experimental[patch]: SmartLLMChain Output Key Customization (#14466)

**Description**
The `SmartLLMChain` was was fixed to output key "resolution".
Unfortunately, this prevents the ability to use multiple `SmartLLMChain`
in a `SequentialChain` because of colliding output keys. This change
simply gives the option the customize the output key to allow for
sequential chaining. The default behavior is the same as the current
behavior.

Now, it's possible to do the following:
```
from langchain.chat_models import ChatOpenAI
from langchain.prompts import PromptTemplate
from langchain_experimental.smart_llm import SmartLLMChain
from langchain.chains import SequentialChain

joke_prompt = PromptTemplate(
    input_variables=["content"],
    template="Tell me a joke about {content}.",
)
review_prompt = PromptTemplate(
    input_variables=["scale", "joke"],
    template="Rate the following joke from 1 to {scale}: {joke}"
)

llm = ChatOpenAI(temperature=0.9, model_name="gpt-4-32k")
joke_chain = SmartLLMChain(llm=llm, prompt=joke_prompt, output_key="joke")
review_chain = SmartLLMChain(llm=llm, prompt=review_prompt, output_key="review")

chain = SequentialChain(
    chains=[joke_chain, review_chain],
    input_variables=["content", "scale"],
    output_variables=["review"],
    verbose=True
)
response = chain.run({"content": "chickens", "scale": "10"})
print(response)
```

---------

Co-authored-by: Erick Friis <erick@langchain.dev>

Author: anishnag

libs/experimental/langchain_experimental/smart_llm/base.py

@@ -66,6 +66,7 @@ def resolve_prompt_inputs(self) -> Dict[str, Any]:
 
     prompt: BasePromptTemplate
     """Prompt object to use."""
+    output_key: str = "resolution"
     ideation_llm: Optional[BaseLanguageModel] = None
     """LLM to use in ideation step. If None given, 'llm' will be used."""
     critique_llm: Optional[BaseLanguageModel] = None
@@ -132,8 +133,8 @@ def input_keys(self) -> List[str]:
     def output_keys(self) -> List[str]:
         """Defines the output keys."""
         if self.return_intermediate_steps:
-            return ["ideas", "critique", "resolution"]
-        return ["resolution"]
+            return ["ideas", "critique", self.output_key]
+        return [self.output_key]
 
     def prep_prompts(
         self,
@@ -169,8 +170,8 @@ def _call(
         self.history.critique = critique
         resolution = self._resolve(stop, run_manager)
         if self.return_intermediate_steps:
-            return {"ideas": ideas, "critique": critique, "resolution": resolution}
-        return {"resolution": resolution}
+            return {"ideas": ideas, "critique": critique, self.output_key: resolution}
+        return {self.output_key: resolution}
 
     def _get_text_from_llm_result(self, result: LLMResult, step: str) -> str:
         """Between steps, only the LLM result text is passed, not the LLMResult object.