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KaifAhmad1andClaude Sonnet 4.6
946a1089c8
- Migrate from mint.json to docs.json (Mintlify v4) - Theme: maple, emerald green + near-black dark / cream light palette (#059669 primary, #0A0A0A dark bg, #FAF7F0 light bg) - Typography: Lexend headings, Inter body - 5-tab navigation: Documentation, Quick Start, API Reference, Cookbook, FAQ - Homepage: removed badge stickers, redundant h2, added blockquote tagline, full 27-module reference table with semantica.mcp_server added - quickstart.md: CodeGroup per pipeline step, pattern vs LLM options, AccordionGroup for patterns and troubleshooting - faq.md: full AccordionGroup structure across 5 sections - reference/explorer.md: NEW — FastAPI explorer, Ontology Hub, Distance Intelligence, CLI reference, REST API endpoints - reference/mcp_server.md: NEW — MCP stdio server, 12 tools with I/O examples, 3 resources, Claude Desktop/VS Code/Windsurf/Cline config - docs.json: explorer added to Output group, mcp_server to Utilities group - Chat, feedback (thumbs/suggest/raise), OG/Twitter metadata, search topbar - All reference pages reformatted with Mintlify JSX components Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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title, description, icon
| title | description | icon |
|---|---|---|
| Parse Module | Document parsing and text extraction — DocumentParser for standard formats and DoclingParser for complex layouts. | file-lines |
Universal data parser supporting documents, web content, structured data, emails, code, and media.
DocumentParser
Standard parser for clean, machine-readable documents.
from semantica.parse import DocumentParser
parser = DocumentParser()
parsed = parser.parse("data/report.pdf")
print(parsed.text) # full clean text
print(parsed.metadata) # title, author, date, page_count, etc.
print(parsed.sections) # document structure
Supported formats: PDF, DOCX, HTML, TXT, JSON, CSV, PPTX, XLSX.
DoclingParser
Advanced parser for complex layouts using the Docling backend.
pip install "semantica[docling]"
from semantica.parse import DoclingParser
parser = DoclingParser(
extract_tables=True, # structured table extraction
extract_images=True, # image OCR
output_format="markdown", # "markdown" | "html" | "json"
)
parsed = parser.parse("data/annual_report.pdf")
print(parsed.text) # full clean text
print(parsed.tables) # structured table data
print(parsed.sections) # document structure
Use DoclingParser for: multi-column PDFs, tables with merged cells, PPTX slides, XLSX spreadsheets, images with OCR, and scanned documents.
OCR Support
parser = DoclingParser(
ocr=True,
ocr_language=["en"],
extract_tables=True,
)
parsed = parser.parse("data/scanned_contract.pdf")
Parsed Document Object
@dataclass
class ParsedDocument:
text: str
sections: List[Section]
tables: List[TableData]
metadata: DocumentMetadata
source_id: str
@dataclass
class DocumentMetadata:
title: Optional[str]
author: Optional[str]
created_date: Optional[datetime]
page_count: int
language: Optional[str]
has_tables: bool
has_images: bool
word_count: int
format: str # "pdf" | "docx" | "pptx" | ...
Integration with FileIngestor
from semantica.ingest import FileIngestor
from semantica.parse import DoclingParser
ingestor = FileIngestor()
parser = DoclingParser(extract_tables=True)
sources = ingestor.ingest("data/reports/")
for source in sources:
parsed = parser.parse(source)