community: Instantiate GPT4AllEmbeddings with parameters (#21238)

### GPT4AllEmbeddings parameters
---

**Description:** 
As of right now the **Embed4All** class inside _GPT4AllEmbeddings_ is
instantiated as it's default which leaves no room to customize the
chosen model and it's behavior. Thus:

- GPT4AllEmbeddings can now be instantiated with custom parameters like
a different model that shall be used.

---------

Co-authored-by: AlexJauchWalser <alexander.jauch-walser@knime.com>

libs/community/langchain_community/embeddings/gpt4all.py

@@ -1,4 +1,4 @@
-from typing import Any, Dict, List
+from typing import Any, Dict, List, Optional
 
 from langchain_core.embeddings import Embeddings
 from langchain_core.pydantic_v1 import BaseModel, root_validator
@@ -14,9 +14,18 @@ class GPT4AllEmbeddings(BaseModel, Embeddings):
 
             from langchain_community.embeddings import GPT4AllEmbeddings
 
-            embeddings = GPT4AllEmbeddings()
+            model_name = "all-MiniLM-L6-v2.gguf2.f16.gguf"
+            gpt4all_kwargs = {'allow_download': 'True'}
+            embeddings = GPT4AllEmbeddings(
+                model_name=model_name,
+                gpt4all_kwargs=gpt4all_kwargs
+            )
     """
 
+    model_name: str
+    n_threads: Optional[int] = None
+    device: Optional[str] = "cpu"
+    gpt4all_kwargs: Optional[dict] = {}
     client: Any  #: :meta private:
 
     @root_validator()
@@ -26,7 +35,12 @@ class GPT4AllEmbeddings(BaseModel, Embeddings):
         try:
             from gpt4all import Embed4All
 
-            values["client"] = Embed4All()
+            values["client"] = Embed4All(
+                model_name=values["model_name"],
+                n_threads=values.get("n_threads"),
+                device=values.get("device"),
+                **values.get("gpt4all_kwargs"),
+            )
         except ImportError:
             raise ImportError(
                 "Could not import gpt4all library. "