Add HuggingFace Hub Embeddings

langchain/embeddings/__init__.py

@@ -1,6 +1,12 @@
 """Wrappers around embedding modules."""
 from langchain.embeddings.cohere import CohereEmbeddings
 from langchain.embeddings.huggingface import HuggingFaceEmbeddings
+from langchain.embeddings.huggingface_hub import HuggingFaceHubEmbeddings
 from langchain.embeddings.openai import OpenAIEmbeddings
 
-__all__ = ["OpenAIEmbeddings", "HuggingFaceEmbeddings", "CohereEmbeddings"]
+__all__ = [
+    "OpenAIEmbeddings",
+    "HuggingFaceEmbeddings",
+    "CohereEmbeddings",
+    "HuggingFaceHubEmbeddings",
+]

langchain/embeddings/huggingface.py

@@ -5,6 +5,8 @@
 
 from langchain.embeddings.base import Embeddings
 
+DEFAULT_MODEL_NAME = "sentence-transformers/all-mpnet-base-v2"
+
 
 class HuggingFaceEmbeddings(BaseModel, Embeddings):
     """Wrapper around sentence_transformers embedding models.
@@ -16,11 +18,11 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings):
 
             from langchain.embeddings import HuggingFaceEmbeddings
             model_name = "sentence-transformers/all-mpnet-base-v2"
-            huggingface = HuggingFaceEmbeddings(model_name=model_name)
+            hf = HuggingFaceEmbeddings(model_name=model_name)
     """
 
     client: Any  #: :meta private:
-    model_name: str = "sentence-transformers/all-mpnet-base-v2"
+    model_name: str = DEFAULT_MODEL_NAME
     """Model name to use."""
 
     def __init__(self, **kwargs: Any):

langchain/embeddings/huggingface_hub.py

@@ -0,0 +1,100 @@
+"""Wrapper around HuggingFace Hub embedding models."""
+from typing import Any, Dict, List, Optional
+
+from pydantic import BaseModel, Extra, root_validator
+
+from langchain.embeddings.base import Embeddings
+from langchain.utils import get_from_dict_or_env
+
+DEFAULT_REPO_ID = "sentence-transformers/all-mpnet-base-v2"
+VALID_TASKS = ("feature-extraction",)
+
+
+class HuggingFaceHubEmbeddings(BaseModel, Embeddings):
+    """Wrapper around HuggingFaceHub embedding models.
+
+    To use, you should have the ``huggingface_hub`` python package installed, and the
+    environment variable ``HUGGINGFACEHUB_API_TOKEN`` set with your API token, or pass
+    it as a named parameter to the constructor.
+
+    Example:
+        .. code-block:: python
+
+            from langchain.embeddings import HuggingFaceHubEmbeddings
+            repo_id = "sentence-transformers/all-mpnet-base-v2"
+            hf = HuggingFaceHubEmbeddings(
+                repo_id=repo_id,
+                task="feature-extraction",
+                huggingfacehub_api_token="my-api-key",
+            )
+    """
+
+    client: Any  #: :meta private:
+    repo_id: str = DEFAULT_REPO_ID
+    """Model name to use."""
+    task: Optional[str] = None
+    """Task to call the model with."""
+    model_kwargs: Optional[dict] = None
+    """Key word arguments to pass to the model."""
+
+    huggingfacehub_api_token: Optional[str] = None
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.forbid
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that api key and python package exists in environment."""
+        huggingfacehub_api_token = get_from_dict_or_env(
+            values, "huggingfacehub_api_token", "HUGGINGFACEHUB_API_TOKEN"
+        )
+        try:
+            from huggingface_hub.inference_api import InferenceApi
+
+            repo_id = values.get("repo_id", DEFAULT_REPO_ID)
+            client = InferenceApi(
+                repo_id=repo_id,
+                token=huggingfacehub_api_token,
+                task=values.get("task"),
+            )
+            if client.task not in VALID_TASKS:
+                raise ValueError(
+                    f"Got invalid task {client.task}, "
+                    f"currently only {VALID_TASKS} are supported"
+                )
+            values["client"] = client
+        except ImportError:
+            raise ValueError(
+                "Could not import huggingface_hub python package. "
+                "Please it install it with `pip install huggingface_hub`."
+            )
+        return values
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Call out to HuggingFaceHub's embedding endpoint for embedding search docs.
+
+        Args:
+            texts: The list of texts to embed.
+
+        Returns:
+            List of embeddings, one for each text.
+        """
+        # replace newlines, which can negatively affect performance.
+        texts = [text.replace("\n", " ") for text in texts]
+        _model_kwargs = self.model_kwargs or {}
+        responses = self.client(inputs=texts, params=_model_kwargs)
+        return responses
+
+    def embed_query(self, text: str) -> List[float]:
+        """Call out to HuggingFaceHub's embedding endpoint for embedding query text.
+
+        Args:
+            text: The text to embed.
+
+        Returns:
+            Embeddings for the text.
+        """
+        response = self.embed_documents([text])[0]
+        return response

tests/integration_tests/embeddings/test_huggingface_hub.py

@@ -0,0 +1,19 @@
+"""Test huggingfacehub embeddings."""
+from langchain.embeddings import HuggingFaceHubEmbeddings
+
+
+def test_huggingfacehub_embedding_documents() -> None:
+    """Test huggingfacehub embeddings."""
+    documents = ["foo bar"]
+    embedding = HuggingFaceHubEmbeddings(task="feature-extraction")
+    output = embedding.embed_documents(documents)
+    assert len(output) == 1
+    assert len(output[0]) == 768
+
+
+def test_huggingfacehub_embedding_query() -> None:
+    """Test huggingfacehub embeddings."""
+    document = "foo bar"
+    embedding = HuggingFaceHubEmbeddings(task="feature-extraction")
+    output = embedding.embed_query(document)
+    assert len(output) == 768