refactor chain

langchain/chains/base.py

@@ -111,6 +111,10 @@ 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]:
+        """Run the logic of this chain and return the output."""
+        raise NotImplementedError("Async call not supported for this chain type.")
+
     def __call__(
         self, inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False
     ) -> Dict[str, Any]:
@@ -125,24 +129,7 @@ class Chain(BaseModel, ABC):
                 chain will be returned. Defaults to False.
 
         """
-        if not isinstance(inputs, dict):
-            _input_keys = set(self.input_keys)
-            if self.memory is not None:
-                # If there are multiple input keys, but some get set by memory so that
-                # only one is not set, we can still figure out which key it is.
-                _input_keys = _input_keys.difference(self.memory.memory_variables)
-            if len(_input_keys) != 1:
-                raise ValueError(
-                    f"A single string input was passed in, but this chain expects "
-                    f"multiple inputs ({_input_keys}). When a chain expects "
-                    f"multiple inputs, please call it by passing in a dictionary, "
-                    "eg `chain({'foo': 1, 'bar': 2})`"
-                )
-            inputs = {list(_input_keys)[0]: inputs}
-        if self.memory is not None:
-            external_context = self.memory.load_memory_variables(inputs)
-            inputs = dict(inputs, **external_context)
-        self._validate_inputs(inputs)
+        inputs = self.prep_inputs(inputs)
         self.callback_manager.on_chain_start(
             {"name": self.__class__.__name__},
             inputs,
@@ -154,6 +141,37 @@ class Chain(BaseModel, ABC):
             self.callback_manager.on_chain_error(e, verbose=self.verbose)
             raise e
         self.callback_manager.on_chain_end(outputs, verbose=self.verbose)
+        return self.prep_outputs(inputs, outputs, return_only_outputs)
+
+    async def async_call(
+        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.
+
+        Args:
+            inputs: Dictionary of inputs, or single input if chain expects
+                only one param.
+            return_only_outputs: boolean for whether to return only outputs in the
+                response. If True, only new keys generated by this chain will be
+                returned. If False, both input keys and new keys generated by this
+                chain will be returned. Defaults to False.
+
+        """
+        inputs = self.prep_inputs(inputs)
+        self.callback_manager.on_chain_start(
+            {"name": self.__class__.__name__},
+            inputs,
+            verbose=self.verbose,
+        )
+        try:
+            outputs = await self._async_call(inputs)
+        except (KeyboardInterrupt, Exception) as e:
+            self.callback_manager.on_chain_error(e, verbose=self.verbose)
+            raise e
+        self.callback_manager.on_chain_end(outputs, verbose=self.verbose)
+        return self.prep_outputs(inputs, outputs, return_only_outputs)
+
+    def prep_outputs(self, inputs, outputs, return_only_outputs):
         self._validate_outputs(outputs)
         if self.memory is not None:
             self.memory.save_context(inputs, outputs)
@@ -162,6 +180,27 @@ class Chain(BaseModel, ABC):
         else:
             return {**inputs, **outputs}
 
+    def prep_inputs(self, inputs):
+        if not isinstance(inputs, dict):
+            _input_keys = set(self.input_keys)
+            if self.memory is not None:
+                # If there are multiple input keys, but some get set by memory so that
+                # only one is not set, we can still figure out which key it is.
+                _input_keys = _input_keys.difference(self.memory.memory_variables)
+            if len(_input_keys) != 1:
+                raise ValueError(
+                    f"A single string input was passed in, but this chain expects "
+                    f"multiple inputs ({_input_keys}). When a chain expects "
+                    f"multiple inputs, please call it by passing in a dictionary, "
+                    "eg `chain({'foo': 1, 'bar': 2})`"
+                )
+            inputs = {list(_input_keys)[0]: inputs}
+        if self.memory is not None:
+            external_context = self.memory.load_memory_variables(inputs)
+            inputs = dict(inputs, **external_context)
+        self._validate_inputs(inputs)
+        return inputs
+
     def apply(self, input_list: List[Dict[str, Any]]) -> List[Dict[str, str]]:
         """Call the chain on all inputs in the list."""
         return [self(inputs) for inputs in input_list]