FAQ

pip install cerevox

from cerevox import Lexa

client = Lexa(api_key="your-api-key")
documents = client.parse(["document.pdf"])

# Get vector DB ready chunks
chunks = documents.get_all_text_chunks(target_size=500)

import asyncio
from cerevox import AsyncLexa

async def main():
    async with AsyncLexa(api_key="your-api-key") as client:
        # Process multiple documents concurrently
        documents = await client.parse([
            "report1.pdf", "report2.docx", "data.xlsx"
        ])
        
        # Batch process with progress tracking
        async for status in client.parse_with_progress(files):
            print(f"Progress: {status.progress}")

asyncio.run(main())

# Get optimally sized chunks for embeddings
chunks = documents.get_all_text_chunks(
    target_size=500,  # Optimal for most embedding models
    overlap=50,       # Maintain context between chunks
    include_metadata=True  # Rich metadata for filtering
)

# Direct integration with vector databases
for chunk in chunks:
    # Each chunk includes source document, page numbers,
    # element types, and confidence scores
    vector_db.upsert(
        id=chunk.id,
        vector=embed(chunk.content),
        metadata=chunk.metadata
    )

# Parse entire S3 folder
documents = client.parse_s3_folder(
    bucket="my-bucket",
    folder="documents/",
    recursive=True
)

# Batch processing example
large_batch = ["doc1.pdf", "doc2.docx", ...] # 100+ files
documents = await client.parse(
    large_batch,
    progress_callback=lambda status: print(f"Progress: {status.completed}/{status.total}")
)

documents = client.parse("financial_report.pdf")

# Access extracted tables
for doc in documents:
    for table in doc.tables:
        print(f"Table on page {table.page_number}")
        print(f"Dimensions: {table.rows}x{table.columns}")
        
        # Export to pandas DataFrame
        df = table.to_pandas()
        
        # Or get raw structured data
        data = table.to_dict()

from cerevox import ProcessingMode

# Standard processing (fastest)
docs = client.parse("document.pdf", mode=ProcessingMode.DEFAULT)

# Advanced processing (highest accuracy)
docs = client.parse("document.pdf", mode=ProcessingMode.ADVANCED)

# Custom chunking parameters
chunks = docs.get_text_chunks(
    target_size=1000,      # Larger chunks for long-form content
    tolerance=0.2,         # 20% size variance allowed
    respect_boundaries=True, # Don't break sentences/paragraphs
    include_tables=True    # Include table content in chunks
)