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https://github.com/shaheennabi/production-ready-tripplanner-multi-ai-agent-project
✈️🌍 Production-Ready TripPlanner Multi-AI Agent Project: Transform your travel planning with AI-driven assistance! From discovering dream destinations, creating custom itineraries, exploring avenues of nature, to finding local attractions and beach spots 🔍💡—all powered by industry-ready AI tools. 🏨🌍
https://github.com/shaheennabi/production-ready-tripplanner-multi-ai-agent-project
agentic-workflow automated-flight-search generative-ai gpt industry-ready langgraph modular-coding multi-ai-agents multiagentssystems openai production-ready serperapi streamlit taskflowai trip-planner weather-api
Last synced: 2 days ago
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✈️🌍 Production-Ready TripPlanner Multi-AI Agent Project: Transform your travel planning with AI-driven assistance! From discovering dream destinations, creating custom itineraries, exploring avenues of nature, to finding local attractions and beach spots 🔍💡—all powered by industry-ready AI tools. 🏨🌍
- Host: GitHub
- URL: https://github.com/shaheennabi/production-ready-tripplanner-multi-ai-agent-project
- Owner: shaheennabi
- License: mit
- Created: 2024-11-17T17:48:10.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-01-01T20:49:02.000Z (about 1 month ago)
- Last Synced: 2025-01-01T21:37:31.761Z (about 1 month ago)
- Topics: agentic-workflow, automated-flight-search, generative-ai, gpt, industry-ready, langgraph, modular-coding, multi-ai-agents, multiagentssystems, openai, production-ready, serperapi, streamlit, taskflowai, trip-planner, weather-api
- Language: Jupyter Notebook
- Homepage:
- Size: 406 KB
- Stars: 5
- Watchers: 3
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
Awesome Lists containing this project
README
# 🌺 🌿 🌱 Production-Ready TripPlanner Multi-AI Agent Project 🍃 🌸 🪴
### Discussions box is open, you can ask anything & i will accept any pull request feel free to contribute (you can later use this project in your resume etc..)
## **Problem Statement**
---
*Note: This project simulates an industry-standard scenario where I am assuming the role of a developer at XYZ Company. The client required a system to provide users with accurate, detailed, and instant information about travel destinations. This system was to be embedded into their platform for seamless user experience, ensuring that users could fetch trip details in one go with pinpoint accuracy.*
*High-Level Note: Some related topics, such as **AI agents**, their pipelines, different types of agents, and **RAG (Retrieval-Augmented Generation) systems** in general, have already been discussed. You can find these details in the **`docs` folder** for further exploration.*
---
# 🌟 **Building a Modular AI System for Destination Insights** 🌟
At **XYZ Company**, we were tasked with creating a robust system for a client that could **fetch and deliver comprehensive trip details** in a single query. The aim was to make the users' lives easier by integrating a **trip planner system** into their platform, enabling seamless access to detailed destination information in one go.
To achieve this, **Taskflow AI** was chosen due to its:
- **Multi-AI agent support**: Making it highly adaptable for complex workflows.
- **Capabilities for modular design**: Ensuring scalability and maintainability for future upgrades.
---
## **My Role as a Developer** 🍃 🌸
As a developer, my manager entrusted me to deliver this project with **high-quality results** within a **strict time frame**. My responsibilities included:
- Designing a system that could provide users with accurate and detailed destination information in one query.
- Ensuring the system had a **modular architecture**, allowing easy future upgrades and scalability.
- Managing cost efficiency while maintaining performance and reliability.
I chose **Taskflow AI** because of its support for **multi-AI agent integration** and its capability to deliver modular and scalable solutions tailored to client needs.
---
## **Project Goals** 🎯
1. **Deliver a System to Meet Client Needs**
- Create a system to provide accurate and instant destination insights, fulfilling the user's requirements seamlessly.
2. **Build a Modular Architecture**
- Design the system to support scalability and future upgrades.
3. **Testing and Quality Assurance**
- Ensure the system's reliability and accuracy through rigorous testing.
4. **Cost-Effective Measures**
- Optimize API and model-related costs without compromising performance.
---
## **My Approach** ✨💚
The project followed a structured approach to ensure efficient execution and high-quality results.
### **Steps in My Approach**
1. **Framework Selection**
- Chose **Taskflow AI** for its multi-AI agent support and capabilities in modular design.
2. **Model Selection**
- Tested multiple models, including:
- **OpenAI Models**:
- The **GPT-3.5 Turbo model** was ultimately chosen due to its:
- **Tool-Calling Excellence**: It demonstrated the best ability to call tools and manage multi-agent tasks seamlessly, ensuring outstanding integration with the system.
- **Token Management**: The model's reasoning capabilities enabled it to self-understand context and optimize token usage, producing exceptional results.
- **Report Generation**: Its power to combine, process, and generate final reports effectively, as showcased in this project.
- **LLaMA Models**: Failed to handle larger token inputs effectively and lacked robust reasoning capabilities, resulting in subpar outcomes.
- **Google Models**: While capable of handling large contexts, they failed in reasoning and memory performance, making them unsuitable for this use case.
- **OpenAI's o1 Series Models**: Offered great performance but were prohibitively expensive for this project.
- Additionally, tested with **Groq inference engine**, which also failed to meet the project's performance indicators.
3. **System Design and Development**
- Built a **modular architecture** using Taskflow AI, ensuring the system could adapt to future needs and upgrades.
4. **Cost Optimization**
- Prioritized minimizing API costs by choosing the **GPT-3.5 Turbo model** and optimizing system performance.
5. **Testing and Delivery**
- Conducted rigorous testing to validate accuracy and reliability.
- Delivered the project on time, exceeding quality expectations.
---
## **Challenges Encountered** 🎋
The project faced several challenges, including:
1. **Model API Costs**: Managing the high costs of API calls while ensuring performance.
2. **Token Limitations**: Handling large queries within the token limits of various models.
3. **Memory Issues**: Ensuring memory efficiency for better performance.
---
## **How I Fixed Challenges** 🌟
- **Model API Costs**:
- Evaluated multiple models and frameworks, but **GPT-3.5 Turbo** was chosen for its cost-effectiveness and outstanding performance.
- **Token Limitations**:
- OpenAI's **GPT-3.5 Turbo** effectively managed token constraints by leveraging its powerful reasoning capabilities, optimizing token usage, and maintaining context to generate accurate results.
- **Memory Challenges**:
- Compared with other models like LLaMA and Google's offerings, **GPT-3.5 Turbo** proved superior in handling memory-intensive tasks and maintaining consistent performance.
---
**Note:** This system is now live, providing users with a seamless and efficient way to fetch trip details in one go. It showcases the potential of modular AI architectures in real-world applications.
## System Design or Project Pipeline
### Main Pipeline
### Let's Start with LLM OpenAI: (GPT-3.5 Turbo):
- In this example, I am loading the **GPT-3.5 Turbo** model from **taskflowai.OpenaiModels**.
- This model is efficient, cost-effective, and performs exceptionally well for most tasks.
- Make sure to load the OpenAI API key properly.
- It is recommended to store the API key securely using a `.env` file or a similar approach.
### Now let's task about the Agents:
Web Research Agent:
- Here, I define the **Web Research Agent**, which is responsible for conducting web-based research and retrieving relevant information.
- I assign the role of "Web Research Agent" to the agent, ensuring it focuses on researching destinations and finding related images.
- The **goal** is set to perform thorough and comprehensive research with a visual focus.
- I specify **attributes** like diligence, thoroughness, and visual-focus to guide the agent's behavior during tasks.
- I load the OpenAI model using `LoadModel.load_openai_model()` to empower the agent with natural language processing capabilities.
- I include tools like:
- **SerperSearch**: For detailed web searches.
- **WikiArticles**: To fetch related articles for context.
- **WikiImages**: To search and retrieve relevant images.
Travel Agent:
- Here, I define the **Travel Agent**, which is designed to assist travelers with their queries, focusing on searching for flights and retrieving weather data.
- The **role** assigned to the agent is "Travel Agent," ensuring its primary function is to help travelers.
- The **goal** is set to assist travelers by providing relevant information such as flight details and weather conditions.
- I specify **attributes** like friendliness, hard work, and attention to detail to ensure the agent delivers thorough and useful responses.
- I load the OpenAI model using `LoadModel.load_openai_model()` to provide natural language processing capabilities.
- I include tools like:
- **SearchFlights**: A tool for searching and retrieving flight information using the Amadeus API.
- **GetWeatherData**: A tool for fetching real-time weather data from weather.com API.
Reporter Agent:
- Here, I define the **Travel Report Agent**, which aggregates data from various agents like the Web Search Agent and Travel Agent to generate comprehensive and summarized travel reports.
- The **role** assigned to the agent is "Travel Report Agent," ensuring its focus on collecting and synthesizing travel-related information.
- The **goal** is to write detailed, visual-oriented, and comprehensive travel reports based on inputs from other agents, covering destination details, dates, weather reports, flight information, and events at the location.
- I specify **attributes** like friendliness, hard work, visual focus, and attention to detail to ensure the agent delivers well-structured, insightful, and informative reports.
- I load the OpenAI model using `LoadModel.load_openai_model()` to provide natural language processing and text generation capabilities.
- The agent relies on tools and data from other agents, such as:
- **Web Search Agent**: Provides destination details and event-related information.
- **Travel Agent**: Supplies weather reports and flight information.
### Now let's talk about Tools
Wiki Image Search Tool:
- Here, I define the **WikiImages tool**, which is responsible for searching images from Wikipedia using **TaskflowAI's WikipediaTools**.
- The **goal** of the tool is to fetch relevant images based on specific queries or topics.
- It leverages **WikipediaTools** from TaskflowAI to access and retrieve image data from Wikipedia.
Wiki Article Search Tool:
- Here, I define the **WikiArticles tool**, which is responsible for fetching articles from Wikipedia using **TaskflowAI's WikipediaTools**.
- The **goal** of the tool is to retrieve relevant articles based on specific queries or topics.
- It utilizes **WikipediaTools** from TaskflowAI to access and fetch article data from Wikipedia.
Serper Search Tool:
- Here, I define the **SerperSearch tool**, which is responsible for performing web searches using **TaskflowAI's WebTools**.
- The **goal** of the tool is to retrieve search results from the web based on specific queries or topics.
- It leverages **WebTools** from TaskflowAI to perform search operations using the SerperSearch API.
Weather Fetching Tool:
- Here, I define the **GetWeatherData tool**, which is responsible for fetching weather data using **TaskflowAI's WebTools** and the **Weather.com API**.
- The **goal** of the tool is to retrieve weather-related information based on location or other relevant queries.
- It utilizes **WebTools** from TaskflowAI to interact with the Weather.com API and fetch weather data.
Search Fligts Tool:
- Here, I define the **SearchFlights tool**, which is responsible for searching flights using **TaskflowAI's AmadeusTools** and the **Amadeus API**.
- The **goal** of the tool is to retrieve flight information based on specific queries such as origin, destination, dates, and other relevant parameters.
- It leverages **AmadeusTools** from TaskflowAI to interact with the Amadeus API for flight searches.
### Ok, so now let's talk the `app.py` where everything is bought together (see how tasks are defined, to later engage with UI)
Research Destination Task (streamlit UI):
- In this code snippet, I import the necessary modules from TaskflowAI, including **Task**, **set_verbosity**, and **Web Research Agent** from `src.agentic.agents.web_research_agent`.
- The `research_destination` function takes a **destination** and **interests** as input to gather detailed information about the destination.
- The function utilizes the **WebResearchAgent**, which combines tools like **WikipediaTools** (for article and image searches) and **SerperSearch** (for web searches) to extract relevant data.
- The `Task.create` method invokes the **WebResearchAgent** to fetch high-quality images, attraction details, and relevant activities based on user interests.
- The research output is formatted in clean markdown, ensuring well-structured and informative reports.
Research Events Task (streamlit UI):
- The `research_events` function utilizes the **WebResearchAgent** imported from `src.agentic.agents.web_research_agent`.
- The **WebResearchAgent** employs tools like **WikipediaTools** (for image searches) and **SerperSearch** (for web search) to collect event-related data.
- The function generates a detailed report about events in a specified **destination** during given **dates**, based on provided **interests**.
- The instruction includes:
- Event name, date, time, venue/location, ticket information, and a short description.
- Images formatted as `` or ``.
- Ensuring URLs are complete and starting with `http://` or `https://`.
- Formatting the entire response in clean markdown with images placed naturally within the content.
Research Weather (streamlit UI):
- The `research_weather` function uses the **TravelAgent** imported from `src.agentic.agents.travel_agent`.
- The **TravelAgent** employs tools like **GetWeatherData**, which fetches weather information using the Weather.com API.
- The function generates a weather report for a specific **destination** and **dates**.
- The instruction includes:
- Temperature ranges.
- Precipitation chances.
- General weather patterns.
- Recommendations for clothing and gear.
Search Flights Task (streamlit UI):
- The `search_flights` function uses the **TravelAgent**, imported from `src.agentic.agents.travel_agent`.
- The **TravelAgent** utilizes tools like **SearchFlights**, which leverages the Amadeus API to search for flight options.
- The function identifies flights from a **current location** to a **destination** on specific **dates**.
- The instruction specifies:
- Finding the top 3 affordable and convenient flight options.
- Providing concise bullet-point details for each option.
Write Travel Report Task (streamlit UI):
- The `write_travel_report` function uses the **ReporterAgent**, imported from `src.agentic.agents.reporter_agent`.
- The **ReporterAgent** combines multiple reports (destination, events, weather, and flight reports) into a single comprehensive travel report.
- The instruction for creating the travel report includes:
- Retaining all images from the destination and events reports.
- Organizing information with a clear and logical structure.
- Preserving all markdown formatting.
- Ensuring images are properly displayed with captions.
- Incorporating all essential information from each individual report section.
### Have a Look (How it Appears via UI While Running the `Main Pipeline`)
* end to `main pipeline`
### Deployment Pipeline
See the **deployment diagram** to gain a better understanding of the infrastructure and components involved in the deployment process.
After completing the **main pipeline**, it's now time to deploy it on the cloud. The steps below outline the process for deploying the Dockerized application to AWS using GitHub Actions **Self Hosted Runner**.
Prerequisites:
- Access to AWS.
- Necessary API keys and secrets stored securely as GitHub secrets.
- SSH access to the EC2 instance with an EC2 key pair's PGP key added to GitHub secrets.
Steps to Deploy:
1. **Checkout the Code**
Pull the latest code from the `main` branch to ensure the most up-to-date version of the application is deployed.
2. **Verify Docker Setup**
Confirm that Docker is installed and properly configured on the self-hosted runner by checking permissions and verifying the Docker daemon.
3. **Build the Docker Image**
Create a Docker image for the Streamlit application using the necessary build arguments like API keys.
4. **Authenticate to AWS**
Log in to AWS using the provided credentials to access the Elastic Container Registry (ECR).
5. **Push Docker Image to AWS ECR**
Tag the Docker image and push it to AWS Elastic Container Registry for deployment.
6. **Deploy to EC2 Instance**
- Pull the latest Docker image from AWS ECR onto the EC2 instance.
- Stop and remove any running instance of the application.
- Run the updated Docker container with the necessary environment variables and expose it on the desired port.
Ensure you store all the API keys (e.g., `OPENAI_API_KEY`, `WEATHER_API_KEY`, `SERPER_API_KEY`, `AMADEUS_API_KEY`, `AMADEUS_API_SECRET`) securely in GitHub secrets.
Ensure you add your EC2 key pair's PGP key to GitHub secrets to allow SSH access to the EC2 instance.
If you want to see the detailed YAML configuration file for this workflow, navigate to `.github/workflows/deploy.yml`.
If you need **Docker** and **GitHub Self-Hosted Runner** commands, I have provided them in the `scripts.sh` file. However, note that GitHub **Self-Hosted Runner** commands are different for every account. Go to -> **Repo Settings** -> **Actions** -> **Runners** -> New Self-Hosted Runner.
### Some shots (after deploying to EC2)
---
## Guide for Developers 🌿🎇✨💚🎆🌱🎇✨💚🎆
Dear Developers,
To create a similar project, set up your environment using Python 3.10 or above with Conda:
```bash
conda create -p your_env_name python=3.10
```
Activate the env:
```bash
conda activate your_env_path
```
Then, install the required packages:
```bash
pip install -r requirements.txt
```
I recommend using TaskflowAI here because of its modularity design, which is crucial for building scalable AI/ML applications. When productionizing AI or ML apps, having a modular design from the beginning is essential. So, I recommend following the main pipeline and deployment steps as outlined.
You can fork this repo as TaskflowAI is simple and easy to understand. Here is the documentation link: [TaskflowAI Documentation](https://www.taskflowai.org/). Feel free to explore more or contribute. It provides tools to work with multi-AI agents and multi-agent system design easily, and there are also other frameworks such as Langchain's **langgraph**, **crewai** etc, you can use those too.
Happy coding and building multi-ai-agent-system! 🎉💚
### Project tree structure
```bash
├── .github/workflows/
│ └── deploy.yml
├── deployment/
│ └── app.py
├── docs/
│ ├── Agentic RAG Pipeline.md
│ └── Types of Agentic RAG.md
├── flowcharts/
│ └── project_pipeline.jpg
├── log/
│ └── timestamp(log)
├── notebooks/
│ └── TripPlanner_Multi_AI_Agent_Experimental.ipynb
├── src/agentic/
│ ├── agents/
│ │ ├── reporter_agent.py
│ │ ├── travel_agent.py
│ │ └── web_research_agent.py
│ ├── exception/
│ │ └── __init__.py
│ ├── logger/
│ │ └── __init__.py
│ ├── tools/
│ │ ├── get_weather_data.py
│ │ ├── search_articles.py
│ │ ├── search_flights.py
│ │ ├── search_images.py
│ │ └── serper_search.py
│ └── utils/
│ ├── __init__.py
│ └── main_utils.py
├── .gitignore
├── demo.py
├── LICENSE
├── README.md
├── requirements.txt
├── scripts.sh
├── setup.py
└── template.py
```
## 📜 License
This project is licensed under the **MIT License**.
You are free to use, modify, and share this project, as long as proper credit is given to the original contributors.
For more details, check the [LICENSE](LICENSE) file. 🏛️