Pydantic AI + Web Scraper + Llama 3.3 Python = Powerful AI Research Agent
In this story, I have a super quick tutorial showing you how to create a multi-agent chatbot with Pydantic AI, Web Scraper and Llama 3.3 to make a powerful Agent Chatbot for your business or personal use. In Retrieval-Augmented Generation (RAG) and large language model (LLM)--based workflows, structured output improves accuracy and clarity and makes the data easier to understand. Many of us know how frustrating it can be to validate or convert data into the right format. When working with interface data, you deal with complex data formats. If you’re not careful, you could end up with bugs that are really hard to find. That’s where Pydantic comes in. It is a well-known tool for data validation and plays a key role behind the scenes. OpenAI, Anthropic, LangChain, and LlamaIndex all use Pydantic as a core component, responsible for important functions such as data validation. Not long ago, the Pydantic team launched PydanticAI, an AI agent framework based on Pydantic. It is designed to simplify the complexity of AI application development and address various pain points in AI agent development. On Friday, December 6, Eastern Time, Meta announced the launch of a new Llama series generative AI model: Llama 3.3 with 7 billion parameters, also known as Llama 3.3 70B. CEO Zuckerberg said on his social media Instagram that this is the last major AI model update this year, and the next step will be the debut of Llama 4 next year. Llama 3.3 is now available for download from online sources such as the oLlama official website and the AI development platform Hugging Face. Llama 3.3 outperformed Google’s Gemini 1.5 Pro, OpenAI’s GPT-4o, and Amazon’s Nova Pro released earlier this week in industry benchmarks. Zuckerberg said this is the last major AI model update this year, and the next step will be the appearance of Llama 4 next year. So, Let me give you a quick demo of a live chatbot to show you what I mean. Check Video : Link Let me ask a simple: What is the latest LLM released this year If you look at how the Pydantic AI generates the output, you will see that when I ask a question and click on the search button, the research function retrieves the current date, passes the query and date to the AI agent (search_agent), and fetches search results using the Tavily client. The AI agent processes these results organizes them into structured fields (ResearchResult), and returns summarized content, including a title, main article, and bullet points. This streamlined system combines AI capabilities with a user-friendly interface to deliver concise and visually appealing information retrieval and summarization. In this video, we will discuss what Pydantic AI is, what are the features of Pydantic AI, the differences between Langchain, llamaindex and Pydantic AI and how Pydantic AI can be used to create a super AI Agent. Before we start!
In this story, I have a super quick tutorial showing you how to create a multi-agent chatbot with Pydantic AI, Web Scraper and Llama 3.3 to make a powerful Agent Chatbot for your business or personal use.
In Retrieval-Augmented Generation (RAG) and large language model (LLM)--based workflows, structured output improves accuracy and clarity and makes the data easier to understand.
Many of us know how frustrating it can be to validate or convert data into the right format. When working with interface data, you deal with complex data formats. If you’re not careful, you could end up with bugs that are really hard to find.
That’s where Pydantic comes in. It is a well-known tool for data validation and plays a key role behind the scenes. OpenAI, Anthropic, LangChain, and LlamaIndex all use Pydantic as a core component, responsible for important functions such as data validation.
Not long ago, the Pydantic team launched PydanticAI, an AI agent framework based on Pydantic. It is designed to simplify the complexity of AI application development and address various pain points in AI agent development.
On Friday, December 6, Eastern Time, Meta announced the launch of a new Llama series generative AI model: Llama 3.3 with 7 billion parameters, also known as Llama 3.3 70B. CEO Zuckerberg said on his social media Instagram that this is the last major AI model update this year, and the next step will be the debut of Llama 4 next year.
Llama 3.3 is now available for download from online sources such as the oLlama official website and the AI development platform Hugging Face.
Llama 3.3 outperformed Google’s Gemini 1.5 Pro, OpenAI’s GPT-4o, and Amazon’s Nova Pro released earlier this week in industry benchmarks. Zuckerberg said this is the last major AI model update this year, and the next step will be the appearance of Llama 4 next year.
So, Let me give you a quick demo of a live chatbot to show you what I mean.
Check Video : Link
Let me ask a simple: What is the latest LLM released this year If you look at how the Pydantic AI generates the output, you will see that when I ask a question and click on the search button, the research function retrieves the current date, passes the query and date to the AI agent (search_agent), and fetches search results using the Tavily client. The AI agent processes these results organizes them into structured fields (ResearchResult), and returns summarized content, including a title, main article, and bullet points. This streamlined system combines AI capabilities with a user-friendly interface to deliver concise and visually appealing information retrieval and summarization.
In this video, we will discuss what Pydantic AI is, what are the features of Pydantic AI, the differences between Langchain, llamaindex and Pydantic AI and how Pydantic AI can be used to create a super AI Agent.
