crewAI/docs/tools/csv-search-tool.mdx

---
title: CSVSearchTool
description: A tool for semantic search within CSV files using RAG capabilities
icon: table
---

## CSVSearchTool

The CSVSearchTool enables semantic search capabilities for CSV files using Retrieval-Augmented Generation (RAG). It can process CSV files either specified during initialization or at runtime, making it flexible for various use cases.

## Installation

```bash
pip install 'crewai[tools]'
```

## Usage Example

```python
from crewai import Agent
from crewai_tools import CSVSearchTool

# Method 1: Initialize with specific CSV file
csv_tool = CSVSearchTool(csv="path/to/data.csv")

# Method 2: Initialize without CSV (specify at runtime)
flexible_csv_tool = CSVSearchTool()

# Create an agent with the tool
data_analyst = Agent(
    role='Data Analyst',
    goal='Search and analyze CSV data semantically',
    backstory='Expert at analyzing and extracting insights from CSV data.',
    tools=[csv_tool],
    verbose=True
)
```

## Input Schema

### Fixed CSV Schema (when CSV path provided during initialization)
```python
class FixedCSVSearchToolSchema(BaseModel):
    search_query: str = Field(
        description="Mandatory search query you want to use to search the CSV's content"
    )
```

### Flexible CSV Schema (when CSV path provided at runtime)
```python
class CSVSearchToolSchema(FixedCSVSearchToolSchema):
    csv: str = Field(
        description="Mandatory csv path you want to search"
    )
```

## Function Signature

```python
def __init__(
    self,
    csv: Optional[str] = None,
    **kwargs
):
    """
    Initialize the CSV search tool.

    Args:
        csv (Optional[str]): Path to CSV file (optional)
        **kwargs: Additional arguments for RAG tool configuration
    """

def _run(
    self,
    search_query: str,
    **kwargs: Any
) -> str:
    """
    Execute semantic search on CSV content.

    Args:
        search_query (str): Query to search in the CSV
        **kwargs: Additional arguments including csv path if not initialized

    Returns:
        str: Relevant content from the CSV matching the query
    """
```

## Best Practices

1. CSV File Handling:
   - Ensure CSV files are properly formatted
   - Use absolute paths for reliability
   - Verify file permissions before processing

2. Search Optimization:
   - Use specific, focused search queries
   - Consider column names and data structure
   - Test with sample queries first

3. Performance Considerations:
   - Pre-initialize with CSV for repeated searches
   - Handle large CSV files appropriately
   - Monitor memory usage with big datasets

4. Error Handling:
   - Verify CSV file existence
   - Handle malformed CSV data
   - Manage file access permissions

## Integration Example

```python
from crewai import Agent, Task, Crew
from crewai_tools import CSVSearchTool

# Initialize tool with specific CSV
csv_tool = CSVSearchTool(csv="/path/to/sales_data.csv")

# Create agent
analyst = Agent(
    role='Data Analyst',
    goal='Extract insights from sales data',
    backstory='Expert at analyzing sales data and trends.',
    tools=[csv_tool]
)

# Define task
analysis_task = Task(
    description="""Find all sales records from the CSV 
    that relate to product returns in Q4 2023.""",
    agent=analyst
)

# The tool will use:
# {
#   "search_query": "product returns Q4 2023"
# }

# Create crew
crew = Crew(
    agents=[analyst],
    tasks=[analysis_task]
)

# Execute
result = crew.kickoff()
```

## Advanced Usage

### Dynamic CSV Selection
```python
# Initialize without CSV
flexible_tool = CSVSearchTool()

# Search different CSVs
result1 = flexible_tool.run(
    search_query="revenue 2023",
    csv="/path/to/finance.csv"
)

result2 = flexible_tool.run(
    search_query="customer feedback",
    csv="/path/to/surveys.csv"
)
```

### Multiple CSV Analysis
```python
# Create tools for different CSVs
sales_tool = CSVSearchTool(csv="/path/to/sales.csv")
inventory_tool = CSVSearchTool(csv="/path/to/inventory.csv")

# Create agent with multiple tools
analyst = Agent(
    role='Business Analyst',
    goal='Cross-reference sales and inventory data',
    tools=[sales_tool, inventory_tool]
)
```

### Error Handling Example
```python
try:
    csv_tool = CSVSearchTool(csv="/path/to/data.csv")
    result = csv_tool.run(
        search_query="important metrics"
    )
    print(result)
except Exception as e:
    print(f"Error processing CSV: {str(e)}")
```

## Notes

- Inherits from RagTool for semantic search
- Supports dynamic CSV file specification
- Uses embedchain for data processing
- Maintains search context across queries
- Thread-safe operations
- Efficient semantic search capabilities
- Supports various CSV formats
- Handles large datasets effectively
- Preserves CSV structure in search
- Enables natural language queries