Removed duplicate BaseModel dependencies (#2471)

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

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 (
-    FakeEmbeddings,
-)
+from tests.integration_tests.vectorstores.fake_embeddings import 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