Working with Microsoft Fabric

%pip install -qU pixeltable

import pixeltable as pxt

# Remove the 'fabric_demo' directory and its contents, if it exists
pxt.drop_dir('fabric_demo', force=True)
pxt.create_dir('fabric_demo')

from pixeltable.functions import fabric

# Create a table for customer support tickets
tickets = pxt.create_table(
    'fabric_demo.support_tickets',
    {
        'ticket_id': pxt.Int,
        'customer_message': pxt.String,
        'priority': pxt.String,
    },
)

# Add a computed column that automatically generates AI responses
# No API keys needed - Fabric handles authentication!
messages = [
    {
        'role': 'system',
        'content': 'You are a helpful customer support agent. Be concise and professional.',
    },
    {'role': 'user', 'content': tickets.customer_message},
]

tickets.add_computed_column(
    ai_response=fabric.chat_completions(
        messages,
        model='gpt-4.1',
        model_kwargs={'max_tokens': 200, 'temperature': 0.7},
    )
)

# Parse the response to extract just the message content
tickets.add_computed_column(
    response_text=tickets.ai_response.choices[0].message.content
)

# Insert data - AI responses are generated automatically
tickets.insert(
    [
        {
            'ticket_id': 1,
            'customer_message': 'How do I reset my password?',
            'priority': 'low',
        },
        {
            'ticket_id': 2,
            'customer_message': "My order hasn't arrived after 2 weeks",
            'priority': 'high',
        },
        {
            'ticket_id': 3,
            'customer_message': 'Can I change my subscription plan?',
            'priority': 'medium',
        },
    ]
)

# Query results with AI-generated responses
tickets.select(
    tickets.ticket_id, tickets.customer_message, tickets.response_text
).show()

# Create a table for complex reasoning tasks
reasoning_tasks = pxt.create_table(
    'fabric_demo.reasoning', {'task_id': pxt.Int, 'problem': pxt.String}
)

messages = [{'role': 'user', 'content': reasoning_tasks.problem}]

reasoning_tasks.add_computed_column(
    reasoning_output=fabric.chat_completions(
        messages,
        model='gpt-5',  # Reasoning model
        model_kwargs={
            'max_completion_tokens': 1000  # Note: max_completion_tokens, not max_tokens
        },
    )
)

reasoning_tasks.add_computed_column(
    solution=reasoning_tasks.reasoning_output.choices[0].message.content
)

# Insert a complex reasoning task
reasoning_tasks.insert(
    [
        {
            'task_id': 1,
            'problem': 'Explain how to implement a binary search tree with self-balancing capabilities. Include time complexity analysis.',
        }
    ]
)

reasoning_tasks.select(
    reasoning_tasks.problem, reasoning_tasks.solution
).show()

# Create a knowledge base table
knowledge_base = pxt.create_table(
    'fabric_demo.knowledge',
    {'doc_id': pxt.Int, 'content': pxt.String, 'category': pxt.String},
)

# Add embeddings column
knowledge_base.add_computed_column(
    embedding=fabric.embeddings(
        knowledge_base.content, model='text-embedding-ada-002'
    )
)

# Insert some documents
knowledge_base.insert(
    [
        {
            'doc_id': 1,
            'content': 'Pixeltable is a Python library for AI data workflows with built-in versioning.',
            'category': 'product',
        },
        {
            'doc_id': 2,
            'content': 'Microsoft Fabric provides a unified analytics platform for data engineering and AI.',
            'category': 'platform',
        },
        {
            'doc_id': 3,
            'content': 'Azure OpenAI Service offers powerful language models through REST APIs.',
            'category': 'service',
        },
    ]
)

# Add an embedding index for fast similarity search
knowledge_base.add_embedding_index(
    'content',
    embedding=fabric.embeddings.using(model='text-embedding-ada-002'),
)

# Perform similarity search
sim = knowledge_base.content.similarity('AI platform for data science')
knowledge_base.select(
    knowledge_base.content, knowledge_base.category, sim=sim
).order_by(sim, asc=False).limit(2).show()

# Create a table for questions
questions = pxt.create_table(
    'fabric_demo.questions',
    {'question_id': pxt.Int, 'question': pxt.String},
)


# Find similar documents using similarity search
@pxt.query
def retrieve_context(question: str, top_k: int = 2) -> list[dict]:
    sim = knowledge_base.content.similarity(question)
    return (
        knowledge_base.select(knowledge_base.content)
        .order_by(sim, asc=False)
        .limit(top_k)
        .collect()['content']
    )


# Add context retrieval
questions.add_computed_column(
    context=retrieve_context(questions.question, top_k=2)
)

# Build RAG prompt with retrieved context
questions.add_computed_column(
    rag_messages=[
        {
            'role': 'system',
            'content': "Answer the question based on the provided context. If the context doesn't contain relevant information, say so.",
        },
        {
            'role': 'user',
            'content': f'Context: {questions.context}\n\nQuestion: {questions.question}',
        },
    ]
)

# Generate answer using gpt-4.1
questions.add_computed_column(
    answer_response=fabric.chat_completions(
        questions.rag_messages,
        model='gpt-4.1',
        model_kwargs={'max_tokens': 300},
    )
)

questions.add_computed_column(
    answer=questions.answer_response.choices[0].message.content
)

# Ask a question
questions.insert(
    [{'question_id': 1, 'question': 'What is Microsoft Fabric used for?'}]
)

questions.select(
    questions.question, questions.context, questions.answer
).show()