DataAnalyzr constructor

DataAnalyzr(
    analysis_type: Literal["sql", "ml", "skip"],
    api_key: Optional[str] = None,
    class_params: Optional[dict] = None,
    generator_llm: Optional[LiteLLM] = None,
    analysis_llm: Optional[LiteLLM] = None,
    context: Optional[str] = None,
    log_params: Optional[dict] = None,
) -> DataAnalyzr

Constructor method for initializing a DataAnalyzr object.

Parameters

Literal['sql', 'ml', 'skip']

required

The type of analysis to be performed.

analysis_type = "sql"

string

API key for accessing LLM services. May also be set as an environment variable.

api_key = "sk-apikey"

dictionary

Dictionary of class parameters. Accepts max_retries, time_limit, and auto_train.

class_params = {
    "max_retries": 5, # int
    "time_limit": 60, # int
    "auto_train": True, # bool
}

Show details

integer

Maximum number of retries for the LLM calls and analysis. Default is 10.

integer

Time limit in seconds for the LLM calls and analysis. Default is 45 for analysis and 60 for visualisation.

boolean

Whether to automatically add questions with their SQL query or Python code to the vector store. Default is True.

LiteLLM

LLM instance for generating analysis. Default LLM used is GPT-4o.

from lyzr.base import LyzrLLMFactory
generator_llm = LyzrLLMFactory.from_defaults(model="gpt-4-turbo")

For details on configuring the LLM, see the Large Language Models guide.

LiteLLM

LLM instance for performing analysis. Default LLM used is GPT-4o.

from lyzr.base import LyzrLLMFactory
analysis_llm = LyzrLLMFactory.from_defaults(model="gpt-4o")

For details on configuring the LLM, see the Large Language Models guide.

string or dictionary

Context for analysis and response generation. Default is an empty string.

This context is used in the analysis, insights, recommendations, and tasks methods.
When passed as a string, the same context is applied to all sections - analysis, insights, recommendations, and tasks.
When passed as a dictionary, it should have keys analysis, visualisation, insights, recommendations, and tasks.
To override the class context for a specific query, pass the context to the ask method.

context = "Sales data analysis for Q1 2023"
# or
context = {
    "analysis": "Sales data analysis for Q1 2023",
    "visualisation": "Distribution plot for sales data",
    "insights": "Key trends in sales data",
    "recommendations": "Improve sales performance",
    "tasks": "Analyze sales data",
}

Show details

string

Context for the analysis method.

string

Context for the visualisation method.

string

Context for the insights method.

string

Context for the recommendations method.

string

Context for the tasks method.

dictionary

Dictionary of logging parameters. Accepts log_filename, log_level, and print_log.

log_params = {
    "log_filename": "dataanalyzr.csv", # str
    "log_level": "INFO", # str
    "print_log": False, # bool
}

Show details

string

Name of the log file. Default is dataanalyzr.csv.

string

Log level for the logger. Default is INFO.

boolean

Whether to print logs to the console. Default is False.

Returns

DataAnalyzr

DataAnalyzr object for performing conversational data analysis.

Example usage

from lyzr.base import LyzrLLMFactory
from lyzr.data_analyzr.data_analyzr import DataAnalyzr

analysis_type = "sql"
log_params = {
    "log_filename": "dataanalyzr.log",
    "log_level": "DEBUG",
    "print_log": True,
}
class_params = {
    "max_retries": 5,
    "time_limit": 60,
    "auto_train": True,
}
generator_llm = LyzrLLMFactory.from_defaults(model="gpt-4-turbo")
analysis_llm = LyzrLLMFactory.from_defaults(model="gpt-4o")
context = "Sales data analysis for Q1 2023"

data_analyzr = DataAnalyzr(
    analysis_type=analysis_type,
    api_key=api_key,
    class_params=class_params,
    generator_llm=generator_llm,
    analysis_llm=analysis_llm,
    context=context,
    log_params=log_params,
)

Introduction

Key Concepts

DataAnalyzr constructor

Parameters

Returns

Example usage

​Parameters

​Returns

​Example usage

Parameters

Returns

Example usage