Bug-Fix[Community] Fix FastEmbedEmbeddings (#26764)

#26759 

- Fix https://github.com/langchain-ai/langchain/issues/26759 
- Change `model` param from private to public, which may not be
initiated.
- Add test case

libs/community/langchain_community/embeddings/fastembed.py

@@ -65,7 +65,7 @@ class FastEmbedEmbeddings(BaseModel, Embeddings):
     Defaults to `None`.
     """
 
-    _model: Any = None  # : :meta private:
+    model: Any = None  # : :meta private:
 
     model_config = ConfigDict(extra="allow", protected_namespaces=())
 
@@ -91,7 +91,7 @@ class FastEmbedEmbeddings(BaseModel, Embeddings):
                 'FastEmbedEmbeddings requires `pip install -U "fastembed>=0.2.0"`.'
             )
 
-        values["_model"] = fastembed.TextEmbedding(
+        values["model"] = fastembed.TextEmbedding(
             model_name=model_name,
             max_length=max_length,
             cache_dir=cache_dir,
@@ -110,11 +110,11 @@ class FastEmbedEmbeddings(BaseModel, Embeddings):
         """
         embeddings: List[np.ndarray]
         if self.doc_embed_type == "passage":
-            embeddings = self._model.passage_embed(
+            embeddings = self.model.passage_embed(
                 texts, batch_size=self.batch_size, parallel=self.parallel
             )
         else:
-            embeddings = self._model.embed(
+            embeddings = self.model.embed(
                 texts, batch_size=self.batch_size, parallel=self.parallel
             )
         return [e.tolist() for e in embeddings]
@@ -129,7 +129,7 @@ class FastEmbedEmbeddings(BaseModel, Embeddings):
             Embeddings for the text.
         """
         query_embeddings: np.ndarray = next(
-            self._model.query_embed(
+            self.model.query_embed(
                 text, batch_size=self.batch_size, parallel=self.parallel
             )
         )

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

@@ -80,3 +80,11 @@ async def test_fastembed_async_embedding_query(
     embedding = FastEmbedEmbeddings(model_name=model_name, max_length=max_length)  # type: ignore[call-arg]
     output = await embedding.aembed_query(document)
     assert len(output) == 384
+
+
+def test_fastembed_embedding_query_with_default_params() -> None:
+    """Test fastembed embeddings for query with default model params"""
+    document = "foo bar"
+    embedding = FastEmbedEmbeddings()  # type: ignore[call-arg]
+    output = embedding.embed_query(document)
+    assert len(output) == 384