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`
  </Accordion>

  <Accordion title="VoyageAI">
```python
    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`
  </Accordion>

  <Accordion title="Ollama">
```python
    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`
  </Accordion>

  <Accordion title="Amazon Bedrock">
```python
    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`)
  </Accordion>

  <Accordion title="Azure OpenAI">
```python
    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`
  </Accordion>

  <Accordion title="Google Generative AI">
```python
    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`
  </Accordion>

  <Accordion title="Google Vertex AI">
```python
    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`
  </Accordion>

  <Accordion title="Jina AI">
```python
    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`
  </Accordion>

  <Accordion title="HuggingFace">
```python
    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`
  </Accordion>

  <Accordion title="Instructor">
```python
    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`
  </Accordion>

  <Accordion title="Sentence Transformer">
```python
    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`
  </Accordion>

  <Accordion title="ONNX">
```python
    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)
  </Accordion>

  <Accordion title="OpenCLIP">
```python
    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`
  </Accordion>

  <Accordion title="Text2Vec">
```python
    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`
  </Accordion>

  <Accordion title="Roboflow">
```python
    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`
  </Accordion>

  <Accordion title="WatsonX (IBM)">
```python
    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`
  </Accordion>

  <Accordion title="Custom">
```python
    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.
  </Accordion>
</AccordionGroup>

### 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.