Add embedding function to tools

README.md

@@ -179,6 +179,9 @@ export CHROMA_SSL="true"
 
 # Optional: Specify path to .env file (defaults to .chroma_env)
 export CHROMA_DOTENV_PATH="/path/to/your/.env" 
+
+# with Huggingface model
+export CHROMA_HF_MODEL="hf-model-name"
 ```
 
 #### Embedding Function Environment Variables

pyproject.toml

@@ -19,20 +19,22 @@ dependencies = [
     "chromadb>=1.0.10",
     "cohere>=5.14.2",
     "httpx>=0.28.1",
+    "huggingface-hub[hf-xet]>=0.30.1",
     "mcp[cli]>=1.2.1",
     "openai>=1.70.0",
     "pillow>=11.1.0",
     "pytest>=8.3.5",
     "pytest-asyncio>=0.26.0",
     "python-dotenv>=0.19.0",
+    "sentence-transformers>=4.1.0",
     "typing-extensions>=4.13.1",
     "voyageai>=0.3.2",
 ]
 
-[project.urls]
-Homepage = "https://github.com/chroma-core/chroma-mcp"
-Documentation = "https://github.com/chroma-core/chroma-mcp#readme"
-Issues = "https://github.com/chroma-core/chroma-mcp/issues"
+# [project.urls]
+# Homepage = "https://github.com/chroma-core/chroma-mcp"
+# Documentation = "https://github.com/chroma-core/chroma-mcp#readme"
+# Issues = "https://github.com/chroma-core/chroma-mcp/issues"
 
 [build-system]
 requires = ["hatchling"]

src/chroma_mcp/embedding/local_huggingface.py

@@ -0,0 +1,96 @@
+"""
+Local huggingface embedding function.
+Model should be download to local to run offline first. Follow: https://huggingface.co/docs/hub/en/models-downloading
+"""
+
+from chromadb.api.types import Embeddings, Documents, EmbeddingFunction, Space
+from typing import List, Dict, Any, Optional
+import os
+import numpy as np
+from chromadb.utils.embedding_functions.schemas import validate_config_schema
+from sentence_transformers import SentenceTransformer
+from typing import cast
+
+
+class HuggingFaceLocalEmbeddingFunction(EmbeddingFunction[Documents]):
+    """
+    This class is used to get embeddings for a list of texts using the HuggingFace API.
+    It requires an API key and a model name. The default model name is "sentence-transformers/all-MiniLM-L6-v2".
+    """
+
+    def __init__(
+        self,
+        model_name: str = os.getenv('CHROMA_HF_MODEL',  "sentence-transformers/all-MiniLM-L6-v2" )
+    ):
+        """
+        Initialize the HuggingFaceEmbeddingFunction.
+
+        Args:
+            model_name (str, optional): The name of the model to use for text embeddings.
+                Defaults to "sentence-transformers/all-MiniLM-L6-v2".
+        """
+
+        self.model = SentenceTransformer(model_name)
+        self.model_name = model_name
+
+    def __call__(self, input: Documents) -> Embeddings:
+        """
+        Get the embeddings for a list of texts.
+
+        Args:
+            input (Documents): A list of texts to get embeddings for.
+
+        Returns:
+            Embeddings: The embeddings for the texts.
+
+        Example:
+            >>> hugging_face = HuggingFaceEmbeddingFunction(api_key_env_var="CHROMA_HUGGINGFACE_API_KEY")
+            >>> texts = ["Hello, world!", "How are you?"]
+            >>> embeddings = hugging_face(texts)
+        """
+        response = self.model.encode(
+            input,
+        )
+        # Convert to numpy arrays
+        return cast(Embeddings, response.tolist())
+
+    @staticmethod
+    def name() -> str:
+        return "huggingface"
+
+    def default_space(self) -> Space:
+        return "cosine"
+
+    def supported_spaces(self) -> List[Space]:
+        return ["cosine", "l2", "ip"]
+
+    @staticmethod
+    def build_from_config(config: Dict[str, Any]) -> "EmbeddingFunction[Documents]":
+        model_name = config.get("model_name")
+        assert model_name is not None, "Model name must be provided in the config"
+
+        return HuggingFaceLocalEmbeddingFunction(model_name=model_name)
+
+    def get_config(self) -> Dict[str, Any]:
+        return {"model_name": self.model_name}
+
+    def validate_config_update(
+        self, old_config: Dict[str, Any], new_config: Dict[str, Any]
+    ) -> None:
+        if "model_name" in new_config:
+            raise ValueError(
+                "The model name cannot be changed after the embedding function has been initialized."
+            )
+
+    @staticmethod
+    def validate_config(config: Dict[str, Any]) -> None:
+        """
+        Validate the configuration using the JSON schema.
+
+        Args:
+            config: Configuration to validate
+
+        Raises:
+            ValidationError: If the configuration does not match the schema
+        """
+        validate_config_schema(config, "huggingface")