llm_model
(str): LLM <provider::model>
to use for text processing and analysis (e.g., "openai::gpt-4o-mini"
).main_theme
(str): The main theme to screen for. Sub-themes are generated from this.companies
(List[Company]): List of companies to analyze.start_date
(str): Start date for searching relevant documents (YYYY-MM-DD).end_date
(str): End date for searching relevant documents (YYYY-MM-DD).document_type
(DocumentType): Type of documents to search (NEWS
, FILINGS
, TRANSCRIPTS
).fiscal_year
(int, optional): Fiscal year to analyze.sources
(Optional[List[str]]): Filter search results by document sources.rerank_threshold
(Optional[float]): Threshold for reranking search results.focus
(str, optional): Focus for sub-theme generation.ThematicScreener
instance.document_limit
(int, optional): Max documents per query (default: 10).batch_size
(int, optional): Number of entities per batch (default: 10).frequency
(str, optional): Date range frequency ('Y'
, 'M'
, 'W'
, 'D'
, default: '3M'
).word_range
(Tuple[int, int], optional): Word count range for motivations (default: (50, 100)
).export_path
(str, optional): Path to export results as Excel.dict
with:
df_labeled
: DataFrame with labeled search results.df_company
: DataFrame with company-level output.df_industry
: DataFrame with industry-level output.theme_tree
: ThemeTree object used for screening.