semantica.parsestructured text, layout, tables, and metadata from unstructured documents:
DocumentParser: broad format support (PDF, DOCX, HTML, JSON, CSV, PPTX, XLSX), no extra dependenciesDoclingParser: complex layouts, merged-cell tables, multi-column PDFs, OCR (pip install docling)Both return a consistent dict with full_text, metadata, pages, and tables keys
parse_batch() processes multiple files in parallel with configurable error handling
Getting Started Installation The parse module works out of the box for standard formats:
from semantica.parse import DocumentParser parser = DocumentParser() result = parser.parse( "document.pdf" ) print (result[ "full_text" ]) # Extracted text content
For enhanced table extraction and complex layouts, install the Docling dependency:
from semantica.parse import DoclingParser parser = DoclingParser( export_format = "markdown" ) result = parser.parse( "document.pdf" , extract_tables = True ) print (result[ "tables" ]) # Enhanced table extraction
First Document Parsing from semantica.parse import DocumentParser # Parse any supported format parser = DocumentParser() result = parser.parse( "annual_report.pdf" ) # Access extracted content text = result[ "full_text" ] # Complete document text metadata = result[ "metadata" ] # Document properties pages = result.get( "pages" , []) # Page-level content print ( f "Extracted { len (text) } characters from { metadata.get( 'page_count' , 0 ) } pages" )
Parser Selection Guide Zero extra dependencies. Use for clean PDFs, Word docs, HTML, and structured formats. Formats PDF, DOCX, HTML, TXT, JSON, CSV, PPTX, XLSX Speed Fast Setup None: included in base install Best for Clean documents, broad format support, production pipelines
from semantica.parse import DocumentParser parser = DocumentParser() result = parser.parse( "contract.pdf" ) print (result[ "full_text" ]) # Extracted text print (result[ "metadata" ]) # Title, author, page count, ... print ( len (result.get( "pages" , []))) # Per-page breakdown
Superior table extraction, OCR, multi-column PDFs. Requires pip install docling. Formats PDF, DOCX, PPTX, XLSX, HTML, images Speed Slower (deep layout analysis) Setup pip install doclingBest for Merged-cell tables, scanned documents, multi-column layouts
from semantica.parse import DoclingParser parser = DoclingParser( export_format = "markdown" ) result = parser.parse( "financial_report.pdf" , extract_tables = True , extract_text = True , ) for i, table in enumerate (result[ "tables" ]): print ( f "Table { i + 1 } : { table[ 'row_count' ] } rows × { table[ 'col_count' ] } columns" ) print ( f " Page: { table[ 'page_number' ] } " ) for row in table[ "rows" ][: 3 ]: print ( " | " .join(row))
Start with DocumentParser. Switch to DoclingParser only when you need better table extraction or encounter complex PDF layouts.
Process multiple files in parallel with per-file error isolation. from semantica.parse import DocumentParser parser = DocumentParser() results = parser.parse_batch( [ "doc1.pdf" , "doc2.docx" , "doc3.html" ], continue_on_error = True , # skip failed files instead of raising ) print ( f "Parsed: { results[ 'success_count' ] } / { results[ 'total' ] } " ) for item in results[ "successful" ]: print ( f " { item[ 'file_path' ] } : { len (item[ 'result' ][ 'full_text' ]) } chars" ) for item in results[ "failed" ]: print ( f "FAILED: { item[ 'file_path' ] } : { item[ 'error' ] } " )
continue_on_error=True is recommended for production batch jobs where individual files may be corrupted or unsupported.
Exported Classes Class Role DocumentParserAuto-detects format: delegates to format-specific parser (PDF, DOCX, HTML, JSON, CSV, …) DoclingParserComplex layouts, merged-cell tables, multi-column PDFs, and OCR (pip install docling) DoclingMetadataDocument metadata from Docling parsing PDFParserPDF text and metadata extraction WebParserURL fetch + HTML parsing EmailParser.eml / .msg email files with attachment extractionCodeParserSource code files with syntax-aware block detection
DocumentParser Standard parser for clean, machine-readable documents:
from semantica.parse import DocumentParser parser = DocumentParser() result = parser.parse( "data/report.pdf" ) print (result[ "full_text" ]) # Complete extracted text print (result[ "metadata" ]) # Document properties (title, author, page_count, etc.) if "pages" in result: # Page-level content (when available) print ( f "Pages: { len (result[ 'pages' ]) } " )
Supported formats: PDF, DOCX, HTML, TXT, JSON, CSV, PPTX, XLSX.
DoclingParser Advanced parser using the Docling backend: handles layouts that DocumentParser cannot:
from semantica.parse import DoclingParser parser = DoclingParser( export_format = "markdown" , # Export format: "markdown" | "html" | "json" enable_ocr = False # Enable OCR for scanned documents ) result = parser.parse( "data/annual_report.pdf" , extract_tables = True , # Extract structured tables extract_images = False , # Extract image regions extract_text = True # Extract text content ) print (result[ "full_text" ]) # Complete extracted text print (result[ "tables" ]) # Structured table data if "pages" in result: # Page-level content print ( f "Pages: { len (result[ 'pages' ]) } " )
Use DoclingParser for:
Multi-column PDF layouts
Tables with merged cells or complex headers
PPTX slides with embedded charts
XLSX spreadsheets with formulas
Scanned documents with OCR
Academic papers and technical reports
OCR Support parser = DoclingParser( enable_ocr = True , # Enable OCR via PdfPipelineOptions export_format = "markdown" ) result = parser.parse( "data/scanned_contract.pdf" ) print (result[ "full_text" ]) # OCR-extracted text
Format Extension Parser Used Notes PDF .pdfPDFParser / DoclingParserText, tables, metadata; Docling adds OCR Word .docxBuilt-in Text, headings, tables, metadata HTML .html, .htmHTMLParser / WebParserWebParser fetches remote URLsMarkdown .mdBuilt-in Preserves heading hierarchy Plain text .txtTXTParserMinimal metadata JSON .jsonJSONParserOne object per line or array CSV / TSV .csv, .tsvCSVParserHeader auto-detected Excel .xlsx, .xlsBuilt-in Sheet selection supported PowerPoint .pptxBuilt-in DoclingParser for embedded chartsEmail .eml, .msgEmailParserAttachments extracted XML .xmlXMLIngestorXXE-safe, optional XSD validation Archive .zip, .tarFileIngestorRecursive extraction Source code .py, .js, .java, …CodeParserAST-aware block detection
Parser Output Structure Both parsers return dictionaries with the following structure:
result = { "full_text" : str , # Complete extracted text "metadata" : dict , # Document properties and statistics "pages" : List[ dict ], # Page-level content (when available) "tables" : List[ dict ], # Structured table data (DoclingParser) "images" : List[ dict ], # Image regions (DoclingParser) "total_pages" : int , # Total page count "export_format" : str # Format used for text extraction (DoclingParser) }
metadata = { "file_path" : str , # Source file path "page_count" : int , # Number of pages "format" : str , # File format ("pdf", "docx", etc.) # Additional fields vary by parser and document type }
DocumentParser Methods Method Returns Description parse(source)dictAuto-detect format and extract text, metadata, tables parse_batch(sources)dictProcess multiple sources in parallel extract_text(path)strExtract only text content from document extract_metadata(path)dictExtract only metadata from document
Integration with FileIngestor The most common pattern: ingest a directory then parse each source:
from semantica.ingest import FileIngestor from semantica.parse import DoclingParser ingestor = FileIngestor() parser = DoclingParser( export_format = "markdown" ) sources = ingestor.ingest( "data/reports/" ) for source in sources: result = parser.parse(source) # Access extracted content text = result[ "full_text" ] tables = result[ "tables" ] metadata = result[ "metadata" ]
Docling is an optional dependency. If docling is not installed, DoclingParser raises an ImportError with installation instructions: pip install docling. DocumentParser is always available and requires no extras.
Ingest Load files before parsing.
Split Chunk parsed text for embedding and extraction.
Docling Integration Full Docling integration setup guide.
Semantic Extract Extract entities and relations from parsed text.
