Building an Enterprise-Level AI Agent for Document Transformation

Introduction to LlamaReport and LlamaCloud

LlamaReport is an API-first solution designed to automate the transformation of unstructured documents into structured, organization-ready reports. It offers a flexible templating system and intelligent document processing capabilities, enabling enterprises to generate comprehensive reports efficiently.


from llama_index import LlamaReport, LlamaCloud

# Initialize LlamaReport with your API key
report_agent = LlamaReport(api_key="your_api_key")

# Configure document processing
cloud_config = LlamaCloud.Config(
    parsing_mode="intelligent",
    supported_formats=["pdf", "pptx", "xlsx"]
)

Key Features and Technical Components

1. Intelligent Document Processing


# Document ingestion and parsing
document_processor = LlamaCloud.DocumentProcessor(config=cloud_config)

# Process multiple documents
processed_docs = document_processor.batch_process([
    "report1.pdf",
    "presentation.pptx",
    "data.xlsx"
])

2. Template Design and Plan Generation


# Define report template
template = LlamaReport.Template.from_markdown("template.md")

# Generate report plan
plan = report_agent.create_plan(
    template=template,
    documents=processed_docs,
    allow_human_review=True
)

3. Report Generation and AI-Assisted Editing


# Generate initial report
report = report_agent.generate_report(plan=plan)

# Apply AI-powered edits
edited_report = report_agent.apply_edits(
    report=report,
    style_guide="company_style_guide.md",
    review_required=True
)

How LlamaReport Works

1. Document Upload & Template Definition

Source documents serve as the knowledge base for report generation. Templates can take various forms:

Markdown templates with section-specific instructions
Questionnaires with blank fields to fill in
Example blog posts with template parsing instructions
Python docstring-style comments for field extraction


# Example template definition
template = LlamaReport.Template.from_markdown("""
# Company Analysis Report

## Executive Summary
{executive_summary}

## Market Analysis
{market_analysis}

## Financial Overview
{financial_metrics}
""")

2. Plan Generation

The system constructs a plan with dependencies to ensure logical report generation:


# Generate plan with dependencies
plan = report_agent.create_plan(
    template=template,
    documents=processed_docs,
    dependencies={
        "financial_metrics": ["market_analysis"],
        "executive_summary": ["market_analysis", "financial_metrics"]
    }
)

3. Report Generation

Reports are generated with maximum parallelization where possible:


# Configure parallel processing
config = LlamaReport.Config(
    max_parallel_blocks=4,
    enable_streaming=True
)

# Generate report with progress tracking
report = report_agent.generate_report(
    plan=plan,
    config=config,
    progress_callback=lambda x: print(f"Progress: {x}%")
)

4. AI-Assisted Editing

The system can suggest and apply intelligent edits to the generated report:


# Request and apply edits
edits = report_agent.suggest_edits(
    report=report,
    style_guide="company_style.md",
    focus_areas=["clarity", "consistency", "tone"]
)

# Review and apply suggested edits
final_report = report_agent.apply_edits(
    report=report,
    edits=edits,
    require_approval=True
)

Developer API Integration

LlamaReport offers a comprehensive API for seamless integration:


from llama_index.report import ReportAPI

# Initialize API client
api = ReportAPI(api_key="your_api_key")

# Stream report generation events
async for event in api.stream_report_generation(plan_id):
    if event.type == "progress":
        print(f"Progress: {event.data.percentage}%")
    elif event.type == "completion":
        report = event.data.report

Coming Soon

Enhanced report generation quality
Token-level streaming API options
Expanded template library
Comprehensive documentation
Example notebooks and tutorials

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