add default async (#11141)

libs/langchain/langchain/chains/base.py

@@ -5,7 +5,6 @@ import json
 import logging
 import warnings
 from abc import ABC, abstractmethod
-from functools import partial
 from pathlib import Path
 from typing import Any, Dict, List, Optional, Type, Union
 
@@ -97,12 +96,6 @@ class Chain(RunnableSerializable[Dict[str, Any], Dict[str, Any]], ABC):
         config: Optional[RunnableConfig] = None,
         **kwargs: Any,
     ) -> Dict[str, Any]:
-        if type(self)._acall == Chain._acall:
-            # If the chain does not implement async, fall back to default implementation
-            return await asyncio.get_running_loop().run_in_executor(
-                None, partial(self.invoke, input, config, **kwargs)
-            )
-
         config = config or {}
         return await self.acall(
             input,
@@ -246,7 +239,9 @@ class Chain(RunnableSerializable[Dict[str, Any], Dict[str, Any]], ABC):
             A dict of named outputs. Should contain all outputs specified in
                 `Chain.output_keys`.
         """
-        raise NotImplementedError("Async call not supported for this chain type.")
+        return await asyncio.get_running_loop().run_in_executor(
+            None, self._call, inputs, run_manager
+        )
 
     def __call__(
         self,

libs/langchain/langchain/chat_models/base.py

@@ -577,10 +577,7 @@ class BaseChatModel(BaseLanguageModel[BaseMessageChunk], ABC):
     ) -> ChatResult:
         """Top Level call"""
         return await asyncio.get_running_loop().run_in_executor(
-            None,
-            partial(
-                self._generate, messages, stop=stop, run_manager=run_manager, **kwargs
-            ),
+            None, partial(self._generate, **kwargs), messages, stop, run_manager
         )
 
     def _stream(

libs/langchain/langchain/llms/base.py

@@ -248,12 +248,6 @@ class BaseLLM(BaseLanguageModel[str], ABC):
         stop: Optional[List[str]] = None,
         **kwargs: Any,
     ) -> str:
-        if type(self)._agenerate == BaseLLM._agenerate:
-            # model doesn't implement async invoke, so use default implementation
-            return await asyncio.get_running_loop().run_in_executor(
-                None, partial(self.invoke, input, config, stop=stop, **kwargs)
-            )
-
         config = config or {}
         llm_result = await self.agenerate_prompt(
             [self._convert_input(input)],
@@ -319,13 +313,6 @@ class BaseLLM(BaseLanguageModel[str], ABC):
     ) -> List[str]:
         if not inputs:
             return []
-
-        if type(self)._agenerate == BaseLLM._agenerate:
-            # model doesn't implement async batch, so use default implementation
-            return await asyncio.get_running_loop().run_in_executor(
-                None, partial(self.batch, **kwargs), inputs, config
-            )
-
         config = get_config_list(config, len(inputs))
         max_concurrency = config[0].get("max_concurrency")
 
@@ -478,7 +465,9 @@ class BaseLLM(BaseLanguageModel[str], ABC):
         **kwargs: Any,
     ) -> LLMResult:
         """Run the LLM on the given prompts."""
-        raise NotImplementedError()
+        return await asyncio.get_running_loop().run_in_executor(
+            None, partial(self._generate, **kwargs), prompts, stop, run_manager
+        )
 
     def _stream(
         self,
@@ -1035,7 +1024,9 @@ class LLM(BaseLLM):
         **kwargs: Any,
     ) -> str:
         """Run the LLM on the given prompt and input."""
-        raise NotImplementedError()
+        return await asyncio.get_running_loop().run_in_executor(
+            None, partial(self._call, **kwargs), prompt, stop, run_manager
+        )
 
     def _generate(
         self,
@@ -1064,12 +1055,6 @@ class LLM(BaseLLM):
         run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
         **kwargs: Any,
     ) -> LLMResult:
-        if type(self)._acall == LLM._acall:
-            # model doesn't implement async call, so use default implementation
-            return await asyncio.get_running_loop().run_in_executor(
-                None, partial(self._generate, prompts, stop, run_manager, **kwargs)
-            )
-
         """Run the LLM on the given prompt and input."""
         generations = []
         new_arg_supported = inspect.signature(self._acall).parameters.get("run_manager")

libs/langchain/langchain/schema/document.py

@@ -1,6 +1,8 @@
 from __future__ import annotations
 
+import asyncio
 from abc import ABC, abstractmethod
+from functools import partial
 from typing import Any, Sequence
 
 from langchain.load.serializable import Serializable
@@ -72,7 +74,6 @@ class BaseDocumentTransformer(ABC):
             A list of transformed Documents.
         """
 
-    @abstractmethod
     async def atransform_documents(
         self, documents: Sequence[Document], **kwargs: Any
     ) -> Sequence[Document]:
@@ -84,3 +85,6 @@ class BaseDocumentTransformer(ABC):
         Returns:
             A list of transformed Documents.
         """
+        return await asyncio.get_running_loop().run_in_executor(
+            None, partial(self.transform_documents, **kwargs), documents
+        )

libs/langchain/langchain/schema/embeddings.py

@@ -1,3 +1,4 @@
+import asyncio
 from abc import ABC, abstractmethod
 from typing import List
 
@@ -15,8 +16,12 @@ class Embeddings(ABC):
 
     async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
         """Asynchronous Embed search docs."""
-        raise NotImplementedError
+        return await asyncio.get_running_loop().run_in_executor(
+            None, self.embed_documents, texts
+        )
 
     async def aembed_query(self, text: str) -> List[float]:
         """Asynchronous Embed query text."""
-        raise NotImplementedError
+        return await asyncio.get_running_loop().run_in_executor(
+            None, self.embed_query, text
+        )

libs/langchain/langchain/schema/retriever.py

@@ -1,7 +1,9 @@
 from __future__ import annotations
 
+import asyncio
 import warnings
 from abc import ABC, abstractmethod
+from functools import partial
 from inspect import signature
 from typing import TYPE_CHECKING, Any, Dict, List, Optional
 
@@ -121,10 +123,6 @@ class BaseRetriever(RunnableSerializable[str, List[Document]], ABC):
         config: Optional[RunnableConfig] = None,
         **kwargs: Optional[Any],
     ) -> List[Document]:
-        if type(self).aget_relevant_documents == BaseRetriever.aget_relevant_documents:
-            # If the retriever doesn't implement async, use default implementation
-            return await super().ainvoke(input, config)
-
         config = config or {}
         return await self.aget_relevant_documents(
             input,
@@ -156,7 +154,9 @@ class BaseRetriever(RunnableSerializable[str, List[Document]], ABC):
         Returns:
             List of relevant documents
         """
-        raise NotImplementedError()
+        return await asyncio.get_running_loop().run_in_executor(
+            None, partial(self._get_relevant_documents, run_manager=run_manager), query
+        )
 
     def get_relevant_documents(
         self,

libs/langchain/langchain/schema/vectorstore.py

@@ -87,7 +87,9 @@ class VectorStore(ABC):
         **kwargs: Any,
     ) -> List[str]:
         """Run more texts through the embeddings and add to the vectorstore."""
-        raise NotImplementedError
+        return await asyncio.get_running_loop().run_in_executor(
+            None, partial(self.add_texts, **kwargs), texts, metadatas
+        )
 
     def add_documents(self, documents: List[Document], **kwargs: Any) -> List[str]:
         """Run more documents through the embeddings and add to the vectorstore.
@@ -451,7 +453,9 @@ class VectorStore(ABC):
         **kwargs: Any,
     ) -> VST:
         """Return VectorStore initialized from texts and embeddings."""
-        raise NotImplementedError
+        return await asyncio.get_running_loop().run_in_executor(
+            None, partial(cls.from_texts, **kwargs), texts, embedding, metadatas
+        )
 
     def _get_retriever_tags(self) -> List[str]:
         """Get tags for retriever."""

libs/langchain/langchain/text_splitter.py

@@ -21,6 +21,7 @@ Note: **MarkdownHeaderTextSplitter** and **HTMLHeaderTextSplitter do not derive
 
 from __future__ import annotations
 
+import asyncio
 import copy
 import logging
 import pathlib
@@ -28,6 +29,7 @@ import re
 from abc import ABC, abstractmethod
 from dataclasses import dataclass
 from enum import Enum
+from functools import partial
 from io import BytesIO, StringIO
 from typing import (
     AbstractSet,
@@ -284,7 +286,9 @@ class TextSplitter(BaseDocumentTransformer, ABC):
         self, documents: Sequence[Document], **kwargs: Any
     ) -> Sequence[Document]:
         """Asynchronously transform a sequence of documents by splitting them."""
-        raise NotImplementedError
+        return await asyncio.get_running_loop().run_in_executor(
+            None, partial(self.transform_documents, **kwargs), documents
+        )
 
 
 class CharacterTextSplitter(TextSplitter):

libs/langchain/langchain/tools/base.py

@@ -217,10 +217,6 @@ class ChildTool(BaseTool):
         config: Optional[RunnableConfig] = None,
         **kwargs: Any,
     ) -> Any:
-        if type(self)._arun == BaseTool._arun:
-            # If the tool does not implement async, fall back to default implementation
-            return await super().ainvoke(input, config, **kwargs)
-
         config = config or {}
         return await self.arun(
             input,