luismartinezs python-data-science-template .cursorrules file for Python

Calls to LLM models use the ell python library.

These are examples of usage:

[EXAMPLES]

Basic Prompt:

```py
@ell.complex(model="gpt-4")
def generate_story(prompt: str) -> List[Message]:
    '''You are a creative story writer''' # System prompt
    return [
        ell.user(f"Write a short story based on this prompt: {prompt}")
    ]

story : ell.Message = generate_story("A robot discovers emotions")
print(story.text)  # Access the text content of the last message
```

Multi-turn Conversation:

```py
@ell.complex(model="gpt-4")
def chat_bot(message_history: List[Message]) -> List[Message]:
    return [
        ell.system("You are a helpful assistant."),
    ] + message_history

conversation = [
    ell.user("Hello, who are you?"),
    ell.assistant("I'm an AI assistant. How can I help you today?"),
    ell.user("Can you explain quantum computing?")
]
response : ell.Message = chat_bot(conversation)
print(response.text)  # Print the assistant's response
```

Tool Usage:

```py
@ell.tool()
def get_weather(location: str) -> str:
    # Implementation to fetch weather
    return f"The weather in {location} is sunny."

@ell.complex(model="gpt-4", tools=[get_weather])
def weather_assistant(message_history: List[Message]) -> List[Message]:
    return [
        ell.system("You are a weather assistant. Use the get_weather tool when needed."),
    ] + message_history

conversation = [
    ell.user("What's the weather like in New York?")
]
response : ell.Message = weather_assistant(conversation)

if response.tool_calls:
    tool_results = response.call_tools_and_collect_as_message()
    print("Tool results:", tool_results.text)

    # Continue the conversation with tool results
    final_response = weather_assistant(conversation + [response, tool_results])
    print("Final response:", final_response.text)
```

Structured Output:

```py
from pydantic import BaseModel

class PersonInfo(BaseModel):
    name: str
    age: int

@ell.complex(model="gpt-4", response_format=PersonInfo)
def extract_person_info(text: str) -> List[Message]:
    return [
        ell.system("Extract person information from the given text."),
        ell.user(text)
    ]

text = "John Doe is a 30-year-old software engineer."
result : ell.Message = extract_person_info(text)
person_info = result.parsed
print(f"Name: {person_info.name}, Age: {person_info.age}")
```