cli/README.md

Lineagentic-flow CLI

A command-line interface for the Lineagentic-flow framework that provides agentic data lineage parsing across various data processing script types.

Installation

The CLI is automatically installed when you install the lineagentic-flow package:

pip install -e .

Usage

The CLI provides two main commands: analyze and field-lineage.

Basic Commands

Analyze Query/Code for Lineage

lineagentic analyze --agent-name sql-lineage-agent --query "your code here"

Running Analysis

Using a Specific Agent

lineagentic analyze --agent-name sql-lineage-agent --query "SELECT a,b FROM table1"

Using a File as Input

lineagentic analyze --agent-name python-lineage-agent --query-file path/to/your/script.py

Specifying a Different Model

lineagentic analyze --agent-name airflow-lineage-agent --model-name gpt-4o --query "your code here"

With Lineage Configuration

lineagentic analyze --agent-name sql-lineage-agent --query "SELECT * FROM users" --job-namespace "my-namespace" --job-name "my-job"

Output Options

Pretty Print Results

lineagentic analyze --agent-name sql --query "your code" --pretty

Save Results to File

lineagentic analyze --agent-name sql --query "your code" --output results.json

Save Results with Pretty Formatting

lineagentic analyze --agent-name python --query "your code" --output results.json --pretty

Enable Verbose Output

lineagentic analyze --agent-name sql --query "your code" --verbose

Available Agents

• sql-lineage-agent: Analyzes SQL queries and scripts (default)
• airflow-lineage-agent: Analyzes Apache Airflow DAGs and workflows
• spark-lineage-agent: Analyzes Apache Spark jobs
• python-lineage-agent: Analyzes Python data processing scripts
• java-lineage-agent: Analyzes Java data processing code

Commands

analyze Command

Analyzes a query or code for lineage information.

Required Arguments

• Either --query or --query-file must be specified

Basic Query Analysis

# Simple SQL query analysis
lineagentic analyze --agent-name sql-lineage-agent --query "SELECT user_id, name FROM users WHERE active = true"

# Analyze with specific agent
lineagentic analyze --agent-name sql-lineage-agent --query "SELECT a, b FROM table1 JOIN table2 ON table1.id = table2.id"

# Analyze Python code
lineagentic analyze --agent-name python-lineage-agent --query "import pandas as pd; df = pd.read_csv('data.csv'); result = df.groupby('category').sum()"

# Analyze Java code
lineagentic analyze --agent-name java-lineage-agent --query "public class DataProcessor { public void processData() { // processing logic } }"

# Analyze Spark code
lineagentic analyze --agent-name spark-lineage-agent --query "val df = spark.read.csv('data.csv'); val result = df.groupBy('category').agg(sum('value'))"

# Analyze Airflow DAG
lineagentic analyze --agent-name airflow-lineage-agent --query "from airflow import DAG; from airflow.operators.python import PythonOperator; dag = DAG('my_dag')"

Reading from File

# Analyze query from file
lineagentic analyze --agent-name sql-lineage-agent --query-file "queries/user_analysis.sql"

# Analyze Python script from file
lineagentic analyze --agent-name python-lineage-agent --query-file "scripts/data_processing.py"

Output Options

# Save results to file
lineagentic analyze --agent-name sql-lineage-agent --query "SELECT * FROM users" --output "results.json"

# Pretty print results
lineagentic analyze --agent-name sql-lineage-agent --query "SELECT * FROM users" --pretty

# Verbose output
lineagentic analyze --agent-name sql-lineage-agent --query "SELECT * FROM users" --verbose

# Don't save to database
lineagentic analyze --agent-name sql-lineage-agent --query "SELECT * FROM users" --no-save

# Don't save to Neo4j
lineagentic analyze --agent-name sql-lineage-agent --query "SELECT * FROM users" --no-neo4j

Common Output Options

Both commands support these output options:

• --output: Output file path for results (JSON format)
• --pretty: Pretty print the output
• --verbose: Enable verbose output

Error Handling

The CLI provides clear error messages for common issues:

• Missing required arguments
• File not found errors
• Agent execution errors
• Invalid agent names

Development

To run the CLI in development mode:

python -m cli.main --help

To run a specific command:

python -m cli.main analyze --agent-name sql --query "SELECT 1" --pretty