Add verbosity to extraction chain

kor/extraction/api.py

@@ -49,6 +49,7 @@ def create_extraction_chain(
     validator: Optional[Validator] = None,
     input_formatter: InputFormatter = None,
     instruction_template: Optional[PromptTemplate] = None,
+    verbose: Optional[bool] = None,
     **encoder_kwargs: Any,
 ) -> LLMChain:
     """Create an extraction chain.
@@ -73,12 +74,13 @@ def create_extraction_chain(
              * "type_description": type description of the node (from TypeDescriptor)
              * "format_instructions": information on how to format the output
                (from Encoder)
+        verbose: if provided, sets the verbosity on the chain, otherwise default
+                 verbosity of the chain will be used
         encoder_kwargs: Keyword arguments to pass to the encoder class
 
     Returns:
         A langchain chain
 
-
     Examples:
 
     .. code-block:: python
@@ -94,6 +96,11 @@ def create_extraction_chain(
         raise ValueError(f"node must be an Object got {type(node)}")
     encoder = initialize_encoder(encoder_or_encoder_class, node, **encoder_kwargs)
     type_descriptor_to_use = initialize_type_descriptors(type_descriptor)
+
+    chain_kwargs = {}
+    if verbose is not None:
+        chain_kwargs["verbose"] = verbose
+
     return LLMChain(
         llm=llm,
         prompt=create_langchain_prompt(
@@ -104,6 +111,7 @@ def create_extraction_chain(
             instruction_template=instruction_template,
             input_formatter=input_formatter,
         ),
+        **chain_kwargs,
     )
 
 

tests/extraction/test_extraction_with_chain.py

@@ -1,6 +1,7 @@
 """Test that the extraction chain works as expected."""
-from typing import Any, Mapping
+from typing import Any, Mapping, Optional
 
+import langchain
 import pytest
 from langchain import PromptTemplate
 from langchain.chains import LLMChain
@@ -102,6 +103,24 @@ def test_not_implemented_assertion_raised_for_csv(options: Mapping[str, Any]) ->
         create_extraction_chain(chat_model, **options)
 
 
+@pytest.mark.parametrize("verbose", [True, False, None])
+def test_instantiation_with_verbose_flag(verbose: Optional[bool]) -> None:
+    """Create an extraction chain."""
+    chat_model = ToyChatModel(response="hello")
+    chain = create_extraction_chain(
+        chat_model,
+        SIMPLE_OBJECT_SCHEMA,
+        encoder_or_encoder_class="json",
+        verbose=verbose,
+    )
+    assert isinstance(chain, LLMChain)
+    if verbose is None:
+        expected_verbose = langchain.verbose
+    else:
+        expected_verbose = verbose
+    assert chain.verbose == expected_verbose
+
+
 def test_using_custom_template() -> None:
     """Create an extraction chain with a custom template."""
     template = PromptTemplate(