community[minor]: Allow passing allow_dangerous_deserialization when loading LLM chain (#18894)

### Issue
Recently, the new `allow_dangerous_deserialization` flag was introduced
for preventing unsafe model deserialization that relies on pickle
without user's notice (#18696). Since then some LLMs like Databricks
requires passing in this flag with true to instantiate the model.

However, this breaks existing functionality to loading such LLMs within
a chain using `load_chain` method, because the underlying loader
function
[load_llm_from_config](f96dd57501/libs/langchain/langchain/chains/loading.py (L40))
 (and load_llm) ignores keyword arguments passed in. 

### Solution
This PR fixes this issue by propagating the
`allow_dangerous_deserialization` argument to the class loader iff the
LLM class has that field.

---------

Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>

libs/community/langchain_community/llms/loading.py

@@ -1,15 +1,17 @@
 """Base interface for loading large language model APIs."""
 import json
 from pathlib import Path
-from typing import Union
+from typing import Any, Union
 
 import yaml
 from langchain_core.language_models.llms import BaseLLM
 
 from langchain_community.llms import get_type_to_cls_dict
 
+_ALLOW_DANGEROUS_DESERIALIZATION_ARG = "allow_dangerous_deserialization"
 
-def load_llm_from_config(config: dict) -> BaseLLM:
+
+def load_llm_from_config(config: dict, **kwargs: Any) -> BaseLLM:
     """Load LLM from Config Dict."""
     if "_type" not in config:
         raise ValueError("Must specify an LLM Type in config")
@@ -21,11 +23,17 @@ def load_llm_from_config(config: dict) -> BaseLLM:
         raise ValueError(f"Loading {config_type} LLM not supported")
 
     llm_cls = type_to_cls_dict[config_type]()
-    return llm_cls(**config)
+
+    load_kwargs = {}
+    if _ALLOW_DANGEROUS_DESERIALIZATION_ARG in llm_cls.__fields__:
+        load_kwargs[_ALLOW_DANGEROUS_DESERIALIZATION_ARG] = kwargs.get(
+            _ALLOW_DANGEROUS_DESERIALIZATION_ARG, False
+        )
+
+    return llm_cls(**config, **load_kwargs)
 
 
-def load_llm(file: Union[str, Path]) -> BaseLLM:
-    """Load LLM from file."""
+def load_llm(file: Union[str, Path], **kwargs: Any) -> BaseLLM:
     # Convert file to Path object.
     if isinstance(file, str):
         file_path = Path(file)
@@ -41,4 +49,4 @@ def load_llm(file: Union[str, Path]) -> BaseLLM:
     else:
         raise ValueError("File type must be json or yaml")
     # Load the LLM from the config now.
-    return load_llm_from_config(config)
+    return load_llm_from_config(config, **kwargs)

libs/community/tests/unit_tests/llms/test_databricks.py

@@ -1,4 +1,5 @@
 """test Databricks LLM"""
+from pathlib import Path
 from typing import Any, Dict
 
 import pytest
@@ -8,6 +9,8 @@ from langchain_community.llms.databricks import (
     Databricks,
     _load_pickled_fn_from_hex_string,
 )
+from langchain_community.llms.loading import load_llm
+from tests.integration_tests.llms.utils import assert_llm_equality
 
 
 class MockDatabricksServingEndpointClient:
@@ -55,3 +58,26 @@ def test_serde_transform_input_fn(monkeypatch: MonkeyPatch) -> None:
     request = {"prompt": "What is the meaning of life?"}
     fn = _load_pickled_fn_from_hex_string(params["transform_input_fn"])
     assert fn(**request) == transform_input(**request)
+
+
+def test_saving_loading_llm(monkeypatch: MonkeyPatch, tmp_path: Path) -> None:
+    monkeypatch.setattr(
+        "langchain_community.llms.databricks._DatabricksServingEndpointClient",
+        MockDatabricksServingEndpointClient,
+    )
+    monkeypatch.setenv("DATABRICKS_HOST", "my-default-host")
+    monkeypatch.setenv("DATABRICKS_TOKEN", "my-default-token")
+
+    llm = Databricks(
+        endpoint_name="chat", temperature=0.1, allow_dangerous_deserialization=True
+    )
+    llm.save(file_path=tmp_path / "databricks.yaml")
+
+    # Loading without allowing_dangerous_deserialization=True should raise an error.
+    with pytest.raises(ValueError, match="This code relies on the pickle module."):
+        load_llm(tmp_path / "databricks.yaml")
+
+    loaded_llm = load_llm(
+        tmp_path / "databricks.yaml", allow_dangerous_deserialization=True
+    )
+    assert_llm_equality(llm, loaded_llm)