Removed duplicate BaseModel dependencies (#2471)

Removed duplicate BaseModel dependencies in class inheritances.
Also, sorted imports by `isort`.

langchain/agents/agent.py

@@ -548,7 +548,7 @@ class Agent(BaseSingleActionAgent):
         }
 
 
-class AgentExecutor(Chain, BaseModel):
+class AgentExecutor(Chain):
     """Consists of an agent using tools."""
 
     agent: Union[BaseSingleActionAgent, BaseMultiActionAgent]

langchain/agents/agent_toolkits/openapi/planner.py

@@ -19,9 +19,7 @@ from langchain.agents.agent_toolkits.openapi.planner_prompt import (
     REQUESTS_GET_TOOL_DESCRIPTION,
     REQUESTS_POST_TOOL_DESCRIPTION,
 )
-from langchain.agents.agent_toolkits.openapi.spec import (
+from langchain.agents.agent_toolkits.openapi.spec import ReducedOpenAPISpec
-    ReducedOpenAPISpec,
-)
 from langchain.agents.mrkl.base import ZeroShotAgent
 from langchain.agents.tools import Tool
 from langchain.chains.llm import LLMChain

langchain/agents/agent_toolkits/openapi/planner_prompt.py

@@ -2,7 +2,6 @@
 
 from langchain.prompts.prompt import PromptTemplate
 
-
 API_PLANNER_PROMPT = """You are a planner that plans a sequence of API calls to assist with user queries against an API.
 
 You should:

langchain/agents/load_tools.py

@@ -1,11 +1,11 @@
 # flake8: noqa
 """Load tools."""
-from typing import Any, List, Optional
 import warnings
+from typing import Any, List, Optional
 
 from langchain.agents.tools import Tool
 from langchain.callbacks.base import BaseCallbackManager
-from langchain.chains.api import news_docs, open_meteo_docs, tmdb_docs, podcast_docs
+from langchain.chains.api import news_docs, open_meteo_docs, podcast_docs, tmdb_docs
 from langchain.chains.api.base import APIChain
 from langchain.chains.llm_math.base import LLMMathChain
 from langchain.chains.pal.base import PALChain
@@ -14,16 +14,16 @@ from langchain.requests import TextRequestsWrapper
 from langchain.tools.base import BaseTool
 from langchain.tools.bing_search.tool import BingSearchRun
 from langchain.tools.google_search.tool import GoogleSearchResults, GoogleSearchRun
-from langchain.tools.searx_search.tool import SearxSearchResults, SearxSearchRun
 from langchain.tools.human.tool import HumanInputRun
 from langchain.tools.python.tool import PythonREPLTool
 from langchain.tools.requests.tool import (
+    RequestsDeleteTool,
     RequestsGetTool,
-    RequestsPostTool,
     RequestsPatchTool,
+    RequestsPostTool,
     RequestsPutTool,
-    RequestsDeleteTool,
 )
+from langchain.tools.searx_search.tool import SearxSearchResults, SearxSearchRun
 from langchain.tools.wikipedia.tool import WikipediaQueryRun
 from langchain.tools.wolfram_alpha.tool import WolframAlphaQueryRun
 from langchain.utilities.apify import ApifyWrapper

langchain/agents/react/base.py

@@ -2,8 +2,6 @@
 import re
 from typing import Any, List, Optional, Sequence, Tuple
 
-from pydantic import BaseModel
-
 from langchain.agents.agent import Agent, AgentExecutor
 from langchain.agents.agent_types import AgentType
 from langchain.agents.react.textworld_prompt import TEXTWORLD_PROMPT
@@ -16,7 +14,7 @@ from langchain.prompts.base import BasePromptTemplate
 from langchain.tools.base import BaseTool
 
 
-class ReActDocstoreAgent(Agent, BaseModel):
+class ReActDocstoreAgent(Agent):
     """Agent for the ReAct chain."""
 
     @property
@@ -124,7 +122,7 @@ class DocstoreExplorer:
         return self.document.page_content.split("\n\n")
 
 
-class ReActTextWorldAgent(ReActDocstoreAgent, BaseModel):
+class ReActTextWorldAgent(ReActDocstoreAgent):
     """Agent for the ReAct TextWorld chain."""
 
     @classmethod

langchain/callbacks/arize_callback.py

@@ -77,11 +77,7 @@ class ArizeCallbackHandler(BaseCallbackHandler):
     def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
         """Log data to Arize when an LLM ends."""
 
-        from arize.utils.types import (
+        from arize.utils.types import Embedding, Environments, ModelTypes
-            Embedding,
-            Environments,
-            ModelTypes,
-        )
 
         # Record token usage of the LLM
         if response.llm_output is not None:

langchain/chains/api/base.py

@@ -3,7 +3,7 @@ from __future__ import annotations
 
 from typing import Any, Dict, List, Optional
 
-from pydantic import BaseModel, Field, root_validator
+from pydantic import Field, root_validator
 
 from langchain.chains.api.prompt import API_RESPONSE_PROMPT, API_URL_PROMPT
 from langchain.chains.base import Chain
@@ -13,7 +13,7 @@ from langchain.requests import TextRequestsWrapper
 from langchain.schema import BaseLanguageModel
 
 
-class APIChain(Chain, BaseModel):
+class APIChain(Chain):
     """Chain that makes API calls and summarizes the responses to answer a question."""
 
     api_request_chain: LLMChain

langchain/chains/combine_documents/base.py

@@ -3,14 +3,14 @@
 from abc import ABC, abstractmethod
 from typing import Any, Dict, List, Optional, Tuple
 
-from pydantic import BaseModel, Field
+from pydantic import Field
 
 from langchain.chains.base import Chain
 from langchain.docstore.document import Document
 from langchain.text_splitter import RecursiveCharacterTextSplitter, TextSplitter
 
 
-class BaseCombineDocumentsChain(Chain, BaseModel, ABC):
+class BaseCombineDocumentsChain(Chain, ABC):
     """Base interface for chains combining documents."""
 
     input_key: str = "input_documents"  #: :meta private:
@@ -66,7 +66,7 @@ class BaseCombineDocumentsChain(Chain, BaseModel, ABC):
         return extra_return_dict
 
 
-class AnalyzeDocumentChain(Chain, BaseModel):
+class AnalyzeDocumentChain(Chain):
     """Chain that splits documents, then analyzes it in pieces."""
 
     input_key: str = "input_document"  #: :meta private:

langchain/chains/combine_documents/map_reduce.py

@@ -4,7 +4,7 @@ from __future__ import annotations
 
 from typing import Any, Callable, Dict, List, Optional, Protocol, Tuple
 
-from pydantic import BaseModel, Extra, root_validator
+from pydantic import Extra, root_validator
 
 from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
 from langchain.chains.llm import LLMChain
@@ -59,7 +59,7 @@ def _collapse_docs(
     return Document(page_content=result, metadata=combined_metadata)
 
 
-class MapReduceDocumentsChain(BaseCombineDocumentsChain, BaseModel):
+class MapReduceDocumentsChain(BaseCombineDocumentsChain):
     """Combining documents by mapping a chain over them, then combining results."""
 
     llm_chain: LLMChain

langchain/chains/combine_documents/map_rerank.py

@@ -4,7 +4,7 @@ from __future__ import annotations
 
 from typing import Any, Dict, List, Optional, Sequence, Tuple, Union, cast
 
-from pydantic import BaseModel, Extra, root_validator
+from pydantic import Extra, root_validator
 
 from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
 from langchain.chains.llm import LLMChain
@@ -12,7 +12,7 @@ from langchain.docstore.document import Document
 from langchain.output_parsers.regex import RegexParser
 
 
-class MapRerankDocumentsChain(BaseCombineDocumentsChain, BaseModel):
+class MapRerankDocumentsChain(BaseCombineDocumentsChain):
     """Combining documents by mapping a chain over them, then reranking results."""
 
     llm_chain: LLMChain

langchain/chains/combine_documents/refine.py

@@ -4,7 +4,7 @@ from __future__ import annotations
 
 from typing import Any, Dict, List, Tuple
 
-from pydantic import BaseModel, Extra, Field, root_validator
+from pydantic import Extra, Field, root_validator
 
 from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
 from langchain.chains.llm import LLMChain
@@ -17,7 +17,7 @@ def _get_default_document_prompt() -> PromptTemplate:
     return PromptTemplate(input_variables=["page_content"], template="{page_content}")
 
 
-class RefineDocumentsChain(BaseCombineDocumentsChain, BaseModel):
+class RefineDocumentsChain(BaseCombineDocumentsChain):
     """Combine documents by doing a first pass and then refining on more documents."""
 
     initial_llm_chain: LLMChain

langchain/chains/combine_documents/stuff.py

@@ -2,7 +2,7 @@
 
 from typing import Any, Dict, List, Optional, Tuple
 
-from pydantic import BaseModel, Extra, Field, root_validator
+from pydantic import Extra, Field, root_validator
 
 from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
 from langchain.chains.llm import LLMChain
@@ -15,7 +15,7 @@ def _get_default_document_prompt() -> PromptTemplate:
     return PromptTemplate(input_variables=["page_content"], template="{page_content}")
 
 
-class StuffDocumentsChain(BaseCombineDocumentsChain, BaseModel):
+class StuffDocumentsChain(BaseCombineDocumentsChain):
     """Chain that combines documents by stuffing into context."""
 
     llm_chain: LLMChain

langchain/chains/constitutional_ai/principles.py

@@ -1,5 +1,6 @@
 # flake8: noqa
 from typing import Dict
+
 from langchain.chains.constitutional_ai.models import ConstitutionalPrinciple
 
 PRINCIPLES: Dict[str, ConstitutionalPrinciple] = {}

langchain/chains/conversation/base.py

@@ -1,7 +1,7 @@
 """Chain that carries on a conversation and calls an LLM."""
 from typing import Dict, List
 
-from pydantic import BaseModel, Extra, Field, root_validator
+from pydantic import Extra, Field, root_validator
 
 from langchain.chains.conversation.prompt import PROMPT
 from langchain.chains.llm import LLMChain
@@ -10,7 +10,7 @@ from langchain.prompts.base import BasePromptTemplate
 from langchain.schema import BaseMemory
 
 
-class ConversationChain(LLMChain, BaseModel):
+class ConversationChain(LLMChain):
     """Chain to have a conversation and load context from memory.
 
     Example:

langchain/chains/conversational_retrieval/base.py

@@ -6,7 +6,7 @@ from abc import abstractmethod
 from pathlib import Path
 from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
-from pydantic import BaseModel, Extra, Field, root_validator
+from pydantic import Extra, Field, root_validator
 
 from langchain.chains.base import Chain
 from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
@@ -28,7 +28,7 @@ def _get_chat_history(chat_history: List[Tuple[str, str]]) -> str:
     return buffer
 
 
-class BaseConversationalRetrievalChain(Chain, BaseModel):
+class BaseConversationalRetrievalChain(Chain):
     """Chain for chatting with an index."""
 
     combine_docs_chain: BaseCombineDocumentsChain
@@ -116,7 +116,7 @@ class BaseConversationalRetrievalChain(Chain, BaseModel):
         super().save(file_path)
 
 
-class ConversationalRetrievalChain(BaseConversationalRetrievalChain, BaseModel):
+class ConversationalRetrievalChain(BaseConversationalRetrievalChain):
     """Chain for chatting with an index."""
 
     retriever: BaseRetriever
@@ -175,7 +175,7 @@ class ConversationalRetrievalChain(BaseConversationalRetrievalChain, BaseModel):
         )
 
 
-class ChatVectorDBChain(BaseConversationalRetrievalChain, BaseModel):
+class ChatVectorDBChain(BaseConversationalRetrievalChain):
     """Chain for chatting with a vector database."""
 
     vectorstore: VectorStore = Field(alias="vectorstore")

langchain/chains/hyde/base.py

@@ -7,7 +7,7 @@ from __future__ import annotations
 from typing import Dict, List
 
 import numpy as np
-from pydantic import BaseModel, Extra
+from pydantic import Extra
 
 from langchain.chains.base import Chain
 from langchain.chains.hyde.prompts import PROMPT_MAP
@@ -16,7 +16,7 @@ from langchain.embeddings.base import Embeddings
 from langchain.llms.base import BaseLLM
 
 
-class HypotheticalDocumentEmbedder(Chain, Embeddings, BaseModel):
+class HypotheticalDocumentEmbedder(Chain, Embeddings):
     """Generate hypothetical document for query, and then embed that.
 
     Based on https://arxiv.org/abs/2212.10496

langchain/chains/llm.py

@@ -3,7 +3,7 @@ from __future__ import annotations
 
 from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
 
-from pydantic import BaseModel, Extra
+from pydantic import Extra
 
 from langchain.chains.base import Chain
 from langchain.input import get_colored_text
@@ -12,7 +12,7 @@ from langchain.prompts.prompt import PromptTemplate
 from langchain.schema import BaseLanguageModel, LLMResult, PromptValue
 
 
-class LLMChain(Chain, BaseModel):
+class LLMChain(Chain):
     """Chain to run queries against LLMs.
 
     Example:

langchain/chains/llm_bash/base.py

@@ -1,7 +1,7 @@
 """Chain that interprets a prompt and executes bash code to perform bash operations."""
 from typing import Dict, List
 
-from pydantic import BaseModel, Extra
+from pydantic import Extra
 
 from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
@@ -11,7 +11,7 @@ from langchain.schema import BaseLanguageModel
 from langchain.utilities.bash import BashProcess
 
 
-class LLMBashChain(Chain, BaseModel):
+class LLMBashChain(Chain):
     """Chain that interprets a prompt and executes bash code to perform bash operations.
 
     Example:

langchain/chains/llm_checker/base.py

@@ -3,7 +3,7 @@
 
 from typing import Dict, List
 
-from pydantic import BaseModel, Extra
+from pydantic import Extra
 
 from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
@@ -18,7 +18,7 @@ from langchain.llms.base import BaseLLM
 from langchain.prompts import PromptTemplate
 
 
-class LLMCheckerChain(Chain, BaseModel):
+class LLMCheckerChain(Chain):
     """Chain for question-answering with self-verification.
 
     Example:

langchain/chains/llm_math/base.py

@@ -1,7 +1,7 @@
 """Chain that interprets a prompt and executes python code to do math."""
 from typing import Dict, List
 
-from pydantic import BaseModel, Extra
+from pydantic import Extra
 
 from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
@@ -11,7 +11,7 @@ from langchain.python import PythonREPL
 from langchain.schema import BaseLanguageModel
 
 
-class LLMMathChain(Chain, BaseModel):
+class LLMMathChain(Chain):
     """Chain that interprets a prompt and executes python code to do math.
 
     Example:

langchain/chains/llm_requests.py

@@ -3,7 +3,7 @@ from __future__ import annotations
 
 from typing import Dict, List
 
-from pydantic import BaseModel, Extra, Field, root_validator
+from pydantic import Extra, Field, root_validator
 
 from langchain.chains import LLMChain
 from langchain.chains.base import Chain
@@ -14,7 +14,7 @@ DEFAULT_HEADERS = {
 }
 
 
-class LLMRequestsChain(Chain, BaseModel):
+class LLMRequestsChain(Chain):
     """Chain that hits a URL and then uses an LLM to parse results."""
 
     llm_chain: LLMChain

langchain/chains/llm_summarization_checker/base.py

@@ -3,7 +3,7 @@
 from pathlib import Path
 from typing import Dict, List
 
-from pydantic import BaseModel, Extra
+from pydantic import Extra
 
 from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
@@ -27,7 +27,7 @@ ARE_ALL_TRUE_PROMPT = PromptTemplate.from_file(
 )
 
 
-class LLMSummarizationCheckerChain(Chain, BaseModel):
+class LLMSummarizationCheckerChain(Chain):
     """Chain for question-answering with self-verification.
 
     Example:

langchain/chains/mapreduce.py

@@ -7,7 +7,7 @@ from __future__ import annotations
 
 from typing import Dict, List
 
-from pydantic import BaseModel, Extra
+from pydantic import Extra
 
 from langchain.chains.base import Chain
 from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
@@ -20,7 +20,7 @@ from langchain.prompts.base import BasePromptTemplate
 from langchain.text_splitter import TextSplitter
 
 
-class MapReduceChain(Chain, BaseModel):
+class MapReduceChain(Chain):
     """Map-reduce chain."""
 
     combine_documents_chain: BaseCombineDocumentsChain

langchain/chains/moderation.py

@@ -1,13 +1,13 @@
 """Pass input through a moderation endpoint."""
 from typing import Any, Dict, List, Optional
 
-from pydantic import BaseModel, root_validator
+from pydantic import root_validator
 
 from langchain.chains.base import Chain
 from langchain.utils import get_from_dict_or_env
 
 
-class OpenAIModerationChain(Chain, BaseModel):
+class OpenAIModerationChain(Chain):
     """Pass input through a moderation endpoint.
 
     To use, you should have the ``openai`` python package installed, and the

langchain/chains/natbot/base.py

@@ -3,7 +3,7 @@ from __future__ import annotations
 
 from typing import Dict, List
 
-from pydantic import BaseModel, Extra
+from pydantic import Extra
 
 from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
@@ -12,7 +12,7 @@ from langchain.llms.base import BaseLLM
 from langchain.llms.openai import OpenAI
 
 
-class NatBotChain(Chain, BaseModel):
+class NatBotChain(Chain):
     """Implement an LLM driven browser.
 
     Example:

langchain/chains/pal/base.py

@@ -6,7 +6,7 @@ from __future__ import annotations
 
 from typing import Any, Dict, List, Optional
 
-from pydantic import BaseModel, Extra
+from pydantic import Extra
 
 from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
@@ -17,7 +17,7 @@ from langchain.python import PythonREPL
 from langchain.schema import BaseLanguageModel
 
 
-class PALChain(Chain, BaseModel):
+class PALChain(Chain):
     """Implements Program-Aided Language Models."""
 
     llm: BaseLanguageModel

langchain/chains/qa_with_sources/base.py

@@ -6,7 +6,7 @@ import re
 from abc import ABC, abstractmethod
 from typing import Any, Dict, List, Optional
 
-from pydantic import BaseModel, Extra, root_validator
+from pydantic import Extra, root_validator
 
 from langchain.chains.base import Chain
 from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
@@ -24,7 +24,7 @@ from langchain.prompts.base import BasePromptTemplate
 from langchain.schema import BaseLanguageModel
 
 
-class BaseQAWithSourcesChain(Chain, BaseModel, ABC):
+class BaseQAWithSourcesChain(Chain, ABC):
     """Question answering with sources over documents."""
 
     combine_documents_chain: BaseCombineDocumentsChain
@@ -149,7 +149,7 @@ class BaseQAWithSourcesChain(Chain, BaseModel, ABC):
         return result
 
 
-class QAWithSourcesChain(BaseQAWithSourcesChain, BaseModel):
+class QAWithSourcesChain(BaseQAWithSourcesChain):
     """Question answering with sources over documents."""
 
     input_docs_key: str = "docs"  #: :meta private:

langchain/chains/qa_with_sources/retrieval.py

@@ -2,7 +2,7 @@
 
 from typing import Any, Dict, List
 
-from pydantic import BaseModel, Field
+from pydantic import Field
 
 from langchain.chains.combine_documents.stuff import StuffDocumentsChain
 from langchain.chains.qa_with_sources.base import BaseQAWithSourcesChain
@@ -10,7 +10,7 @@ from langchain.docstore.document import Document
 from langchain.schema import BaseRetriever
 
 
-class RetrievalQAWithSourcesChain(BaseQAWithSourcesChain, BaseModel):
+class RetrievalQAWithSourcesChain(BaseQAWithSourcesChain):
     """Question-answering with sources over an index."""
 
     retriever: BaseRetriever = Field(exclude=True)

langchain/chains/qa_with_sources/vector_db.py

@@ -3,7 +3,7 @@
 import warnings
 from typing import Any, Dict, List
 
-from pydantic import BaseModel, Field, root_validator
+from pydantic import Field, root_validator
 
 from langchain.chains.combine_documents.stuff import StuffDocumentsChain
 from langchain.chains.qa_with_sources.base import BaseQAWithSourcesChain
@@ -11,7 +11,7 @@ from langchain.docstore.document import Document
 from langchain.vectorstores.base import VectorStore
 
 
-class VectorDBQAWithSourcesChain(BaseQAWithSourcesChain, BaseModel):
+class VectorDBQAWithSourcesChain(BaseQAWithSourcesChain):
     """Question-answering with sources over a vector database."""
 
     vectorstore: VectorStore = Field(exclude=True)

langchain/chains/question_answering/map_reduce_prompt.py

@@ -1,14 +1,11 @@
 # flake8: noqa
-from langchain.prompts.prompt import PromptTemplate
+from langchain.chains.prompt_selector import ConditionalPromptSelector, is_chat_model
 from langchain.prompts.chat import (
-    SystemMessagePromptTemplate,
-    HumanMessagePromptTemplate,
     ChatPromptTemplate,
+    HumanMessagePromptTemplate,
+    SystemMessagePromptTemplate,
 )
-from langchain.chains.prompt_selector import (
+from langchain.prompts.prompt import PromptTemplate
-    ConditionalPromptSelector,
-    is_chat_model,
-)
 
 question_prompt_template = """Use the following portion of a long document to see if any of the text is relevant to answer the question. 
 Return any relevant text verbatim.

langchain/chains/question_answering/map_rerank_prompt.py

@@ -1,6 +1,6 @@
 # flake8: noqa
-from langchain.prompts import PromptTemplate
 from langchain.output_parsers.regex import RegexParser
+from langchain.prompts import PromptTemplate
 
 output_parser = RegexParser(
     regex=r"(.*?)\nScore: (.*)",

langchain/chains/question_answering/refine_prompts.py

@@ -1,16 +1,12 @@
 # flake8: noqa
-from langchain.prompts.prompt import PromptTemplate
+from langchain.chains.prompt_selector import ConditionalPromptSelector, is_chat_model
 from langchain.prompts.chat import (
-    SystemMessagePromptTemplate,
-    HumanMessagePromptTemplate,
-    ChatPromptTemplate,
     AIMessagePromptTemplate,
+    ChatPromptTemplate,
+    HumanMessagePromptTemplate,
+    SystemMessagePromptTemplate,
 )
-from langchain.chains.prompt_selector import (
+from langchain.prompts.prompt import PromptTemplate
-    ConditionalPromptSelector,
-    is_chat_model,
-)
-
 
 DEFAULT_REFINE_PROMPT_TMPL = (
     "The original question is as follows: {question}\n"

langchain/chains/question_answering/stuff_prompt.py

@@ -1,16 +1,12 @@
 # flake8: noqa
+from langchain.chains.prompt_selector import ConditionalPromptSelector, is_chat_model
 from langchain.prompts import PromptTemplate
-from langchain.chains.prompt_selector import (
-    ConditionalPromptSelector,
-    is_chat_model,
-)
 from langchain.prompts.chat import (
     ChatPromptTemplate,
-    SystemMessagePromptTemplate,
     HumanMessagePromptTemplate,
+    SystemMessagePromptTemplate,
 )
 
-
 prompt_template = """Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.
 
 {context}

langchain/chains/retrieval_qa/base.py

@@ -5,7 +5,7 @@ import warnings
 from abc import abstractmethod
 from typing import Any, Dict, List, Optional
 
-from pydantic import BaseModel, Extra, Field, root_validator
+from pydantic import Extra, Field, root_validator
 
 from langchain.chains.base import Chain
 from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
@@ -18,7 +18,7 @@ from langchain.schema import BaseLanguageModel, BaseRetriever, Document
 from langchain.vectorstores.base import VectorStore
 
 
-class BaseRetrievalQA(Chain, BaseModel):
+class BaseRetrievalQA(Chain):
     combine_documents_chain: BaseCombineDocumentsChain
     """Chain to use to combine the documents."""
     input_key: str = "query"  #: :meta private:
@@ -143,7 +143,7 @@ class BaseRetrievalQA(Chain, BaseModel):
             return {self.output_key: answer}
 
 
-class RetrievalQA(BaseRetrievalQA, BaseModel):
+class RetrievalQA(BaseRetrievalQA):
     """Chain for question-answering against an index.
 
     Example:
@@ -166,7 +166,7 @@ class RetrievalQA(BaseRetrievalQA, BaseModel):
         return await self.retriever.aget_relevant_documents(question)
 
 
-class VectorDBQA(BaseRetrievalQA, BaseModel):
+class VectorDBQA(BaseRetrievalQA):
     """Chain for question-answering against a vector database."""
 
     vectorstore: VectorStore = Field(exclude=True, alias="vectorstore")

langchain/chains/sequential.py

@@ -1,13 +1,13 @@
 """Chain pipeline where the outputs of one step feed directly into next."""
 from typing import Dict, List
 
-from pydantic import BaseModel, Extra, root_validator
+from pydantic import Extra, root_validator
 
 from langchain.chains.base import Chain
 from langchain.input import get_color_mapping
 
 
-class SequentialChain(Chain, BaseModel):
+class SequentialChain(Chain):
     """Chain where the outputs of one chain feed directly into next."""
 
     chains: List[Chain]
@@ -94,7 +94,7 @@ class SequentialChain(Chain, BaseModel):
         return {k: known_values[k] for k in self.output_variables}
 
 
-class SimpleSequentialChain(Chain, BaseModel):
+class SimpleSequentialChain(Chain):
     """Simple chain where the outputs of one step feed directly into next."""
 
     chains: List[Chain]

langchain/chains/sql_database/base.py

@@ -3,7 +3,7 @@ from __future__ import annotations
 
 from typing import Any, Dict, List
 
-from pydantic import BaseModel, Extra, Field
+from pydantic import Extra, Field
 
 from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
@@ -13,7 +13,7 @@ from langchain.schema import BaseLanguageModel
 from langchain.sql_database import SQLDatabase
 
 
-class SQLDatabaseChain(Chain, BaseModel):
+class SQLDatabaseChain(Chain):
     """Chain for interacting with SQL Database.
 
     Example:
@@ -107,7 +107,7 @@ class SQLDatabaseChain(Chain, BaseModel):
         return "sql_database_chain"
 
 
-class SQLDatabaseSequentialChain(Chain, BaseModel):
+class SQLDatabaseSequentialChain(Chain):
     """Chain for querying SQL database that is a sequential chain.
 
     The chain is as follows:

langchain/chains/transform.py

@@ -1,12 +1,10 @@
 """Chain that runs an arbitrary python function."""
 from typing import Callable, Dict, List
 
-from pydantic import BaseModel
-
 from langchain.chains.base import Chain
 
 
-class TransformChain(Chain, BaseModel):
+class TransformChain(Chain):
     """Chain transform chain output.
 
     Example:

langchain/chat_models/azure_openai.py

@@ -6,9 +6,7 @@ from typing import Any, Dict
 
 from pydantic import root_validator
 
-from langchain.chat_models.openai import (
+from langchain.chat_models.openai import ChatOpenAI
-    ChatOpenAI,
-)
 from langchain.utils import get_from_dict_or_env
 
 logger = logging.getLogger(__file__)

langchain/chat_models/base.py

@@ -2,7 +2,7 @@ import asyncio
 from abc import ABC, abstractmethod
 from typing import List, Optional
 
-from pydantic import BaseModel, Extra, Field, validator
+from pydantic import Extra, Field, validator
 
 import langchain
 from langchain.callbacks import get_callback_manager
@@ -23,7 +23,7 @@ def _get_verbosity() -> bool:
     return langchain.verbose
 
 
-class BaseChatModel(BaseLanguageModel, BaseModel, ABC):
+class BaseChatModel(BaseLanguageModel, ABC):
     verbose: bool = Field(default_factory=_get_verbosity)
     """Whether to print out response text."""
     callback_manager: BaseCallbackManager = Field(default_factory=get_callback_manager)

langchain/chat_models/openai.py

@@ -5,7 +5,7 @@ import logging
 import sys
 from typing import Any, Callable, Dict, List, Mapping, Optional, Tuple
 
-from pydantic import BaseModel, Extra, Field, root_validator
+from pydantic import Extra, Field, root_validator
 from tenacity import (
     before_sleep_log,
     retry,
@@ -91,7 +91,7 @@ def _convert_message_to_dict(message: BaseMessage) -> dict:
     return message_dict
 
 
-class ChatOpenAI(BaseChatModel, BaseModel):
+class ChatOpenAI(BaseChatModel):
     """Wrapper around OpenAI Chat large language models.
 
     To use, you should have the ``openai`` python package installed, and the

langchain/chat_models/promptlayer_openai.py

@@ -2,13 +2,11 @@
 import datetime
 from typing import List, Optional
 
-from pydantic import BaseModel
-
 from langchain.chat_models import ChatOpenAI
 from langchain.schema import BaseMessage, ChatResult
 
 
-class PromptLayerChatOpenAI(ChatOpenAI, BaseModel):
+class PromptLayerChatOpenAI(ChatOpenAI):
     """Wrapper around OpenAI Chat large language models and PromptLayer.
 
     To use, you should have the ``openai`` and ``promptlayer`` python

langchain/document_loaders/__init__.py

@@ -11,9 +11,7 @@ from langchain.document_loaders.azure_blob_storage_file import (
 )
 from langchain.document_loaders.bigquery import BigQueryLoader
 from langchain.document_loaders.blackboard import BlackboardLoader
-from langchain.document_loaders.college_confidential import (
+from langchain.document_loaders.college_confidential import CollegeConfidentialLoader
-    CollegeConfidentialLoader,
-)
 from langchain.document_loaders.conllu import CoNLLULoader
 from langchain.document_loaders.csv_loader import CSVLoader
 from langchain.document_loaders.dataframe import DataFrameLoader
@@ -66,9 +64,7 @@ from langchain.document_loaders.url import UnstructuredURLLoader
 from langchain.document_loaders.url_selenium import SeleniumURLLoader
 from langchain.document_loaders.web_base import WebBaseLoader
 from langchain.document_loaders.whatsapp_chat import WhatsAppChatLoader
-from langchain.document_loaders.word_document import (
+from langchain.document_loaders.word_document import UnstructuredWordDocumentLoader
-    UnstructuredWordDocumentLoader,
-)
 from langchain.document_loaders.youtube import (
     GoogleApiClient,
     GoogleApiYoutubeLoader,

langchain/embeddings/aleph_alpha.py

@@ -54,9 +54,7 @@ class AlephAlphaAsymmetricSemanticEmbedding(BaseModel, Embeddings):
             values, "aleph_alpha_api_key", "ALEPH_ALPHA_API_KEY"
         )
         try:
-            from aleph_alpha_client import (
+            from aleph_alpha_client import Client
-                Client,
-            )
         except ImportError:
             raise ValueError(
                 "Could not import aleph_alpha_client python package. "

langchain/embeddings/self_hosted.py

@@ -1,7 +1,7 @@
 """Running custom embedding models on self-hosted remote hardware."""
 from typing import Any, Callable, List
 
-from pydantic import BaseModel, Extra
+from pydantic import Extra
 
 from langchain.embeddings.base import Embeddings
 from langchain.llms import SelfHostedPipeline
@@ -16,7 +16,7 @@ def _embed_documents(pipeline: Any, *args: Any, **kwargs: Any) -> List[List[floa
     return pipeline(*args, **kwargs)
 
 
-class SelfHostedEmbeddings(SelfHostedPipeline, Embeddings, BaseModel):
+class SelfHostedEmbeddings(SelfHostedPipeline, Embeddings):
     """Runs custom embedding models on self-hosted remote hardware.
 
     Supported hardware includes auto-launched instances on AWS, GCP, Azure,

langchain/embeddings/self_hosted_hugging_face.py

@@ -3,8 +3,6 @@ import importlib
 import logging
 from typing import Any, Callable, List, Optional
 
-from pydantic import BaseModel
-
 from langchain.embeddings.self_hosted import SelfHostedEmbeddings
 
 DEFAULT_MODEL_NAME = "sentence-transformers/all-mpnet-base-v2"
@@ -59,7 +57,7 @@ def load_embedding_model(model_id: str, instruct: bool = False, device: int = 0)
     return client
 
 
-class SelfHostedHuggingFaceEmbeddings(SelfHostedEmbeddings, BaseModel):
+class SelfHostedHuggingFaceEmbeddings(SelfHostedEmbeddings):
     """Runs sentence_transformers embedding models on self-hosted remote hardware.
 
     Supported hardware includes auto-launched instances on AWS, GCP, Azure,

langchain/evaluation/qa/generate_prompt.py

@@ -1,6 +1,6 @@
 # flake8: noqa
-from langchain.prompts import PromptTemplate
 from langchain.output_parsers.regex import RegexParser
+from langchain.prompts import PromptTemplate
 
 template = """You are a teacher coming up with questions to ask on a quiz. 
 Given the following document, please generate a question and answer based on that document.

langchain/llms/ai21.py

@@ -19,7 +19,7 @@ class AI21PenaltyData(BaseModel):
     applyToEmojis: bool = True
 
 
-class AI21(LLM, BaseModel):
+class AI21(LLM):
     """Wrapper around AI21 large language models.
 
     To use, you should have the environment variable ``AI21_API_KEY``

langchain/llms/aleph_alpha.py

@@ -1,14 +1,14 @@
 """Wrapper around Aleph Alpha APIs."""
 from typing import Any, Dict, List, Optional, Sequence
 
-from pydantic import BaseModel, Extra, root_validator
+from pydantic import Extra, root_validator
 
 from langchain.llms.base import LLM
 from langchain.llms.utils import enforce_stop_tokens
 from langchain.utils import get_from_dict_or_env
 
 
-class AlephAlpha(LLM, BaseModel):
+class AlephAlpha(LLM):
     """Wrapper around Aleph Alpha large language models.
 
     To use, you should have the ``aleph_alpha_client`` python package installed, and the

langchain/llms/anthropic.py

@@ -2,13 +2,13 @@
 import re
 from typing import Any, Dict, Generator, List, Mapping, Optional
 
-from pydantic import BaseModel, Extra, root_validator
+from pydantic import Extra, root_validator
 
 from langchain.llms.base import LLM
 from langchain.utils import get_from_dict_or_env
 
 
-class Anthropic(LLM, BaseModel):
+class Anthropic(LLM):
     r"""Wrapper around Anthropic large language models.
 
     To use, you should have the ``anthropic`` python package installed, and the

langchain/llms/bananadev.py

@@ -2,7 +2,7 @@
 import logging
 from typing import Any, Dict, List, Mapping, Optional
 
-from pydantic import BaseModel, Extra, Field, root_validator
+from pydantic import Extra, Field, root_validator
 
 from langchain.llms.base import LLM
 from langchain.llms.utils import enforce_stop_tokens
@@ -11,7 +11,7 @@ from langchain.utils import get_from_dict_or_env
 logger = logging.getLogger(__name__)
 
 
-class Banana(LLM, BaseModel):
+class Banana(LLM):
     """Wrapper around Banana large language models.
 
     To use, you should have the ``banana-dev`` python package installed,

langchain/llms/base.py

@@ -5,7 +5,7 @@ from pathlib import Path
 from typing import Any, Dict, List, Mapping, Optional, Tuple, Union
 
 import yaml
-from pydantic import BaseModel, Extra, Field, validator
+from pydantic import Extra, Field, validator
 
 import langchain
 from langchain.callbacks import get_callback_manager
@@ -53,7 +53,7 @@ def update_cache(
     return llm_output
 
 
-class BaseLLM(BaseLanguageModel, BaseModel, ABC):
+class BaseLLM(BaseLanguageModel, ABC):
     """LLM wrapper should take in a prompt and return a string."""
 
     cache: Optional[bool] = None

langchain/llms/cerebriumai.py

@@ -2,7 +2,7 @@
 import logging
 from typing import Any, Dict, List, Mapping, Optional
 
-from pydantic import BaseModel, Extra, Field, root_validator
+from pydantic import Extra, Field, root_validator
 
 from langchain.llms.base import LLM
 from langchain.llms.utils import enforce_stop_tokens
@@ -11,7 +11,7 @@ from langchain.utils import get_from_dict_or_env
 logger = logging.getLogger(__name__)
 
 
-class CerebriumAI(LLM, BaseModel):
+class CerebriumAI(LLM):
     """Wrapper around CerebriumAI large language models.
 
     To use, you should have the ``cerebrium`` python package installed, and the

langchain/llms/cohere.py

@@ -2,7 +2,7 @@
 import logging
 from typing import Any, Dict, List, Optional
 
-from pydantic import BaseModel, Extra, root_validator
+from pydantic import Extra, root_validator
 
 from langchain.llms.base import LLM
 from langchain.llms.utils import enforce_stop_tokens
@@ -11,7 +11,7 @@ from langchain.utils import get_from_dict_or_env
 logger = logging.getLogger(__name__)
 
 
-class Cohere(LLM, BaseModel):
+class Cohere(LLM):
     """Wrapper around Cohere large language models.
 
     To use, you should have the ``cohere`` python package installed, and the

langchain/llms/deepinfra.py

@@ -2,7 +2,7 @@
 from typing import Any, Dict, List, Mapping, Optional
 
 import requests
-from pydantic import BaseModel, Extra, root_validator
+from pydantic import Extra, root_validator
 
 from langchain.llms.base import LLM
 from langchain.llms.utils import enforce_stop_tokens
@@ -11,7 +11,7 @@ from langchain.utils import get_from_dict_or_env
 DEFAULT_MODEL_ID = "google/flan-t5-xl"
 
 
-class DeepInfra(LLM, BaseModel):
+class DeepInfra(LLM):
     """Wrapper around DeepInfra deployed models.
 
     To use, you should have the ``requests`` python package installed, and the

langchain/llms/fake.py

@@ -1,12 +1,10 @@
 """Fake LLM wrapper for testing purposes."""
 from typing import Any, List, Mapping, Optional
 
-from pydantic import BaseModel
-
 from langchain.llms.base import LLM
 
 
-class FakeListLLM(LLM, BaseModel):
+class FakeListLLM(LLM):
     """Fake LLM wrapper for testing purposes."""
 
     responses: List

langchain/llms/forefrontai.py

@@ -2,14 +2,14 @@
 from typing import Any, Dict, List, Mapping, Optional
 
 import requests
-from pydantic import BaseModel, Extra, root_validator
+from pydantic import Extra, root_validator
 
 from langchain.llms.base import LLM
 from langchain.llms.utils import enforce_stop_tokens
 from langchain.utils import get_from_dict_or_env
 
 
-class ForefrontAI(LLM, BaseModel):
+class ForefrontAI(LLM):
     """Wrapper around ForefrontAI large language models.
 
     To use, you should have the environment variable ``FOREFRONTAI_API_KEY``

langchain/llms/gooseai.py

@@ -2,7 +2,7 @@
 import logging
 from typing import Any, Dict, List, Mapping, Optional
 
-from pydantic import BaseModel, Extra, Field, root_validator
+from pydantic import Extra, Field, root_validator
 
 from langchain.llms.base import LLM
 from langchain.utils import get_from_dict_or_env
@@ -10,7 +10,7 @@ from langchain.utils import get_from_dict_or_env
 logger = logging.getLogger(__name__)
 
 
-class GooseAI(LLM, BaseModel):
+class GooseAI(LLM):
     """Wrapper around OpenAI large language models.
 
     To use, you should have the ``openai`` python package installed, and the

langchain/llms/gpt4all.py

@@ -1,13 +1,13 @@
 """Wrapper for the GPT4All model."""
 from typing import Any, Dict, List, Mapping, Optional, Set
 
-from pydantic import BaseModel, Extra, Field, root_validator
+from pydantic import Extra, Field, root_validator
 
 from langchain.llms.base import LLM
 from langchain.llms.utils import enforce_stop_tokens
 
 
-class GPT4All(LLM, BaseModel):
+class GPT4All(LLM):
     r"""Wrapper around GPT4All language models.
 
     To use, you should have the ``pyllamacpp`` python package installed, the

langchain/llms/huggingface_endpoint.py

@@ -2,7 +2,7 @@
 from typing import Any, Dict, List, Mapping, Optional
 
 import requests
-from pydantic import BaseModel, Extra, root_validator
+from pydantic import Extra, root_validator
 
 from langchain.llms.base import LLM
 from langchain.llms.utils import enforce_stop_tokens
@@ -11,7 +11,7 @@ from langchain.utils import get_from_dict_or_env
 VALID_TASKS = ("text2text-generation", "text-generation")
 
 
-class HuggingFaceEndpoint(LLM, BaseModel):
+class HuggingFaceEndpoint(LLM):
     """Wrapper around HuggingFaceHub Inference Endpoints.
 
     To use, you should have the ``huggingface_hub`` python package installed, and the

langchain/llms/huggingface_hub.py

@@ -1,7 +1,7 @@
 """Wrapper around HuggingFace APIs."""
 from typing import Any, Dict, List, Mapping, Optional
 
-from pydantic import BaseModel, Extra, root_validator
+from pydantic import Extra, root_validator
 
 from langchain.llms.base import LLM
 from langchain.llms.utils import enforce_stop_tokens
@@ -11,7 +11,7 @@ DEFAULT_REPO_ID = "gpt2"
 VALID_TASKS = ("text2text-generation", "text-generation")
 
 
-class HuggingFaceHub(LLM, BaseModel):
+class HuggingFaceHub(LLM):
     """Wrapper around HuggingFaceHub  models.
 
     To use, you should have the ``huggingface_hub`` python package installed, and the

langchain/llms/huggingface_pipeline.py

@@ -3,7 +3,7 @@ import importlib.util
 import logging
 from typing import Any, List, Mapping, Optional
 
-from pydantic import BaseModel, Extra
+from pydantic import Extra
 
 from langchain.llms.base import LLM
 from langchain.llms.utils import enforce_stop_tokens
@@ -15,7 +15,7 @@ VALID_TASKS = ("text2text-generation", "text-generation")
 logger = logging.getLogger()
 
 
-class HuggingFacePipeline(LLM, BaseModel):
+class HuggingFacePipeline(LLM):
     """Wrapper around HuggingFace Pipeline API.
 
     To use, you should have the ``transformers`` python package installed.

langchain/llms/llamacpp.py

@@ -2,14 +2,14 @@
 import logging
 from typing import Any, Dict, List, Optional
 
-from pydantic import BaseModel, Field, root_validator
+from pydantic import Field, root_validator
 
 from langchain.llms.base import LLM
 
 logger = logging.getLogger(__name__)
 
 
-class LlamaCpp(LLM, BaseModel):
+class LlamaCpp(LLM):
     """Wrapper around the llama.cpp model.
 
     To use, you should have the llama-cpp-python library installed, and provide the

langchain/llms/manifest.py

@@ -1,12 +1,12 @@
 """Wrapper around HazyResearch's Manifest library."""
 from typing import Any, Dict, List, Mapping, Optional
 
-from pydantic import BaseModel, Extra, root_validator
+from pydantic import Extra, root_validator
 
 from langchain.llms.base import LLM
 
 
-class ManifestWrapper(LLM, BaseModel):
+class ManifestWrapper(LLM):
     """Wrapper around HazyResearch's Manifest library."""
 
     client: Any  #: :meta private:

langchain/llms/modal.py

@@ -3,7 +3,7 @@ import logging
 from typing import Any, Dict, List, Mapping, Optional
 
 import requests
-from pydantic import BaseModel, Extra, Field, root_validator
+from pydantic import Extra, Field, root_validator
 
 from langchain.llms.base import LLM
 from langchain.llms.utils import enforce_stop_tokens
@@ -11,7 +11,7 @@ from langchain.llms.utils import enforce_stop_tokens
 logger = logging.getLogger(__name__)
 
 
-class Modal(LLM, BaseModel):
+class Modal(LLM):
     """Wrapper around Modal large language models.
 
     To use, you should have the ``modal-client`` python package installed.

langchain/llms/nlpcloud.py

@@ -1,13 +1,13 @@
 """Wrapper around NLPCloud APIs."""
 from typing import Any, Dict, List, Mapping, Optional
 
-from pydantic import BaseModel, Extra, root_validator
+from pydantic import Extra, root_validator
 
 from langchain.llms.base import LLM
 from langchain.utils import get_from_dict_or_env
 
 
-class NLPCloud(LLM, BaseModel):
+class NLPCloud(LLM):
     """Wrapper around NLPCloud large language models.
 
     To use, you should have the ``nlpcloud`` python package installed, and the

langchain/llms/openai.py

@@ -17,7 +17,7 @@ from typing import (
     Union,
 )
 
-from pydantic import BaseModel, Extra, Field, root_validator
+from pydantic import Extra, Field, root_validator
 from tenacity import (
     before_sleep_log,
     retry,
@@ -113,7 +113,7 @@ async def acompletion_with_retry(
     return await _completion_with_retry(**kwargs)
 
 
-class BaseOpenAI(BaseLLM, BaseModel):
+class BaseOpenAI(BaseLLM):
     """Wrapper around OpenAI large language models.
 
     To use, you should have the ``openai`` python package installed, and the
@@ -534,7 +534,7 @@ class AzureOpenAI(BaseOpenAI):
         return {**{"engine": self.deployment_name}, **super()._invocation_params}
 
 
-class OpenAIChat(BaseLLM, BaseModel):
+class OpenAIChat(BaseLLM):
     """Wrapper around OpenAI Chat large language models.
 
     To use, you should have the ``openai`` python package installed, and the

langchain/llms/petals.py

@@ -2,7 +2,7 @@
 import logging
 from typing import Any, Dict, List, Mapping, Optional
 
-from pydantic import BaseModel, Extra, Field, root_validator
+from pydantic import Extra, Field, root_validator
 
 from langchain.llms.base import LLM
 from langchain.llms.utils import enforce_stop_tokens
@@ -11,7 +11,7 @@ from langchain.utils import get_from_dict_or_env
 logger = logging.getLogger(__name__)
 
 
-class Petals(LLM, BaseModel):
+class Petals(LLM):
     """Wrapper around Petals Bloom models.
 
     To use, you should have the ``petals`` python package installed, and the

langchain/llms/promptlayer_openai.py

@@ -2,13 +2,11 @@
 import datetime
 from typing import List, Optional
 
-from pydantic import BaseModel
-
 from langchain.llms import OpenAI, OpenAIChat
 from langchain.schema import LLMResult
 
 
-class PromptLayerOpenAI(OpenAI, BaseModel):
+class PromptLayerOpenAI(OpenAI):
     """Wrapper around OpenAI large language models.
 
     To use, you should have the ``openai`` and ``promptlayer`` python
@@ -106,7 +104,7 @@ class PromptLayerOpenAI(OpenAI, BaseModel):
         return generated_responses
 
 
-class PromptLayerOpenAIChat(OpenAIChat, BaseModel):
+class PromptLayerOpenAIChat(OpenAIChat):
     """Wrapper around OpenAI large language models.
 
     To use, you should have the ``openai`` and ``promptlayer`` python

langchain/llms/replicate.py

@@ -2,7 +2,7 @@
 import logging
 from typing import Any, Dict, List, Mapping, Optional
 
-from pydantic import BaseModel, Extra, Field, root_validator
+from pydantic import Extra, Field, root_validator
 
 from langchain.llms.base import LLM
 from langchain.utils import get_from_dict_or_env
@@ -10,7 +10,7 @@ from langchain.utils import get_from_dict_or_env
 logger = logging.getLogger(__name__)
 
 
-class Replicate(LLM, BaseModel):
+class Replicate(LLM):
     """Wrapper around Replicate models.
 
     To use, you should have the ``replicate`` python package installed,

langchain/llms/sagemaker_endpoint.py

@@ -2,7 +2,7 @@
 from abc import ABC, abstractmethod
 from typing import Any, Dict, List, Mapping, Optional, Union
 
-from pydantic import BaseModel, Extra, root_validator
+from pydantic import Extra, root_validator
 
 from langchain.llms.base import LLM
 from langchain.llms.utils import enforce_stop_tokens
@@ -55,7 +55,7 @@ class ContentHandlerBase(ABC):
         """
 
 
-class SagemakerEndpoint(LLM, BaseModel):
+class SagemakerEndpoint(LLM):
     """Wrapper around custom Sagemaker Inference Endpoints.
 
     To use, you must supply the endpoint name from your deployed

langchain/llms/self_hosted.py

@@ -4,7 +4,7 @@ import logging
 import pickle
 from typing import Any, Callable, List, Mapping, Optional
 
-from pydantic import BaseModel, Extra
+from pydantic import Extra
 
 from langchain.llms.base import LLM
 from langchain.llms.utils import enforce_stop_tokens
@@ -61,7 +61,7 @@ def _send_pipeline_to_device(pipeline: Any, device: int) -> Any:
     return pipeline
 
 
-class SelfHostedPipeline(LLM, BaseModel):
+class SelfHostedPipeline(LLM):
     """Run model inference on self-hosted remote hardware.
 
     Supported hardware includes auto-launched instances on AWS, GCP, Azure,

langchain/llms/self_hosted_hugging_face.py

@@ -3,7 +3,7 @@ import importlib.util
 import logging
 from typing import Any, Callable, List, Mapping, Optional
 
-from pydantic import BaseModel, Extra
+from pydantic import Extra
 
 from langchain.llms.self_hosted import SelfHostedPipeline
 from langchain.llms.utils import enforce_stop_tokens
@@ -108,7 +108,7 @@ def _load_transformer(
     return pipeline
 
 
-class SelfHostedHuggingFaceLLM(SelfHostedPipeline, BaseModel):
+class SelfHostedHuggingFaceLLM(SelfHostedPipeline):
     """Wrapper around HuggingFace Pipeline API to run on self-hosted remote hardware.
 
     Supported hardware includes auto-launched instances on AWS, GCP, Azure,

langchain/llms/stochasticai.py

@@ -4,7 +4,7 @@ import time
 from typing import Any, Dict, List, Mapping, Optional
 
 import requests
-from pydantic import BaseModel, Extra, Field, root_validator
+from pydantic import Extra, Field, root_validator
 
 from langchain.llms.base import LLM
 from langchain.llms.utils import enforce_stop_tokens
@@ -13,7 +13,7 @@ from langchain.utils import get_from_dict_or_env
 logger = logging.getLogger(__name__)
 
 
-class StochasticAI(LLM, BaseModel):
+class StochasticAI(LLM):
     """Wrapper around StochasticAI large language models.
 
     To use, you should have the environment variable ``STOCHASTICAI_API_KEY``

langchain/llms/writer.py

@@ -2,14 +2,14 @@
 from typing import Any, Dict, List, Mapping, Optional
 
 import requests
-from pydantic import BaseModel, Extra, root_validator
+from pydantic import Extra, root_validator
 
 from langchain.llms.base import LLM
 from langchain.llms.utils import enforce_stop_tokens
 from langchain.utils import get_from_dict_or_env
 
 
-class Writer(LLM, BaseModel):
+class Writer(LLM):
     """Wrapper around Writer large language models.
 
     To use, you should have the environment variable ``WRITER_API_KEY``

langchain/memory/buffer.py

@@ -1,13 +1,13 @@
 from typing import Any, Dict, List, Optional
 
-from pydantic import BaseModel, root_validator
+from pydantic import root_validator
 
 from langchain.memory.chat_memory import BaseChatMemory, BaseMemory
 from langchain.memory.utils import get_prompt_input_key
 from langchain.schema import get_buffer_string
 
 
-class ConversationBufferMemory(BaseChatMemory, BaseModel):
+class ConversationBufferMemory(BaseChatMemory):
     """Buffer for storing conversation memory."""
 
     human_prefix: str = "Human"
@@ -39,7 +39,7 @@ class ConversationBufferMemory(BaseChatMemory, BaseModel):
         return {self.memory_key: self.buffer}
 
 
-class ConversationStringBufferMemory(BaseMemory, BaseModel):
+class ConversationStringBufferMemory(BaseMemory):
     """Buffer for storing conversation memory."""
 
     human_prefix: str = "Human"

langchain/memory/buffer_window.py

@@ -1,12 +1,10 @@
 from typing import Any, Dict, List
 
-from pydantic import BaseModel
-
 from langchain.memory.chat_memory import BaseChatMemory
 from langchain.schema import BaseMessage, get_buffer_string
 
 
-class ConversationBufferWindowMemory(BaseChatMemory, BaseModel):
+class ConversationBufferWindowMemory(BaseChatMemory):
     """Buffer for storing conversation memory."""
 
     human_prefix: str = "Human"

langchain/memory/chat_memory.py

@@ -5,10 +5,7 @@ from pydantic import Field
 
 from langchain.memory.chat_message_histories.in_memory import ChatMessageHistory
 from langchain.memory.utils import get_prompt_input_key
-from langchain.schema import (
+from langchain.schema import BaseChatMessageHistory, BaseMemory
-    BaseChatMessageHistory,
-    BaseMemory,
-)
 
 
 class BaseChatMemory(BaseMemory, ABC):

langchain/memory/combined.py

@@ -1,11 +1,9 @@
 from typing import Any, Dict, List
 
-from pydantic import BaseModel
-
 from langchain.schema import BaseMemory
 
 
-class CombinedMemory(BaseMemory, BaseModel):
+class CombinedMemory(BaseMemory):
     """Class for combining multiple memories' data together."""
 
     memories: List[BaseMemory]

langchain/memory/entity.py

@@ -1,7 +1,5 @@
 from typing import Any, Dict, List, Optional
 
-from pydantic import BaseModel
-
 from langchain.chains.llm import LLMChain
 from langchain.memory.chat_memory import BaseChatMemory
 from langchain.memory.prompt import (
@@ -13,7 +11,7 @@ from langchain.prompts.base import BasePromptTemplate
 from langchain.schema import BaseLanguageModel, BaseMessage, get_buffer_string
 
 
-class ConversationEntityMemory(BaseChatMemory, BaseModel):
+class ConversationEntityMemory(BaseChatMemory):
     """Entity extractor & summarizer to memory."""
 
     human_prefix: str = "Human"

langchain/memory/kg.py

@@ -1,6 +1,6 @@
 from typing import Any, Dict, List, Type, Union
 
-from pydantic import BaseModel, Field
+from pydantic import Field
 
 from langchain.chains.llm import LLMChain
 from langchain.graphs import NetworkxEntityGraph
@@ -20,7 +20,7 @@ from langchain.schema import (
 )
 
 
-class ConversationKGMemory(BaseChatMemory, BaseModel):
+class ConversationKGMemory(BaseChatMemory):
     """Knowledge graph memory for storing conversation memory.
 
     Integrates with external knowledge graph to store and retrieve

langchain/memory/simple.py

@@ -1,11 +1,9 @@
 from typing import Any, Dict, List
 
-from pydantic import BaseModel
-
 from langchain.schema import BaseMemory
 
 
-class SimpleMemory(BaseMemory, BaseModel):
+class SimpleMemory(BaseMemory):
     """Simple memory for storing context or other bits of information that shouldn't
     ever change between prompts.
     """

langchain/memory/summary.py

@@ -34,7 +34,7 @@ class SummarizerMixin(BaseModel):
         return chain.predict(summary=existing_summary, new_lines=new_lines)
 
 
-class ConversationSummaryMemory(BaseChatMemory, SummarizerMixin, BaseModel):
+class ConversationSummaryMemory(BaseChatMemory, SummarizerMixin):
     """Conversation summarizer to memory."""
 
     buffer: str = ""

langchain/memory/summary_buffer.py

@@ -1,13 +1,13 @@
 from typing import Any, Dict, List
 
-from pydantic import BaseModel, root_validator
+from pydantic import root_validator
 
 from langchain.memory.chat_memory import BaseChatMemory
 from langchain.memory.summary import SummarizerMixin
 from langchain.schema import BaseMessage, get_buffer_string
 
 
-class ConversationSummaryBufferMemory(BaseChatMemory, SummarizerMixin, BaseModel):
+class ConversationSummaryBufferMemory(BaseChatMemory, SummarizerMixin):
     """Buffer with summarizer for storing conversation memory."""
 
     max_token_limit: int = 2000

langchain/memory/token_buffer.py

@@ -1,12 +1,10 @@
 from typing import Any, Dict, List
 
-from pydantic import BaseModel
-
 from langchain.memory.chat_memory import BaseChatMemory
 from langchain.schema import BaseLanguageModel, BaseMessage, get_buffer_string
 
 
-class ConversationTokenBufferMemory(BaseChatMemory, BaseModel):
+class ConversationTokenBufferMemory(BaseChatMemory):
     """Buffer for storing conversation memory."""
 
     human_prefix: str = "Human"

langchain/output_parsers/regex.py

@@ -3,12 +3,10 @@ from __future__ import annotations
 import re
 from typing import Dict, List, Optional
 
-from pydantic import BaseModel
-
 from langchain.schema import BaseOutputParser
 
 
-class RegexParser(BaseOutputParser, BaseModel):
+class RegexParser(BaseOutputParser):
     """Class to parse the output into a dictionary."""
 
     regex: str

langchain/output_parsers/regex_dict.py

@@ -3,12 +3,10 @@ from __future__ import annotations
 import re
 from typing import Dict, Optional
 
-from pydantic import BaseModel
-
 from langchain.schema import BaseOutputParser
 
 
-class RegexDictParser(BaseOutputParser, BaseModel):
+class RegexDictParser(BaseOutputParser):
     """Class to parse the output into a dictionary."""
 
     regex_pattern: str = r"{}:\s?([^.'\n']*)\.?"  # : :meta private:

langchain/prompts/example_selector/ngram_overlap.py

@@ -20,8 +20,8 @@ def ngram_overlap_score(source: List[str], example: List[str]) -> float:
     https://www.nltk.org/_modules/nltk/translate/bleu_score.html
     https://aclanthology.org/P02-1040.pdf
     """
-    from nltk.translate.bleu_score import (  # type: ignore
+    from nltk.translate.bleu_score import (
-        SmoothingFunction,
+        SmoothingFunction,  # type: ignore
         sentence_bleu,
     )
 

langchain/prompts/example_selector/semantic_similarity.py

@@ -99,7 +99,7 @@ class SemanticSimilarityExampleSelector(BaseExampleSelector, BaseModel):
         return cls(vectorstore=vectorstore, k=k, input_keys=input_keys)
 
 
-class MaxMarginalRelevanceExampleSelector(SemanticSimilarityExampleSelector, BaseModel):
+class MaxMarginalRelevanceExampleSelector(SemanticSimilarityExampleSelector):
     """ExampleSelector that selects examples based on Max Marginal Relevance.
 
     This was shown to improve performance in this paper:

langchain/prompts/few_shot.py

@@ -1,7 +1,7 @@
 """Prompt template that contains few shot examples."""
 from typing import Any, Dict, List, Optional
 
-from pydantic import BaseModel, Extra, root_validator
+from pydantic import Extra, root_validator
 
 from langchain.prompts.base import (
     DEFAULT_FORMATTER_MAPPING,
@@ -12,7 +12,7 @@ from langchain.prompts.example_selector.base import BaseExampleSelector
 from langchain.prompts.prompt import PromptTemplate
 
 
-class FewShotPromptTemplate(StringPromptTemplate, BaseModel):
+class FewShotPromptTemplate(StringPromptTemplate):
     """Prompt template that contains few shot examples."""
 
     examples: Optional[List[dict]] = None

langchain/prompts/few_shot_with_templates.py

@@ -1,17 +1,14 @@
 """Prompt template that contains few shot examples."""
 from typing import Any, Dict, List, Optional
 
-from pydantic import BaseModel, Extra, root_validator
+from pydantic import Extra, root_validator
 
-from langchain.prompts.base import (
+from langchain.prompts.base import DEFAULT_FORMATTER_MAPPING, StringPromptTemplate
-    DEFAULT_FORMATTER_MAPPING,
-    StringPromptTemplate,
-)
 from langchain.prompts.example_selector.base import BaseExampleSelector
 from langchain.prompts.prompt import PromptTemplate
 
 
-class FewShotPromptWithTemplates(StringPromptTemplate, BaseModel):
+class FewShotPromptWithTemplates(StringPromptTemplate):
     """Prompt template that contains few shot examples."""
 
     examples: Optional[List[dict]] = None

langchain/prompts/prompt.py

@@ -5,7 +5,7 @@ from pathlib import Path
 from string import Formatter
 from typing import Any, Dict, List, Union
 
-from pydantic import BaseModel, Extra, root_validator
+from pydantic import Extra, root_validator
 
 from langchain.prompts.base import (
     DEFAULT_FORMATTER_MAPPING,
@@ -14,7 +14,7 @@ from langchain.prompts.base import (
 )
 
 
-class PromptTemplate(StringPromptTemplate, BaseModel):
+class PromptTemplate(StringPromptTemplate):
     """Schema to represent a prompt for an LLM.
 
     Example:

tests/integration_tests/vectorstores/test_pgvector.py

@@ -6,9 +6,7 @@ from sqlalchemy.orm import Session
 
 from langchain.docstore.document import Document
 from langchain.vectorstores.pgvector import PGVector
-from tests.integration_tests.vectorstores.fake_embeddings import (
+from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
-    FakeEmbeddings,
-)
 
 CONNECTION_STRING = PGVector.connection_string_from_db_params(
     driver=os.environ.get("TEST_PGVECTOR_DRIVER", "psycopg2"),

tests/unit_tests/agents/test_agent.py

@@ -2,8 +2,6 @@
 
 from typing import Any, List, Mapping, Optional
 
-from pydantic import BaseModel
-
 from langchain.agents import AgentExecutor, AgentType, initialize_agent
 from langchain.agents.tools import Tool
 from langchain.callbacks.base import CallbackManager
@@ -11,7 +9,7 @@ from langchain.llms.base import LLM
 from tests.unit_tests.callbacks.fake_callback_handler import FakeCallbackHandler
 
 
-class FakeListLLM(LLM, BaseModel):
+class FakeListLLM(LLM):
     """Fake LLM for testing that outputs elements of a list."""
 
     responses: List[str]

tests/unit_tests/agents/test_react.py

@@ -2,8 +2,6 @@
 
 from typing import Any, List, Mapping, Optional, Union
 
-from pydantic import BaseModel
-
 from langchain.agents.react.base import ReActChain, ReActDocstoreAgent
 from langchain.agents.tools import Tool
 from langchain.docstore.base import Docstore
@@ -23,7 +21,7 @@ Made in 2022."""
 _FAKE_PROMPT = PromptTemplate(input_variables=["input"], template="{input}")
 
 
-class FakeListLLM(LLM, BaseModel):
+class FakeListLLM(LLM):
     """Fake LLM for testing that outputs elements of a list."""
 
     responses: List[str]

tests/unit_tests/chains/test_base.py

@@ -2,7 +2,6 @@
 from typing import Any, Dict, List, Optional
 
 import pytest
-from pydantic import BaseModel
 
 from langchain.callbacks.base import CallbackManager
 from langchain.chains.base import Chain
@@ -10,7 +9,7 @@ from langchain.schema import BaseMemory
 from tests.unit_tests.callbacks.fake_callback_handler import FakeCallbackHandler
 
 
-class FakeMemory(BaseMemory, BaseModel):
+class FakeMemory(BaseMemory):
     """Fake memory class for testing purposes."""
 
     @property
@@ -33,7 +32,7 @@ class FakeMemory(BaseMemory, BaseModel):
         pass
 
 
-class FakeChain(Chain, BaseModel):
+class FakeChain(Chain):
     """Fake chain class for testing purposes."""
 
     be_correct: bool = True

tests/unit_tests/chains/test_hyde.py

@@ -2,7 +2,6 @@
 from typing import List, Optional
 
 import numpy as np
-from pydantic import BaseModel
 
 from langchain.chains.hyde.base import HypotheticalDocumentEmbedder
 from langchain.chains.hyde.prompts import PROMPT_MAP
@@ -23,7 +22,7 @@ class FakeEmbeddings(Embeddings):
         return list(np.random.uniform(0, 1, 10))
 
 
-class FakeLLM(BaseLLM, BaseModel):
+class FakeLLM(BaseLLM):
     """Fake LLM wrapper for testing purposes."""
 
     n: int = 1

tests/unit_tests/chains/test_natbot.py

@@ -2,13 +2,11 @@
 
 from typing import Any, List, Mapping, Optional
 
-from pydantic import BaseModel
-
 from langchain.chains.natbot.base import NatBotChain
 from langchain.llms.base import LLM
 
 
-class FakeLLM(LLM, BaseModel):
+class FakeLLM(LLM):
     """Fake LLM wrapper for testing purposes."""
 
     def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:

tests/unit_tests/chains/test_sequential.py

@@ -2,14 +2,13 @@
 from typing import Dict, List
 
 import pytest
-from pydantic import BaseModel
 
 from langchain.chains.base import Chain
 from langchain.chains.sequential import SequentialChain, SimpleSequentialChain
 from langchain.memory.simple import SimpleMemory
 
 
-class FakeChain(Chain, BaseModel):
+class FakeChain(Chain):
     """Fake Chain for testing purposes."""
 
     input_variables: List[str]

tests/unit_tests/llms/fake_llm.py

@@ -1,12 +1,10 @@
 """Fake LLM wrapper for testing purposes."""
 from typing import Any, List, Mapping, Optional
 
-from pydantic import BaseModel
-
 from langchain.llms.base import LLM
 
 
-class FakeLLM(LLM, BaseModel):
+class FakeLLM(LLM):
     """Fake LLM wrapper for testing purposes."""
 
     queries: Optional[Mapping] = None