Add async versions of predict() and predict_messages() (#4867)

# Add async versions of predict() and predict_messages()

#4615 introduced a unifying interface for "base" and "chat" LLM models
via the new `predict()` and `predict_messages()` methods that allow both
types of models to operate on string and message-based inputs,
respectively.

This PR adds async versions of the same (`apredict()` and
`apredict_messages()`) that are identical except for their use of
`agenerate()` in place of `generate()`, which means they repurpose all
existing work on the async backend.


## Who can review?

Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
        @hwchase17 (follows his work on #4615)
        @agola11 (async)

---------

Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>

langchain/base_language.py

@@ -58,6 +58,16 @@ class BaseLanguageModel(BaseModel, ABC):
     ) -> BaseMessage:
         """Predict message from messages."""
 
+    @abstractmethod
+    async def apredict(self, text: str, *, stop: Optional[Sequence[str]] = None) -> str:
+        """Predict text from text."""
+
+    @abstractmethod
+    async def apredict_messages(
+        self, messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None
+    ) -> BaseMessage:
+        """Predict message from messages."""
+
     def get_token_ids(self, text: str) -> List[int]:
         """Get the token present in the text."""
         return _get_token_ids_default_method(text)

langchain/chat_models/base.py

@@ -183,6 +183,19 @@ class BaseChatModel(BaseLanguageModel, ABC):
         else:
             raise ValueError("Unexpected generation type")
 
+    async def _call_async(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        callbacks: Callbacks = None,
+    ) -> BaseMessage:
+        result = await self.agenerate([messages], stop=stop, callbacks=callbacks)
+        generation = result.generations[0][0]
+        if isinstance(generation, ChatGeneration):
+            return generation.message
+        else:
+            raise ValueError("Unexpected generation type")
+
     def call_as_llm(self, message: str, stop: Optional[List[str]] = None) -> str:
         return self.predict(message, stop=stop)
 
@@ -203,6 +216,23 @@ class BaseChatModel(BaseLanguageModel, ABC):
             _stop = list(stop)
         return self(messages, stop=_stop)
 
+    async def apredict(self, text: str, *, stop: Optional[Sequence[str]] = None) -> str:
+        if stop is None:
+            _stop = None
+        else:
+            _stop = list(stop)
+        result = await self._call_async([HumanMessage(content=text)], stop=_stop)
+        return result.content
+
+    async def apredict_messages(
+        self, messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None
+    ) -> BaseMessage:
+        if stop is None:
+            _stop = None
+        else:
+            _stop = list(stop)
+        return await self._call_async(messages, stop=_stop)
+
     @property
     def _identifying_params(self) -> Mapping[str, Any]:
         """Get the identifying parameters."""

langchain/llms/base.py

@@ -299,6 +299,13 @@ class BaseLLM(BaseLanguageModel, ABC):
             .text
         )
 
+    async def _call_async(
+        self, prompt: str, stop: Optional[List[str]] = None, callbacks: Callbacks = None
+    ) -> str:
+        """Check Cache and run the LLM on the given prompt and input."""
+        result = await self.agenerate([prompt], stop=stop, callbacks=callbacks)
+        return result.generations[0][0].text
+
     def predict(self, text: str, *, stop: Optional[Sequence[str]] = None) -> str:
         if stop is None:
             _stop = None
@@ -317,6 +324,24 @@ class BaseLLM(BaseLanguageModel, ABC):
         content = self(text, stop=_stop)
         return AIMessage(content=content)
 
+    async def apredict(self, text: str, *, stop: Optional[Sequence[str]] = None) -> str:
+        if stop is None:
+            _stop = None
+        else:
+            _stop = list(stop)
+        return await self._call_async(text, stop=_stop)
+
+    async def apredict_messages(
+        self, messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None
+    ) -> BaseMessage:
+        text = get_buffer_string(messages)
+        if stop is None:
+            _stop = None
+        else:
+            _stop = list(stop)
+        content = await self._call_async(text, stop=_stop)
+        return AIMessage(content=content)
+
     @property
     def _identifying_params(self) -> Mapping[str, Any]:
         """Get the identifying parameters."""