chore: bump CrewAI version to 0.201.1 and update dependencies in project templates (#3605)

- Update version in __init__.py to 0.201.1
- Modify dependency versions in pyproject.toml for crew, flow, and tool templates to require CrewAI 0.201.1

src/crewai/__init__.py

@@ -40,7 +40,7 @@ def _suppress_pydantic_deprecation_warnings() -> None:
 
 _suppress_pydantic_deprecation_warnings()
 
-__version__ = "0.201.0"
+__version__ = "0.201.1"
 _telemetry_submitted = False
 
 

src/crewai/cli/templates/crew/pyproject.toml

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]>=0.201.0,<1.0.0"
+    "crewai[tools]>=0.201.1,<1.0.0"
 ]
 
 [project.scripts]

src/crewai/cli/templates/flow/pyproject.toml

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]>=0.201.0,<1.0.0",
+    "crewai[tools]>=0.201.1,<1.0.0",
 ]
 
 [project.scripts]

src/crewai/cli/templates/tool/pyproject.toml

@@ -5,7 +5,7 @@ description = "Power up your crews with {{folder_name}}"
 readme = "README.md"
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]>=0.201.0"
+    "crewai[tools]>=0.201.1"
 ]
 
 [tool.crewai]

src/crewai/rag/core/base_embeddings_callable.py

@@ -140,3 +140,10 @@ class EmbeddingFunction(Protocol[D]):
             return validate_embeddings(normalized)
 
         cls.__call__ = wrapped_call  # type: ignore[method-assign]
+
+    def embed_query(self, input: D) -> Embeddings:
+        """
+        Get the embeddings for a query input.
+        This method is optional, and if not implemented, the default behavior is to call __call__.
+        """
+        return self.__call__(input=input)

src/crewai/rag/embeddings/factory.py

@@ -2,8 +2,11 @@
 
 from __future__ import annotations
 
+import warnings
 from typing import TYPE_CHECKING, TypeVar, overload
 
+from typing_extensions import deprecated
+
 from crewai.rag.core.base_embeddings_callable import EmbeddingFunction
 from crewai.rag.core.base_embeddings_provider import BaseEmbeddingsProvider
 from crewai.utilities.import_utils import import_and_validate_definition
@@ -59,9 +62,12 @@ if TYPE_CHECKING:
         HuggingFaceProviderSpec,
     )
     from crewai.rag.embeddings.providers.ibm.embedding_callable import (
-        WatsonEmbeddingFunction,
+        WatsonXEmbeddingFunction,
+    )
+    from crewai.rag.embeddings.providers.ibm.types import (
+        WatsonProviderSpec,
+        WatsonXProviderSpec,
     )
-    from crewai.rag.embeddings.providers.ibm.types import WatsonProviderSpec
     from crewai.rag.embeddings.providers.instructor.types import InstructorProviderSpec
     from crewai.rag.embeddings.providers.jina.types import JinaProviderSpec
     from crewai.rag.embeddings.providers.microsoft.types import AzureProviderSpec
@@ -100,7 +106,8 @@ PROVIDER_PATHS = {
     "sentence-transformer": "crewai.rag.embeddings.providers.sentence_transformer.sentence_transformer_provider.SentenceTransformerProvider",
     "text2vec": "crewai.rag.embeddings.providers.text2vec.text2vec_provider.Text2VecProvider",
     "voyageai": "crewai.rag.embeddings.providers.voyageai.voyageai_provider.VoyageAIProvider",
-    "watson": "crewai.rag.embeddings.providers.ibm.watson.WatsonProvider",
+    "watson": "crewai.rag.embeddings.providers.ibm.watsonx.WatsonXProvider",  # Deprecated alias
+    "watsonx": "crewai.rag.embeddings.providers.ibm.watsonx.WatsonXProvider",
 }
 
 
@@ -169,7 +176,14 @@ def build_embedder_from_dict(
 
 
 @overload
-def build_embedder_from_dict(spec: WatsonProviderSpec) -> WatsonEmbeddingFunction: ...
+def build_embedder_from_dict(spec: WatsonXProviderSpec) -> WatsonXEmbeddingFunction: ...
+
+
+@overload
+@deprecated(
+    'The "WatsonProviderSpec" provider spec is deprecated and will be removed in v1.0.0. Use "WatsonXProviderSpec" instead.'
+)
+def build_embedder_from_dict(spec: WatsonProviderSpec) -> WatsonXEmbeddingFunction: ...
 
 
 @overload
@@ -233,6 +247,14 @@ def build_embedder_from_dict(spec):
     if not provider_name:
         raise ValueError("Missing 'provider' key in specification")
 
+    if provider_name == "watson":
+        warnings.warn(
+            'The "watson" provider key is deprecated and will be removed in v1.0.0. '
+            'Use "watsonx" instead.',
+            DeprecationWarning,
+            stacklevel=2,
+        )
+
     if provider_name not in PROVIDER_PATHS:
         raise ValueError(
             f"Unknown provider: {provider_name}. Available providers: {list(PROVIDER_PATHS.keys())}"
@@ -300,7 +322,14 @@ def build_embedder(spec: VoyageAIProviderSpec) -> VoyageAIEmbeddingFunction: ...
 
 
 @overload
-def build_embedder(spec: WatsonProviderSpec) -> WatsonEmbeddingFunction: ...
+def build_embedder(spec: WatsonXProviderSpec) -> WatsonXEmbeddingFunction: ...
+
+
+@overload
+@deprecated(
+    'The "WatsonProviderSpec" provider spec is deprecated and will be removed in v1.0.0. Use "WatsonXProviderSpec" instead.'
+)
+def build_embedder(spec: WatsonProviderSpec) -> WatsonXEmbeddingFunction: ...
 
 
 @overload

src/crewai/rag/embeddings/providers/aws/bedrock.py

@@ -46,7 +46,7 @@ class BedrockProvider(BaseEmbeddingsProvider[AmazonBedrockEmbeddingFunction]):
     model_name: str = Field(
         default="amazon.titan-embed-text-v1",
         description="Model name to use for embeddings",
-        validation_alias="BEDROCK_MODEL_NAME",
+        validation_alias="EMBEDDINGS_BEDROCK_MODEL_NAME",
     )
     session: Any = Field(
         default_factory=create_aws_session, description="AWS session object"

src/crewai/rag/embeddings/providers/cohere/cohere_provider.py

@@ -15,10 +15,10 @@ class CohereProvider(BaseEmbeddingsProvider[CohereEmbeddingFunction]):
         default=CohereEmbeddingFunction, description="Cohere embedding function class"
     )
     api_key: str = Field(
-        description="Cohere API key", validation_alias="COHERE_API_KEY"
+        description="Cohere API key", validation_alias="EMBEDDINGS_COHERE_API_KEY"
     )
     model_name: str = Field(
         default="large",
         description="Model name to use for embeddings",
-        validation_alias="COHERE_MODEL_NAME",
+        validation_alias="EMBEDDINGS_COHERE_MODEL_NAME",
     )

src/crewai/rag/embeddings/providers/google/generative_ai.py

@@ -18,13 +18,13 @@ class GenerativeAiProvider(BaseEmbeddingsProvider[GoogleGenerativeAiEmbeddingFun
     model_name: str = Field(
         default="models/embedding-001",
         description="Model name to use for embeddings",
-        validation_alias="GOOGLE_GENERATIVE_AI_MODEL_NAME",
+        validation_alias="EMBEDDINGS_GOOGLE_GENERATIVE_AI_MODEL_NAME",
     )
     api_key: str = Field(
-        description="Google API key", validation_alias="GOOGLE_API_KEY"
+        description="Google API key", validation_alias="EMBEDDINGS_GOOGLE_API_KEY"
     )
     task_type: str = Field(
         default="RETRIEVAL_DOCUMENT",
         description="Task type for embeddings",
-        validation_alias="GOOGLE_GENERATIVE_AI_TASK_TYPE",
+        validation_alias="EMBEDDINGS_GOOGLE_GENERATIVE_AI_TASK_TYPE",
     )

src/crewai/rag/embeddings/providers/google/vertex.py

@@ -18,18 +18,18 @@ class VertexAIProvider(BaseEmbeddingsProvider[GoogleVertexEmbeddingFunction]):
     model_name: str = Field(
         default="textembedding-gecko",
         description="Model name to use for embeddings",
-        validation_alias="GOOGLE_VERTEX_MODEL_NAME",
+        validation_alias="EMBEDDINGS_GOOGLE_VERTEX_MODEL_NAME",
     )
     api_key: str = Field(
-        description="Google API key", validation_alias="GOOGLE_CLOUD_API_KEY"
+        description="Google API key", validation_alias="EMBEDDINGS_GOOGLE_CLOUD_API_KEY"
     )
     project_id: str = Field(
         default="cloud-large-language-models",
         description="GCP project ID",
-        validation_alias="GOOGLE_CLOUD_PROJECT",
+        validation_alias="EMBEDDINGS_GOOGLE_CLOUD_PROJECT",
     )
     region: str = Field(
         default="us-central1",
         description="GCP region",
-        validation_alias="GOOGLE_CLOUD_REGION",
+        validation_alias="EMBEDDINGS_GOOGLE_CLOUD_REGION",
     )

src/crewai/rag/embeddings/providers/huggingface/huggingface_provider.py

@@ -16,5 +16,5 @@ class HuggingFaceProvider(BaseEmbeddingsProvider[HuggingFaceEmbeddingServer]):
         description="HuggingFace embedding function class",
     )
     url: str = Field(
-        description="HuggingFace API URL", validation_alias="HUGGINGFACE_URL"
+        description="HuggingFace API URL", validation_alias="EMBEDDINGS_HUGGINGFACE_URL"
     )

src/crewai/rag/embeddings/providers/ibm/__init__.py

@@ -1,15 +1,17 @@
 """IBM embedding providers."""
 
 from crewai.rag.embeddings.providers.ibm.types import (
-    WatsonProviderConfig,
     WatsonProviderSpec,
+    WatsonXProviderConfig,
+    WatsonXProviderSpec,
 )
-from crewai.rag.embeddings.providers.ibm.watson import (
-    WatsonProvider,
+from crewai.rag.embeddings.providers.ibm.watsonx import (
+    WatsonXProvider,
 )
 
 __all__ = [
-    "WatsonProvider",
-    "WatsonProviderConfig",
     "WatsonProviderSpec",
+    "WatsonXProvider",
+    "WatsonXProviderConfig",
+    "WatsonXProviderSpec",
 ]

src/crewai/rag/embeddings/providers/ibm/embedding_callable.py

@@ -1,25 +1,30 @@
-"""IBM Watson embedding function implementation."""
+"""IBM WatsonX embedding function implementation."""
 
 from typing import cast
 
+from chromadb.api.types import Documents, EmbeddingFunction, Embeddings
 from typing_extensions import Unpack
 
-from crewai.rag.core.base_embeddings_callable import EmbeddingFunction
-from crewai.rag.core.types import Documents, Embeddings
-from crewai.rag.embeddings.providers.ibm.types import WatsonProviderConfig
+from crewai.rag.embeddings.providers.ibm.types import WatsonXProviderConfig
 
 
-class WatsonEmbeddingFunction(EmbeddingFunction[Documents]):
-    """Embedding function for IBM Watson models."""
+class WatsonXEmbeddingFunction(EmbeddingFunction[Documents]):
+    """Embedding function for IBM WatsonX models."""
 
-    def __init__(self, **kwargs: Unpack[WatsonProviderConfig]) -> None:
-        """Initialize Watson embedding function.
+    def __init__(self, **kwargs: Unpack[WatsonXProviderConfig]) -> None:
+        """Initialize WatsonX embedding function.
 
         Args:
-            **kwargs: Configuration parameters for Watson Embeddings and Credentials.
+            **kwargs: Configuration parameters for WatsonX Embeddings and Credentials.
         """
+        super().__init__(**kwargs)
         self._config = kwargs
 
+    @staticmethod
+    def name() -> str:
+        """Return the name of the embedding function for ChromaDB compatibility."""
+        return "watsonx"
+
     def __call__(self, input: Documents) -> Embeddings:
         """Generate embeddings for input documents.
 
@@ -40,7 +45,7 @@ class WatsonEmbeddingFunction(EmbeddingFunction[Documents]):
 
         except ImportError as e:
             raise ImportError(
-                "ibm-watsonx-ai is required for watson embeddings. "
+                "ibm-watsonx-ai is required for watsonx embeddings. "
                 "Install it with: uv add ibm-watsonx-ai"
             ) from e
 
@@ -150,5 +155,5 @@ class WatsonEmbeddingFunction(EmbeddingFunction[Documents]):
             embeddings = embedding.embed_documents(input)
             return cast(Embeddings, embeddings)
         except Exception as e:
-            print(f"Error during Watson embedding: {e}")
+            print(f"Error during WatsonX embedding: {e}")
             raise

src/crewai/rag/embeddings/providers/ibm/types.py

@@ -1,12 +1,12 @@
-"""Type definitions for IBM Watson embedding providers."""
+"""Type definitions for IBM WatsonX embedding providers."""
 
 from typing import Annotated, Any, Literal
 
-from typing_extensions import Required, TypedDict
+from typing_extensions import Required, TypedDict, deprecated
 
 
-class WatsonProviderConfig(TypedDict, total=False):
-    """Configuration for Watson provider."""
+class WatsonXProviderConfig(TypedDict, total=False):
+    """Configuration for WatsonX provider."""
 
     model_id: str
     url: str
@@ -37,8 +37,22 @@ class WatsonProviderConfig(TypedDict, total=False):
     proxies: dict
 
 
+class WatsonXProviderSpec(TypedDict, total=False):
+    """WatsonX provider specification."""
+
+    provider: Required[Literal["watsonx"]]
+    config: WatsonXProviderConfig
+
+
+@deprecated(
+    'The "WatsonProviderSpec" provider spec is deprecated and will be removed in v1.0.0. Use "WatsonXProviderSpec" instead.'
+)
 class WatsonProviderSpec(TypedDict, total=False):
-    """Watson provider specification."""
+    """Watson provider specification (deprecated).
+
+    Notes:
+        - This is deprecated. Use WatsonXProviderSpec with provider="watsonx" instead.
+    """
 
     provider: Required[Literal["watson"]]
-    config: WatsonProviderConfig
+    config: WatsonXProviderConfig

src/crewai/rag/embeddings/providers/ibm/watsonx.py

@@ -1,4 +1,4 @@
-"""IBM Watson embeddings provider."""
+"""IBM WatsonX embeddings provider."""
 
 from typing import Any
 
@@ -7,110 +7,130 @@ from typing_extensions import Self
 
 from crewai.rag.core.base_embeddings_provider import BaseEmbeddingsProvider
 from crewai.rag.embeddings.providers.ibm.embedding_callable import (
-    WatsonEmbeddingFunction,
+    WatsonXEmbeddingFunction,
 )
 
 
-class WatsonProvider(BaseEmbeddingsProvider[WatsonEmbeddingFunction]):
-    """IBM Watson embeddings provider.
+class WatsonXProvider(BaseEmbeddingsProvider[WatsonXEmbeddingFunction]):
+    """IBM WatsonX embeddings provider.
 
-    Note: Requires custom implementation as Watson uses a different interface.
+    Note: Requires custom implementation as WatsonX uses a different interface.
     """
 
-    embedding_callable: type[WatsonEmbeddingFunction] = Field(
-        default=WatsonEmbeddingFunction, description="Watson embedding function class"
+    embedding_callable: type[WatsonXEmbeddingFunction] = Field(
+        default=WatsonXEmbeddingFunction, description="WatsonX embedding function class"
     )
     model_id: str = Field(
-        description="Watson model ID", validation_alias="WATSON_MODEL_ID"
+        description="WatsonX model ID", validation_alias="EMBEDDINGS_WATSONX_MODEL_ID"
     )
     params: dict[str, str | dict[str, str]] | None = Field(
         default=None, description="Additional parameters"
     )
-    credentials: Any | None = Field(default=None, description="Watson credentials")
+    credentials: Any | None = Field(default=None, description="WatsonX credentials")
     project_id: str | None = Field(
         default=None,
-        description="Watson project ID",
-        validation_alias="WATSON_PROJECT_ID",
+        description="WatsonX project ID",
+        validation_alias="EMBEDDINGS_WATSONX_PROJECT_ID",
     )
     space_id: str | None = Field(
-        default=None, description="Watson space ID", validation_alias="WATSON_SPACE_ID"
+        default=None,
+        description="WatsonX space ID",
+        validation_alias="EMBEDDINGS_WATSONX_SPACE_ID",
     )
-    api_client: Any | None = Field(default=None, description="Watson API client")
+    api_client: Any | None = Field(default=None, description="WatsonX API client")
     verify: bool | str | None = Field(
-        default=None, description="SSL verification", validation_alias="WATSON_VERIFY"
+        default=None,
+        description="SSL verification",
+        validation_alias="EMBEDDINGS_WATSONX_VERIFY",
     )
     persistent_connection: bool = Field(
         default=True,
         description="Use persistent connection",
-        validation_alias="WATSON_PERSISTENT_CONNECTION",
+        validation_alias="EMBEDDINGS_WATSONX_PERSISTENT_CONNECTION",
     )
     batch_size: int = Field(
         default=100,
         description="Batch size for processing",
-        validation_alias="WATSON_BATCH_SIZE",
+        validation_alias="EMBEDDINGS_WATSONX_BATCH_SIZE",
     )
     concurrency_limit: int = Field(
         default=10,
         description="Concurrency limit",
-        validation_alias="WATSON_CONCURRENCY_LIMIT",
+        validation_alias="EMBEDDINGS_WATSONX_CONCURRENCY_LIMIT",
     )
     max_retries: int | None = Field(
         default=None,
         description="Maximum retries",
-        validation_alias="WATSON_MAX_RETRIES",
+        validation_alias="EMBEDDINGS_WATSONX_MAX_RETRIES",
     )
     delay_time: float | None = Field(
         default=None,
         description="Delay time between retries",
-        validation_alias="WATSON_DELAY_TIME",
+        validation_alias="EMBEDDINGS_WATSONX_DELAY_TIME",
     )
     retry_status_codes: list[int] | None = Field(
         default=None, description="HTTP status codes to retry on"
     )
-    url: str = Field(description="Watson API URL", validation_alias="WATSON_URL")
+    url: str = Field(
+        description="WatsonX API URL", validation_alias="EMBEDDINGS_WATSONX_URL"
+    )
     api_key: str = Field(
-        description="Watson API key", validation_alias="WATSON_API_KEY"
+        description="WatsonX API key", validation_alias="EMBEDDINGS_WATSONX_API_KEY"
     )
     name: str | None = Field(
-        default=None, description="Service name", validation_alias="WATSON_NAME"
+        default=None,
+        description="Service name",
+        validation_alias="EMBEDDINGS_WATSONX_NAME",
     )
     iam_serviceid_crn: str | None = Field(
         default=None,
         description="IAM service ID CRN",
-        validation_alias="WATSON_IAM_SERVICEID_CRN",
+        validation_alias="EMBEDDINGS_WATSONX_IAM_SERVICEID_CRN",
     )
     trusted_profile_id: str | None = Field(
         default=None,
         description="Trusted profile ID",
-        validation_alias="WATSON_TRUSTED_PROFILE_ID",
+        validation_alias="EMBEDDINGS_WATSONX_TRUSTED_PROFILE_ID",
     )
     token: str | None = Field(
-        default=None, description="Bearer token", validation_alias="WATSON_TOKEN"
+        default=None,
+        description="Bearer token",
+        validation_alias="EMBEDDINGS_WATSONX_TOKEN",
     )
     projects_token: str | None = Field(
         default=None,
         description="Projects token",
-        validation_alias="WATSON_PROJECTS_TOKEN",
+        validation_alias="EMBEDDINGS_WATSONX_PROJECTS_TOKEN",
     )
     username: str | None = Field(
-        default=None, description="Username", validation_alias="WATSON_USERNAME"
+        default=None,
+        description="Username",
+        validation_alias="EMBEDDINGS_WATSONX_USERNAME",
     )
     password: str | None = Field(
-        default=None, description="Password", validation_alias="WATSON_PASSWORD"
+        default=None,
+        description="Password",
+        validation_alias="EMBEDDINGS_WATSONX_PASSWORD",
     )
     instance_id: str | None = Field(
         default=None,
         description="Service instance ID",
-        validation_alias="WATSON_INSTANCE_ID",
+        validation_alias="EMBEDDINGS_WATSONX_INSTANCE_ID",
     )
     version: str | None = Field(
-        default=None, description="API version", validation_alias="WATSON_VERSION"
+        default=None,
+        description="API version",
+        validation_alias="EMBEDDINGS_WATSONX_VERSION",
     )
     bedrock_url: str | None = Field(
-        default=None, description="Bedrock URL", validation_alias="WATSON_BEDROCK_URL"
+        default=None,
+        description="Bedrock URL",
+        validation_alias="EMBEDDINGS_WATSONX_BEDROCK_URL",
     )
     platform_url: str | None = Field(
-        default=None, description="Platform URL", validation_alias="WATSON_PLATFORM_URL"
+        default=None,
+        description="Platform URL",
+        validation_alias="EMBEDDINGS_WATSONX_PLATFORM_URL",
     )
     proxies: dict | None = Field(default=None, description="Proxy configuration")
 

src/crewai/rag/embeddings/providers/instructor/instructor_provider.py

@@ -18,15 +18,15 @@ class InstructorProvider(BaseEmbeddingsProvider[InstructorEmbeddingFunction]):
     model_name: str = Field(
         default="hkunlp/instructor-base",
         description="Model name to use",
-        validation_alias="INSTRUCTOR_MODEL_NAME",
+        validation_alias="EMBEDDINGS_INSTRUCTOR_MODEL_NAME",
     )
     device: str = Field(
         default="cpu",
         description="Device to run model on (cpu or cuda)",
-        validation_alias="INSTRUCTOR_DEVICE",
+        validation_alias="EMBEDDINGS_INSTRUCTOR_DEVICE",
     )
     instruction: str | None = Field(
         default=None,
         description="Instruction for embeddings",
-        validation_alias="INSTRUCTOR_INSTRUCTION",
+        validation_alias="EMBEDDINGS_INSTRUCTOR_INSTRUCTION",
     )

src/crewai/rag/embeddings/providers/jina/jina_provider.py

@@ -14,9 +14,11 @@ class JinaProvider(BaseEmbeddingsProvider[JinaEmbeddingFunction]):
     embedding_callable: type[JinaEmbeddingFunction] = Field(
         default=JinaEmbeddingFunction, description="Jina embedding function class"
     )
-    api_key: str = Field(description="Jina API key", validation_alias="JINA_API_KEY")
+    api_key: str = Field(
+        description="Jina API key", validation_alias="EMBEDDINGS_JINA_API_KEY"
+    )
     model_name: str = Field(
         default="jina-embeddings-v2-base-en",
         description="Model name to use for embeddings",
-        validation_alias="JINA_MODEL_NAME",
+        validation_alias="EMBEDDINGS_JINA_MODEL_NAME",
     )

src/crewai/rag/embeddings/providers/microsoft/azure.py

@@ -17,26 +17,28 @@ class AzureProvider(BaseEmbeddingsProvider[OpenAIEmbeddingFunction]):
         default=OpenAIEmbeddingFunction,
         description="Azure OpenAI embedding function class",
     )
-    api_key: str = Field(description="Azure API key", validation_alias="OPENAI_API_KEY")
+    api_key: str = Field(
+        description="Azure API key", validation_alias="EMBEDDINGS_OPENAI_API_KEY"
+    )
     api_base: str | None = Field(
         default=None,
         description="Azure endpoint URL",
-        validation_alias="OPENAI_API_BASE",
+        validation_alias="EMBEDDINGS_OPENAI_API_BASE",
     )
     api_type: str = Field(
         default="azure",
         description="API type for Azure",
-        validation_alias="OPENAI_API_TYPE",
+        validation_alias="EMBEDDINGS_OPENAI_API_TYPE",
     )
     api_version: str | None = Field(
         default=None,
         description="Azure API version",
-        validation_alias="OPENAI_API_VERSION",
+        validation_alias="EMBEDDINGS_OPENAI_API_VERSION",
     )
     model_name: str = Field(
         default="text-embedding-ada-002",
         description="Model name to use for embeddings",
-        validation_alias="OPENAI_MODEL_NAME",
+        validation_alias="EMBEDDINGS_OPENAI_MODEL_NAME",
     )
     default_headers: dict[str, Any] | None = Field(
         default=None, description="Default headers for API requests"
@@ -44,15 +46,15 @@ class AzureProvider(BaseEmbeddingsProvider[OpenAIEmbeddingFunction]):
     dimensions: int | None = Field(
         default=None,
         description="Embedding dimensions",
-        validation_alias="OPENAI_DIMENSIONS",
+        validation_alias="EMBEDDINGS_OPENAI_DIMENSIONS",
     )
     deployment_id: str | None = Field(
         default=None,
         description="Azure deployment ID",
-        validation_alias="OPENAI_DEPLOYMENT_ID",
+        validation_alias="EMBEDDINGS_OPENAI_DEPLOYMENT_ID",
     )
     organization_id: str | None = Field(
         default=None,
         description="Organization ID",
-        validation_alias="OPENAI_ORGANIZATION_ID",
+        validation_alias="EMBEDDINGS_OPENAI_ORGANIZATION_ID",
     )

src/crewai/rag/embeddings/providers/ollama/ollama_provider.py

@@ -17,9 +17,9 @@ class OllamaProvider(BaseEmbeddingsProvider[OllamaEmbeddingFunction]):
     url: str = Field(
         default="http://localhost:11434/api/embeddings",
         description="Ollama API endpoint URL",
-        validation_alias="OLLAMA_URL",
+        validation_alias="EMBEDDINGS_OLLAMA_URL",
     )
     model_name: str = Field(
         description="Model name to use for embeddings",
-        validation_alias="OLLAMA_MODEL_NAME",
+        validation_alias="EMBEDDINGS_OLLAMA_MODEL_NAME",
     )

src/crewai/rag/embeddings/providers/onnx/onnx_provider.py

@@ -15,5 +15,5 @@ class ONNXProvider(BaseEmbeddingsProvider[ONNXMiniLM_L6_V2]):
     preferred_providers: list[str] | None = Field(
         default=None,
         description="Preferred ONNX execution providers",
-        validation_alias="ONNX_PREFERRED_PROVIDERS",
+        validation_alias="EMBEDDINGS_ONNX_PREFERRED_PROVIDERS",
     )

src/crewai/rag/embeddings/providers/openai/openai_provider.py

@@ -18,25 +18,29 @@ class OpenAIProvider(BaseEmbeddingsProvider[OpenAIEmbeddingFunction]):
         description="OpenAI embedding function class",
     )
     api_key: str | None = Field(
-        default=None, description="OpenAI API key", validation_alias="OPENAI_API_KEY"
+        default=None,
+        description="OpenAI API key",
+        validation_alias="EMBEDDINGS_OPENAI_API_KEY",
     )
     model_name: str = Field(
         default="text-embedding-ada-002",
         description="Model name to use for embeddings",
-        validation_alias="OPENAI_MODEL_NAME",
+        validation_alias="EMBEDDINGS_OPENAI_MODEL_NAME",
     )
     api_base: str | None = Field(
         default=None,
         description="Base URL for API requests",
-        validation_alias="OPENAI_API_BASE",
+        validation_alias="EMBEDDINGS_OPENAI_API_BASE",
     )
     api_type: str | None = Field(
         default=None,
         description="API type (e.g., 'azure')",
-        validation_alias="OPENAI_API_TYPE",
+        validation_alias="EMBEDDINGS_OPENAI_API_TYPE",
     )
     api_version: str | None = Field(
-        default=None, description="API version", validation_alias="OPENAI_API_VERSION"
+        default=None,
+        description="API version",
+        validation_alias="EMBEDDINGS_OPENAI_API_VERSION",
     )
     default_headers: dict[str, Any] | None = Field(
         default=None, description="Default headers for API requests"
@@ -44,15 +48,15 @@ class OpenAIProvider(BaseEmbeddingsProvider[OpenAIEmbeddingFunction]):
     dimensions: int | None = Field(
         default=None,
         description="Embedding dimensions",
-        validation_alias="OPENAI_DIMENSIONS",
+        validation_alias="EMBEDDINGS_OPENAI_DIMENSIONS",
     )
     deployment_id: str | None = Field(
         default=None,
         description="Azure deployment ID",
-        validation_alias="OPENAI_DEPLOYMENT_ID",
+        validation_alias="EMBEDDINGS_OPENAI_DEPLOYMENT_ID",
     )
     organization_id: str | None = Field(
         default=None,
         description="OpenAI organization ID",
-        validation_alias="OPENAI_ORGANIZATION_ID",
+        validation_alias="EMBEDDINGS_OPENAI_ORGANIZATION_ID",
     )

src/crewai/rag/embeddings/providers/openclip/openclip_provider.py

@@ -18,15 +18,15 @@ class OpenCLIPProvider(BaseEmbeddingsProvider[OpenCLIPEmbeddingFunction]):
     model_name: str = Field(
         default="ViT-B-32",
         description="Model name to use",
-        validation_alias="OPENCLIP_MODEL_NAME",
+        validation_alias="EMBEDDINGS_OPENCLIP_MODEL_NAME",
     )
     checkpoint: str = Field(
         default="laion2b_s34b_b79k",
         description="Model checkpoint",
-        validation_alias="OPENCLIP_CHECKPOINT",
+        validation_alias="EMBEDDINGS_OPENCLIP_CHECKPOINT",
     )
     device: str | None = Field(
         default="cpu",
         description="Device to run model on",
-        validation_alias="OPENCLIP_DEVICE",
+        validation_alias="EMBEDDINGS_OPENCLIP_DEVICE",
     )

src/crewai/rag/embeddings/providers/roboflow/roboflow_provider.py

@@ -16,10 +16,12 @@ class RoboflowProvider(BaseEmbeddingsProvider[RoboflowEmbeddingFunction]):
         description="Roboflow embedding function class",
     )
     api_key: str = Field(
-        default="", description="Roboflow API key", validation_alias="ROBOFLOW_API_KEY"
+        default="",
+        description="Roboflow API key",
+        validation_alias="EMBEDDINGS_ROBOFLOW_API_KEY",
     )
     api_url: str = Field(
         default="https://infer.roboflow.com",
         description="Roboflow API URL",
-        validation_alias="ROBOFLOW_API_URL",
+        validation_alias="EMBEDDINGS_ROBOFLOW_API_URL",
     )

src/crewai/rag/embeddings/providers/sentence_transformer/sentence_transformer_provider.py

@@ -20,15 +20,15 @@ class SentenceTransformerProvider(
     model_name: str = Field(
         default="all-MiniLM-L6-v2",
         description="Model name to use",
-        validation_alias="SENTENCE_TRANSFORMER_MODEL_NAME",
+        validation_alias="EMBEDDINGS_SENTENCE_TRANSFORMER_MODEL_NAME",
     )
     device: str = Field(
         default="cpu",
         description="Device to run model on (cpu or cuda)",
-        validation_alias="SENTENCE_TRANSFORMER_DEVICE",
+        validation_alias="EMBEDDINGS_SENTENCE_TRANSFORMER_DEVICE",
     )
     normalize_embeddings: bool = Field(
         default=False,
         description="Whether to normalize embeddings",
-        validation_alias="SENTENCE_TRANSFORMER_NORMALIZE_EMBEDDINGS",
+        validation_alias="EMBEDDINGS_SENTENCE_TRANSFORMER_NORMALIZE_EMBEDDINGS",
     )

src/crewai/rag/embeddings/providers/text2vec/text2vec_provider.py

@@ -18,5 +18,5 @@ class Text2VecProvider(BaseEmbeddingsProvider[Text2VecEmbeddingFunction]):
     model_name: str = Field(
         default="shibing624/text2vec-base-chinese",
         description="Model name to use",
-        validation_alias="TEXT2VEC_MODEL_NAME",
+        validation_alias="EMBEDDINGS_TEXT2VEC_MODEL_NAME",
     )

src/crewai/rag/embeddings/providers/voyageai/embedding_callable.py

@@ -2,10 +2,9 @@
 
 from typing import cast
 
+from chromadb.api.types import Documents, EmbeddingFunction, Embeddings
 from typing_extensions import Unpack
 
-from crewai.rag.core.base_embeddings_callable import EmbeddingFunction
-from crewai.rag.core.types import Documents, Embeddings
 from crewai.rag.embeddings.providers.voyageai.types import VoyageAIProviderConfig
 
 
@@ -33,6 +32,11 @@ class VoyageAIEmbeddingFunction(EmbeddingFunction[Documents]):
             timeout=kwargs.get("timeout"),
         )
 
+    @staticmethod
+    def name() -> str:
+        """Return the name of the embedding function for ChromaDB compatibility."""
+        return "voyageai"
+
     def __call__(self, input: Documents) -> Embeddings:
         """Generate embeddings for input documents.
 

src/crewai/rag/embeddings/providers/voyageai/voyageai_provider.py

@@ -18,38 +18,38 @@ class VoyageAIProvider(BaseEmbeddingsProvider[VoyageAIEmbeddingFunction]):
     model: str = Field(
         default="voyage-2",
         description="Model to use for embeddings",
-        validation_alias="VOYAGEAI_MODEL",
+        validation_alias="EMBEDDINGS_VOYAGEAI_MODEL",
     )
     api_key: str = Field(
-        description="Voyage AI API key", validation_alias="VOYAGEAI_API_KEY"
+        description="Voyage AI API key", validation_alias="EMBEDDINGS_VOYAGEAI_API_KEY"
     )
     input_type: str | None = Field(
         default=None,
         description="Input type for embeddings",
-        validation_alias="VOYAGEAI_INPUT_TYPE",
+        validation_alias="EMBEDDINGS_VOYAGEAI_INPUT_TYPE",
     )
     truncation: bool = Field(
         default=True,
         description="Whether to truncate inputs",
-        validation_alias="VOYAGEAI_TRUNCATION",
+        validation_alias="EMBEDDINGS_VOYAGEAI_TRUNCATION",
     )
     output_dtype: str | None = Field(
         default=None,
         description="Output data type",
-        validation_alias="VOYAGEAI_OUTPUT_DTYPE",
+        validation_alias="EMBEDDINGS_VOYAGEAI_OUTPUT_DTYPE",
     )
     output_dimension: int | None = Field(
         default=None,
         description="Output dimension",
-        validation_alias="VOYAGEAI_OUTPUT_DIMENSION",
+        validation_alias="EMBEDDINGS_VOYAGEAI_OUTPUT_DIMENSION",
     )
     max_retries: int = Field(
         default=0,
         description="Maximum retries for API calls",
-        validation_alias="VOYAGEAI_MAX_RETRIES",
+        validation_alias="EMBEDDINGS_VOYAGEAI_MAX_RETRIES",
     )
     timeout: float | None = Field(
         default=None,
         description="Timeout for API calls",
-        validation_alias="VOYAGEAI_TIMEOUT",
+        validation_alias="EMBEDDINGS_VOYAGEAI_TIMEOUT",
     )

src/crewai/rag/embeddings/types.py

@@ -11,7 +11,10 @@ from crewai.rag.embeddings.providers.google.types import (
     VertexAIProviderSpec,
 )
 from crewai.rag.embeddings.providers.huggingface.types import HuggingFaceProviderSpec
-from crewai.rag.embeddings.providers.ibm.types import WatsonProviderSpec
+from crewai.rag.embeddings.providers.ibm.types import (
+    WatsonProviderSpec,
+    WatsonXProviderSpec,
+)
 from crewai.rag.embeddings.providers.instructor.types import InstructorProviderSpec
 from crewai.rag.embeddings.providers.jina.types import JinaProviderSpec
 from crewai.rag.embeddings.providers.microsoft.types import AzureProviderSpec
@@ -44,7 +47,8 @@ ProviderSpec = (
     | Text2VecProviderSpec
     | VertexAIProviderSpec
     | VoyageAIProviderSpec
-    | WatsonProviderSpec
+    | WatsonProviderSpec  # Deprecated, use WatsonXProviderSpec
+    | WatsonXProviderSpec
 )
 
 AllowedEmbeddingProviders = Literal[
@@ -65,7 +69,8 @@ AllowedEmbeddingProviders = Literal[
     "sentence-transformer",
     "text2vec",
     "voyageai",
-    "watson",
+    "watsonx",
+    "watson",  # for backward compatibility until v1.0.0
 ]
 
 EmbedderConfig: TypeAlias = (

tests/rag/embeddings/test_embedding_factory.py

@@ -150,8 +150,8 @@ class TestEmbeddingFactory:
         )
 
     @patch("crewai.rag.embeddings.factory.import_and_validate_definition")
-    def test_build_embedder_watson(self, mock_import):
-        """Test building Watson embedder."""
+    def test_build_embedder_watsonx(self, mock_import):
+        """Test building WatsonX embedder."""
         mock_provider_class = MagicMock()
         mock_provider_instance = MagicMock()
         mock_embedding_function = MagicMock()
@@ -161,10 +161,10 @@ class TestEmbeddingFactory:
         mock_provider_instance.embedding_callable.return_value = mock_embedding_function
 
         config = {
-            "provider": "watson",
+            "provider": "watsonx",
             "config": {
                 "model_id": "ibm/slate-125m-english-rtrvr",
-                "api_key": "watson-key",
+                "api_key": "watsonx-key",
                 "url": "https://us-south.ml.cloud.ibm.com",
                 "project_id": "test-project",
             },
@@ -173,7 +173,7 @@ class TestEmbeddingFactory:
         build_embedder(config)
 
         mock_import.assert_called_once_with(
-            "crewai.rag.embeddings.providers.ibm.watson.WatsonProvider"
+            "crewai.rag.embeddings.providers.ibm.watsonx.WatsonXProvider"
         )
 
     def test_build_embedder_unknown_provider(self):