Polar Llama

import polars as pl
from polar_llama import string_to_message, inference_async

# Create a DataFrame with system prompts and user questions
df = pl.DataFrame({
    "system_prompt": [
        "You are a helpful assistant.",
        "You are a math expert.",
        "You are a creative writer."
    ],
    "user_question": [
        "What's the weather like today?",
        "Solve x^2 + 5x + 6 = 0",
        "Write a haiku about coding"
    ]
})

# Convert both columns to messages
df = df.with_columns([
    string_to_message("system_prompt", message_type="system").alias("system_message"),
    string_to_message("user_question", message_type="user").alias("user_message")
])

# Combine messages into conversations
from polar_llama import combine_messages, inference_messages
df = df.with_columns(
    combine_messages("system_message", "user_message").alias("conversation")
)

# Run inference with combined messages
df = df.with_columns(
    inference_messages("conversation",
           provider="openai",
           model="gpt-4").alias("response")
)

print(df.select(["user_question", "response"]))

import polars as pl
from polar_llama import string_to_message, inference_async, Provider

# Load customer feedback data
feedback_df = pl.DataFrame({
    'customer_id': [101, 102, 103, 104, 105],
    'feedback': [
        'The product is amazing but shipping was slow',
        'Great quality, highly recommend!',
        'Disappointed with customer service',
        'Perfect for my needs, will buy again',
        'Product arrived damaged, requesting refund'
    ]
})

# Create sentiment analysis prompts
sentiment_prompt = """Analyze the sentiment of this customer feedback
and classify it as Positive, Negative, or Neutral.
Also provide a brief reason.

Feedback: {feedback}"""

df = feedback_df.with_columns(
    prompt=pl.format(sentiment_prompt, pl.col('feedback'))
)

# Convert to messages and run inference
df = df.with_columns(
    message=string_to_message("prompt", message_type='user')
)

df = df.with_columns(
    sentiment_analysis=inference_async('message',
                                      provider=Provider.OPENAI,
                                      model='gpt-4o-mini')
)

# Extract key insights
print(df.select(['customer_id', 'feedback', 'sentiment_analysis']))

import polars as pl
from polar_llama import string_to_message, inference_async, Provider

# Product data
products = pl.DataFrame({
    'product_name': ['Wireless Headphones', 'Smart Watch', 'Laptop Stand'],
    'features': [
        'noise-canceling, 30-hour battery, comfortable',
        'fitness tracking, heart rate monitor, waterproof',
        'adjustable height, aluminum build, cable management'
    ],
    'target_audience': ['music lovers', 'fitness enthusiasts', 'remote workers']
})

# Create dynamic prompts for each product
prompt_template = """Create a compelling product description for:
Product: {product_name}
Features: {features}
Target Audience: {target_audience}

Write in a marketing tone, 2-3 sentences."""

df = products.with_columns(
    prompt=pl.format(prompt_template,
                    pl.col('product_name'),
                    pl.col('features'),
                    pl.col('target_audience'))
)

# Convert to messages
df = df.with_columns(
    message=string_to_message("prompt", message_type='user')
)

# Generate descriptions in parallel
df = df.with_columns(
    description=inference_async('message',
                               provider=Provider.ANTHROPIC,
                               model='claude-3-haiku-20240307')
)

print(df.select(['product_name', 'description']))

import polars as pl
from polar_llama import string_to_message, inference_async, Provider

# Code snippets to review
code_samples = pl.DataFrame({
    'file_name': ['auth.py', 'database.py', 'api.py'],
    'code_snippet': [
        '''
def authenticate(password):
    if password == "admin123":
        return True
    return False
        ''',
        '''
def get_user(user_id):
    query = f"SELECT * FROM users WHERE id = {user_id}"
    return db.execute(query)
        ''',
        '''
def process_data(data):
    result = []
    for item in data:
        result.append(item * 2)
    return result
        '''
    ]
})

# Create review prompts
review_template = """Review this code for:
1. Security issues
2. Performance problems
3. Best practice violations

Code:
{code}

Provide specific suggestions for improvement."""

df = code_samples.with_columns(
    prompt=pl.format(review_template, pl.col('code_snippet'))
)

# Get AI code reviews
df = df.with_columns(
    message=string_to_message("prompt", message_type='user')
)

df = df.with_columns(
    review=inference_async('message',
                          provider=Provider.OPENAI,
                          model='gpt-4')
)

print(df.select(['file_name', 'review']))