langchain provider fix (#759)

* first

* temp format

* minor

* more notes

* minor fixes

* more fixes

trulens_eval/trulens_eval/feedback/provider/endpoint/base.py

@@ -954,7 +954,6 @@ class DummyEndpoint(Endpoint):
     overloaded_prob: float
 
     def __new__(cls, *args, **kwargs):
-
         return super(Endpoint, cls).__new__(cls, name="dummyendpoint")
 
     def __init__(

trulens_eval/trulens_eval/feedback/provider/endpoint/langchain.py

@@ -4,6 +4,7 @@
 
 from langchain.chat_models.base import BaseChatModel
 from langchain.llms.base import BaseLLM
+import pydantic
 
 from trulens_eval.feedback.provider.endpoint.base import Endpoint
 from trulens_eval.feedback.provider.endpoint.base import EndpointCallback
@@ -28,7 +29,10 @@ class LangchainEndpoint(Endpoint):
     Langchain endpoint.
     """
 
-    chain: Union[BaseLLM, BaseChatModel]
+    # Cannot validate BaseLLM / BaseChatModel as they are pydantic v1 and there
+    # is some bug involving their use within pydantic v2.
+    # https://github.com/langchain-ai/langchain/issues/10112
+    chain: Any # Union[BaseLLM, BaseChatModel]
 
     def __new__(cls, *args, **kwargs):
         return super(Endpoint, cls).__new__(cls, name="langchain")
@@ -51,7 +55,8 @@ def __init__(self, chain: Union[BaseLLM, BaseChatModel], *args, **kwargs):
 
         if not (isinstance(chain, BaseLLM) or isinstance(chain, BaseChatModel)):
             raise ValueError(
-                f"`chain` must be of type {BaseLLM.__name__} or {BaseChatModel.__name__}"
+                f"`chain` must be of type {BaseLLM.__name__} or {BaseChatModel.__name__}. " 
+                f"If you are using DEFERRED mode, this may be due to our inability to serialize `chain`."
             )
 
         kwargs["chain"] = chain

trulens_eval/trulens_eval/feedback/provider/langchain.py

@@ -19,8 +19,8 @@ class Langchain(LLMProvider):
     def __init__(
         self,
         chain: Union[BaseLLM, BaseChatModel],
-        model_engine: str = "",
         *args,
+        model_engine: str = "",
         **kwargs
     ):
         """
@@ -38,10 +38,10 @@ def __init__(
         Args:
             chain (Union[BaseLLM, BaseChatModel]): Langchain LLMs or chat models
         """
-        self_kwargs = kwargs.copy()
+        self_kwargs = dict(kwargs)
         self_kwargs["model_engine"] = model_engine or type(chain).__name__
         self_kwargs["endpoint"] = LangchainEndpoint(
-            *args, chain=chain, **kwargs.copy()
+            *args, chain=chain, **kwargs
         )
 
         super().__init__(**self_kwargs)