From 6be67279fba808f9b67ebaa18fc62140e3672471 Mon Sep 17 00:00:00 2001
From: Tim Asp <707699+timothyasp@users.noreply.github.com>
Date: Wed, 29 Mar 2023 22:12:50 -0700
Subject: [PATCH] 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 | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/langchain/chains/llm.py b/langchain/chains/llm.py
index 46b3cfb7ce..62cc7a9112 100644
--- a/langchain/chains/llm.py
+++ b/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]]]: