Add apredict_and_parse to LLM (#2164)

`predict_and_parse` exists, and it's a nice abstraction to allow for
applying output parsers to LLM generations. And async is very useful.

As an aside, the difference between `call/acall`, `predict/apredict` and
`generate/agenerate` isn't entirely
clear to me other than they all call into the LLM in slightly different
ways.

Is there some documentation or a good way to think about these
differences?

One thought:  

output parsers should just work magically for all those LLM calls. If
the `output_parser` arg is set on the prompt, the LLM has access, so it
seems like extra work on the user's end to have to call
`output_parser.parse`

If this sounds reasonable, happy to throw something together. @hwchase17

langchain/chains/llm.py

@@ -174,6 +174,16 @@ class LLMChain(Chain, BaseModel):
         else:
             return result
 
+    async def apredict_and_parse(
+        self, **kwargs: Any
+    ) -> Union[str, List[str], Dict[str, str]]:
+        """Call apredict and then parse the results."""
+        result = await self.apredict(**kwargs)
+        if self.prompt.output_parser is not None:
+            return self.prompt.output_parser.parse(result)
+        else:
+            return result
+
     def apply_and_parse(
         self, input_list: List[Dict[str, Any]]
     ) -> Sequence[Union[str, List[str], Dict[str, str]]]: