fix(embeddings): huggingface hub embeddings and TEI (#14489)

**Description:** This PR fixes `HuggingFaceHubEmbeddings` by making the
API token optional (as in the client beneath). Most models don't require
one. I also updated the notebook for TEI (text-embeddings-inference)
accordingly as requested here #14288. In addition, I fixed a mistake in
the POST call parameters.

**Tag maintainers:** @baskaryan

docs/docs/integrations/text_embedding/text_embeddings_inference.ipynb

@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "579f0677-aa06-4ad8-a816-3520c8d6923c",
    "metadata": {
     "tags": []
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "22b09777-5ba3-4fbe-81cf-a702a55df9c4",
    "metadata": {
     "tags": []
@@ -62,45 +62,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "c26fca9f-cfdb-45e5-a0bd-f677ff8b9d92",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdin",
-     "output_type": "stream",
-     "text": [
-      "Enter your HF API Key:\n",
-      "\n",
-      " ········\n"
-     ]
-    }
-   ],
-   "source": [
-    "from getpass import getpass\n",
-    "\n",
-    "huggingfacehub_api_token = getpass(\"Enter your HF API Key:\\n\\n\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "id": "f9a92970-16f4-458c-b186-2a83e9f7d840",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "embeddings = HuggingFaceHubEmbeddings(\n",
-    "    model=\"http://localhost:8080\", huggingfacehub_api_token=huggingfacehub_api_token\n",
-    ")"
+    "embeddings = HuggingFaceHubEmbeddings(model=\"http://localhost:8080\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "id": "42105438-9fee-460a-9c52-b7c595722758",
    "metadata": {
     "tags": []
@@ -112,7 +86,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "id": "20167762-0988-4205-bbd4-1f20fd9dd247",
    "metadata": {
     "tags": []
@@ -124,7 +98,7 @@
        "[0.018113142, 0.00302585, -0.049911194]"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -136,7 +110,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "id": "54b87cf6-86ad-46f5-b2cd-17eb43cb4d0b",
    "metadata": {
     "tags": []
@@ -145,6 +119,29 @@
    "source": [
     "doc_result = embeddings.embed_documents([text])"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "6fba8be9-fabf-4972-8334-aa56ed9893e1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0.018113142, 0.00302585, -0.049911194]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "doc_result[0][:3]"
+   ]
   }
  ],
  "metadata": {

libs/community/langchain_community/embeddings/huggingface_hub.py

@@ -1,9 +1,9 @@
 import json
+import os
 from typing import Any, Dict, List, Optional
 
 from langchain_core.embeddings import Embeddings
 from langchain_core.pydantic_v1 import BaseModel, Extra, root_validator
-from langchain_core.utils import get_from_dict_or_env
 
 DEFAULT_MODEL = "sentence-transformers/all-mpnet-base-v2"
 VALID_TASKS = ("feature-extraction",)
@@ -48,9 +48,10 @@ class HuggingFaceHubEmbeddings(BaseModel, Embeddings):
     @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"
+        huggingfacehub_api_token = values["huggingfacehub_api_token"] or os.getenv(
+            "HUGGINGFACEHUB_API_TOKEN"
         )
+
         try:
             from huggingface_hub import InferenceClient
 
@@ -92,7 +93,7 @@ class HuggingFaceHubEmbeddings(BaseModel, Embeddings):
         texts = [text.replace("\n", " ") for text in texts]
         _model_kwargs = self.model_kwargs or {}
         responses = self.client.post(
-            json={"inputs": texts, "parameters": _model_kwargs, "task": self.task}
+            json={"inputs": texts, "parameters": _model_kwargs}, task=self.task
         )
         return json.loads(responses.decode())