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certainly! `pydantic` is a data validation and settings management library in python, and `pydanticai` is an extension for building ai agents using pydantic models. this tutorial will cover the basics of setting up a simple ai agent using `pydanticai`.

prerequisites
1. python installed (preferably 3.6 or higher).
2. basic understanding of python and pydantic.
3. install necessary libraries:

```bash
pip install pydantic pydanticai
```

step 1: create a basic pydantic model

first, we will define a pydantic model that represents the data structure for our ai agent. let’s build an agent that can respond to user queries.

```python
from pydantic import basemodel

class userquery(basemodel):
question: str
user_name: str
```

step 2: define the ai agent

next, we will create an ai agent class that uses the pydantic model. this agent will have a method to generate responses based on the user query.

```python
from pydanticai import ai

class simpleagent(ai):
def respond(self, user_query: userquery) - str:
a simple hardcoded response for demonstration
if "hello" in user_query.question.lower():
return f"hello, {user_query.user_name}! how can i assist you today?"
elif "bye" in user_query.question.lower():
return "goodbye! have a great day!"
else:
return "i'm not sure how to respond to that."
```

step 3: instantiate the ai agent and process user queries

now, we can create an instance of our `simpleagent` and use it to respond to user queries.

```python
def main():
agent = simpleagent()

example user query
query = userquery(question="hello!", user_name="alice")

response = agent.respond(query)
print(response)

another example
query = userquery(question="what is the weather?", user_name="bob")

response = agent.respond(query)
print(response)

if __name__ == "__main__":
main()
```

step 4: running the code

save the code above in a file ...

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