Formatting

src/neo4j_graphrag/generation/graphrag.py

@@ -154,7 +154,9 @@ def build_query(
                 chat_history=chat_history
             )
             summary = self.llm.invoke(
-                input=summarization_prompt, system_instruction=summarization_prompt.SYSTEM_MESSAGE).content
+                input=summarization_prompt,
+                system_instruction=summarization_prompt.SYSTEM_MESSAGE,
+            ).content
             return ConversationTemplate().format(
                 summary=summary, current_query=query_text
             )

src/neo4j_graphrag/llm/anthropic_llm.py

@@ -22,7 +22,6 @@
 from neo4j_graphrag.llm.types import LLMResponse, MessageList, UserMessage
 
 if TYPE_CHECKING:
-    import anthropic
     from anthropic.types.message_param import MessageParam
 
 

src/neo4j_graphrag/llm/base.py

@@ -43,7 +43,10 @@ def __init__(
 
     @abstractmethod
     def invoke(
-        self, input: str, chat_history: Optional[list[dict[str, str]]] = None, system_instruction: Optional[str] = None
+        self,
+        input: str,
+        chat_history: Optional[list[dict[str, str]]] = None,
+        system_instruction: Optional[str] = None,
     ) -> LLMResponse:
         """Sends a text input to the LLM and retrieves a response.
 

src/neo4j_graphrag/llm/cohere_llm.py

@@ -27,7 +27,6 @@
 )
 
 if TYPE_CHECKING:
-    import cohere
     from cohere import ChatMessages
 
 

src/neo4j_graphrag/llm/ollama_llm.py

@@ -22,7 +22,6 @@
 from .types import LLMResponse, SystemMessage, UserMessage, MessageList
 
 if TYPE_CHECKING:
-    import ollama
     from ollama import Message
 
 

src/neo4j_graphrag/llm/openai_llm.py

@@ -61,10 +61,17 @@ def __init__(
         super().__init__(model_name, model_params, system_instruction)
 
     def get_messages(
-        self, input: str, chat_history: Optional[list[Any]] = None, system_instruction: Optional[str] = None
+        self,
+        input: str,
+        chat_history: Optional[list[Any]] = None,
+        system_instruction: Optional[str] = None,
     ) -> Iterable[ChatCompletionMessageParam]:
         messages = []
-        system_message = system_instruction if system_instruction is not None else self.system_instruction
+        system_message = (
+            system_instruction
+            if system_instruction is not None
+            else self.system_instruction
+        )
         if system_message:
             messages.append(SystemMessage(content=system_message).model_dump())
         if chat_history:
@@ -77,7 +84,10 @@ def get_messages(
         return messages
 
     def invoke(
-        self, input: str, chat_history: Optional[list[Any]] = None, system_instruction: Optional[str] = None
+        self,
+        input: str,
+        chat_history: Optional[list[Any]] = None,
+        system_instruction: Optional[str] = None,
     ) -> LLMResponse:
         """Sends a text input to the OpenAI chat completion model
         and returns the response's content.

src/neo4j_graphrag/llm/vertexai_llm.py

@@ -104,7 +104,10 @@ def get_messages(
         return messages
 
     def invoke(
-        self, input: str, chat_history: Optional[list[Any]] = None, system_instruction: Optional[str] = None
+        self,
+        input: str,
+        chat_history: Optional[list[Any]] = None,
+        system_instruction: Optional[str] = None,
     ) -> LLMResponse:
         """Sends text to the LLM and returns a response.
 
@@ -116,9 +119,15 @@ def invoke(
         Returns:
             LLMResponse: The response from the LLM.
         """
-        system_message = system_instruction if system_instruction is not None else self.system_instruction
+        system_message = (
+            system_instruction
+            if system_instruction is not None
+            else self.system_instruction
+        )
         self.model = GenerativeModel(
-            model_name=self.model_name, system_instruction=[system_message], **self.model_params
+            model_name=self.model_name,
+            system_instruction=[system_message],
+            **self.model_params,
         )
         try:
             messages = self.get_messages(input, chat_history)