`RagTool`

Description

The RagTool is designed to answer questions by leveraging the power of Retrieval-Augmented Generation (RAG) through CrewAI’s native RAG system. 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

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, a CrewAIRagAdapter will be used.
config: Optional. Configuration for the underlying CrewAI RAG system. Accepts a RagToolConfig TypedDict with optional embedding_model (ProviderSpec) and vectordb (VectorDbConfig) keys. All configuration values provided programmatically take precedence over environment variables.

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")

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]
    )

Advanced Configuration

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

Code

from crewai_tools import RagTool
from crewai_tools.tools.rag import RagToolConfig, VectorDbConfig, ProviderSpec

# Create a RAG tool with custom configuration

vectordb: VectorDbConfig = {
    "provider": "qdrant",
    "config": {
        "collection_name": "my-collection"
    }
}

embedding_model: ProviderSpec = {
    "provider": "openai",
    "config": {
        "model_name": "text-embedding-3-small"
    }
}

config: RagToolConfig = {
    "vectordb": vectordb,
    "embedding_model": embedding_model
}

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

Embedding Model Configuration

The embedding_model parameter accepts a crewai.rag.embeddings.types.ProviderSpec dictionary with the structure:

{
    "provider": "provider-name",  # Required
    "config": {                    # Optional
        # Provider-specific configuration
    }
}

Supported Providers

OpenAI

main.py

from crewai.rag.embeddings.providers.openai.types import OpenAIProviderSpec

embedding_model: OpenAIProviderSpec = {
    "provider": "openai",
    "config": {
        "api_key": "your-api-key",
        "model_name": "text-embedding-ada-002",
        "dimensions": 1536,
        "organization_id": "your-org-id",
        "api_base": "https://api.openai.com/v1",
        "api_version": "v1",
        "default_headers": {"Custom-Header": "value"}
    }
}

Config Options:

api_key (str): OpenAI API key
model_name (str): Model to use. Default: text-embedding-ada-002. Options: text-embedding-3-small, text-embedding-3-large, text-embedding-ada-002
dimensions (int): Number of dimensions for the embedding
organization_id (str): OpenAI organization ID
api_base (str): Custom API base URL
api_version (str): API version
default_headers (dict): Custom headers for API requests

Environment Variables:

OPENAI_API_KEY or EMBEDDINGS_OPENAI_API_KEY: api_key
OPENAI_ORGANIZATION_ID or EMBEDDINGS_OPENAI_ORGANIZATION_ID: organization_id
OPENAI_MODEL_NAME or EMBEDDINGS_OPENAI_MODEL_NAME: model_name
OPENAI_API_BASE or EMBEDDINGS_OPENAI_API_BASE: api_base
OPENAI_API_VERSION or EMBEDDINGS_OPENAI_API_VERSION: api_version
OPENAI_DIMENSIONS or EMBEDDINGS_OPENAI_DIMENSIONS: dimensions

Cohere

main.py

from crewai.rag.embeddings.providers.cohere.types import CohereProviderSpec

embedding_model: CohereProviderSpec = {
    "provider": "cohere",
    "config": {
        "api_key": "your-api-key",
        "model_name": "embed-english-v3.0"
    }
}

Config Options:

api_key (str): Cohere API key
model_name (str): Model to use. Default: large. Options: embed-english-v3.0, embed-multilingual-v3.0, large, small

Environment Variables:

COHERE_API_KEY or EMBEDDINGS_COHERE_API_KEY: api_key
EMBEDDINGS_COHERE_MODEL_NAME: model_name

VoyageAI

main.py

from crewai.rag.embeddings.providers.voyageai.types import VoyageAIProviderSpec

embedding_model: VoyageAIProviderSpec = {
    "provider": "voyageai",
    "config": {
        "api_key": "your-api-key",
        "model": "voyage-3",
        "input_type": "document",
        "truncation": True,
        "output_dtype": "float32",
        "output_dimension": 1024,
        "max_retries": 3,
        "timeout": 60.0
    }
}

Config Options:

api_key (str): VoyageAI API key
model (str): Model to use. Default: voyage-2. Options: voyage-3, voyage-3-lite, voyage-code-3, voyage-large-2
input_type (str): Type of input. Options: document (for storage), query (for search)
truncation (bool): Whether to truncate inputs that exceed max length. Default: True
output_dtype (str): Output data type
output_dimension (int): Dimension of output embeddings
max_retries (int): Maximum number of retry attempts. Default: 0
timeout (float): Request timeout in seconds

Environment Variables:

VOYAGEAI_API_KEY or EMBEDDINGS_VOYAGEAI_API_KEY: api_key
VOYAGEAI_MODEL or EMBEDDINGS_VOYAGEAI_MODEL: model
VOYAGEAI_INPUT_TYPE or EMBEDDINGS_VOYAGEAI_INPUT_TYPE: input_type
VOYAGEAI_TRUNCATION or EMBEDDINGS_VOYAGEAI_TRUNCATION: truncation
VOYAGEAI_OUTPUT_DTYPE or EMBEDDINGS_VOYAGEAI_OUTPUT_DTYPE: output_dtype
VOYAGEAI_OUTPUT_DIMENSION or EMBEDDINGS_VOYAGEAI_OUTPUT_DIMENSION: output_dimension
VOYAGEAI_MAX_RETRIES or EMBEDDINGS_VOYAGEAI_MAX_RETRIES: max_retries
VOYAGEAI_TIMEOUT or EMBEDDINGS_VOYAGEAI_TIMEOUT: timeout

Ollama

main.py

from crewai.rag.embeddings.providers.ollama.types import OllamaProviderSpec

embedding_model: OllamaProviderSpec = {
    "provider": "ollama",
    "config": {
        "model_name": "llama2",
        "url": "http://localhost:11434/api/embeddings"
    }
}

Config Options:

model_name (str): Ollama model name (e.g., llama2, mistral, nomic-embed-text)
url (str): Ollama API endpoint URL. Default: http://localhost:11434/api/embeddings

Environment Variables:

OLLAMA_MODEL or EMBEDDINGS_OLLAMA_MODEL: model_name
OLLAMA_URL or EMBEDDINGS_OLLAMA_URL: url

Amazon Bedrock

main.py

from crewai.rag.embeddings.providers.aws.types import BedrockProviderSpec

embedding_model: BedrockProviderSpec = {
    "provider": "amazon-bedrock",
    "config": {
        "model_name": "amazon.titan-embed-text-v2:0",
        "session": boto3_session
    }
}

Config Options:

model_name (str): Bedrock model ID. Default: amazon.titan-embed-text-v1. Options: amazon.titan-embed-text-v1, amazon.titan-embed-text-v2:0, cohere.embed-english-v3, cohere.embed-multilingual-v3
session (Any): Boto3 session object for AWS authentication

Environment Variables:

AWS_ACCESS_KEY_ID: AWS access key
AWS_SECRET_ACCESS_KEY: AWS secret key
AWS_REGION: AWS region (e.g., us-east-1)

Azure OpenAI

main.py

from crewai.rag.embeddings.providers.microsoft.types import AzureProviderSpec

embedding_model: AzureProviderSpec = {
    "provider": "azure",
    "config": {
        "deployment_id": "your-deployment-id",
        "api_key": "your-api-key",
        "api_base": "https://your-resource.openai.azure.com",
        "api_version": "2024-02-01",
        "model_name": "text-embedding-ada-002",
        "api_type": "azure"
    }
}

Config Options:

deployment_id (str): Required - Azure OpenAI deployment ID
api_key (str): Azure OpenAI API key
api_base (str): Azure OpenAI resource endpoint
api_version (str): API version. Example: 2024-02-01
model_name (str): Model name. Default: text-embedding-ada-002
api_type (str): API type. Default: azure
dimensions (int): Output dimensions
default_headers (dict): Custom headers

Environment Variables:

AZURE_OPENAI_API_KEY or EMBEDDINGS_AZURE_API_KEY: api_key
AZURE_OPENAI_ENDPOINT or EMBEDDINGS_AZURE_API_BASE: api_base
EMBEDDINGS_AZURE_DEPLOYMENT_ID: deployment_id
EMBEDDINGS_AZURE_API_VERSION: api_version
EMBEDDINGS_AZURE_MODEL_NAME: model_name
EMBEDDINGS_AZURE_API_TYPE: api_type
EMBEDDINGS_AZURE_DIMENSIONS: dimensions

Google Generative AI

main.py

from crewai.rag.embeddings.providers.google.types import GenerativeAiProviderSpec

embedding_model: GenerativeAiProviderSpec = {
    "provider": "google-generativeai",
    "config": {
        "api_key": "your-api-key",
        "model_name": "gemini-embedding-001",
        "task_type": "RETRIEVAL_DOCUMENT"
    }
}

Config Options:

api_key (str): Google AI API key
model_name (str): Model name. Default: gemini-embedding-001. Options: gemini-embedding-001, text-embedding-005, text-multilingual-embedding-002
task_type (str): Task type for embeddings. Default: RETRIEVAL_DOCUMENT. Options: RETRIEVAL_DOCUMENT, RETRIEVAL_QUERY

Environment Variables:

GOOGLE_API_KEY, GEMINI_API_KEY, or EMBEDDINGS_GOOGLE_API_KEY: api_key
EMBEDDINGS_GOOGLE_GENERATIVE_AI_MODEL_NAME: model_name
EMBEDDINGS_GOOGLE_GENERATIVE_AI_TASK_TYPE: task_type

Google Vertex AI

main.py

from crewai.rag.embeddings.providers.google.types import VertexAIProviderSpec

embedding_model: VertexAIProviderSpec = {
    "provider": "google-vertex",
    "config": {
        "model_name": "text-embedding-004",
        "project_id": "your-project-id",
        "region": "us-central1",
        "api_key": "your-api-key"
    }
}

Config Options:

model_name (str): Model name. Default: textembedding-gecko. Options: text-embedding-004, textembedding-gecko, textembedding-gecko-multilingual
project_id (str): Google Cloud project ID. Default: cloud-large-language-models
region (str): Google Cloud region. Default: us-central1
api_key (str): API key for authentication

Environment Variables:

GOOGLE_APPLICATION_CREDENTIALS: Path to service account JSON file
GOOGLE_CLOUD_PROJECT or EMBEDDINGS_GOOGLE_VERTEX_PROJECT_ID: project_id
EMBEDDINGS_GOOGLE_VERTEX_MODEL_NAME: model_name
EMBEDDINGS_GOOGLE_VERTEX_REGION: region
EMBEDDINGS_GOOGLE_VERTEX_API_KEY: api_key

Jina AI

main.py

from crewai.rag.embeddings.providers.jina.types import JinaProviderSpec

embedding_model: JinaProviderSpec = {
    "provider": "jina",
    "config": {
        "api_key": "your-api-key",
        "model_name": "jina-embeddings-v3"
    }
}

Config Options:

api_key (str): Jina AI API key
model_name (str): Model name. Default: jina-embeddings-v2-base-en. Options: jina-embeddings-v3, jina-embeddings-v2-base-en, jina-embeddings-v2-small-en

Environment Variables:

JINA_API_KEY or EMBEDDINGS_JINA_API_KEY: api_key
EMBEDDINGS_JINA_MODEL_NAME: model_name

HuggingFace

main.py

from crewai.rag.embeddings.providers.huggingface.types import HuggingFaceProviderSpec

embedding_model: HuggingFaceProviderSpec = {
    "provider": "huggingface",
    "config": {
        "url": "https://api-inference.huggingface.co/models/sentence-transformers/all-MiniLM-L6-v2"
    }
}

Config Options:

url (str): Full URL to HuggingFace inference API endpoint

Environment Variables:

HUGGINGFACE_URL or EMBEDDINGS_HUGGINGFACE_URL: url

Instructor

main.py

from crewai.rag.embeddings.providers.instructor.types import InstructorProviderSpec

embedding_model: InstructorProviderSpec = {
    "provider": "instructor",
    "config": {
        "model_name": "hkunlp/instructor-xl",
        "device": "cuda",
        "instruction": "Represent the document"
    }
}

Config Options:

model_name (str): HuggingFace model ID. Default: hkunlp/instructor-base. Options: hkunlp/instructor-xl, hkunlp/instructor-large, hkunlp/instructor-base
device (str): Device to run on. Default: cpu. Options: cpu, cuda, mps
instruction (str): Instruction prefix for embeddings

Environment Variables:

EMBEDDINGS_INSTRUCTOR_MODEL_NAME: model_name
EMBEDDINGS_INSTRUCTOR_DEVICE: device
EMBEDDINGS_INSTRUCTOR_INSTRUCTION: instruction

Sentence Transformer

main.py

from crewai.rag.embeddings.providers.sentence_transformer.types import SentenceTransformerProviderSpec

embedding_model: SentenceTransformerProviderSpec = {
    "provider": "sentence-transformer",
    "config": {
        "model_name": "all-mpnet-base-v2",
        "device": "cuda",
        "normalize_embeddings": True
    }
}

Config Options:

model_name (str): Sentence Transformers model name. Default: all-MiniLM-L6-v2. Options: all-mpnet-base-v2, all-MiniLM-L6-v2, paraphrase-multilingual-MiniLM-L12-v2
device (str): Device to run on. Default: cpu. Options: cpu, cuda, mps
normalize_embeddings (bool): Whether to normalize embeddings. Default: False

Environment Variables:

EMBEDDINGS_SENTENCE_TRANSFORMER_MODEL_NAME: model_name
EMBEDDINGS_SENTENCE_TRANSFORMER_DEVICE: device
EMBEDDINGS_SENTENCE_TRANSFORMER_NORMALIZE_EMBEDDINGS: normalize_embeddings

ONNX

main.py

from crewai.rag.embeddings.providers.onnx.types import ONNXProviderSpec

embedding_model: ONNXProviderSpec = {
    "provider": "onnx",
    "config": {
        "preferred_providers": ["CUDAExecutionProvider", "CPUExecutionProvider"]
    }
}

Config Options:

preferred_providers (list[str]): List of ONNX execution providers in order of preference

Environment Variables:

EMBEDDINGS_ONNX_PREFERRED_PROVIDERS: preferred_providers (comma-separated list)

OpenCLIP

main.py

from crewai.rag.embeddings.providers.openclip.types import OpenCLIPProviderSpec

embedding_model: OpenCLIPProviderSpec = {
    "provider": "openclip",
    "config": {
        "model_name": "ViT-B-32",
        "checkpoint": "laion2b_s34b_b79k",
        "device": "cuda"
    }
}

Config Options:

model_name (str): OpenCLIP model architecture. Default: ViT-B-32. Options: ViT-B-32, ViT-B-16, ViT-L-14
checkpoint (str): Pretrained checkpoint name. Default: laion2b_s34b_b79k. Options: laion2b_s34b_b79k, laion400m_e32, openai
device (str): Device to run on. Default: cpu. Options: cpu, cuda

Environment Variables:

EMBEDDINGS_OPENCLIP_MODEL_NAME: model_name
EMBEDDINGS_OPENCLIP_CHECKPOINT: checkpoint
EMBEDDINGS_OPENCLIP_DEVICE: device

Text2Vec

main.py

from crewai.rag.embeddings.providers.text2vec.types import Text2VecProviderSpec

embedding_model: Text2VecProviderSpec = {
    "provider": "text2vec",
    "config": {
        "model_name": "shibing624/text2vec-base-multilingual"
    }
}

Config Options:

model_name (str): Text2Vec model name from HuggingFace. Default: shibing624/text2vec-base-chinese. Options: shibing624/text2vec-base-multilingual, shibing624/text2vec-base-chinese

Environment Variables:

EMBEDDINGS_TEXT2VEC_MODEL_NAME: model_name

Roboflow

main.py

from crewai.rag.embeddings.providers.roboflow.types import RoboflowProviderSpec

embedding_model: RoboflowProviderSpec = {
    "provider": "roboflow",
    "config": {
        "api_key": "your-api-key",
        "api_url": "https://infer.roboflow.com"
    }
}

Config Options:

api_key (str): Roboflow API key. Default: "" (empty string)
api_url (str): Roboflow inference API URL. Default: https://infer.roboflow.com

Environment Variables:

ROBOFLOW_API_KEY or EMBEDDINGS_ROBOFLOW_API_KEY: api_key
ROBOFLOW_API_URL or EMBEDDINGS_ROBOFLOW_API_URL: api_url

WatsonX (IBM)

main.py

from crewai.rag.embeddings.providers.ibm.types import WatsonXProviderSpec

embedding_model: WatsonXProviderSpec = {
    "provider": "watsonx",
    "config": {
        "model_id": "ibm/slate-125m-english-rtrvr",
        "url": "https://us-south.ml.cloud.ibm.com",
        "api_key": "your-api-key",
        "project_id": "your-project-id",
        "batch_size": 100,
        "concurrency_limit": 10,
        "persistent_connection": True
    }
}

Config Options:

model_id (str): WatsonX model identifier
url (str): WatsonX API endpoint
api_key (str): IBM Cloud API key
project_id (str): WatsonX project ID
space_id (str): WatsonX space ID (alternative to project_id)
batch_size (int): Batch size for embeddings. Default: 100
concurrency_limit (int): Maximum concurrent requests. Default: 10
persistent_connection (bool): Use persistent connections. Default: True
Plus 20+ additional authentication and configuration options

Environment Variables:

WATSONX_API_KEY or EMBEDDINGS_WATSONX_API_KEY: api_key
WATSONX_URL or EMBEDDINGS_WATSONX_URL: url
WATSONX_PROJECT_ID or EMBEDDINGS_WATSONX_PROJECT_ID: project_id
EMBEDDINGS_WATSONX_MODEL_ID: model_id
EMBEDDINGS_WATSONX_SPACE_ID: space_id
EMBEDDINGS_WATSONX_BATCH_SIZE: batch_size
EMBEDDINGS_WATSONX_CONCURRENCY_LIMIT: concurrency_limit
EMBEDDINGS_WATSONX_PERSISTENT_CONNECTION: persistent_connection

Custom

main.py

from crewai.rag.core.base_embeddings_callable import EmbeddingFunction
from crewai.rag.embeddings.providers.custom.types import CustomProviderSpec

class MyEmbeddingFunction(EmbeddingFunction):
    def __call__(self, input):
        # Your custom embedding logic
        return embeddings

embedding_model: CustomProviderSpec = {
    "provider": "custom",
    "config": {
        "embedding_callable": MyEmbeddingFunction
    }
}

Config Options:

embedding_callable (type[EmbeddingFunction]): Custom embedding function class

Note: Custom embedding functions must implement the EmbeddingFunction protocol defined in crewai.rag.core.base_embeddings_callable. The __call__ method should accept input data and return embeddings as a list of numpy arrays (or compatible format that will be normalized). The returned embeddings are automatically normalized and validated.

Notes

All config fields are optional unless marked as Required
API keys can typically be provided via environment variables instead of config
Default values are shown where applicable

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.

​RagTool

​Description

​Example

​Supported Data Sources

​Parameters

​Adding Content

​Agent Integration Example

​Advanced Configuration

​Embedding Model Configuration

​Supported Providers

​Notes

​Conclusion

`RagTool`

Description

Example

Supported Data Sources

Parameters

Adding Content

Agent Integration Example

Advanced Configuration

Embedding Model Configuration

Supported Providers

Notes

Conclusion