conditional imports (#11017)

libs/langchain/langchain/__init__.py

@@ -1,57 +1,12 @@
 # ruff: noqa: E402
 """Main entrypoint into package."""
+import warnings
 from importlib import metadata
-from typing import Optional
-
-from langchain.agents import MRKLChain, ReActChain, SelfAskWithSearchChain
-from langchain.chains import (
-    ConversationChain,
-    LLMBashChain,
-    LLMChain,
-    LLMCheckerChain,
-    LLMMathChain,
-    QAWithSourcesChain,
-    VectorDBQA,
-    VectorDBQAWithSourcesChain,
-)
-from langchain.docstore import InMemoryDocstore, Wikipedia
-from langchain.llms import (
-    Anthropic,
-    Banana,
-    CerebriumAI,
-    Cohere,
-    ForefrontAI,
-    GooseAI,
-    HuggingFaceHub,
-    HuggingFaceTextGenInference,
-    LlamaCpp,
-    Modal,
-    OpenAI,
-    Petals,
-    PipelineAI,
-    SagemakerEndpoint,
-    StochasticAI,
-    Writer,
-)
-from langchain.llms.huggingface_pipeline import HuggingFacePipeline
-from langchain.prompts import (
-    FewShotPromptTemplate,
-    Prompt,
-    PromptTemplate,
-)
-from langchain.schema.cache import BaseCache
-from langchain.schema.prompt_template import BasePromptTemplate
-from langchain.utilities.arxiv import ArxivAPIWrapper
-from langchain.utilities.golden_query import GoldenQueryAPIWrapper
-from langchain.utilities.google_search import GoogleSearchAPIWrapper
-from langchain.utilities.google_serper import GoogleSerperAPIWrapper
-from langchain.utilities.powerbi import PowerBIDataset
-from langchain.utilities.searx_search import SearxSearchWrapper
-from langchain.utilities.serpapi import SerpAPIWrapper
-from langchain.utilities.sql_database import SQLDatabase
-from langchain.utilities.wikipedia import WikipediaAPIWrapper
-from langchain.utilities.wolfram_alpha import WolframAlphaAPIWrapper
-from langchain.vectorstores import FAISS, ElasticVectorSearch
+from typing import TYPE_CHECKING, Any, Optional
+
+if TYPE_CHECKING:
+    from langchain.schema import BaseCache
+
 
 try:
     __version__ = metadata.version(__package__)
@@ -62,10 +17,200 @@ del metadata  # optional, avoids polluting the results of dir(__package__)
 
 verbose: bool = False
 debug: bool = False
-llm_cache: Optional[BaseCache] = None
+llm_cache: Optional["BaseCache"] = None
+
+
+def __getattr__(name: str) -> Any:
+    warnings.warn(
+        f"Importing {name} from langchain root module is no longer supported."
+    )
+    if name == "MRKLChain":
+        from langchain.agents import MRKLChain
+
+        return MRKLChain
+    elif name == "ReActChain":
+        from langchain.agents import ReActChain
+
+        return ReActChain
+    elif name == "SelfAskWithSearchChain":
+        from langchain.agents import SelfAskWithSearchChain
+
+        return SelfAskWithSearchChain
+    elif name == "ConversationChain":
+        from langchain.chains import ConversationChain
+
+        return ConversationChain
+    elif name == "LLMBashChain":
+        from langchain.chains import LLMBashChain
+
+        return LLMBashChain
+    elif name == "LLMChain":
+        from langchain.chains import LLMChain
+
+        return LLMChain
+    elif name == "LLMCheckerChain":
+        from langchain.chains import LLMCheckerChain
+
+        return LLMCheckerChain
+    elif name == "LLMMathChain":
+        from langchain.chains import LLMMathChain
+
+        return LLMMathChain
+    elif name == "QAWithSourcesChain":
+        from langchain.chains import QAWithSourcesChain
+
+        return QAWithSourcesChain
+    elif name == "VectorDBQA":
+        from langchain.chains import VectorDBQA
+
+        return VectorDBQA
+    elif name == "VectorDBQAWithSourcesChain":
+        from langchain.chains import VectorDBQAWithSourcesChain
+
+        return VectorDBQAWithSourcesChain
+    elif name == "InMemoryDocstore":
+        from langchain.docstore import InMemoryDocstore
+
+        return InMemoryDocstore
+    elif name == "Wikipedia":
+        from langchain.docstore import Wikipedia
+
+        return Wikipedia
+    elif name == "Anthropic":
+        from langchain.llms import Anthropic
+
+        return Anthropic
+    elif name == "Banana":
+        from langchain.llms import Banana
+
+        return Banana
+    elif name == "CerebriumAI":
+        from langchain.llms import CerebriumAI
+
+        return CerebriumAI
+    elif name == "Cohere":
+        from langchain.llms import Cohere
+
+        return Cohere
+    elif name == "ForefrontAI":
+        from langchain.llms import ForefrontAI
+
+        return ForefrontAI
+    elif name == "GooseAI":
+        from langchain.llms import GooseAI
+
+        return GooseAI
+    elif name == "HuggingFaceHub":
+        from langchain.llms import HuggingFaceHub
+
+        return HuggingFaceHub
+    elif name == "HuggingFaceTextGenInference":
+        from langchain.llms import HuggingFaceTextGenInference
+
+        return HuggingFaceTextGenInference
+    elif name == "LlamaCpp":
+        from langchain.llms import LlamaCpp
+
+        return LlamaCpp
+    elif name == "Modal":
+        from langchain.llms import Modal
+
+        return Modal
+    elif name == "OpenAI":
+        from langchain.llms import OpenAI
+
+        return OpenAI
+    elif name == "Petals":
+        from langchain.llms import Petals
+
+        return Petals
+    elif name == "PipelineAI":
+        from langchain.llms import PipelineAI
+
+        return PipelineAI
+    elif name == "SagemakerEndpoint":
+        from langchain.llms import SagemakerEndpoint
+
+        return SagemakerEndpoint
+    elif name == "StochasticAI":
+        from langchain.llms import StochasticAI
+
+        return StochasticAI
+    elif name == "Writer":
+        from langchain.llms import Writer
+
+        return Writer
+    elif name == "HuggingFacePipeline":
+        from langchain.llms.huggingface_pipeline import HuggingFacePipeline
+
+        return HuggingFacePipeline
+    elif name == "FewShotPromptTemplate":
+        from langchain.prompts import FewShotPromptTemplate
+
+        return FewShotPromptTemplate
+    elif name == "Prompt":
+        from langchain.prompts import Prompt
+
+        return Prompt
+    elif name == "PromptTemplate":
+        from langchain.prompts import PromptTemplate
+
+        return PromptTemplate
+    elif name == "BasePromptTemplate":
+        from langchain.schema.prompt_template import BasePromptTemplate
+
+        return BasePromptTemplate
+    elif name == "ArxivAPIWrapper":
+        from langchain.utilities import ArxivAPIWrapper
+
+        return ArxivAPIWrapper
+    elif name == "GoldenQueryAPIWrapper":
+        from langchain.utilities import GoldenQueryAPIWrapper
+
+        return GoldenQueryAPIWrapper
+    elif name == "GoogleSearchAPIWrapper":
+        from langchain.utilities import GoogleSearchAPIWrapper
+
+        return GoogleSearchAPIWrapper
+    elif name == "GoogleSerperAPIWrapper":
+        from langchain.utilities import GoogleSerperAPIWrapper
+
+        return GoogleSerperAPIWrapper
+    elif name == "PowerBIDataset":
+        from langchain.utilities import PowerBIDataset
+
+        return PowerBIDataset
+    elif name == "SearxSearchWrapper":
+        from langchain.utilities import SearxSearchWrapper
+
+        return SearxSearchWrapper
+    elif name == "WikipediaAPIWrapper":
+        from langchain.utilities import WikipediaAPIWrapper
+
+        return WikipediaAPIWrapper
+    elif name == "WolframAlphaAPIWrapper":
+        from langchain.utilities import WolframAlphaAPIWrapper
+
+        return WolframAlphaAPIWrapper
+    elif name == "SQLDatabase":
+        from langchain.utilities import SQLDatabase
+
+        return SQLDatabase
+    elif name == "FAISS":
+        from langchain.vectorstores import FAISS
+
+        return FAISS
+    elif name == "ElasticVectorSearch":
+        from langchain.vectorstores import ElasticVectorSearch
+
+        return ElasticVectorSearch
+    # For backwards compatibility
+    elif name == "SerpAPIChain":
+        from langchain.utilities import SerpAPIWrapper
 
-# For backwards compatibility
-SerpAPIChain = SerpAPIWrapper
+        return SerpAPIWrapper
+    else:
+        raise AttributeError(f"Could not find: {name}")
 
 
 __all__ = [

libs/langchain/langchain/output_parsers/fix.py

@@ -1,12 +1,14 @@
 from __future__ import annotations
 
-from typing import TypeVar
+from typing import TYPE_CHECKING, TypeVar
 
-from langchain.chains.llm import LLMChain
 from langchain.output_parsers.prompts import NAIVE_FIX_PROMPT
 from langchain.schema import BaseOutputParser, BasePromptTemplate, OutputParserException
 from langchain.schema.language_model import BaseLanguageModel
 
+if TYPE_CHECKING:
+    from langchain.chains.llm import LLMChain
+
 T = TypeVar("T")
 
 
@@ -37,6 +39,8 @@ class OutputFixingParser(BaseOutputParser[T]):
         Returns:
             OutputFixingParser
         """
+        from langchain.chains.llm import LLMChain
+
         chain = LLMChain(llm=llm, prompt=prompt)
         return cls(parser=parser, retry_chain=chain)
 

libs/langchain/langchain/output_parsers/retry.py

@@ -1,8 +1,7 @@
 from __future__ import annotations
 
-from typing import TypeVar
+from typing import TYPE_CHECKING, TypeVar
 
-from langchain.chains.llm import LLMChain
 from langchain.prompts.prompt import PromptTemplate
 from langchain.schema import (
     BaseOutputParser,
@@ -12,6 +11,9 @@ from langchain.schema import (
 )
 from langchain.schema.language_model import BaseLanguageModel
 
+if TYPE_CHECKING:
+    from langchain.chains.llm import LLMChain
+
 NAIVE_COMPLETION_RETRY = """Prompt:
 {prompt}
 Completion:
@@ -56,6 +58,8 @@ class RetryOutputParser(BaseOutputParser[T]):
         parser: BaseOutputParser[T],
         prompt: BasePromptTemplate = NAIVE_RETRY_PROMPT,
     ) -> RetryOutputParser[T]:
+        from langchain.chains.llm import LLMChain
+
         chain = LLMChain(llm=llm, prompt=prompt)
         return cls(parser=parser, retry_chain=chain)
 
@@ -142,6 +146,8 @@ class RetryWithErrorOutputParser(BaseOutputParser[T]):
         Returns:
             A RetryWithErrorOutputParser.
         """
+        from langchain.chains.llm import LLMChain
+
         chain = LLMChain(llm=llm, prompt=prompt)
         return cls(parser=parser, retry_chain=chain)
 

libs/langchain/langchain/tools/vectorstore/tool.py

@@ -4,7 +4,6 @@ import json
 from typing import Any, Dict, Optional
 
 from langchain.callbacks.manager import CallbackManagerForToolRun
-from langchain.chains import RetrievalQA, RetrievalQAWithSourcesChain
 from langchain.llms.openai import OpenAI
 from langchain.pydantic_v1 import BaseModel, Field
 from langchain.schema.language_model import BaseLanguageModel
@@ -48,6 +47,8 @@ class VectorStoreQATool(BaseVectorStoreTool, BaseTool):
         run_manager: Optional[CallbackManagerForToolRun] = None,
     ) -> str:
         """Use the tool."""
+        from langchain.chains.retrieval_qa.base import RetrievalQA
+
         chain = RetrievalQA.from_chain_type(
             self.llm, retriever=self.vectorstore.as_retriever()
         )
@@ -78,6 +79,11 @@ class VectorStoreQAWithSourcesTool(BaseVectorStoreTool, BaseTool):
         run_manager: Optional[CallbackManagerForToolRun] = None,
     ) -> str:
         """Use the tool."""
+
+        from langchain.chains.qa_with_sources.retrieval import (
+            RetrievalQAWithSourcesChain,
+        )
+
         chain = RetrievalQAWithSourcesChain.from_chain_type(
             self.llm, retriever=self.vectorstore.as_retriever()
         )