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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:
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]
    )

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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:
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")

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Agent Integration Example

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

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Advanced Configuration

You can customize the behavior of the RagTool by providing a configuration dictionary:
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 for details. Make sure to review the configuration options available in the .yaml file.

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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.