Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339)

Part of upgrading our CI to use Poetry 1.6.1.

libs/experimental/langchain_experimental/autonomous_agents/autogpt/prompt.py

@@ -12,7 +12,30 @@ from langchain_experimental.autonomous_agents.autogpt.prompt_generator import ge
 from langchain_experimental.pydantic_v1 import BaseModel
 
 
-class AutoGPTPrompt(BaseChatPromptTemplate, BaseModel):
+# This class has a metaclass conflict: both `BaseChatPromptTemplate` and `BaseModel`
+# define a metaclass to use, and the two metaclasses attempt to define
+# the same functions but in mutually-incompatible ways.
+# It isn't clear how to resolve this, and this code predates mypy
+# beginning to perform that check.
+#
+# Mypy errors:
+# ```
+# Definition of "__private_attributes__" in base class "BaseModel" is
+#   incompatible with definition in base class "BaseModel"  [misc]
+# Definition of "__repr_name__" in base class "Representation" is
+#   incompatible with definition in base class "BaseModel"  [misc]
+# Definition of "__pretty__" in base class "Representation" is
+#   incompatible with definition in base class "BaseModel"  [misc]
+# Definition of "__repr_str__" in base class "Representation" is
+#   incompatible with definition in base class "BaseModel"  [misc]
+# Definition of "__rich_repr__" in base class "Representation" is
+#   incompatible with definition in base class "BaseModel"  [misc]
+# Metaclass conflict: the metaclass of a derived class must be
+#   a (non-strict) subclass of the metaclasses of all its bases  [misc]
+# ```
+#
+# TODO: look into refactoring this class in a way that avoids the mypy type errors
+class AutoGPTPrompt(BaseChatPromptTemplate, BaseModel):  # type: ignore[misc]
     """Prompt for AutoGPT."""
 
     ai_name: str

libs/experimental/langchain_experimental/autonomous_agents/baby_agi/baby_agi.py

@@ -19,7 +19,27 @@ from langchain_experimental.autonomous_agents.baby_agi.task_prioritization impor
 from langchain_experimental.pydantic_v1 import BaseModel, Field
 
 
-class BabyAGI(Chain, BaseModel):
+# This class has a metaclass conflict: both `Chain` and `BaseModel` define a metaclass
+# to use, and the two metaclasses attempt to define the same functions but
+# in mutually-incompatible ways. It isn't clear how to resolve this,
+# and this code predates mypy beginning to perform that check.
+#
+# Mypy errors:
+# ```
+# Definition of "__repr_str__" in base class "Representation" is
+#   incompatible with definition in base class "BaseModel"  [misc]
+# Definition of "__repr_name__" in base class "Representation" is
+#   incompatible with definition in base class "BaseModel"  [misc]
+# Definition of "__rich_repr__" in base class "Representation" is
+#   incompatible with definition in base class "BaseModel"  [misc]
+# Definition of "__pretty__" in base class "Representation" is
+#   incompatible with definition in base class "BaseModel"  [misc]
+# Metaclass conflict: the metaclass of a derived class must be
+#   a (non-strict) subclass of the metaclasses of all its bases  [misc]
+# ```
+#
+# TODO: look into refactoring this class in a way that avoids the mypy type errors
+class BabyAGI(Chain, BaseModel):  # type: ignore[misc]
     """Controller model for the BabyAGI agent."""
 
     task_list: deque = Field(default_factory=deque)

libs/experimental/langchain_experimental/cpal/models.py

@@ -139,7 +139,9 @@ class StoryModel(BaseModel):
         # TODO: when langchain adopts pydantic.v2 replace w/ `__post_init__`
         # misses hints github.com/pydantic/pydantic/issues/1729#issuecomment-1300576214
 
-    @root_validator
+    # TODO: move away from `root_validator` since it is deprecated in pydantic v2
+    #       and causes mypy type-checking failures (hence the `type: ignore`)
+    @root_validator  # type: ignore[call-overload]
     def check_intervention_is_valid(cls, values: dict) -> dict:
         valid_names = [e.name for e in values["causal_operations"].entities]
         for setting in values["intervention"].entity_settings:

libs/experimental/langchain_experimental/llm_bash/base.py

@@ -56,7 +56,9 @@ class LLMBashChain(Chain):
                 values["llm_chain"] = LLMChain(llm=values["llm"], prompt=prompt)
         return values
 
-    @root_validator
+    # TODO: move away from `root_validator` since it is deprecated in pydantic v2
+    #       and causes mypy type-checking failures (hence the `type: ignore`)
+    @root_validator  # type: ignore[call-overload]
     def validate_prompt(cls, values: Dict) -> Dict:
         if values["llm_chain"].prompt.output_parser is None:
             raise ValueError(

libs/experimental/langchain_experimental/llms/jsonformer_decoder.py

@@ -37,7 +37,9 @@ class JsonFormer(HuggingFacePipeline):
     )
     debug: bool = Field(default=False, description="Debug mode.")
 
-    @root_validator
+    # TODO: move away from `root_validator` since it is deprecated in pydantic v2
+    #       and causes mypy type-checking failures (hence the `type: ignore`)
+    @root_validator  # type: ignore[call-overload]
     def check_jsonformer_installation(cls, values: dict) -> dict:
         import_jsonformer()
         return values

libs/experimental/langchain_experimental/llms/rellm_decoder.py

@@ -39,7 +39,9 @@ class RELLM(HuggingFacePipeline):
         default=200, description="Maximum number of new tokens to generate."
     )
 
-    @root_validator
+    # TODO: move away from `root_validator` since it is deprecated in pydantic v2
+    #       and causes mypy type-checking failures (hence the `type: ignore`)
+    @root_validator  # type: ignore[call-overload]
     def check_rellm_installation(cls, values: dict) -> dict:
         import_rellm()
         return values

libs/experimental/langchain_experimental/pal_chain/base.py

@@ -154,7 +154,13 @@ class PALChain(Chain):
         )
         _run_manager.on_text(code, color="green", end="\n", verbose=self.verbose)
         PALChain.validate_code(code, self.code_validations)
-        repl = PythonREPL(_globals=self.python_globals, _locals=self.python_locals)
+
+        # TODO: look into why mypy thinks PythonREPL's type here is `Any`
+        #       and therefore not callable
+        repl = PythonREPL(
+            _globals=self.python_globals,
+            _locals=self.python_locals,
+        )  # type: ignore[misc]
         res = repl.run(code + f"\n{self.get_answer_expr}", timeout=self.timeout)
         output = {self.output_key: res.strip()}
         if self.return_intermediate_steps:

libs/experimental/langchain_experimental/smart_llm/base.py

@@ -83,7 +83,9 @@ class SmartLLMChain(Chain):
     class Config:
         extra = Extra.forbid
 
-    @root_validator
+    # TODO: move away from `root_validator` since it is deprecated in pydantic v2
+    #       and causes mypy type-checking failures (hence the `type: ignore`)
+    @root_validator  # type: ignore[call-overload]
     @classmethod
     def validate_inputs(cls, values: Dict[str, Any]) -> Dict[str, Any]:
         """Ensure we have an LLM for each step."""