# 🍊 Jupy-Juice
> Your AI-powered coding companion for Jupyter notebooks
Build on top of the [Pydantic AI framework](https://ai.pydantic.dev/), and inspired by amazing [nbdev](https://nbdev.fast.ai/) library and it's creators, this library features:
- AI powered chat with your custom agent inside the notebook.
- By defauld agents will:
- have a memory of the code you have written so far.
- create new code based on your prompt
- Default agent is provided with a basic system prompt and tools.
- For more details on how to customize the assistant see the [Pydantic AI documentation](https://ai.pydantic.dev/).
- Add new tools to the assistant
<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->
## Installation
``` bash
pip install git+https://github.com/ndendic/jupy-juice.git
```
or
``` bash
pip install jupy-juice
```
## Configuration
In your environment variables:
1. Set `PYDANTIC_AI_MODEL` in your environment variables
2. Set your model API key - ie. `OPENAI_API_KEY`
### 2. Usage
Import the jupyter_assistant.core module:
``` python
from jupy_juice.core import *
```
Start prompting by adding the `%%prompt` cell magic:
``` python
%%prompt
Hello, how are you?
```
Hello! I’m here to assist you with your tasks. How can I help you today?
jupyter_assistant.core works with default notebook_agent that has basic
create_cell tool and short system_prompt.
``` python
from IPython.display import display, Markdown
display(Markdown(system_prompt))
notebook_agent
```
You are a helpful assistant that operates in a Jupyter notebook. Your
regular text responses are rendered as cell output. You can create new
cells, edit existing cells, and run code. You can also use tools to help
you with your tasks. Today’s date is 2025-01-01.
Agent(model=OpenAIModel(model_name='gpt-4o'), name='agent', end_strategy='early', model_settings=None)
Let’s see how you can create a custom agent and set it as current agent.
Here we’ll use `model` we set earlier and
[`set_agent`](https://ndendic.github.io/jupy-juice/core.html#set_agent)
function to set it as current agent.
``` python
from pydantic_ai import Agent
# Configure with custom system prompt
custom_prompt = system_prompt + """
Please always add emojis to your responses where possible.
You use joining-teacher style of speech but also try to be concise.
"""
my_agent = Agent(model, system_prompt=custom_prompt)
# Set as current agent
set_agent(my_agent)
```
Agent(model=OpenAIModel(model_name='gpt-4o'), name=None, end_strategy='early', model_settings=None)
``` python
%%prompt
Hello, what are the things you can do for me?
```
Hello! I’m here to assist you with various tasks. Here are some of the
things I can do:
1. **Code Assistance**: Help you write, debug, and understand code.
2. **Notebook Management**: Create, modify, and organize Jupyter
notebook cells.
3. **Data Analysis**: Assist with data manipulation, analysis, and
visualization.
4. **Explain Concepts**: Provide explanations for various programming,
data science, or other technical concepts.
5. **Tool Usage**: Show you how to use libraries and tools effectively
in your projects.
6. **Research**: Provide information on a wide range of topics or find
specific details that you need.
7. **Learning**: Assist with learning new programming languages or
technologies by providing guidance, examples, and exercises.
Let me know how I can assist you further!
`last_prompt_result` variable will be automatically created and will
contain the result of the prompt but also some other useful information.
``` python
last_prompt_result.all_messages()
```
[ModelRequest(parts=[UserPromptPart(content='Here is the context of new notebook cells that were added:\nCell[1]:\nSource:\n## Installation\n\n```bash\npip install git+https://github.com/ndendic/jupy-juice.git\n```\nor\n\n```bash\npip install jupy-juice\n```\n\nOutputs:\nNone\n\nCell[2]:\nSource:\n## Configuration\n\nIn your environment variables:\n\n1. Set `PYDANTIC_AI_MODEL` in your environment variables\n2. Set your model API key - ie. `OPENAI_API_KEY`\nOutputs:\nNone\n\nCell[3]:\nSource:\n### 2. Usage\n\nImport the jupyter_assistant.core module:\n\nOutputs:\nNone\n\nCell[4]:\nSource:\nfrom jupy_juice.core import *\nOutputs:\n[{\'ename\': \'ModuleNotFoundError\', \'evalue\': "No module named \'jupyjuice\'", \'output_type\': \'error\', \'traceback\': [\'\\x1b[0;31m---------------------------------------------------------------------------\\x1b[0m\', \'\\x1b[0;31mModuleNotFoundError\\x1b[0m Traceback (most recent call last)\', \'Cell \\x1b[0;32mIn[1], line 1\\x1b[0m\\n\\x1b[0;32m----> 1\\x1b[0m \\x1b[38;5;28;01mfrom\\x1b[39;00m \\x1b[38;5;21;01mjupyjuice\\x1b[39;00m\\x1b[38;5;21;01m.\\x1b[39;00m\\x1b[38;5;21;01mcore\\x1b[39;00m \\x1b[38;5;28;01mimport\\x1b[39;00m \\x1b[38;5;241m*\\x1b[39m\\n\', "\\x1b[0;31mModuleNotFoundError\\x1b[0m: No module named \'jupyjuice\'"]}]\n\nCell[5]:\nSource:\nStart prompting by adding the `%%prompt` cell magic:\nOutputs:\nNone', timestamp=datetime.datetime(2025, 1, 1, 18, 28, 12, 950409, tzinfo=datetime.timezone.utc), part_kind='user-prompt')], kind='request'),
ModelResponse(parts=[TextPart(content='I understand the new notebook context. How can I help?', part_kind='text')], timestamp=datetime.datetime(2025, 1, 1, 18, 28, 12, 950489, tzinfo=datetime.timezone.utc), kind='response'),
ModelRequest(parts=[UserPromptPart(content='Hello, how are you?', timestamp=datetime.datetime(2025, 1, 1, 18, 28, 12, 951411, tzinfo=datetime.timezone.utc), part_kind='user-prompt')], kind='request'),
ModelResponse(parts=[TextPart(content="Hello! I'm here to assist you with your tasks. How can I help you today?", part_kind='text')], timestamp=datetime.datetime(2025, 1, 1, 18, 28, 13, tzinfo=datetime.timezone.utc), kind='response'),
ModelRequest(parts=[UserPromptPart(content='Here is the context of new notebook cells that were added:\nCell[7]:\nSource:\njupyter_assistant.core works with default notebook_agent that has basic create_cell tool and short system_prompt. \nOutputs:\nNone\n\nCell[8]:\nSource:\nfrom IPython.display import display, Markdown\n\ndisplay(Markdown(system_prompt))\nnotebook_agent\nOutputs:\n[{\'data\': {\'text/markdown\': [\'\\n\', \'You are a helpful assistant that operates in a Jupyter notebook.\\n\', \'Your regular text responses are rendered as cell output.\\n\', \'You can create new cells, edit existing cells, and run code.\\n\', \'You can also use tools to help you with your tasks.\\n\', "Today\'s date is 2024-12-31.\\n"], \'text/plain\': [\'<IPython.core.display.Markdown object>\']}, \'metadata\': {}, \'output_type\': \'display_data\'}, {\'data\': {\'text/plain\': ["Agent(model=OpenAIModel(model_name=\'gpt-4o\'), name=\'agent\', end_strategy=\'early\', model_settings=None)"]}, \'execution_count\': None, \'metadata\': {}, \'output_type\': \'execute_result\'}]\n\nCell[9]:\nSource:\nLet\'s see how you can create a custom agent and set it as current agent.\nHere we\'ll use `model` we set earlier and `set_agent` function to set it as current agent.\nOutputs:\nNone', timestamp=datetime.datetime(2025, 1, 1, 18, 28, 13, 843879, tzinfo=datetime.timezone.utc), part_kind='user-prompt')], kind='request'),
ModelResponse(parts=[TextPart(content='I understand the new notebook context. How can I help?', part_kind='text')], timestamp=datetime.datetime(2025, 1, 1, 18, 28, 13, 843889, tzinfo=datetime.timezone.utc), kind='response'),
ModelRequest(parts=[UserPromptPart(content='Hello, what are the things you can do for me?', timestamp=datetime.datetime(2025, 1, 1, 18, 28, 13, 844030, tzinfo=datetime.timezone.utc), part_kind='user-prompt')], kind='request'),
ModelResponse(parts=[TextPart(content="Hello! I'm here to assist you with various tasks. Here are some of the things I can do:\n\n1. **Code Assistance**: Help you write, debug, and understand code.\n2. **Notebook Management**: Create, modify, and organize Jupyter notebook cells.\n3. **Data Analysis**: Assist with data manipulation, analysis, and visualization.\n4. **Explain Concepts**: Provide explanations for various programming, data science, or other technical concepts.\n5. **Tool Usage**: Show you how to use libraries and tools effectively in your projects.\n6. **Research**: Provide information on a wide range of topics or find specific details that you need.\n7. **Learning**: Assist with learning new programming languages or technologies by providing guidance, examples, and exercises.\n\nLet me know how I can assist you further!", part_kind='text')], timestamp=datetime.datetime(2025, 1, 1, 18, 28, 14, tzinfo=datetime.timezone.utc), kind='response')]
From the example above, you can see that in addition to the standard
system prompt, agent will also get the context that provides notebook
cells created so far with the whole chat history.
This context provides all the cells since the notebook was opened and
any cells that we created between the last prompt we run and the current
prompt.
This is useful for creating more complex agents that can use the history
of the conversation to create more accurate responses.
Now let’s ask our agent to create us some code.
``` python
%%prompt
can you create me a pydantic model to store User data?
```
I have created a Pydantic model to store User data. You can find it in
the new code cell. Here it is:
``` python
from pydantic import BaseModel
class User(BaseModel):
user_id: int
username: str
email: str
is_active: bool = True
# Example usage:
# user = User(user_id=1, username='john_doe', email='john@example.com')
# print(user)
```
You can create instances of this model to store user information with
fields for `user_id`, `username`, `email`, and an optional `is_active`
status.
``` python
from pydantic import BaseModel
class User(BaseModel):
user_id: int
username: str
email: str
is_active: bool = True
# Example usage:
# user = User(user_id=1, username='john_doe', email='john@example.com')
# print(user)
```
Let’s check the last 3 messages to see how agent utilized the tool.
``` python
last_prompt_result.all_messages()[-3:]
```
[ModelResponse(parts=[ToolCallPart(tool_name='create_cell', args=ArgsJson(args_json='{"content":"from pydantic import BaseModel\\n\\nclass User(BaseModel):\\n user_id: int\\n username: str\\n email: str\\n is_active: bool = True\\n\\n# Example usage:\\n# user = User(user_id=1, username=\'john_doe\', email=\'john@example.com\')\\n# print(user)","cell_type":"code"}'), tool_call_id='call_U4ayyEDdMn0tSDAlJyxY7uls', part_kind='tool-call')], timestamp=datetime.datetime(2025, 1, 1, 18, 28, 15, tzinfo=datetime.timezone.utc), kind='response'),
ModelRequest(parts=[ToolReturnPart(tool_name='create_cell', content="Created new code with content: from pydantic import BaseModel\n\nclass User(BaseModel):\n user_id: int\n username: str\n email: str\n is_active: bool = True\n\n# Example usage:\n# user = User(user_id=1, username='john_doe', email='john@example.com')\n# print(user)", tool_call_id='call_U4ayyEDdMn0tSDAlJyxY7uls', timestamp=datetime.datetime(2025, 1, 1, 18, 28, 17, 304, tzinfo=datetime.timezone.utc), part_kind='tool-return')], kind='request'),
ModelResponse(parts=[TextPart(content="I have created a Pydantic model to store User data. You can find it in the new code cell. Here it is:\n\n```python\nfrom pydantic import BaseModel\n\nclass User(BaseModel):\n user_id: int\n username: str\n email: str\n is_active: bool = True\n\n# Example usage:\n# user = User(user_id=1, username='john_doe', email='john@example.com')\n# print(user)\n```\n\nYou can create instances of this model to store user information with fields for `user_id`, `username`, `email`, and an optional `is_active` status.", part_kind='text')], timestamp=datetime.datetime(2025, 1, 1, 18, 28, 17, tzinfo=datetime.timezone.utc), kind='response')]
Let’s ask our agent to summarize our conversation so far.
``` python
Can you please summarize our conversation so far?
```
Certainly! Here’s a summary of our conversation:
1. **Setup Instructions**:
- You shared notebook cells with installation and configuration
instructions for a package related to Jupyter.
2. **Module Import Attempts**:
- There was an attempt to import a module that resulted in a
`ModuleNotFoundError`.
3. **Agent Introduction**:
- You provided details about a Jupyter agent and how to utilize it
within the notebook.
4. **Creating Custom Agents**:
- Information was given on setting up a custom agent using
previously configured models.
5. **Creating a Pydantic User Model**:
- I created a Pydantic model for storing user data with fields for
user ID, username, email, and active status. You also added a
similar model with additional fields.
6. **Review of Messages and Tool Utilization**:
- You reviewed messages and saw how tools were utilized to create
and retrieve information in the notebook.
7. **Request for Summary**:
- You asked for a summary of our entire conversation.
If there’s anything specific you’d like to discuss or need further
assistance with, feel free to let me know!
If there are certain cells that you don’t want to include in the
summary, you can add the `#| exclude` parameter to exclude them.
``` python
def multiply_numbers(a, b):
return a * b
```
``` python
%%prompt
Do we have any multiplication functions in this notebook?
```
As of the information from our conversation and the provided notebook
context, there don’t appear to be any specific multiplication functions
defined in the notebook cells. If you’d like, I can help you create a
multiplication function. Would you like me to do that?
You can also check the usage of the last prompt.
``` python
last_prompt_result.usage()
```
Usage(requests=1, request_tokens=4224, response_tokens=51, total_tokens=4275, details={'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0, 'cached_tokens': 3968})
Raw data
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"requires_python": ">=3.7",
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"author": "ndendic",
"author_email": "Nikola Dendic <ndendic@gmail.com>",
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"platform": null,
"description": "# \ud83c\udf4a Jupy-Juice\n\n> Your AI-powered coding companion for Jupyter notebooks\n\nBuild on top of the [Pydantic AI framework](https://ai.pydantic.dev/), and inspired by amazing [nbdev](https://nbdev.fast.ai/) library and it's creators, this library features:\n- AI powered chat with your custom agent inside the notebook.\n- By defauld agents will: \n - have a memory of the code you have written so far.\n - create new code based on your prompt\n- Default agent is provided with a basic system prompt and tools.\n- For more details on how to customize the assistant see the [Pydantic AI documentation](https://ai.pydantic.dev/).\n- Add new tools to the assistant\n\n<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->\n\n## Installation\n\n``` bash\npip install git+https://github.com/ndendic/jupy-juice.git\n```\n\nor\n\n``` bash\npip install jupy-juice\n```\n\n## Configuration\n\nIn your environment variables:\n\n1. Set `PYDANTIC_AI_MODEL` in your environment variables\n2. Set your model API key - ie. `OPENAI_API_KEY`\n\n### 2. Usage\n\nImport the jupyter_assistant.core module:\n\n``` python\nfrom jupy_juice.core import *\n```\n\nStart prompting by adding the `%%prompt` cell magic:\n\n``` python\n%%prompt\nHello, how are you?\n```\n\nHello! I\u2019m here to assist you with your tasks. How can I help you today?\n\njupyter_assistant.core works with default notebook_agent that has basic\ncreate_cell tool and short system_prompt.\n\n``` python\nfrom IPython.display import display, Markdown\n\ndisplay(Markdown(system_prompt))\nnotebook_agent\n```\n\nYou are a helpful assistant that operates in a Jupyter notebook. Your\nregular text responses are rendered as cell output. You can create new\ncells, edit existing cells, and run code. You can also use tools to help\nyou with your tasks. Today\u2019s date is 2025-01-01.\n\n Agent(model=OpenAIModel(model_name='gpt-4o'), name='agent', end_strategy='early', model_settings=None)\n\nLet\u2019s see how you can create a custom agent and set it as current agent.\nHere we\u2019ll use `model` we set earlier and\n[`set_agent`](https://ndendic.github.io/jupy-juice/core.html#set_agent)\nfunction to set it as current agent.\n\n``` python\nfrom pydantic_ai import Agent\n\n# Configure with custom system prompt\ncustom_prompt = system_prompt + \"\"\"\nPlease always add emojis to your responses where possible.\nYou use joining-teacher style of speech but also try to be concise.\n\"\"\"\nmy_agent = Agent(model, system_prompt=custom_prompt)\n\n# Set as current agent\nset_agent(my_agent)\n```\n\n Agent(model=OpenAIModel(model_name='gpt-4o'), name=None, end_strategy='early', model_settings=None)\n\n``` python\n%%prompt\nHello, what are the things you can do for me?\n```\n\nHello! I\u2019m here to assist you with various tasks. Here are some of the\nthings I can do:\n\n1. **Code Assistance**: Help you write, debug, and understand code.\n2. **Notebook Management**: Create, modify, and organize Jupyter\n notebook cells.\n3. **Data Analysis**: Assist with data manipulation, analysis, and\n visualization.\n4. **Explain Concepts**: Provide explanations for various programming,\n data science, or other technical concepts.\n5. **Tool Usage**: Show you how to use libraries and tools effectively\n in your projects.\n6. **Research**: Provide information on a wide range of topics or find\n specific details that you need.\n7. **Learning**: Assist with learning new programming languages or\n technologies by providing guidance, examples, and exercises.\n\nLet me know how I can assist you further!\n\n`last_prompt_result` variable will be automatically created and will\ncontain the result of the prompt but also some other useful information.\n\n``` python\nlast_prompt_result.all_messages()\n```\n\n [ModelRequest(parts=[UserPromptPart(content='Here is the context of new notebook cells that were added:\\nCell[1]:\\nSource:\\n## Installation\\n\\n```bash\\npip install git+https://github.com/ndendic/jupy-juice.git\\n```\\nor\\n\\n```bash\\npip install jupy-juice\\n```\\n\\nOutputs:\\nNone\\n\\nCell[2]:\\nSource:\\n## Configuration\\n\\nIn your environment variables:\\n\\n1. Set `PYDANTIC_AI_MODEL` in your environment variables\\n2. Set your model API key - ie. `OPENAI_API_KEY`\\nOutputs:\\nNone\\n\\nCell[3]:\\nSource:\\n### 2. Usage\\n\\nImport the jupyter_assistant.core module:\\n\\nOutputs:\\nNone\\n\\nCell[4]:\\nSource:\\nfrom jupy_juice.core import *\\nOutputs:\\n[{\\'ename\\': \\'ModuleNotFoundError\\', \\'evalue\\': \"No module named \\'jupyjuice\\'\", \\'output_type\\': \\'error\\', \\'traceback\\': [\\'\\\\x1b[0;31m---------------------------------------------------------------------------\\\\x1b[0m\\', \\'\\\\x1b[0;31mModuleNotFoundError\\\\x1b[0m Traceback (most recent call last)\\', \\'Cell \\\\x1b[0;32mIn[1], line 1\\\\x1b[0m\\\\n\\\\x1b[0;32m----> 1\\\\x1b[0m \\\\x1b[38;5;28;01mfrom\\\\x1b[39;00m \\\\x1b[38;5;21;01mjupyjuice\\\\x1b[39;00m\\\\x1b[38;5;21;01m.\\\\x1b[39;00m\\\\x1b[38;5;21;01mcore\\\\x1b[39;00m \\\\x1b[38;5;28;01mimport\\\\x1b[39;00m \\\\x1b[38;5;241m*\\\\x1b[39m\\\\n\\', \"\\\\x1b[0;31mModuleNotFoundError\\\\x1b[0m: No module named \\'jupyjuice\\'\"]}]\\n\\nCell[5]:\\nSource:\\nStart prompting by adding the `%%prompt` cell magic:\\nOutputs:\\nNone', timestamp=datetime.datetime(2025, 1, 1, 18, 28, 12, 950409, tzinfo=datetime.timezone.utc), part_kind='user-prompt')], kind='request'),\n ModelResponse(parts=[TextPart(content='I understand the new notebook context. How can I help?', part_kind='text')], timestamp=datetime.datetime(2025, 1, 1, 18, 28, 12, 950489, tzinfo=datetime.timezone.utc), kind='response'),\n ModelRequest(parts=[UserPromptPart(content='Hello, how are you?', timestamp=datetime.datetime(2025, 1, 1, 18, 28, 12, 951411, tzinfo=datetime.timezone.utc), part_kind='user-prompt')], kind='request'),\n ModelResponse(parts=[TextPart(content=\"Hello! I'm here to assist you with your tasks. How can I help you today?\", part_kind='text')], timestamp=datetime.datetime(2025, 1, 1, 18, 28, 13, tzinfo=datetime.timezone.utc), kind='response'),\n ModelRequest(parts=[UserPromptPart(content='Here is the context of new notebook cells that were added:\\nCell[7]:\\nSource:\\njupyter_assistant.core works with default notebook_agent that has basic create_cell tool and short system_prompt. \\nOutputs:\\nNone\\n\\nCell[8]:\\nSource:\\nfrom IPython.display import display, Markdown\\n\\ndisplay(Markdown(system_prompt))\\nnotebook_agent\\nOutputs:\\n[{\\'data\\': {\\'text/markdown\\': [\\'\\\\n\\', \\'You are a helpful assistant that operates in a Jupyter notebook.\\\\n\\', \\'Your regular text responses are rendered as cell output.\\\\n\\', \\'You can create new cells, edit existing cells, and run code.\\\\n\\', \\'You can also use tools to help you with your tasks.\\\\n\\', \"Today\\'s date is 2024-12-31.\\\\n\"], \\'text/plain\\': [\\'<IPython.core.display.Markdown object>\\']}, \\'metadata\\': {}, \\'output_type\\': \\'display_data\\'}, {\\'data\\': {\\'text/plain\\': [\"Agent(model=OpenAIModel(model_name=\\'gpt-4o\\'), name=\\'agent\\', end_strategy=\\'early\\', model_settings=None)\"]}, \\'execution_count\\': None, \\'metadata\\': {}, \\'output_type\\': \\'execute_result\\'}]\\n\\nCell[9]:\\nSource:\\nLet\\'s see how you can create a custom agent and set it as current agent.\\nHere we\\'ll use `model` we set earlier and `set_agent` function to set it as current agent.\\nOutputs:\\nNone', timestamp=datetime.datetime(2025, 1, 1, 18, 28, 13, 843879, tzinfo=datetime.timezone.utc), part_kind='user-prompt')], kind='request'),\n ModelResponse(parts=[TextPart(content='I understand the new notebook context. How can I help?', part_kind='text')], timestamp=datetime.datetime(2025, 1, 1, 18, 28, 13, 843889, tzinfo=datetime.timezone.utc), kind='response'),\n ModelRequest(parts=[UserPromptPart(content='Hello, what are the things you can do for me?', timestamp=datetime.datetime(2025, 1, 1, 18, 28, 13, 844030, tzinfo=datetime.timezone.utc), part_kind='user-prompt')], kind='request'),\n ModelResponse(parts=[TextPart(content=\"Hello! I'm here to assist you with various tasks. Here are some of the things I can do:\\n\\n1. **Code Assistance**: Help you write, debug, and understand code.\\n2. **Notebook Management**: Create, modify, and organize Jupyter notebook cells.\\n3. **Data Analysis**: Assist with data manipulation, analysis, and visualization.\\n4. **Explain Concepts**: Provide explanations for various programming, data science, or other technical concepts.\\n5. **Tool Usage**: Show you how to use libraries and tools effectively in your projects.\\n6. **Research**: Provide information on a wide range of topics or find specific details that you need.\\n7. **Learning**: Assist with learning new programming languages or technologies by providing guidance, examples, and exercises.\\n\\nLet me know how I can assist you further!\", part_kind='text')], timestamp=datetime.datetime(2025, 1, 1, 18, 28, 14, tzinfo=datetime.timezone.utc), kind='response')]\n\nFrom the example above, you can see that in addition to the standard\nsystem prompt, agent will also get the context that provides notebook\ncells created so far with the whole chat history.\n\nThis context provides all the cells since the notebook was opened and\nany cells that we created between the last prompt we run and the current\nprompt.\n\nThis is useful for creating more complex agents that can use the history\nof the conversation to create more accurate responses.\n\nNow let\u2019s ask our agent to create us some code.\n\n``` python\n%%prompt\ncan you create me a pydantic model to store User data?\n```\n\nI have created a Pydantic model to store User data. You can find it in\nthe new code cell. Here it is:\n\n``` python\nfrom pydantic import BaseModel\n\nclass User(BaseModel):\n user_id: int\n username: str\n email: str\n is_active: bool = True\n\n# Example usage:\n# user = User(user_id=1, username='john_doe', email='john@example.com')\n# print(user)\n```\n\nYou can create instances of this model to store user information with\nfields for `user_id`, `username`, `email`, and an optional `is_active`\nstatus.\n\n``` python\nfrom pydantic import BaseModel\n\nclass User(BaseModel):\n user_id: int\n username: str\n email: str\n is_active: bool = True\n\n# Example usage:\n# user = User(user_id=1, username='john_doe', email='john@example.com')\n# print(user)\n```\n\nLet\u2019s check the last 3 messages to see how agent utilized the tool.\n\n``` python\nlast_prompt_result.all_messages()[-3:]\n```\n\n [ModelResponse(parts=[ToolCallPart(tool_name='create_cell', args=ArgsJson(args_json='{\"content\":\"from pydantic import BaseModel\\\\n\\\\nclass User(BaseModel):\\\\n user_id: int\\\\n username: str\\\\n email: str\\\\n is_active: bool = True\\\\n\\\\n# Example usage:\\\\n# user = User(user_id=1, username=\\'john_doe\\', email=\\'john@example.com\\')\\\\n# print(user)\",\"cell_type\":\"code\"}'), tool_call_id='call_U4ayyEDdMn0tSDAlJyxY7uls', part_kind='tool-call')], timestamp=datetime.datetime(2025, 1, 1, 18, 28, 15, tzinfo=datetime.timezone.utc), kind='response'),\n ModelRequest(parts=[ToolReturnPart(tool_name='create_cell', content=\"Created new code with content: from pydantic import BaseModel\\n\\nclass User(BaseModel):\\n user_id: int\\n username: str\\n email: str\\n is_active: bool = True\\n\\n# Example usage:\\n# user = User(user_id=1, username='john_doe', email='john@example.com')\\n# print(user)\", tool_call_id='call_U4ayyEDdMn0tSDAlJyxY7uls', timestamp=datetime.datetime(2025, 1, 1, 18, 28, 17, 304, tzinfo=datetime.timezone.utc), part_kind='tool-return')], kind='request'),\n ModelResponse(parts=[TextPart(content=\"I have created a Pydantic model to store User data. You can find it in the new code cell. Here it is:\\n\\n```python\\nfrom pydantic import BaseModel\\n\\nclass User(BaseModel):\\n user_id: int\\n username: str\\n email: str\\n is_active: bool = True\\n\\n# Example usage:\\n# user = User(user_id=1, username='john_doe', email='john@example.com')\\n# print(user)\\n```\\n\\nYou can create instances of this model to store user information with fields for `user_id`, `username`, `email`, and an optional `is_active` status.\", part_kind='text')], timestamp=datetime.datetime(2025, 1, 1, 18, 28, 17, tzinfo=datetime.timezone.utc), kind='response')]\n\nLet\u2019s ask our agent to summarize our conversation so far.\n\n``` python\nCan you please summarize our conversation so far?\n```\n\nCertainly! Here\u2019s a summary of our conversation:\n\n1. **Setup Instructions**:\n - You shared notebook cells with installation and configuration\n instructions for a package related to Jupyter.\n2. **Module Import Attempts**:\n - There was an attempt to import a module that resulted in a\n `ModuleNotFoundError`.\n3. **Agent Introduction**:\n - You provided details about a Jupyter agent and how to utilize it\n within the notebook.\n4. **Creating Custom Agents**:\n - Information was given on setting up a custom agent using\n previously configured models.\n5. **Creating a Pydantic User Model**:\n - I created a Pydantic model for storing user data with fields for\n user ID, username, email, and active status. You also added a\n similar model with additional fields.\n6. **Review of Messages and Tool Utilization**:\n - You reviewed messages and saw how tools were utilized to create\n and retrieve information in the notebook.\n7. **Request for Summary**:\n - You asked for a summary of our entire conversation.\n\nIf there\u2019s anything specific you\u2019d like to discuss or need further\nassistance with, feel free to let me know!\n\nIf there are certain cells that you don\u2019t want to include in the\nsummary, you can add the `#| exclude` parameter to exclude them.\n\n``` python\ndef multiply_numbers(a, b):\n return a * b\n```\n\n``` python\n%%prompt\nDo we have any multiplication functions in this notebook?\n```\n\nAs of the information from our conversation and the provided notebook\ncontext, there don\u2019t appear to be any specific multiplication functions\ndefined in the notebook cells. If you\u2019d like, I can help you create a\nmultiplication function. Would you like me to do that?\n\nYou can also check the usage of the last prompt.\n\n``` python\nlast_prompt_result.usage()\n```\n\n Usage(requests=1, request_tokens=4224, response_tokens=51, total_tokens=4275, details={'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0, 'cached_tokens': 3968})\n",
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