crewAI/docs/tools/rag-tool.mdx

---
title: RagTool
description: Base class for Retrieval-Augmented Generation (RAG) tools with flexible adapter support
icon: database
---

## RagTool

The RagTool serves as the base class for all Retrieval-Augmented Generation (RAG) tools in the CrewAI ecosystem. It provides a flexible adapter-based architecture for implementing knowledge base functionality with semantic search capabilities.

## Installation

```bash
pip install 'crewai[tools]'
```

## Usage Example

```python
from crewai import Agent
from crewai_tools import RagTool
from crewai_tools.adapters import EmbedchainAdapter
from embedchain import App

# Create custom adapter
class CustomAdapter(RagTool.Adapter):
    def query(self, question: str) -> str:
        # Implement custom query logic
        return "Answer based on knowledge base"

    def add(self, *args, **kwargs) -> None:
        # Implement custom add logic
        pass

# Method 1: Use default EmbedchainAdapter
rag_tool = RagTool(
    name="Custom Knowledge Base",
    description="Specialized knowledge base for domain data",
    summarize=True
)

# Method 2: Use custom adapter
custom_tool = RagTool(
    name="Custom Knowledge Base",
    adapter=CustomAdapter(),
    summarize=False
)

# Create an agent with the tool
researcher = Agent(
    role='Knowledge Base Researcher',
    goal='Search and analyze knowledge base content',
    backstory='Expert at finding relevant information in specialized datasets.',
    tools=[rag_tool],
    verbose=True
)
```

## Adapter Interface

```python
class Adapter(BaseModel, ABC):
    @abstractmethod
    def query(self, question: str) -> str:
        """
        Query the knowledge base with a question.
        
        Args:
            question (str): Query to search in knowledge base
            
        Returns:
            str: Answer based on knowledge base content
        """
        
    @abstractmethod
    def add(self, *args: Any, **kwargs: Any) -> None:
        """
        Add content to the knowledge base.
        
        Args:
            *args: Variable length argument list
            **kwargs: Arbitrary keyword arguments
        """
```

## Function Signature

```python
def __init__(
    self,
    name: str = "Knowledge base",
    description: str = "A knowledge base that can be used to answer questions.",
    summarize: bool = False,
    adapter: Optional[Adapter] = None,
    config: Optional[dict[str, Any]] = None,
    **kwargs
):
    """
    Initialize the RAG tool.

    Args:
        name (str): Tool name
        description (str): Tool description
        summarize (bool): Enable answer summarization
        adapter (Optional[Adapter]): Custom adapter implementation
        config (Optional[dict]): Configuration for default adapter
        **kwargs: Additional arguments for base tool
    """

def _run(
    self,
    query: str,
    **kwargs: Any
) -> str:
    """
    Execute query against knowledge base.

    Args:
        query (str): Question to ask
        **kwargs: Additional arguments

    Returns:
        str: Answer from knowledge base
    """
```

## Best Practices

1. Adapter Implementation:
   - Define clear interfaces
   - Handle edge cases
   - Implement error handling
   - Document behavior

2. Knowledge Base Management:
   - Organize content logically
   - Update content regularly
   - Monitor performance
   - Handle large datasets

3. Query Optimization:
   - Structure queries clearly
   - Consider context
   - Handle ambiguity
   - Validate inputs

4. Error Handling:
   - Handle missing data
   - Manage timeouts
   - Provide clear messages
   - Log issues

## Integration Example

```python
from crewai import Agent, Task, Crew
from crewai_tools import RagTool
from embedchain import App

# Initialize tool with custom configuration
rag_tool = RagTool(
    name="Technical Documentation KB",
    description="Knowledge base for technical documentation",
    summarize=True,
    config={
        "collection_name": "tech_docs",
        "chunking": {
            "chunk_size": 500,
            "chunk_overlap": 50
        }
    }
)

# Add content to knowledge base
rag_tool.add(
    "Technical documentation content here...",
    data_type="text"
)

# Create agent
researcher = Agent(
    role='Documentation Expert',
    goal='Extract technical information from documentation',
    backstory='Expert at analyzing technical documentation.',
    tools=[rag_tool]
)

# Define task
research_task = Task(
    description="""Find all mentions of API endpoints 
    and their authentication requirements.""",
    agent=researcher
)

# Create crew
crew = Crew(
    agents=[researcher],
    tasks=[research_task]
)

# Execute
result = crew.kickoff()
```

## Advanced Usage

### Custom Adapter Implementation
```python
from typing import Any
from pydantic import BaseModel
from abc import ABC, abstractmethod

class SpecializedAdapter(RagTool.Adapter):
    def __init__(self, config: dict):
        self.config = config
        self.knowledge_base = {}

    def query(self, question: str) -> str:
        # Implement specialized query logic
        return self._process_query(question)

    def add(self, content: str, **kwargs: Any) -> None:
        # Implement specialized content addition
        self._process_content(content, **kwargs)

# Use custom adapter
specialized_tool = RagTool(
    name="Specialized KB",
    adapter=SpecializedAdapter(config={"mode": "advanced"})
)
```

### Configuration Management
```python
# Configure default EmbedchainAdapter
config = {
    "collection_name": "custom_collection",
    "embedding": {
        "model": "sentence-transformers/all-mpnet-base-v2",
        "dimensions": 768
    },
    "chunking": {
        "chunk_size": 1000,
        "chunk_overlap": 100
    }
}

tool = RagTool(config=config)
```

### Error Handling Example
```python
try:
    rag_tool = RagTool()
    
    # Add content
    rag_tool.add(
        "Documentation content...",
        data_type="text"
    )
    
    # Query content
    result = rag_tool.run(
        query="What are the system requirements?"
    )
    print(result)
except Exception as e:
    print(f"Error using knowledge base: {str(e)}")
```

## Notes

- Base class for RAG tools
- Flexible adapter pattern
- Default EmbedchainAdapter
- Custom adapter support
- Content management
- Query processing
- Error handling
- Configuration options
- Performance optimization
- Memory management