feat(embeddings): text-embeddings-inference (#14288)

- **Description:** Added a notebook to illustrate how to use
`text-embeddings-inference` from huggingface. As
`HuggingFaceHubEmbeddings` was using a deprecated client, I made the
most of this PR updating that too.

- **Issue:** #13286 

- **Dependencies**: None

- **Tag maintainer:** @baskaryan

libs/langchain/langchain/embeddings/huggingface_hub.py

@@ -1,3 +1,4 @@
+import json
 from typing import Any, Dict, List, Optional
 
 from langchain_core.embeddings import Embeddings
@@ -5,7 +6,7 @@ from langchain_core.pydantic_v1 import BaseModel, Extra, root_validator
 
 from langchain.utils import get_from_dict_or_env
 
-DEFAULT_REPO_ID = "sentence-transformers/all-mpnet-base-v2"
+DEFAULT_MODEL = "sentence-transformers/all-mpnet-base-v2"
 VALID_TASKS = ("feature-extraction",)
 
 
@@ -20,17 +21,19 @@ class HuggingFaceHubEmbeddings(BaseModel, Embeddings):
         .. code-block:: python
 
             from langchain.embeddings import HuggingFaceHubEmbeddings
-            repo_id = "sentence-transformers/all-mpnet-base-v2"
+            model = "sentence-transformers/all-mpnet-base-v2"
             hf = HuggingFaceHubEmbeddings(
-                repo_id=repo_id,
+                model=model,
                 task="feature-extraction",
                 huggingfacehub_api_token="my-api-key",
             )
     """
 
     client: Any  #: :meta private:
-    repo_id: str = DEFAULT_REPO_ID
+    model: Optional[str] = None
     """Model name to use."""
+    repo_id: Optional[str] = None
+    """Huggingfacehub repository id, for backward compatibility."""
     task: Optional[str] = "feature-extraction"
     """Task to call the model with."""
     model_kwargs: Optional[dict] = None
@@ -50,22 +53,23 @@ class HuggingFaceHubEmbeddings(BaseModel, Embeddings):
             values, "huggingfacehub_api_token", "HUGGINGFACEHUB_API_TOKEN"
         )
         try:
-            from huggingface_hub.inference_api import InferenceApi
+            from huggingface_hub import InferenceClient
 
-            repo_id = values["repo_id"]
-            if not repo_id.startswith("sentence-transformers"):
-                raise ValueError(
-                    "Currently only 'sentence-transformers' embedding models "
-                    f"are supported. Got invalid 'repo_id' {repo_id}."
-                )
-            client = InferenceApi(
-                repo_id=repo_id,
+            if values["model"]:
+                values["repo_id"] = values["model"]
+            elif values["repo_id"]:
+                values["model"] = values["repo_id"]
+            else:
+                values["model"] = DEFAULT_MODEL
+                values["repo_id"] = DEFAULT_MODEL
+
+            client = InferenceClient(
+                model=values["model"],
                 token=huggingfacehub_api_token,
-                task=values.get("task"),
             )
-            if client.task not in VALID_TASKS:
+            if values["task"] not in VALID_TASKS:
                 raise ValueError(
-                    f"Got invalid task {client.task}, "
+                    f"Got invalid task {values['task']}, "
                     f"currently only {VALID_TASKS} are supported"
                 )
             values["client"] = client
@@ -88,8 +92,10 @@ class HuggingFaceHubEmbeddings(BaseModel, Embeddings):
         # 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
+        responses = self.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.