CSV Agent

The CSV Agent specializes in processing, analyzing, and manipulating CSV data files. It provides intelligent parsing, data cleaning, analysis, and transformation capabilities while handling various CSV formats and error conditions.

CSV Agent workflow and architecture

Configuration Parameters

Required Input Parameters

file_path: Path to the CSV file
text: Query or instruction for processing
agent_type: Type of analysis to perform

Optional Configuration

llm: Language model configuration
- model_name: Name of the language model
- temperature: Response creativity (0-1)
- max_tokens: Maximum response length
handle_parse_errors: Error handling strategy
- skip_bad_lines: Skip lines with parsing errors
- error_handling: 'strict' | 'ignore' | 'coerce'
- log_errors: Enable error logging
max_iterations: Maximum processing iterations
agent_description: Custom agent behavior instructions

Output Format

{
  "agent": {
    "type": string,
    "description": string,
    "iterations_used": number,
    "processing_stats": {
      "rows_processed": number,
      "errors_encountered": number,
      "processing_time": number
    }
  },
  "response": {
    "result": any,
    "analysis": {
      "summary": string,
      "insights": array,
      "visualizations": array
    },
    "data_quality": {
      "missing_values": object,
      "data_types": object,
      "anomalies": array
    },
    "transformations": [
      {
        "type": string,
        "description": string,
        "affected_columns": array
      }
    ]
  }
}

Features

Intelligent CSV parsing
Data cleaning and preprocessing
Statistical analysis
Pattern detection
Data validation
Format conversion
Missing value handling
Data visualization

Note: Large CSV files are processed in chunks to manage memory efficiently. Consider file size and available resources when setting max_iterations.

Tip: Use appropriate error handling strategies based on your data quality requirements. Enable logging for debugging and audit purposes.

Example Usage

const csvAgent = new CSVAgent({
  file_path: "./data/sales_data.csv",
  agent_type: "analysis",
  agent_description: "Analyze sales trends and patterns",
  llm: {
    model_name: "gpt-4",
    temperature: 0.3
  },
  handle_parse_errors: {
    skip_bad_lines: true,
    error_handling: "coerce",
    log_errors: true
  },
  max_iterations: 1000
});

const results = await csvAgent.process({
  text: "Analyze monthly sales trends and identify top performing products"
});