community: Added functions to make async calls to HuggingFaceHub's embedding endpoint in HuggingFaceHubEmbeddings class (#15737)

**Description:**
Added aembed_documents() and aembed_query() async functions in
HuggingFaceHubEmbeddings class in
langchain_community\embeddings\huggingface_hub.py file. It will support
to make async calls to HuggingFaceHub's
embedding endpoint and generate embeddings asynchronously.

Test Cases: Added test_huggingfacehub_embedding_async_documents() and
test_huggingfacehub_embedding_async_query()
functions in test_huggingface_hub.py file to test the two async
functions created in HuggingFaceHubEmbeddings class.

Documentation: Updated huggingfacehub.ipynb with steps to install
huggingface_hub package and use
HuggingFaceHubEmbeddings.

**Dependencies:** None,
**Twitter handle:** I do not have a Twitter account

---------

Co-authored-by: H161961 <Raunak.Raunak@Honeywell.com>

docs/docs/integrations/text_embedding/huggingfacehub.ipynb

@@ -106,7 +106,7 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdin",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
       "Enter your HF Inference API Key:\n",
@@ -148,6 +148,75 @@
     "query_result = embeddings.embed_query(text)\n",
     "query_result[:3]"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19ef2d31",
+   "metadata": {},
+   "source": [
+    "## Hugging Face Hub\n",
+    "We can also generate embeddings locally via the Hugging Face Hub package, which requires us to install ``huggingface_hub ``"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "39e85945",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install huggingface_hub"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c78a2779",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.embeddings import HuggingFaceHubEmbeddings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "116f3ce7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "embeddings = HuggingFaceHubEmbeddings()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d6f97ee9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text = \"This is a test document.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fb6adc67",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query_result = embeddings.embed_query(text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1f42c311",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query_result[:3]"
+   ]
   }
  ],
  "metadata": {

libs/community/langchain_community/embeddings/huggingface_hub.py

@@ -29,6 +29,7 @@ class HuggingFaceHubEmbeddings(BaseModel, Embeddings):
     """
 
     client: Any  #: :meta private:
+    async_client: Any  #: :meta private:
     model: Optional[str] = None
     """Model name to use."""
     repo_id: Optional[str] = None
@@ -53,7 +54,7 @@ class HuggingFaceHubEmbeddings(BaseModel, Embeddings):
         )
 
         try:
-            from huggingface_hub import InferenceClient
+            from huggingface_hub import AsyncInferenceClient, InferenceClient
 
             if values["model"]:
                 values["repo_id"] = values["model"]
@@ -67,12 +68,20 @@ class HuggingFaceHubEmbeddings(BaseModel, Embeddings):
                 model=values["model"],
                 token=huggingfacehub_api_token,
             )
+
+            async_client = AsyncInferenceClient(
+                model=values["model"],
+                token=huggingfacehub_api_token,
+            )
+
             if values["task"] not in VALID_TASKS:
                 raise ValueError(
                     f"Got invalid task {values['task']}, "
                     f"currently only {VALID_TASKS} are supported"
                 )
             values["client"] = client
+            values["async_client"] = async_client
+
         except ImportError:
             raise ImportError(
                 "Could not import huggingface_hub python package. "
@@ -97,6 +106,23 @@ class HuggingFaceHubEmbeddings(BaseModel, Embeddings):
         )
         return json.loads(responses.decode())
 
+    async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Async Call 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 = await self.async_client.post(
+            json={"inputs": texts, "parameters": _model_kwargs}, task=self.task
+        )
+        return json.loads(responses.decode())
+
     def embed_query(self, text: str) -> List[float]:
         """Call out to HuggingFaceHub's embedding endpoint for embedding query text.
 
@@ -108,3 +134,15 @@ class HuggingFaceHubEmbeddings(BaseModel, Embeddings):
         """
         response = self.embed_documents([text])[0]
         return response
+
+    async def aembed_query(self, text: str) -> List[float]:
+        """Async Call to HuggingFaceHub's embedding endpoint for embedding query text.
+
+        Args:
+            text: The text to embed.
+
+        Returns:
+            Embeddings for the text.
+        """
+        response = await self.aembed_documents([text])[0]
+        return response

libs/community/tests/integration_tests/embeddings/test_huggingface_hub.py

@@ -13,6 +13,15 @@ def test_huggingfacehub_embedding_documents() -> None:
     assert len(output[0]) == 768
 
 
+async def test_huggingfacehub_embedding_async_documents() -> None:
+    """Test huggingfacehub embeddings."""
+    documents = ["foo bar"]
+    embedding = HuggingFaceHubEmbeddings()
+    output = await embedding.aembed_documents(documents)
+    assert len(output) == 1
+    assert len(output[0]) == 768
+
+
 def test_huggingfacehub_embedding_query() -> None:
     """Test huggingfacehub embeddings."""
     document = "foo bar"
@@ -21,6 +30,14 @@ def test_huggingfacehub_embedding_query() -> None:
     assert len(output) == 768
 
 
+async def test_huggingfacehub_embedding_async_query() -> None:
+    """Test huggingfacehub embeddings."""
+    document = "foo bar"
+    embedding = HuggingFaceHubEmbeddings()
+    output = await embedding.aembed_query(document)
+    assert len(output) == 768
+
+
 def test_huggingfacehub_embedding_invalid_repo() -> None:
     """Test huggingfacehub embedding repo id validation."""
     # Only sentence-transformers models are currently supported.