community: Fix FastEmbedEmbeddings (#24462)

## Description

This PR:
- Fixes the validation error in `FastEmbedEmbeddings`.
- Adds support for `batch_size`, `parallel` params.
- Removes support for very old FastEmbed versions.
- Updates the FastEmbed doc with the new params.

Associated Issues:
- Resolves #24039
- Resolves #https://github.com/qdrant/fastembed/issues/296

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

@@ -11,8 +11,9 @@ from langchain_community.embeddings.fastembed import FastEmbedEmbeddings
 @pytest.mark.parametrize("max_length", [50, 512])
 @pytest.mark.parametrize("doc_embed_type", ["default", "passage"])
 @pytest.mark.parametrize("threads", [0, 10])
+@pytest.mark.parametrize("batch_size", [1, 10])
 def test_fastembed_embedding_documents(
-    model_name: str, max_length: int, doc_embed_type: str, threads: int
+    model_name: str, max_length: int, doc_embed_type: str, threads: int, batch_size: int
 ) -> None:
     """Test fastembed embeddings for documents."""
     documents = ["foo bar", "bar foo"]
@@ -21,6 +22,7 @@ def test_fastembed_embedding_documents(
         max_length=max_length,
         doc_embed_type=doc_embed_type,  # type: ignore[arg-type]
         threads=threads,
+        batch_size=batch_size,
     )
     output = embedding.embed_documents(documents)
     assert len(output) == 2
@@ -31,10 +33,15 @@ def test_fastembed_embedding_documents(
     "model_name", ["sentence-transformers/all-MiniLM-L6-v2", "BAAI/bge-small-en-v1.5"]
 )
 @pytest.mark.parametrize("max_length", [50, 512])
-def test_fastembed_embedding_query(model_name: str, max_length: int) -> None:
+@pytest.mark.parametrize("batch_size", [1, 10])
+def test_fastembed_embedding_query(
+    model_name: str, max_length: int, batch_size: int
+) -> None:
     """Test fastembed embeddings for query."""
     document = "foo bar"
-    embedding = FastEmbedEmbeddings(model_name=model_name, max_length=max_length)  # type: ignore[call-arg]
+    embedding = FastEmbedEmbeddings(
+        model_name=model_name, max_length=max_length, batch_size=batch_size
+    )  # type: ignore[call-arg]
     output = embedding.embed_query(document)
     assert len(output) == 384