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]
    )

# PDF 파일 추가
rag_tool.add(data_type="file", path="path/to/your/document.pdf")

# 웹 페이지 추가
rag_tool.add(data_type="web_page", url="https://example.com")

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

# 파일이 있는 디렉터리 추가
rag_tool.add(data_type="directory", path="path/to/your/directory")

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]
    )

from crewai_tools import RagTool

# 사용자 지정 구성으로 RAG 도구 생성
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)