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Write your pageRAG Tool The RagTool is a dynamic knowledge base tool for answering questions using Retrieval-Augmented Generation. ​ 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: Code Copy 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: Code Copy # 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: Code Copy 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: Code Copy 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" } } } rag_tool = RagTool(config=config, summarize=True) ​ 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.
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