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RagTool

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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.

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Example

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

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]
    )

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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

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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.

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Adding Content

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

# 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")

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Agent Integration Example

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

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]
    )

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Advanced Configuration

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

from crewai_tools import RagTool

# Create a RAG tool with custom configuration
config = {
    "app": {
        "name": "custom_app",
    },
    "llm": {
        "provider": "openai",
        "config": {
            "model": "gpt-4",
        }
    },
    "embedder": {
        "provider": "openai",
        "config": {
            "model": "text-embedding-ada-002"
        }
    }
}

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

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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.