crewAI/docs/edge/en/tools/file-document/txtsearchtool.mdx

---
title: TXT RAG Search
description: The `TXTSearchTool` is designed to perform a RAG (Retrieval-Augmented Generation) search within the content of a text file.
icon: file-lines
mode: "wide"
---

## Overview

<Note>
    We are still working on improving tools, so there might be unexpected behavior or changes in the future.
</Note>

This tool is used to perform a RAG (Retrieval-Augmented Generation) search within the content of a text file. 
It allows for semantic searching of a query within a specified text file's content, 
making it an invaluable resource for quickly extracting information or finding specific sections of text based on the query provided.

## Installation

To use the `TXTSearchTool`, you first need to install the `crewai_tools` package. 
This can be done using pip, a package manager for Python. 
Open your terminal or command prompt and enter the following command:

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

This command will download and install the TXTSearchTool along with any necessary dependencies.

## Example

The following example demonstrates how to use the TXTSearchTool to search within a text file. 
This example shows both the initialization of the tool with a specific text file and the subsequent search within that file's content.

```python Code
from crewai_tools import TXTSearchTool

# Initialize the tool to search within any text file's content 
# the agent learns about during its execution
tool = TXTSearchTool()

# OR

# Initialize the tool with a specific text file, 
# so the agent can search within the given text file's content
tool = TXTSearchTool(txt='path/to/text/file.txt')
```

## Arguments
- `txt` (str): **Optional**. The path to the text file you want to search. 
This argument is only required if the tool was not initialized with a specific text file; 
otherwise, the search will be conducted within the initially provided text file.

## Custom model and embeddings

By default, the tool uses OpenAI for both embeddings and summarization. 
To customize the model, you can use a config dictionary as follows:

```python Code
from chromadb.config import Settings

tool = TXTSearchTool(
    config={
        # Required: embeddings provider + config
        "embedding_model": {
            "provider": "openai",  # or google-generativeai, cohere, ollama, ...
            "config": {
                "model": "text-embedding-3-small",
                # "api_key": "sk-...",  # optional if env var is set (e.g., OPENAI_API_KEY or EMBEDDINGS_OPENAI_API_KEY)
                # Provider examples:
                # Google → model_name: "gemini-embedding-001", task_type: "RETRIEVAL_DOCUMENT"
                # Cohere → model: "embed-english-v3.0"
                # Ollama → model: "nomic-embed-text"
            },
        },

        # Required: vector database config
        "vectordb": {
            "provider": "chromadb",  # or "qdrant"
            "config": {
                # Chroma settings (optional persistence)
                # "settings": Settings(
                #     persist_directory="/content/chroma",
                #     allow_reset=True,
                #     is_persistent=True,
                # ),

                # Qdrant vector params example:
                # from qdrant_client.models import VectorParams, Distance
                # "vectors_config": VectorParams(size=384, distance=Distance.COSINE),

                # Note: collection name is controlled by the tool (default: "rag_tool_collection").
            }
        },
    }
)
```