use agenerate

langchain/llms/base.py

@@ -83,7 +83,7 @@ class BaseLLM(BaseModel, ABC):
         """Run the LLM on the given prompts."""
 
     @abstractmethod
-    async def _async_generate(
+    async def _agenerate(
         self, prompts: List[str], stop: Optional[List[str]] = None
     ) -> LLMResult:
         """Run the LLM on the given prompts."""
@@ -128,7 +128,7 @@ class BaseLLM(BaseModel, ABC):
         generations = [existing_prompts[i] for i in range(len(prompts))]
         return LLMResult(generations=generations, llm_output=llm_output)
 
-    async def async_generate(
+    async def agenerate(
         self, prompts: List[str], stop: Optional[List[str]] = None
     ) -> LLMResult:
         disregard_cache = self.cache is not None and not self.cache
@@ -142,7 +142,7 @@ class BaseLLM(BaseModel, ABC):
                 {"name": self.__class__.__name__}, prompts, verbose=self.verbose
             )
             try:
-                output = await self._async_generate(prompts, stop=stop)
+                output = await self._agenerate(prompts, stop=stop)
             except (KeyboardInterrupt, Exception) as e:
                 self.callback_manager.on_llm_error(e, verbose=self.verbose)
                 raise e
@@ -156,7 +156,7 @@ class BaseLLM(BaseModel, ABC):
                 {"name": self.__class__.__name__}, missing_prompts, verbose=self.verbose
             )
             try:
-                new_results = await self._async_generate(missing_prompts, stop=stop)
+                new_results = await self._agenerate(missing_prompts, stop=stop)
             except (KeyboardInterrupt, Exception) as e:
                 self.callback_manager.on_llm_error(e, verbose=self.verbose)
                 raise e
@@ -268,7 +268,7 @@ class LLM(BaseLLM):
             generations.append([Generation(text=text)])
         return LLMResult(generations=generations)
 
-    async def _async_generate(
+    async def _agenerate(
         self, prompts: List[str], stop: Optional[List[str]] = None
     ) -> LLMResult:
         """Run the LLM on the given prompt and input."""

langchain/llms/openai.py

@@ -186,7 +186,7 @@ class BaseOpenAI(BaseLLM, BaseModel):
                     token_usage[_key] += response["usage"][_key]
         return self.create_llm_result(choices, prompts, token_usage)
 
-    async def _async_generate(
+    async def _agenerate(
         self, prompts: List[str], stop: Optional[List[str]] = None
     ) -> LLMResult:
         params = self._invocation_params

tests/unit_tests/chains/test_hyde.py

@@ -33,7 +33,7 @@ class FakeLLM(BaseLLM, BaseModel):
     ) -> LLMResult:
         return LLMResult(generations=[[Generation(text="foo") for _ in range(self.n)]])
 
-    async def _async_generate(
+    async def _agenerate(
         self, prompts: List[str], stop: Optional[List[str]] = None
     ) -> LLMResult:
         return LLMResult(generations=[[Generation(text="foo") for _ in range(self.n)]])

tests/unit_tests/llms/llm_test.py

@@ -10,7 +10,7 @@ def generate_serially():
 
 
 async def async_generate(llm):
-    resp = await llm.async_generate(["Hello, how are you?"])
+    resp = await llm.agenerate(["Hello, how are you?"])
     # print(resp)