> ## Documentation Index
> Fetch the complete documentation index at: https://docs.crewai.com/llms.txt
> Use this file to discover all available pages before exploring further.

# RAG 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 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:

```python Code theme={null}
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:

```python Code theme={null}
# 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:

```python Code theme={null}
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:

```python Code theme={null}
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:

```python theme={null}
{
    "provider": "provider-name",  # Required
    "config": {                    # Optional
        # Provider-specific configuration
    }
}
```

### Supported Providers

> **OpenAI**
>
> ```python main.py theme={null}
> 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**
>
> ```python main.py theme={null}
> 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**
>
> ```python main.py theme={null}
> 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**
>
> ```python main.py theme={null}
> 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**
>
> ```python main.py theme={null}
> 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**
>
> ```python main.py theme={null}
> 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**
>
> ```python main.py theme={null}
> 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**
>
> ```python main.py theme={null}
> 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**
>
> ```python main.py theme={null}
> 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**
>
> ```python main.py theme={null}
> 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**
>
> ```python main.py theme={null}
> 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`, `xpu`
> * `instruction` (str): Instruction prefix for embeddings
>
> **Environment Variables:**
>
> * `EMBEDDINGS_INSTRUCTOR_MODEL_NAME`: `model_name`
> * `EMBEDDINGS_INSTRUCTOR_DEVICE`: `device`
> * `EMBEDDINGS_INSTRUCTOR_INSTRUCTION`: `instruction`

> **Sentence Transformer**
>
> ```python main.py theme={null}
> 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`, `xpu`
> * `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**
>
> ```python main.py theme={null}
> 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**
>
> ```python main.py theme={null}
> 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`, `mps`, `xpu`
>
> **Environment Variables:**
>
> * `EMBEDDINGS_OPENCLIP_MODEL_NAME`: `model_name`
> * `EMBEDDINGS_OPENCLIP_CHECKPOINT`: `checkpoint`
> * `EMBEDDINGS_OPENCLIP_DEVICE`: `device`

> **Text2Vec**
>
> ```python main.py theme={null}
> 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**
>
> ```python main.py theme={null}
> 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)**
>
> ```python main.py theme={null}
> 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**
>
> ```python main.py theme={null}
> 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.


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