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

---
title: TXTSearchTool
description: A semantic search tool for text files using RAG capabilities
icon: magnifying-glass-document
---

## TXTSearchTool

The TXTSearchTool is a specialized Retrieval-Augmented Generation (RAG) tool that enables semantic search within text files. It inherits from the base RagTool class and provides both fixed and dynamic text file searching capabilities.

## Installation

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

## Usage Example

```python
from crewai import Agent
from crewai_tools import TXTSearchTool

# Method 1: Dynamic file path
txt_search = TXTSearchTool()

# Method 2: Fixed file path
fixed_txt_search = TXTSearchTool(txt="path/to/fixed/document.txt")

# Create an agent with the tool
researcher = Agent(
    role='Research Assistant',
    goal='Search through text documents semantically',
    backstory='Expert at finding relevant information in documents using semantic search.',
    tools=[txt_search],
    verbose=True
)
```

## Input Schema

The tool supports two input schemas depending on initialization:

### Dynamic File Path Schema
```python
class TXTSearchToolSchema(BaseModel):
    search_query: str  # The semantic search query
    txt: str          # Path to the text file to search
```

### Fixed File Path Schema
```python
class FixedTXTSearchToolSchema(BaseModel):
    search_query: str  # The semantic search query
```

## Function Signature

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

    Args:
        txt (Optional[str]): Fixed path to a text file. If provided, the tool will only search this file.
        **kwargs: Additional arguments passed to the parent RagTool
    """

def _run(self, search_query: str, **kwargs: Any) -> Any:
    """
    Perform semantic search on the text file.

    Args:
        search_query (str): The semantic search query
        **kwargs: Additional arguments (including 'txt' for dynamic file path)

    Returns:
        str: Relevant text passages based on semantic search
    """
```

## Best Practices

1. Choose initialization method based on use case:
   - Use fixed file path when repeatedly searching the same document
   - Use dynamic file path when searching different documents
2. Write clear, semantic search queries
3. Handle potential file access errors in agent prompts
4. Consider memory usage for large text files

## Integration Example

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

# Example 1: Fixed document search
documentation_search = TXTSearchTool(txt="api_documentation.txt")

# Example 2: Dynamic document search
flexible_search = TXTSearchTool()

# Create agents
doc_analyst = Agent(
    role='Documentation Analyst',
    goal='Find relevant API documentation sections',
    backstory='Expert at analyzing technical documentation.',
    tools=[documentation_search]
)

file_analyst = Agent(
    role='File Analyst',
    goal='Search through various text files',
    backstory='Specialist in finding information across multiple documents.',
    tools=[flexible_search]
)

# Define tasks
fixed_search_task = Task(
    description="""Find all API endpoints related to user authentication 
    in the documentation.""",
    agent=doc_analyst
)

# The agent will use:
# {
#   "search_query": "user authentication API endpoints"
# }

dynamic_search_task = Task(
    description="""Search through the logs.txt file for any database 
    connection errors.""",
    agent=file_analyst
)

# The agent will use:
# {
#   "search_query": "database connection errors",
#   "txt": "logs.txt"
# }

# Create crew
crew = Crew(
    agents=[doc_analyst, file_analyst],
    tasks=[fixed_search_task, dynamic_search_task]
)

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

## Notes

- Inherits from RagTool for semantic search capabilities
- Supports both fixed and dynamic text file paths
- Uses embeddings for semantic search
- Optimized for text file analysis
- Thread-safe operations
- Automatically handles file loading and embedding