crewAI/docs/tools/ragtool.mdx

---
title: RAG Tool
description: The `RagTool` is a dynamic knowledge base tool for answering questions using Retrieval-Augmented Generation.
icon: vector-square
---

# `RagTool`

## Description

The `RagTool` is designed to answer questions by leveraging the power of Retrieval-Augmented Generation (RAG) through EmbedChain.
It provides a dynamic knowledge base that can be queried to retrieve relevant information from various data sources.
This tool is particularly useful for applications that require access to a vast array of information and need to provide contextually relevant answers.

## Example

The following example demonstrates how to initialize the tool and use it with different data sources:

```python Code
from crewai_tools import RagTool

# Create a RAG tool with default settings
rag_tool = RagTool()

# Add content from a file
rag_tool.add(data_type="file", path="path/to/your/document.pdf")

# Add content from a web page
rag_tool.add(data_type="web_page", url="https://example.com")

# Define an agent with the RagTool
@agent
def knowledge_expert(self) -> Agent:
    '''
    This agent uses the RagTool to answer questions about the knowledge base.
    '''
    return Agent(
        config=self.agents_config["knowledge_expert"],
        allow_delegation=False,
        tools=[rag_tool]
    )
```

## Supported Data Sources

The `RagTool` can be used with a wide variety of data sources, including:

- 📰 PDF files
- 📊 CSV files
- 📃 JSON files
- 📝 Text
- 📁 Directories/Folders
- 🌐 HTML Web pages
- 📽️ YouTube Channels
- 📺 YouTube Videos
- 📚 Documentation websites
- 📝 MDX files
- 📄 DOCX files
- 🧾 XML files
- 📬 Gmail
- 📝 GitHub repositories
- 🐘 PostgreSQL databases
- 🐬 MySQL databases
- 🤖 Slack conversations
- 💬 Discord messages
- 🗨️ Discourse forums
- 📝 Substack newsletters
- 🐝 Beehiiv content
- 💾 Dropbox files
- 🖼️ Images
- ⚙️ Custom data sources

## Parameters

The `RagTool` accepts the following parameters:

- **summarize**: Optional. Whether to summarize the retrieved content. Default is `False`.
- **adapter**: Optional. A custom adapter for the knowledge base. If not provided, an EmbedchainAdapter will be used.
- **config**: Optional. Configuration for the underlying EmbedChain App.

## Adding Content

You can add content to the knowledge base using the `add` method:

```python Code
# Add a PDF file
rag_tool.add(data_type="file", path="path/to/your/document.pdf")

# Add a web page
rag_tool.add(data_type="web_page", url="https://example.com")

# Add a YouTube video
rag_tool.add(data_type="youtube_video", url="https://www.youtube.com/watch?v=VIDEO_ID")

# Add a directory of files
rag_tool.add(data_type="directory", path="path/to/your/directory")
```

## Agent Integration Example

Here's how to integrate the `RagTool` with a CrewAI agent:

```python Code
from crewai import Agent
from crewai.project import agent
from crewai_tools import RagTool

# Initialize the tool and add content
rag_tool = RagTool()
rag_tool.add(data_type="web_page", url="https://docs.crewai.com")
rag_tool.add(data_type="file", path="company_data.pdf")

# Define an agent with the RagTool
@agent
def knowledge_expert(self) -> Agent:
    return Agent(
        config=self.agents_config["knowledge_expert"],
        allow_delegation=False,
        tools=[rag_tool]
    )
```

## Advanced Configuration

You can customize the behavior of the `RagTool` by providing a configuration dictionary:

```python Code
from crewai_tools import RagTool

# Create a RAG tool with custom configuration
config = {
    "app": {
        "name": "custom_app",
    },
    "llm": {
        "provider": "openai",
        "config": {
            "model": "gpt-4",
        }
    },
    "embedding_model": {
        "provider": "openai",
        "config": {
            "model": "text-embedding-ada-002"
        }
    },
    "vectordb": {
        "provider": "elasticsearch",
        "config": {
            "collection_name": "my-collection",
            "cloud_id": "deployment-name:xxxx",
            "api_key": "your-key",
            "verify_certs": False
        }
    },
    "chunker": {
        "chunk_size": 400,
        "chunk_overlap": 100,
        "length_function": "len",
        "min_chunk_size": 0
    }
}

rag_tool = RagTool(config=config, summarize=True)
```

The internal RAG tool utilizes the Embedchain adapter, allowing you to pass any configuration options that are supported by Embedchain.
You can refer to the [Embedchain documentation](https://docs.embedchain.ai/components/introduction) for details.
Make sure to review the configuration options available in the .yaml file.

## Conclusion
The `RagTool` provides a powerful way to create and query knowledge bases from various data sources. By leveraging Retrieval-Augmented Generation, it enables agents to access and retrieve relevant information efficiently, enhancing their ability to provide accurate and contextually appropriate responses.