diff --git a/libs/community/langchain_community/chat_models/kinetica.py b/libs/community/langchain_community/chat_models/kinetica.py
index a8c52e6a6533d..26003e2ecff5a 100644
--- a/libs/community/langchain_community/chat_models/kinetica.py
+++ b/libs/community/langchain_community/chat_models/kinetica.py
@@ -8,10 +8,11 @@
 import re
 from importlib.metadata import version
 from pathlib import Path
-from typing import TYPE_CHECKING, Any, Dict, List, Optional, cast
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, cast, Dict
 
 if TYPE_CHECKING:
     import gpudb
+    import pandas as pd
 
 from langchain_core.callbacks import CallbackManagerForLLMRun
 from langchain_core.language_models.chat_models import BaseChatModel
@@ -24,7 +25,6 @@
 from langchain_core.output_parsers.transform import BaseOutputParser
 from langchain_core.outputs import ChatGeneration, ChatResult, Generation
 from langchain_core.pydantic_v1 import BaseModel, Field, root_validator
-from pandas import DataFrame
 
 LOG = logging.getLogger(__name__)
 
@@ -36,11 +36,11 @@ class _KdtSuggestContext(BaseModel):
 
     table: Optional[str] = Field(default=None, title="Name of table")
     description: Optional[str] = Field(default=None, title="Table description")
-    columns: list[str] = Field(default=None, title="Table columns list")
-    rules: Optional[list[str]] = Field(
+    columns: List[str] = Field(default=None, title="Table columns list")
+    rules: Optional[List[str]] = Field(
         default=None, title="Rules that apply to the table."
     )
-    samples: Optional[dict] = Field(
+    samples: Optional[Dict] = Field(
         default=None, title="Samples that apply to the entire context."
     )
 
@@ -77,7 +77,7 @@ class _KdtSuggestPayload(BaseModel):
     """pydantic API request type"""
 
     question: Optional[str]
-    context: list[_KdtSuggestContext]
+    context: List[_KdtSuggestContext]
 
     def get_system_str(self) -> str:
         lines = []
@@ -88,7 +88,7 @@ def get_system_str(self) -> str:
             lines.append(context_str)
         return "\n\n".join(lines)
 
-    def get_messages(self) -> list[dict]:
+    def get_messages(self) -> List[Dict]:
         messages = []
         for context in self.context:
             if context.samples is None:
@@ -101,7 +101,7 @@ def get_messages(self) -> list[dict]:
                 messages.append(dict(role="assistant", content=answer))
         return messages
 
-    def to_completion(self) -> dict:
+    def to_completion(self) -> Dict:
         messages = []
         messages.append(dict(role="system", content=self.get_system_str()))
         messages.extend(self.get_messages())
@@ -146,7 +146,7 @@ class _KdtSqlResponse(BaseModel):
     object: str
     created: int
     model: str
-    choices: list[_KdtChoice]
+    choices: List[_KdtChoice]
     usage: _KdtUsage
     prompt: str = Field(default=None, title="The input question")
 
@@ -165,14 +165,14 @@ class _KineticaLlmFileContextParser:
     PARSER = re.compile(r"^<\|(?P<role>\w+)\|>\W*(?P<content>.*)$", re.DOTALL)
 
     @classmethod
-    def parse_dialogue_file(cls, input_file: os.PathLike) -> dict:
+    def parse_dialogue_file(cls, input_file: os.PathLike) -> Dict:
         path = Path(input_file)
         schema = path.name.removesuffix(".txt")
         lines = open(input_file).read()
         return cls.parse_dialogue(lines, schema)
 
     @classmethod
-    def parse_dialogue(cls, text: str, schema: str) -> dict:
+    def parse_dialogue(cls, text: str, schema: str) -> Dict:
         messages = []
         system = None
 
@@ -286,7 +286,7 @@ def _llm_type(self) -> str:
         return "kinetica-sqlassist"
 
     @property
-    def _identifying_params(self) -> dict[str, Any]:
+    def _identifying_params(self) -> Dict[str, Any]:
         return dict(
             kinetica_version=str(self.kdbc.server_version), api_version=version("gpudb")
         )
@@ -320,7 +320,7 @@ def _generate(
             llm_output=llm_output,
         )
 
-    def load_messages_from_context(self, context_name: str) -> list:
+    def load_messages_from_context(self, context_name: str) -> List:
         """Load a lanchain prompt from a Kinetica context."""
 
         # query kinetica for the prompt
@@ -340,7 +340,7 @@ def load_messages_from_context(self, context_name: str) -> list:
         messages = [self._convert_message_from_dict(m) for m in dict_messages]
         return messages
 
-    def _submit_completion(self, messages: list[dict]) -> _KdtSqlResponse:
+    def _submit_completion(self, messages: List[Dict]) -> _KdtSqlResponse:
         """Submit a /chat/completions request to Kinetica."""
 
         request = dict(messages=messages)
@@ -438,7 +438,7 @@ def _convert_dict_to_messages(cls, sa_data: Dict) -> List[BaseMessage]:
         messages = sa_data["messages"]
         LOG.info(f"Importing prompt for schema: {schema}")
 
-        result_list: list[BaseMessage] = []
+        result_list: List[BaseMessage] = []
         result_list.append(SystemMessage(content=system))
         result_list.extend([cls._convert_message_from_dict(m) for m in messages])
         return result_list
@@ -448,7 +448,7 @@ class KineticaSqlResponse(BaseModel):
     """Response containing SQL and the fetched data."""
 
     sql: str = Field(description="Result SQL")
-    dataframe: DataFrame = Field(description="Result Data")
+    dataframe: "pd.DataFrame" = Field(description="Result Data")
 
     class Config:
         """Configuration for this pydantic object."""