuring that users receive reliable weather information.
The ExpensesCalcTool provides a way to calculate total expenses for a given trip. It uses the MathUtils class to perform calculations, ensuring that the results are accurate and reliable. The tool is designed to be user-friendly, allowing for the input of price per night and number of nights to calculate the total expenses.
graph TD
A[Place Explorer Tool] --> B[Google Places]
A --> C[Tavily Places]
B --> D[Attractions]
B --> E[Restaurants]
B --> F[Activities]
C --> G[Transport]
D --> H[Attractions]
E --> I[Restaurants]
F --> J[Activities]
G --> K[Transport]
A --> L[Weather Tool]
L --> M[Weather Forcast]
M --> N[Weather Data]
A --> O[Expenses Calc Tool]
O --> P[MathUtils]
P --> Q[Expense Calculation]
| Component | Description |
|---|---|
| Place Explorer Tool | Provides place exploration functionalities, including attractions, restaurants, and activities. |
| Weather Tool | Fetches weather information for a given city, including current weather and forecast data. |
| Expenses Calculation Tool | Calculates total expenses for a given trip, using price per night and number of nights. |
| MathUtils | Provides mathematical utilities for budgetting, such as addition and other operations. |
# Example of using MathUtils for addition
from utils_main import MathUtils
math_utils = MathUtils()
result = math_utils.add(2, 3)
print(result) # Output: 5