First, many thanks for your clear explanation. What is the best advice for a beginner when choosing between Pydantic and LangChain to start with agentive AI.
Pydantic is easy to learn and very straightforward but it is in a very early stage. Langchain and llama-index is there for quite a long time and well suited for end to end development, it has everything that we need to build an AI Agent. My suggestion would be either langchain/langgraph or llama-index. Moreover If you understand how an AI agent works then the framework does not matter, you can learn any framework in just 1 or 2 days. Try to build an agent from scratch in plain Python, it will help you a lot. Happy Learning
Develop a robust and user-friendly flight booking agent that leverages Pydantic AI for data validation, model definition, and efficient data handling. The agent should allow users to search for flights, compare prices, and book tickets seamlessly. Functional Requirements: User Interface: Search Functionality: Allow users to input the following: Origin Airport Destination Airport Departure Date Return Date (optional for one-way trips) Number of Passengers Class (Economy, Business, First) Preferred Airlines (optional) Implement robust input validation using Pydantic models to ensure data integrity. Flight Search Results: Display a list of available flights, including: Airline Flight Number Departure/Arrival Times Duration Number of Stops Prices Enable sorting and filtering of results based on price, duration, number of stops, and airline. Booking Process: Allow users to select a flight and proceed to the booking page. Collect passenger information (name, contact, etc.) using Pydantic models for data validation. Display a summary of the booking with a breakdown of costs. Implement secure payment processing integration (e.g., Stripe, PayPal). Issue electronic tickets and confirmations. Data Handling: Flight Data Acquisition: Integrate with a flight data API (e.g., Amadeus, Skyscanner, Google Flights) to retrieve real-time flight information. Implement efficient data fetching and caching mechanisms to minimize API calls and improve performance. Data Model Definition: Create Pydantic models to represent: Airports (code, name, city, country) Airlines (code, name) Flights (origin, destination, departure_time, arrival_time, duration, price, etc.) Passengers (name, contact, etc.) Bookings (flight, passengers, payment, etc.) Leverage Pydantic's data validation features to ensure data consistency and prevent errors. Pydantic AI Utilization: Data Validation: Utilize Pydantic's rich type annotations and validation rules to ensure data integrity throughout the application. Data Modeling: Define clear and concise data models using Pydantic to represent all application entities. Data Serialization/Deserialization: Leverage Pydantic's built-in serialization and deserialization capabilities for efficient data exchange between different parts of the application. Data Transformation: Use Pydantic to transform and manipulate data as needed for various operations (e.g., filtering, sorting, calculating prices). Non-Functional Requirements: Performance: The application should be highly responsive and efficient, with minimal latency during flight searches and booking processes. Usability: The user interface should be intuitive and easy to navigate, providing a seamless and enjoyable user experience. Security: Implement robust security measures to protect user data and prevent fraud. Scalability: The system should be designed to handle a growing number of users and flight searches. Maintainability: The codebase should be well-structured, documented, and easy to maintain and update.
We can say it something similar to lang graph but it deals with updating the user details in run time,,,we may or may not do with lang graph built in functionalities.
Hi bro I'm pavan im a Gen Ai engineer....I'm working to build a medical ceiling assistant. Im using The bolt .New Ai...I completely. 90% build succes but I have issue they the i got and Error Opna AI model GPT 4 and the ApI key was not working problem. How i can trouble shoot not understand can u help me on this
Best of luck and thanks Vishu for easy to understand video..🎉
Great new channel find. Nice teaching style, thx. Have been building with swarm and this flushes out the functionality that is missing.
Thank you, Vichu. Nice, clear work. PydanticAI has great potential.
Good luck from Morocco , Keep going , You can make it (y)
Thank you for the informative video and explanation. Could you please share the URL to your notebook or the code?
First, many thanks for your clear explanation.
What is the best advice for a beginner when choosing between Pydantic and LangChain to start with agentive AI.
Pydantic is easy to learn and very straightforward but it is in a very early stage.
Langchain and llama-index is there for quite a long time and well suited for end to end development, it has everything that we need to build an AI Agent.
My suggestion would be either langchain/langgraph or llama-index. Moreover If you understand how an AI agent works then the framework does not matter, you can learn any framework in just 1 or 2 days. Try to build an agent from scratch in plain Python, it will help you a lot.
Happy Learning
Kindly explain the difference/significance between sync and async in this context
can you build an ai flight booking agent and interact with a wbe ui
Please share your complete requirements.
@@learnwithvichu to use agent flight booking - to make booings -
Develop a robust and user-friendly flight booking agent that leverages Pydantic AI for data validation, model definition, and efficient data handling.
The agent should allow users to search for flights, compare prices, and book tickets seamlessly.
Functional Requirements:
User Interface:
Search Functionality:
Allow users to input the following:
Origin Airport
Destination Airport
Departure Date
Return Date (optional for one-way trips)
Number of Passengers
Class (Economy, Business, First)
Preferred Airlines (optional)
Implement robust input validation using Pydantic models to ensure data integrity.
Flight Search Results:
Display a list of available flights, including:
Airline
Flight Number
Departure/Arrival Times
Duration
Number of Stops
Prices
Enable sorting and filtering of results based on price, duration, number of stops, and airline.
Booking Process:
Allow users to select a flight and proceed to the booking page.
Collect passenger information (name, contact, etc.) using Pydantic models for data validation.
Display a summary of the booking with a breakdown of costs.
Implement secure payment processing integration (e.g., Stripe, PayPal).
Issue electronic tickets and confirmations.
Data Handling:
Flight Data Acquisition:
Integrate with a flight data API (e.g., Amadeus, Skyscanner, Google Flights) to retrieve real-time flight information.
Implement efficient data fetching and caching mechanisms to minimize API calls and improve performance.
Data Model Definition:
Create Pydantic models to represent:
Airports (code, name, city, country)
Airlines (code, name)
Flights (origin, destination, departure_time, arrival_time, duration, price, etc.)
Passengers (name, contact, etc.)
Bookings (flight, passengers, payment, etc.)
Leverage Pydantic's data validation features to ensure data consistency and prevent errors.
Pydantic AI Utilization:
Data Validation: Utilize Pydantic's rich type annotations and validation rules to ensure data integrity throughout the application.
Data Modeling: Define clear and concise data models using Pydantic to represent all application entities.
Data Serialization/Deserialization: Leverage Pydantic's built-in serialization and deserialization capabilities for efficient data exchange between different parts of the application.
Data Transformation: Use Pydantic to transform and manipulate data as needed for various operations (e.g., filtering, sorting, calculating prices).
Non-Functional Requirements:
Performance: The application should be highly responsive and efficient, with minimal latency during flight searches and booking processes.
Usability: The user interface should be intuitive and easy to navigate, providing a seamless and enjoyable user experience.
Security: Implement robust security measures to protect user data and prevent fraud.
Scalability: The system should be designed to handle a growing number of users and flight searches.
Maintainability: The codebase should be well-structured, documented, and easy to maintain and update.
@@fancypetsulove Done
Great video Vishnu. Can you share your code please? Thanks!
Nice vídeo.
PydanticAI have “state” like langgraph?
We can say it something similar to lang graph but it deals with updating the user details in run time,,,we may or may not do with lang graph built in functionalities.
Nice video. Can you please tell me which screen recording software you are using to record your videos?
I am using OBS.
@@learnwithvichu Thanks for sharing the details.
I am one of your followers in linkedin 😀....
@@learnwithvichu Nice.
Nice
What is RunCOntext? i font understand that ctx : RunCOntext code. Please explain. Thanks
Hi bro I'm pavan im a Gen Ai engineer....I'm working to build a medical ceiling assistant. Im using The bolt .New Ai...I completely. 90% build succes but I have issue they the i got and Error Opna AI model GPT 4 and the ApI key was not working problem. How i can trouble shoot not understand can u help me on this
Sure, get my mail id from GitHub and share your contact.
Can you create a video with react and ai agent connection
Sure will do..
Bro create with interface for chatting