cr

langchain/chains/base.py

@@ -111,7 +111,7 @@ class Chain(BaseModel, ABC):
     def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
         """Run the logic of this chain and return the output."""
 
-    async def _async_call(self, inputs: Dict[str, str]) -> Dict[str, str]:
+    async def _acall(self, inputs: Dict[str, str]) -> Dict[str, str]:
         """Run the logic of this chain and return the output."""
         raise NotImplementedError("Async call not supported for this chain type.")
 
@@ -143,7 +143,7 @@ class Chain(BaseModel, ABC):
         self.callback_manager.on_chain_end(outputs, verbose=self.verbose)
         return self.prep_outputs(inputs, outputs, return_only_outputs)
 
-    async def async_call(
+    async def acall(
         self, inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False
     ) -> Dict[str, Any]:
         """Run the logic of this chain and add to output if desired.
@@ -164,7 +164,7 @@ class Chain(BaseModel, ABC):
             verbose=self.verbose,
         )
         try:
-            outputs = await self._async_call(inputs)
+            outputs = await self._acall(inputs)
         except (KeyboardInterrupt, Exception) as e:
             self.callback_manager.on_chain_error(e, verbose=self.verbose)
             raise e

langchain/chains/llm.py

@@ -56,6 +56,17 @@ class LLMChain(Chain, BaseModel):
 
     def generate(self, input_list: List[Dict[str, Any]]) -> LLMResult:
         """Generate LLM result from inputs."""
+        prompts, stop = self.prep_prompts(input_list)
+        response = self.llm.generate(prompts, stop=stop)
+        return response
+
+    async def agenerate(self, input_list: List[Dict[str, Any]]) -> LLMResult:
+        """Generate LLM result from inputs."""
+        prompts, stop = self.prep_prompts(input_list)
+        response = await self.llm.agenerate(prompts, stop=stop)
+        return response
+
+    def prep_prompts(self, input_list):
         stop = None
         if "stop" in input_list[0]:
             stop = input_list[0]["stop"]
@@ -71,12 +82,19 @@ class LLMChain(Chain, BaseModel):
                     "If `stop` is present in any inputs, should be present in all."
                 )
             prompts.append(prompt)
-        response = self.llm.generate(prompts, stop=stop)
-        return response
+        return prompts, stop
 
     def apply(self, input_list: List[Dict[str, Any]]) -> List[Dict[str, str]]:
         """Utilize the LLM generate method for speed gains."""
         response = self.generate(input_list)
+        return self.create_outputs(response)
+
+    async def aapply(self, input_list: List[Dict[str, Any]]) -> List[Dict[str, str]]:
+        """Utilize the LLM generate method for speed gains."""
+        response = await self.agenerate(input_list)
+        return self.create_outputs(response)
+
+    def create_outputs(self, response):
         outputs = []
         for generation in response.generations:
             # Get the text of the top generated string.
@@ -87,6 +105,9 @@ class LLMChain(Chain, BaseModel):
     def _call(self, inputs: Dict[str, Any]) -> Dict[str, str]:
         return self.apply([inputs])[0]
 
+    async def _acall(self, inputs: Dict[str, Any]) -> Dict[str, str]:
+        return (await self.aapply([inputs]))[0]
+
     def predict(self, **kwargs: Any) -> str:
         """Format prompt with kwargs and pass to LLM.
 
@@ -103,6 +124,22 @@ class LLMChain(Chain, BaseModel):
         """
         return self(kwargs)[self.output_key]
 
+    async def apredict(self, **kwargs: Any) -> str:
+        """Format prompt with kwargs and pass to LLM.
+
+        Args:
+            **kwargs: Keys to pass to prompt template.
+
+        Returns:
+            Completion from LLM.
+
+        Example:
+            .. code-block:: python
+
+                completion = llm.predict(adjective="funny")
+        """
+        return (await self.acall(kwargs))[self.output_key]
+
     def predict_and_parse(self, **kwargs: Any) -> Union[str, List[str], Dict[str, str]]:
         """Call predict and then parse the results."""
         result = self.predict(**kwargs)