[feat-add] Fastembed Embedding provider integration

lib/crewai-tools/src/crewai_tools/rag/embedding_service.py

@@ -51,6 +51,7 @@ class EmbeddingService:
     - roboflow: Roboflow embeddings (roboflow-embeddings-v2-base-en, etc.)
     - voyageai: Voyage AI embeddings (voyage-2, voyage-large-2, etc.)
     - watsonx: Watson X embeddings (ibm/slate-125m-english-rtrvr, etc.)
+    - fastembed: FastEmbed embeddings (sentence-transformers/all-MiniLM-L6-v2, etc.)
     - custom: Custom embeddings (embedding_callable, etc.)
     - sentence-transformer: Sentence Transformers embeddings (all-MiniLM-L6-v2, etc.)
     - text2vec: Text2Vec embeddings (text2vec-base-en, etc.)
@@ -230,6 +231,11 @@ def _build_provider_config(self) -> dict[str, Any]:
                 "model_name": self.config.model,
                 **self.config.extra_config,
             }
+        elif self.config.provider == "fastembed":
+            base_config["config"] = {
+                "model_name": self.config.model,
+                **self.config.extra_config,
+            }
         elif self.config.provider == "custom":
             # Custom provider requires embedding_callable in extra_config
             base_config["config"] = {
@@ -365,6 +371,7 @@ def list_supported_providers(cls) -> list[str]:
             "amazon-bedrock",
             "cohere",
             "custom",
+            "fastembed",
             "google-generativeai",
             "google-vertex",
             "huggingface",
@@ -497,6 +504,15 @@ def create_watsonx_service(
         """Create a Watson X embedding service."""
         return cls(provider="watsonx", model=model, api_key=api_key, **kwargs)
 
+    @classmethod
+    def create_fastembed_service(
+        cls,
+        model: str = "sentence-transformers/all-MiniLM-L6-v2",
+        **kwargs: Any,
+    ) -> EmbeddingService:
+        """Create a FastEmbed embedding service (local, fast inference)."""
+        return cls(provider="fastembed", model=model, **kwargs)
+
     @classmethod
     def create_custom_service(
         cls,

lib/crewai/src/crewai/rag/embeddings/factory.py

@@ -51,6 +51,10 @@
     from crewai.rag.embeddings.providers.aws.types import BedrockProviderSpec
     from crewai.rag.embeddings.providers.cohere.types import CohereProviderSpec
     from crewai.rag.embeddings.providers.custom.types import CustomProviderSpec
+    from crewai.rag.embeddings.providers.fastembed.embedding_callable import (
+        FastEmbedEmbeddingFunction,
+    )
+    from crewai.rag.embeddings.providers.fastembed.types import FastEmbedProviderSpec
     from crewai.rag.embeddings.providers.google.genai_vertex_embedding import (
         GoogleGenAIVertexEmbeddingFunction,
     )
@@ -92,6 +96,7 @@
     "amazon-bedrock": "crewai.rag.embeddings.providers.aws.bedrock.BedrockProvider",
     "cohere": "crewai.rag.embeddings.providers.cohere.cohere_provider.CohereProvider",
     "custom": "crewai.rag.embeddings.providers.custom.custom_provider.CustomProvider",
+    "fastembed": "crewai.rag.embeddings.providers.fastembed.fastembed_provider.FastEmbedProvider",
     "google-generativeai": "crewai.rag.embeddings.providers.google.generative_ai.GenerativeAiProvider",
     "google": "crewai.rag.embeddings.providers.google.generative_ai.GenerativeAiProvider",
     "google-vertex": "crewai.rag.embeddings.providers.google.vertex.VertexAIProvider",
@@ -142,6 +147,12 @@ def build_embedder_from_dict(spec: CohereProviderSpec) -> CohereEmbeddingFunctio
 def build_embedder_from_dict(spec: CustomProviderSpec) -> EmbeddingFunction[Any]: ...
 
 
+@overload
+def build_embedder_from_dict(
+    spec: FastEmbedProviderSpec,
+) -> FastEmbedEmbeddingFunction: ...
+
+
 @overload
 def build_embedder_from_dict(
     spec: GenerativeAiProviderSpec,
@@ -283,6 +294,10 @@ def build_embedder(spec: CohereProviderSpec) -> CohereEmbeddingFunction: ...
 def build_embedder(spec: CustomProviderSpec) -> EmbeddingFunction[Any]: ...
 
 
+@overload
+def build_embedder(spec: FastEmbedProviderSpec) -> FastEmbedEmbeddingFunction: ...
+
+
 @overload
 def build_embedder(
     spec: GenerativeAiProviderSpec,

lib/crewai/src/crewai/rag/embeddings/providers/fastembed/__init__.py

@@ -0,0 +1,20 @@
+"""FastEmbed embedding providers."""
+
+from crewai.rag.embeddings.providers.fastembed.embedding_callable import (
+    FastEmbedEmbeddingFunction,
+)
+from crewai.rag.embeddings.providers.fastembed.fastembed_provider import (
+    FastEmbedProvider,
+)
+from crewai.rag.embeddings.providers.fastembed.types import (
+    FastEmbedProviderConfig,
+    FastEmbedProviderSpec,
+)
+
+
+__all__ = [
+    "FastEmbedEmbeddingFunction",
+    "FastEmbedProvider",
+    "FastEmbedProviderConfig",
+    "FastEmbedProviderSpec",
+]

lib/crewai/src/crewai/rag/embeddings/providers/fastembed/embedding_callable.py

@@ -0,0 +1,69 @@
+"""FastEmbed embedding function implementation."""
+
+from typing import Any, cast
+
+from chromadb.api.types import Documents, EmbeddingFunction, Embeddings
+from typing_extensions import Unpack
+
+from crewai.rag.embeddings.providers.fastembed.types import FastEmbedProviderConfig
+
+
+class FastEmbedEmbeddingFunction(EmbeddingFunction[Documents]):
+    """Embedding function for FastEmbed text embedding models."""
+
+    def __init__(self, **kwargs: Unpack[FastEmbedProviderConfig]) -> None:
+        """Initialize FastEmbed embedding function.
+
+        Args:
+            **kwargs: Configuration parameters for FastEmbed.
+        """
+        try:
+            from fastembed import TextEmbedding
+        except ImportError as e:
+            raise ImportError(
+                "fastembed is required for fastembed embeddings. "
+                "Install it with: uv add fastembed"
+            ) from e
+
+        model_kwargs: dict[str, Any] = {
+            "model_name": kwargs.get(
+                "model_name", "sentence-transformers/all-MiniLM-L6-v2"
+            )
+        }
+        for key in (
+            "cache_dir",
+            "threads",
+            "providers",
+            "cuda",
+            "device_ids",
+            "lazy_load",
+        ):
+            if key in kwargs and kwargs[key] is not None:
+                model_kwargs[key] = kwargs[key]
+
+        self._model = TextEmbedding(**model_kwargs)
+        self._batch_size = kwargs.get("batch_size", 256)
+        self._parallel = kwargs.get("parallel")
+
+    @staticmethod
+    def name() -> str:
+        """Return the name of the embedding function for ChromaDB compatibility."""
+        return "fastembed"
+
+    def __call__(self, input: Documents) -> Embeddings:
+        """Generate embeddings for input documents.
+
+        Args:
+            input: List of documents to embed.
+
+        Returns:
+            List of embedding vectors.
+        """
+        if isinstance(input, str):
+            input = [input]
+
+        embed_kwargs: dict[str, Any] = {"batch_size": self._batch_size}
+        if self._parallel is not None:
+            embed_kwargs["parallel"] = self._parallel
+
+        return cast(Embeddings, list(self._model.embed(input, **embed_kwargs)))

lib/crewai/src/crewai/rag/embeddings/providers/fastembed/fastembed_provider.py

@@ -0,0 +1,78 @@
+"""FastEmbed embeddings provider."""
+
+from pydantic import AliasChoices, Field
+
+from crewai.rag.core.base_embeddings_provider import BaseEmbeddingsProvider
+from crewai.rag.embeddings.providers.fastembed.embedding_callable import (
+    FastEmbedEmbeddingFunction,
+)
+
+
+class FastEmbedProvider(BaseEmbeddingsProvider[FastEmbedEmbeddingFunction]):
+    """FastEmbed embeddings provider."""
+
+    embedding_callable: type[FastEmbedEmbeddingFunction] = Field(
+        default=FastEmbedEmbeddingFunction,
+        description="FastEmbed embedding function class",
+    )
+    model_name: str = Field(
+        default="sentence-transformers/all-MiniLM-L6-v2",
+        description="Model name to use",
+        validation_alias=AliasChoices(
+            "EMBEDDINGS_FASTEMBED_MODEL_NAME",
+            "FASTEMBED_MODEL_NAME",
+            "model",
+        ),
+    )
+    cache_dir: str | None = Field(
+        default=None,
+        description="Directory to cache downloaded FastEmbed models",
+        validation_alias=AliasChoices(
+            "EMBEDDINGS_FASTEMBED_CACHE_DIR", "FASTEMBED_CACHE_DIR"
+        ),
+    )
+    threads: int | None = Field(
+        default=None,
+        description="Number of threads to use for inference",
+        validation_alias=AliasChoices("EMBEDDINGS_FASTEMBED_THREADS", "FASTEMBED_THREADS"),
+    )
+    providers: list[str] | None = Field(
+        default=None,
+        description="ONNX Runtime execution providers",
+        validation_alias=AliasChoices(
+            "EMBEDDINGS_FASTEMBED_PROVIDERS", "FASTEMBED_PROVIDERS"
+        ),
+    )
+    cuda: bool = Field(
+        default=False,
+        description="Whether to use CUDA execution",
+        validation_alias=AliasChoices("EMBEDDINGS_FASTEMBED_CUDA", "FASTEMBED_CUDA"),
+    )
+    device_ids: list[int] | None = Field(
+        default=None,
+        description="CUDA device IDs to use",
+        validation_alias=AliasChoices(
+            "EMBEDDINGS_FASTEMBED_DEVICE_IDS", "FASTEMBED_DEVICE_IDS"
+        ),
+    )
+    lazy_load: bool = Field(
+        default=False,
+        description="Whether to defer model loading until first embedding call",
+        validation_alias=AliasChoices(
+            "EMBEDDINGS_FASTEMBED_LAZY_LOAD", "FASTEMBED_LAZY_LOAD"
+        ),
+    )
+    batch_size: int = Field(
+        default=256,
+        description="Batch size to use when embedding documents",
+        validation_alias=AliasChoices(
+            "EMBEDDINGS_FASTEMBED_BATCH_SIZE", "FASTEMBED_BATCH_SIZE"
+        ),
+    )
+    parallel: int | None = Field(
+        default=None,
+        description="Number of parallel workers to use when embedding documents",
+        validation_alias=AliasChoices(
+            "EMBEDDINGS_FASTEMBED_PARALLEL", "FASTEMBED_PARALLEL"
+        ),
+    )

lib/crewai/src/crewai/rag/embeddings/providers/fastembed/types.py

@@ -0,0 +1,26 @@
+"""Type definitions for FastEmbed embedding providers."""
+
+from typing import Annotated, Literal
+
+from typing_extensions import Required, TypedDict
+
+
+class FastEmbedProviderConfig(TypedDict, total=False):
+    """Configuration for FastEmbed provider."""
+
+    model_name: Annotated[str, "sentence-transformers/all-MiniLM-L6-v2"]
+    cache_dir: str | None
+    threads: int | None
+    providers: list[str] | None
+    cuda: Annotated[bool, False]
+    device_ids: list[int] | None
+    lazy_load: Annotated[bool, False]
+    batch_size: Annotated[int, 256]
+    parallel: int | None
+
+
+class FastEmbedProviderSpec(TypedDict, total=False):
+    """FastEmbed provider specification."""
+
+    provider: Required[Literal["fastembed"]]
+    config: FastEmbedProviderConfig

lib/crewai/src/crewai/rag/embeddings/types.py

@@ -6,6 +6,7 @@
 from crewai.rag.embeddings.providers.aws.types import BedrockProviderSpec
 from crewai.rag.embeddings.providers.cohere.types import CohereProviderSpec
 from crewai.rag.embeddings.providers.custom.types import CustomProviderSpec
+from crewai.rag.embeddings.providers.fastembed.types import FastEmbedProviderSpec
 from crewai.rag.embeddings.providers.google.types import (
     GenerativeAiProviderSpec,
     VertexAIProviderSpec,
@@ -34,6 +35,7 @@
     | BedrockProviderSpec
     | CohereProviderSpec
     | CustomProviderSpec
+    | FastEmbedProviderSpec
     | GenerativeAiProviderSpec
     | HuggingFaceProviderSpec
     | InstructorProviderSpec
@@ -55,6 +57,7 @@
     "amazon-bedrock",
     "cohere",
     "custom",
+    "fastembed",
     "google-generativeai",
     "google-vertex",
     "huggingface",

lib/crewai/tests/rag/embeddings/test_backward_compatibility.py

@@ -9,6 +9,7 @@
 from crewai.rag.embeddings.providers.jina.jina_provider import JinaProvider
 from crewai.rag.embeddings.providers.ollama.ollama_provider import OllamaProvider
 from crewai.rag.embeddings.providers.aws.bedrock import BedrockProvider
+from crewai.rag.embeddings.providers.fastembed.fastembed_provider import FastEmbedProvider
 from crewai.rag.embeddings.providers.text2vec.text2vec_provider import Text2VecProvider
 from crewai.rag.embeddings.providers.sentence_transformer.sentence_transformer_provider import (
     SentenceTransformerProvider,
@@ -101,6 +102,13 @@ def test_text2vec_provider_accepts_model_key(self):
         )
         assert provider.model_name == "shibing624/text2vec-base-multilingual"
 
+    def test_fastembed_provider_accepts_model_key(self):
+        """Test FastEmbed provider accepts 'model' as alias for 'model_name'."""
+        provider = FastEmbedProvider(
+            model="sentence-transformers/all-MiniLM-L6-v2",
+        )
+        assert provider.model_name == "sentence-transformers/all-MiniLM-L6-v2"
+
     def test_sentence_transformer_provider_accepts_model_key(self):
         """Test SentenceTransformer provider accepts 'model' as alias."""
         provider = SentenceTransformerProvider(
@@ -361,4 +369,4 @@ def test_legacy_azure_with_model_key(self):
             deployment_id="test-deployment",
             model="text-embedding-3-large",
         )
-        assert provider.model_name == "text-embedding-3-large"
+        assert provider.model_name == "text-embedding-3-large"

lib/crewai/tests/rag/embeddings/test_embedding_factory.py

@@ -154,6 +154,35 @@ def test_build_embedder_cohere(self, mock_import):
             "crewai.rag.embeddings.providers.cohere.cohere_provider.CohereProvider"
         )
 
+    @patch("crewai.rag.embeddings.factory.import_and_validate_definition")
+    def test_build_embedder_fastembed(self, mock_import):
+        """Test building FastEmbed embedder."""
+        mock_provider_class = MagicMock()
+        mock_provider_instance = MagicMock()
+        mock_embedding_function = MagicMock()
+
+        mock_import.return_value = mock_provider_class
+        mock_provider_class.return_value = mock_provider_instance
+        mock_provider_instance.embedding_callable.return_value = mock_embedding_function
+
+        config = {
+            "provider": "fastembed",
+            "config": {
+                "model": "sentence-transformers/all-MiniLM-L6-v2",
+                "cache_dir": ".fastembed_cache",
+            },
+        }
+
+        build_embedder(config)
+
+        mock_import.assert_called_once_with(
+            "crewai.rag.embeddings.providers.fastembed.fastembed_provider.FastEmbedProvider"
+        )
+
+        call_kwargs = mock_provider_class.call_args.kwargs
+        assert call_kwargs["model"] == "sentence-transformers/all-MiniLM-L6-v2"
+        assert call_kwargs["cache_dir"] == ".fastembed_cache"
+
     @patch("crewai.rag.embeddings.factory.import_and_validate_definition")
     def test_build_embedder_voyageai(self, mock_import):
         """Test building VoyageAI embedder."""