Tools (SEC, Explain)

API reference for sovai.get_tools

Module: sovai.get_tools

Functions

sec_search()

def sec_search(search = 'CFO Resgination')

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sec_filing()

def sec_filing(ticker = 'AAPL', form = '10-Q', date_input = '2023-Q3', verbose = False)

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code()

def code(prompt = 'get bankruptcy data for Tesla', verbose = False, run = False)

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sec_graph()

def sec_graph(
    ticker: str = 'AAPL',
    date: str = '2024-Q3',
    verbose: bool = False,
    ontology_type: str = 'causal',
    oai_model: str = 'gpt-4o-mini',
    batch: bool = True,
    batch_size: int = 10,
    sentiment_filter: Optional[Union[float, bool]] = None,
    output_dir: str = './docs',
    use_cache: bool = True,
) -> pd.DataFrame

Generate a knowledge graph from 10-K SEC filings for a given ticker using the specified ontology type.

Parameters

Parameter	Type	Description
ticker	str	Ticker symbol (e.g., AAPL for Apple Inc.)
date	str	Filing date or quarter (default: "2024-Q3").
verbose	bool	Whether to print detailed logs.
ontology_type	str	The ontology type to use for analysis. Choose from: - "connection", "causal", "temporal", "stakeholder", "innovation", "esg", "sentiment"
oai_model	str	OpenAI model to use (default: "gpt-4o-mini").
batch	bool	Whether to process documents in batches.
batch_size	int	Number of documents to process in a batch (default: 10).
sentiment_filter	Optional[Union[float, bool]]	Filter by sentiment scores or leave as None for no filter.
output_dir	str	Directory to save graph outputs (default: "./docs").
use_cache	bool	Whether to use cached results to speed up analysis.

Returns

• pd.DataFrame: DataFrame representing the generated graph with nodes and relationships.

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