AI/ML Functions

def embed_text(
    text: Expression,
    *,
    provider: str | Provider | None = None,
    model: str | None = None,
    **options: str,
) -> Expression:
    """
    Generate text embeddings using specified model and provider.
    
    Parameters:
    - text: Text expression to embed
    - provider: Provider to use for embedding model (e.g., "sentence_transformers", "openai")
    - model: Model name/identifier for embedding generation  
    - options: Additional options to pass for the model
    
    Returns:
    Expression: Vector embedding expression
    
    Examples:
    >>> df.select(embed_text(col("description"), model="all-MiniLM-L6-v2"))
    >>> df.select(embed_text(col("query"), provider="openai", model="text-embedding-ada-002"))
    """

def embed_image(
    image: Expression,
    *,
    provider: str | Provider | None = None,
    model: str | None = None,
    **options: str,
) -> Expression:
    """
    Generate image embeddings using specified model and provider.
    
    Parameters:
    - image: Image expression to embed
    - provider: Provider to use for embedding model (e.g., "transformers", "openai")
    - model: Model name/identifier for image embedding generation
    - options: Additional options to pass for the model
    
    Returns:
    Expression: Vector embedding expression
    
    Examples:
    >>> df.select(embed_image(col("image"), provider="transformers", model="clip-vit-base-patch32"))
    >>> df.select(embed_image(col("photo"), provider="openai", model="clip"))
    """

def llm_generate(
    input_column: Expression,
    model: str = "facebook/opt-125m",
    provider: Literal["vllm", "openai"] = "vllm",
    concurrency: int = 1,
    batch_size: int | None = None,
    num_cpus: int | None = None,
    num_gpus: int | None = None,
    **generation_config: dict[str, Any],
) -> Expression:
    """
    Generate text using large language model with specified provider.
    
    Parameters:
    - input_column: Input text expression for generation
    - model: Model identifier (default: "facebook/opt-125m")
    - provider: LLM provider ("vllm" or "openai")
    - concurrency: Number of concurrent model instances
    - batch_size: Batch size for processing
    - num_cpus: CPU resources to allocate
    - num_gpus: GPU resources to allocate
    - generation_config: Model parameters (temperature, max_tokens, etc.)
    
    Returns:
    Expression: Generated text expression
    
    Examples:
    >>> df.select(llm_generate(col("question"), model="gpt-4o", provider="openai"))
    >>> df.select(llm_generate(col("prompt"), model="facebook/opt-125m", provider="vllm", temperature=0.7))
    """

class DataFrame:
    def to_torch_map_dataset(
        self,
        shard_strategy: Optional[Literal["file"]] = None,
        world_size: Optional[int] = None,
        rank: Optional[int] = None,
    ) -> "torch.utils.data.Dataset":
        """
        Convert DataFrame to PyTorch map-style dataset.
        
        Parameters:
        - shard_strategy: Strategy for distributed sharding
        - world_size: Total number of processes for distributed training
        - rank: Current process rank for distributed training
        
        Returns:
        torch.utils.data.Dataset: PyTorch map-style dataset
        """
    
    def to_torch_iter_dataset(
        self,
        shard_strategy: Optional[Literal["file"]] = None,
        world_size: Optional[int] = None,
        rank: Optional[int] = None,
    ) -> "torch.utils.data.IterableDataset":
        """
        Convert DataFrame to PyTorch iterable dataset.
        
        Parameters:
        - shard_strategy: Strategy for distributed sharding
        - world_size: Total number of processes for distributed training
        - rank: Current process rank for distributed training
        
        Returns:
        torch.utils.data.IterableDataset: PyTorch iterable dataset
        """

def get_device() -> "torch.device":
    """
    Get optimal PyTorch device (CUDA GPU if available, otherwise CPU).
    
    Returns:
    torch.device: Optimal device for computation
    """

def load_openai(name: str | None = None, **options) -> Provider:
    """Load OpenAI provider with configuration."""

def load_sentence_transformers(name: str | None = None, **options) -> Provider:
    """Load Sentence Transformers provider with configuration."""

def load_transformers(name: str | None = None, **options) -> Provider:
    """Load Hugging Face Transformers provider with configuration."""

def load_lm_studio(name: str | None = None, **options) -> Provider:
    """Load LM Studio provider with configuration."""

def load_provider(provider: str, name: str | None = None, **options) -> Provider:
    """Load provider by name with configuration."""

import daft
from daft import col
from daft.functions import embed_image

# Dataset with image paths or URLs
images_df = daft.from_pydict({
    "image_id": [1, 2, 3, 4],
    "image_path": [
        "s3://bucket/images/product1.jpg",
        "s3://bucket/images/product2.jpg", 
        "s3://bucket/images/product3.jpg",
        "s3://bucket/images/query.jpg"
    ]
})

# Load and decode images
decoded_images = images_df.select(
    col("image_id"),
    col("image_path").url.download().image.decode().alias("image")
)

# Generate image embeddings for visual similarity
embedded_images = decoded_images.select(
    col("image_id"),
    col("image"),
    embed_image(col("image"), provider="transformers", model="clip-vit-base-patch32").alias("image_embedding")
)

# Compute visual similarity (using cosine distance)
query_embedding = embedded_images.filter(col("image_id") == 4).collect()[0]["image_embedding"][0]

similarities = embedded_images.select(
    col("image_id"),
    col("image_embedding").embedding.cosine_distance(lit(query_embedding)).alias("similarity")
).filter(col("image_id") != 4).sort("similarity").collect()

import torch
from torch.utils.data import DataLoader

# Create DataFrame with image data and labels
training_df = daft.from_pydict({
    "image_path": ["train/cat1.jpg", "train/dog1.jpg", "train/cat2.jpg"],
    "label": [0, 1, 0]  # 0=cat, 1=dog
})

# Load and preprocess images
processed_df = training_df.select(
    col("image_path").url.download().image.decode().image.resize(224, 224).alias("image"),
    col("label")
)

# Convert to PyTorch dataset for training
torch_dataset = processed_df.to_torch_map_dataset()

# Create DataLoader for training
dataloader = DataLoader(torch_dataset, batch_size=32, shuffle=True)

# Use in PyTorch training loop
for batch in dataloader:
    images = batch["image"]
    labels = batch["label"]
    # Training code here...

import daft
from daft import col
from daft.functions import embed_text

# Create dataset with text descriptions
documents = daft.from_pydict({
    "doc_id": [1, 2, 3, 4],
    "title": ["Machine Learning", "Deep Learning", "Data Science", "Statistics"],
    "description": [
        "Algorithms that learn from data",
        "Neural networks with multiple layers", 
        "Extracting insights from data",
        "Mathematical analysis of data"
    ]
})

# Generate embeddings for semantic search
embedded_docs = documents.select(
    col("doc_id"),
    col("title"),
    col("description"),
    embed_text(col("description"), "sentence-transformers/all-MiniLM-L6-v2").alias("embedding")
)

# Create query embedding
query_df = daft.from_pydict({"query": ["artificial intelligence algorithms"]})
query_embedded = query_df.select(
    embed_text(col("query"), "sentence-transformers/all-MiniLM-L6-v2").alias("query_embedding")
)

# Compute similarity (cosine similarity would be implemented as UDF)
@daft.func
def cosine_similarity(vec1: list, vec2: list) -> float:
    import numpy as np
    v1, v2 = np.array(vec1), np.array(vec2)
    return np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))

# Find most similar documents
similarities = embedded_docs.select(
    col("title"),
    cosine_similarity(col("embedding"), query_embedded.collect()[0]["query_embedding"][0]).alias("similarity")
).collect()

from daft.functions import llm_generate

# Dataset with partial content to augment
content_df = daft.from_pydict({
    "topic": ["Climate Change", "Space Exploration", "Renewable Energy"],
    "outline": [
        "Impact on global temperatures",
        "Mars colonization plans", 
        "Solar and wind power efficiency"
    ]
})

# Generate detailed content
augmented_content = content_df.select(
    col("topic"),
    col("outline"),
    llm_generate(
        daft.sql_expr("'Write a detailed paragraph about: ' || outline"),
        "gpt-3.5-turbo",
        max_tokens=200,
        temperature=0.7
    ).alias("detailed_content")
)

# Generate summaries
summaries = content_df.select(
    col("topic"),
    llm_generate(
        daft.sql_expr("'Summarize in one sentence: ' || outline"),
        "gpt-3.5-turbo",
        max_tokens=50
    ).alias("summary")
)

# FAQ dataset
faq_df = daft.from_pydict({
    "category": ["Technical", "Billing", "Support", "General"],
    "question": [
        "How do I install the software?",
        "What payment methods are accepted?",
        "How to contact customer support?",
        "What is your company mission?"
    ],
    "context": [
        "Installation guide and system requirements",
        "Payment processing and billing information",
        "Support channels and contact information", 
        "Company background and mission statement"
    ]
})

# Generate comprehensive answers
qa_responses = faq_df.select(
    col("category"),
    col("question"),
    llm_generate(
        daft.sql_expr("'Context: ' || context || '\nQuestion: ' || question || '\nAnswer:'"),
        "gpt-4",
        max_tokens=150,
        temperature=0.3
    ).alias("answer")
)

# Customer feedback dataset
feedback_df = daft.from_pydict({
    "customer_id": [1, 2, 3, 4, 5],
    "feedback": [
        "Great product, very satisfied!",
        "Terrible service, not recommended",
        "Average experience, could be better",
        "Excellent customer support team",
        "Product quality is disappointing"
    ]
})

# Classify sentiment
sentiment_analysis = feedback_df.select(
    col("customer_id"),
    col("feedback"),
    llm_generate(
        daft.sql_expr("'Classify the sentiment of this feedback as Positive, Negative, or Neutral: ' || feedback"),
        "gpt-3.5-turbo",
        max_tokens=10,
        temperature=0.1
    ).alias("sentiment"),
    embed_text(col("feedback"), "sentence-transformers/all-MiniLM-L6-v2").alias("feedback_embedding")
)

# Extract key themes
theme_extraction = feedback_df.select(
    col("customer_id"),
    col("feedback"),
    llm_generate(
        daft.sql_expr("'Extract 3 key themes from this feedback: ' || feedback"),
        "gpt-3.5-turbo",
        max_tokens=50
    ).alias("themes")
)

# Dataset with mixed content types
content_df = daft.from_pydict({
    "content_id": [1, 2, 3],
    "content_type": ["article", "social_post", "review"],
    "text": [
        "Long-form article about technology trends",
        "Short social media post #trending",
        "Detailed product review with pros and cons"
    ]
})

# Generate type-specific processing
processed_content = content_df.select(
    col("content_id"),
    col("content_type"),
    col("text"),
    # Conditional processing based on content type
    daft.when(col("content_type") == "article")
    .then(llm_generate(
        daft.sql_expr("'Create a compelling headline for this article: ' || text"),
        "gpt-3.5-turbo"
    ))
    .when(col("content_type") == "social_post") 
    .then(llm_generate(
        daft.sql_expr("'Suggest 3 relevant hashtags for: ' || text"),
        "gpt-3.5-turbo"
    ))
    .otherwise(llm_generate(
        daft.sql_expr("'Rate this review from 1-5 stars and explain: ' || text"),
        "gpt-3.5-turbo"
    ))
    .alias("processed_output")
)

# Large dataset for batch AI processing
large_dataset = daft.read_parquet("s3://bucket/customer-reviews/*.parquet")

# Efficient batch processing
batch_ai_results = (large_dataset
    .filter(col("review_text").is_not_null())
    .select(
        col("review_id"),
        col("product_id"),
        col("review_text"),
        # Generate embeddings for similarity search
        embed_text(col("review_text"), "all-MiniLM-L6-v2").alias("review_embedding"),
        # Extract sentiment
        llm_generate(
            daft.sql_expr("'Sentiment (Positive/Negative/Neutral):' || review_text"),
            "gpt-3.5-turbo",
            max_tokens=10
        ).alias("sentiment"),
        # Generate summary for long reviews
        daft.when(daft.col("review_text").str_length() > 500)
        .then(llm_generate(
            daft.sql_expr("'Summarize this review in 2 sentences: ' || review_text"),
            "gpt-3.5-turbo",
            max_tokens=100
        ))
        .otherwise(col("review_text"))
        .alias("summary")
    )
    .repartition(10)  # Distribute processing
    .collect()
)

# Using custom or specialized models
specialized_df = daft.from_pydict({
    "medical_text": [
        "Patient presents with fever and cough",
        "Symptoms include headache and fatigue",
        "No significant medical history reported"
    ]
})

# Medical text analysis with domain-specific model
medical_analysis = specialized_df.select(
    col("medical_text"),
    embed_text(col("medical_text"), "clinical-bert-base").alias("clinical_embedding"),
    llm_generate(
        daft.sql_expr("'Extract medical entities from: ' || medical_text"),
        "biomedical-llm",
        temperature=0.1
    ).alias("medical_entities")
)

# Optimize AI function calls for large datasets
optimized_processing = (large_dataset
    .filter(col("text_length") > 10)  # Pre-filter short texts
    .repartition(20)  # Distribute load
    .select(
        col("id"),
        # Batch similar operations together
        embed_text(col("title") + " " + col("content"), "sentence-transformers/all-MiniLM-L6-v2").alias("combined_embedding")
    )
    .cache()  # Cache results for reuse
)

# Use cached embeddings for multiple downstream tasks
similarity_results = optimized_processing.select(
    col("id"),
    col("combined_embedding")
    # Additional similarity computations would use cached embeddings
)

class Provider:
    """AI model provider interface for custom model integration."""
    
    def configure(self, **kwargs) -> None:
        """Configure provider with authentication and settings."""

class Embedding:
    """Embedding type for vector representations."""
    
    def __init__(self, values: List[float]): ...
    
    def similarity(self, other: "Embedding") -> float:
        """Compute similarity with another embedding."""

# Chain AI operations with standard DataFrame operations
ai_pipeline = (df
    .filter(col("text").is_not_null())
    .select(
        col("id"),
        embed_text(col("text"), "all-MiniLM-L6-v2").alias("embedding")
    )
    .groupby("category")
    .agg(
        col("embedding").list().alias("category_embeddings"),
        col("id").count().alias("doc_count")
    )
    .filter(col("doc_count") > 5)
    .collect()
)

# Robust AI processing with fallbacks
@daft.func
def safe_llm_generate(text: str, model: str) -> str:
    try:
        # This would integrate with actual LLM API
        return f"Generated content for: {text[:50]}..."
    except Exception as e:
        return "Generation failed"

robust_generation = df.select(
    col("input_text"),
    safe_llm_generate(col("input_text"), "gpt-3.5-turbo").alias("generated")
)