diff --git a/libs/neo4j/langchain_neo4j/chains/graph_qa/cypher.py b/libs/neo4j/langchain_neo4j/chains/graph_qa/cypher.py
index 69ff000..3770208 100644
--- a/libs/neo4j/langchain_neo4j/chains/graph_qa/cypher.py
+++ b/libs/neo4j/langchain_neo4j/chains/graph_qa/cypher.py
@@ -164,8 +164,8 @@ class GraphCypherQAChain(Chain):
     """
 
     graph: GraphStore = Field(exclude=True)
-    cypher_generation_chain: Runnable
-    qa_chain: Runnable
+    cypher_generation_chain: Runnable[Dict[str, Any], str]
+    qa_chain: Runnable[Dict[str, Any], str]
     graph_schema: str
     input_key: str = "query"  #: :meta private:
     output_key: str = "result"  #: :meta private:
@@ -239,7 +239,7 @@ def from_llm(
         qa_prompt: Optional[BasePromptTemplate] = None,
         cypher_prompt: Optional[BasePromptTemplate] = None,
         cypher_llm: Optional[BaseLanguageModel] = None,
-        qa_llm: Optional[Union[BaseLanguageModel, Any]] = None,
+        qa_llm: Optional[BaseLanguageModel] = None,
         exclude_types: List[str] = [],
         include_types: List[str] = [],
         validate_cypher: bool = False,
@@ -250,16 +250,28 @@ def from_llm(
         **kwargs: Any,
     ) -> GraphCypherQAChain:
         """Initialize from LLM."""
+        # Ensure at least one LLM is provided
+        if llm is None and qa_llm is None and cypher_llm is None:
+            raise ValueError("At least one LLM must be provided")
 
-        if not cypher_llm and not llm:
-            raise ValueError("Either `llm` or `cypher_llm` parameters must be provided")
-        if not qa_llm and not llm:
-            raise ValueError("Either `llm` or `qa_llm` parameters must be provided")
-        if cypher_llm and qa_llm and llm:
+        # Prevent all three LLMs from being provided simultaneously
+        if llm is not None and qa_llm is not None and cypher_llm is not None:
             raise ValueError(
-                "You can specify up to two of 'cypher_llm', 'qa_llm'"
-                ", and 'llm', but not all three simultaneously."
+                "You can specify up to two of 'cypher_llm', 'qa_llm', and 'llm', but "
+                "not all three simultaneously."
             )
+
+        # Assign default LLMs if specific ones are not provided
+        if llm is not None:
+            qa_llm = qa_llm or llm
+            cypher_llm = cypher_llm or llm
+        else:
+            # If llm is None, both qa_llm and cypher_llm must be provided
+            if qa_llm is None or cypher_llm is None:
+                raise ValueError(
+                    "If `llm` is not provided, both `qa_llm` and `cypher_llm` must be "
+                    "provided."
+                )
         if cypher_prompt:
             if cypher_llm_kwargs:
                 raise ValueError(
@@ -271,6 +283,11 @@ def from_llm(
                 cypher_prompt = cypher_llm_kwargs.pop(
                     "prompt", CYPHER_GENERATION_PROMPT
                 )
+                if not isinstance(cypher_prompt, BasePromptTemplate):
+                    raise ValueError(
+                        "The cypher_llm_kwargs `prompt` must inherit from "
+                        "BasePromptTemplate"
+                    )
             else:
                 cypher_prompt = CYPHER_GENERATION_PROMPT
         if qa_prompt:
@@ -282,6 +299,11 @@ def from_llm(
         else:
             if qa_llm_kwargs:
                 qa_prompt = qa_llm_kwargs.pop("prompt", CYPHER_QA_PROMPT)
+                if not isinstance(qa_prompt, BasePromptTemplate):
+                    raise ValueError(
+                        "The qa_llm_kwargs `prompt` must inherit from "
+                        "BasePromptTemplate"
+                    )
             else:
                 qa_prompt = CYPHER_QA_PROMPT
         use_qa_llm_kwargs = qa_llm_kwargs if qa_llm_kwargs is not None else {}
@@ -289,10 +311,12 @@ def from_llm(
             cypher_llm_kwargs if cypher_llm_kwargs is not None else {}
         )
 
-        qa_llm = qa_llm or llm
         if use_function_response:
             try:
-                qa_llm.bind_tools({})  # type: ignore[union-attr]
+                if hasattr(qa_llm, "bind_tools"):
+                    qa_llm.bind_tools({})
+                else:
+                    raise AttributeError
                 response_prompt = ChatPromptTemplate.from_messages(
                     [
                         SystemMessage(content=function_response_system),
@@ -300,15 +324,13 @@ def from_llm(
                         MessagesPlaceholder(variable_name="function_response"),
                     ]
                 )
-                qa_chain = response_prompt | qa_llm | StrOutputParser()  # type: ignore
+                qa_chain = response_prompt | qa_llm | StrOutputParser()
             except (NotImplementedError, AttributeError):
                 raise ValueError("Provided LLM does not support native tools/functions")
         else:
-            qa_chain = qa_prompt | qa_llm.bind(**use_qa_llm_kwargs) | StrOutputParser()  # type: ignore
-
-        cypher_llm = cypher_llm or llm
+            qa_chain = qa_prompt | qa_llm.bind(**use_qa_llm_kwargs) | StrOutputParser()
         cypher_generation_chain = (
-            cypher_prompt | cypher_llm.bind(**use_cypher_llm_kwargs) | StrOutputParser()  # type: ignore
+            cypher_prompt | cypher_llm.bind(**use_cypher_llm_kwargs) | StrOutputParser()
         )
 
         if exclude_types and include_types:
@@ -379,6 +401,7 @@ def _call(
         else:
             context = []
 
+        final_result: Union[List[Dict[str, Any]], str]
         if self.return_direct:
             final_result = context
         else:
@@ -390,15 +413,14 @@ def _call(
             intermediate_steps.append({"context": context})
             if self.use_function_response:
                 function_response = get_function_response(question, context)
-                final_result = self.qa_chain.invoke(  # type: ignore
+                final_result = self.qa_chain.invoke(
                     {"question": question, "function_response": function_response},
                 )
             else:
-                result = self.qa_chain.invoke(  # type: ignore
+                final_result = self.qa_chain.invoke(
                     {"question": question, "context": context},
                     callbacks=callbacks,
                 )
-                final_result = result  # type: ignore
 
         chain_result: Dict[str, Any] = {self.output_key: final_result}
         if self.return_intermediate_steps: