---
title: PDFSearchTool
description: A tool for semantic search within PDF documents using RAG capabilities
icon: file-search
---

## PDFSearchTool

The PDFSearchTool enables semantic search capabilities for PDF documents using Retrieval-Augmented Generation (RAG). It leverages embedchain's PDFEmbedchainAdapter for efficient PDF processing and supports both fixed and dynamic PDF path specification.

## Installation

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

## Usage Example

```python
from crewai import Agent
from crewai_tools import PDFSearchTool

# Method 1: Initialize with specific PDF
pdf_tool = PDFSearchTool(pdf="/path/to/document.pdf")

# Method 2: Initialize without PDF (specify at runtime)
flexible_pdf_tool = PDFSearchTool()

# Create an agent with the tool
researcher = Agent(
    role='PDF Researcher',
    goal='Search and analyze PDF documents',
    backstory='Expert at finding relevant information in PDFs.',
    tools=[pdf_tool],
    verbose=True
)
```

## Input Schema

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

### Flexible PDF Schema (when PDF path provided at runtime)
```python
class PDFSearchToolSchema(FixedPDFSearchToolSchema):
    pdf: str = Field(
        description="Mandatory pdf path you want to search"
    )
```

## Function Signature

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

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

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

    Args:
        query (str): Search query for the PDF
        **kwargs: Additional arguments including pdf path if not initialized

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

## Best Practices

1. PDF File Handling:
   - Use absolute paths for reliability
   - Verify PDF file existence
   - Handle large PDFs appropriately

2. Search Optimization:
   - Use specific, focused queries
   - Consider document structure
   - Test with sample queries first

3. Performance Considerations:
   - Pre-initialize with PDF for repeated searches
   - Handle large documents efficiently
   - Monitor memory usage

4. Error Handling:
   - Verify PDF file existence
   - Handle malformed PDFs
   - Manage file access permissions

## Integration Example

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

# Initialize tool with specific PDF
pdf_tool = PDFSearchTool(pdf="/path/to/research.pdf")

# Create agent
researcher = Agent(
    role='PDF Researcher',
    goal='Extract insights from research papers',
    backstory='Expert at analyzing research documents.',
    tools=[pdf_tool]
)

# Define task
research_task = Task(
    description="""Find all mentions of machine learning 
    applications in healthcare from the PDF.""",
    agent=researcher
)

# The tool will use:
# {
#   "query": "machine learning applications healthcare"
# }

# Create crew
crew = Crew(
    agents=[researcher],
    tasks=[research_task]
)

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

## Advanced Usage

### Dynamic PDF Selection
```python
# Initialize without PDF
flexible_tool = PDFSearchTool()

# Search different PDFs
research_results = flexible_tool.run(
    query="quantum computing",
    pdf="/path/to/research.pdf"
)

report_results = flexible_tool.run(
    query="financial metrics",
    pdf="/path/to/report.pdf"
)
```

### Multiple PDF Analysis
```python
# Create tools for different PDFs
research_tool = PDFSearchTool(pdf="/path/to/research.pdf")
report_tool = PDFSearchTool(pdf="/path/to/report.pdf")

# Create agent with multiple tools
analyst = Agent(
    role='Document Analyst',
    goal='Cross-reference multiple documents',
    tools=[research_tool, report_tool]
)
```

### Error Handling Example
```python
try:
    pdf_tool = PDFSearchTool()
    results = pdf_tool.run(
        query="important findings",
        pdf="/path/to/document.pdf"
    )
    print(results)
except Exception as e:
    print(f"Error processing PDF: {str(e)}")
```

## Notes

- Inherits from RagTool
- Uses PDFEmbedchainAdapter
- Supports semantic search
- Dynamic PDF specification
- Efficient content retrieval
- Thread-safe operations
- Maintains search context
- Handles large documents
- Supports various PDF formats
- Memory-efficient processing