Doc fixes

django_ai_assistant/helpers/assistants.py

@@ -85,7 +85,7 @@ class AIAssistant(abc.ABC):  # noqa: F821
     """Structured output to use for the assistant.\n
     Defaults to `None`.
     When not `None`, the assistant will return a structured output in the provided format.
-    See https://python.langchain.com/v0.2/docs/how_to/structured_output/ for the available formats.
+    See https://python.langchain.com/v0.3/docs/how_to/structured_output/ for the available formats.
     """
     _user: Any | None
     """The current user the assistant is helping. A model instance.\n
@@ -260,7 +260,7 @@ def get_model_kwargs(self) -> dict[str, Any]:
         return {}
 
     def get_llm(self) -> BaseChatModel:
-        """Get the Langchain LLM instance for the assistant.
+        """Get the LangChain LLM instance for the assistant.
         By default, this uses the OpenAI implementation.\n
         `get_model`, `get_temperature`, and `get_model_kwargs` are used to create the LLM instance.\n
         Override this method to use a different LLM implementation.
@@ -301,7 +301,7 @@ def get_tools(self) -> Sequence[BaseTool]:
         """Get the list of method tools the assistant can use.
         By default, this is the `_method_tools` attribute, which are all `@method_tool`s.\n
         Override and call super to add additional tools,
-        such as [any langchain_community tools](https://python.langchain.com/v0.2/docs/integrations/tools/).
+        such as [any langchain_community tools](https://python.langchain.com/v0.3/docs/integrations/tools/).
 
         Returns:
             Sequence[BaseTool]: The list of tools the assistant can use.
@@ -310,7 +310,7 @@ def get_tools(self) -> Sequence[BaseTool]:
 
     def get_document_separator(self) -> str:
         """Get the RAG document separator to use in the prompt. Only used when `has_rag=True`.\n
-        Defaults to `"\\n\\n"`, which is the Langchain default.\n
+        Defaults to `"\\n\\n"`, which is the LangChain default.\n
         Override this method to use a different separator.
 
         Returns:
@@ -321,7 +321,7 @@ def get_document_separator(self) -> str:
     def get_document_prompt(self) -> PromptTemplate:
         """Get the PromptTemplate template to use when rendering RAG documents in the prompt.
         Only used when `has_rag=True`.\n
-        Defaults to `PromptTemplate.from_template("{page_content}")`, which is the Langchain default.\n
+        Defaults to `PromptTemplate.from_template("{page_content}")`, which is the LangChain default.\n
         Override this method to use a different template.
 
         Returns:
@@ -372,7 +372,7 @@ def get_contextualize_prompt(self) -> ChatPromptTemplate:
         )
 
     def get_history_aware_retriever(self) -> Runnable[dict, RetrieverOutput]:
-        """Get the history-aware retriever Langchain chain for the assistant.\n
+        """Get the history-aware retriever LangChain chain for the assistant.\n
         This is used when `has_rag=True` to fetch documents based on the chat history.\n
         By default, this is a chain that checks if there is chat history,
         and if so, it uses the chat history to generate a new standalone question
@@ -381,10 +381,10 @@ def get_history_aware_retriever(self) -> Runnable[dict, RetrieverOutput]:
         Override this method to use a different history-aware retriever chain.
 
         Read more about the history-aware retriever in the
-        [Langchain docs](https://python.langchain.com/v0.2/docs/how_to/qa_chat_history_how_to/).
+        [LangChain docs](https://python.langchain.com/v0.2/docs/how_to/qa_chat_history_how_to/).
 
         Returns:
-            Runnable[dict, RetrieverOutput]: a history-aware retriever Langchain chain.
+            Runnable[dict, RetrieverOutput]: a history-aware retriever LangChain chain.
         """
         llm = self.get_llm()
         retriever = self.get_retriever()
@@ -403,7 +403,7 @@ def get_history_aware_retriever(self) -> Runnable[dict, RetrieverOutput]:
 
     @with_cast_id
     def as_graph(self, thread_id: Any | None = None) -> Runnable[dict, dict]:
-        """Create the Langchain graph for the assistant.\n
+        """Create the LangGraph graph for the assistant.\n
         This graph is an agent that supports chat history, tool calling, and RAG (if `has_rag=True`).\n
         `as_graph` uses many other methods to create the graph for the assistant.
         Prefer to override the other methods to customize the graph for the assistant.
@@ -537,7 +537,7 @@ def record_response(state: AgentState):
 
     @with_cast_id
     def invoke(self, *args: Any, thread_id: Any | None, **kwargs: Any) -> dict:
-        """Invoke the assistant Langchain graph with the given arguments and keyword arguments.\n
+        """Invoke the assistant LangChain graph with the given arguments and keyword arguments.\n
         This is the lower-level method to run the assistant.\n
         The graph is created by the `as_graph` method.\n
 

django_ai_assistant/helpers/use_cases.py

@@ -129,7 +129,7 @@ def create_message(
     if not can_create_message(thread=thread, user=user, request=request):
         raise AIUserNotAllowedError("User is not allowed to create messages in this thread")
 
-    # TODO: Check if we can separate the message creation from the chain invoke
+    # TODO: Check if we can separate the message creation from the invoke
     assistant = assistant_cls(user=user, request=request)
     assistant_message = assistant.invoke(
         {"input": content},

django_ai_assistant/models.py

@@ -54,7 +54,7 @@ def __repr__(self) -> str:
 
     def get_messages(self, include_extra_messages: bool = False) -> list[BaseMessage]:
         """
-        Get Langchain messages objects from the thread.
+        Get LangChain messages objects from the thread.
 
         Args:
             include_extra_messages (bool): Whether to include non-chat messages (like tool calls).
@@ -91,7 +91,7 @@ class Message(models.Model):
     """Thread to which the message belongs."""
     thread_id: Any
     message = models.JSONField()
-    """Message content. This is a serialized Langchain `BaseMessage` that was serialized
+    """Message content. This is a serialized LangChain `BaseMessage` that was serialized
     with `message_to_dict` and can be deserialized with `messages_from_dict`."""
     created_at = models.DateTimeField(auto_now_add=True)
     """Date and time when the message was created.

docs/tutorial.md

@@ -178,7 +178,7 @@ class IssueManagementAIAssistant(AIAssistant):
 
 ### Using pre-implemented tools
 
-Django AI Assistant works with [any LangChain-compatible tool](https://python.langchain.com/v0.2/docs/integrations/tools/).
+Django AI Assistant works with [any LangChain-compatible tool](https://python.langchain.com/v0.3/docs/integrations/tools/).
 Just override the `get_tools` method in your AI Assistant class to include the tools you want to use.
 
 For example, you can use the `TavilySearch` tool to provide your AI Assistant with the ability to search the web
@@ -219,8 +219,9 @@ class MovieSearchAIAssistant(AIAssistant):
 ```
 
 !!! note
-    As of now, Django AI Assistant is powered by [LangChain](https://python.langchain.com/v0.2/docs/introduction/),
-    but previous knowledge on LangChain is NOT necessary to use this library, at least for the main use cases.
+    As of now, Django AI Assistant is powered by [LangChain](https://python.langchain.com/v0.3/docs/introduction/)
+    and [LangGraph](https://langchain-ai.github.io/langgraph/tutorials/introduction/),
+    but knowledge on these tools is NOT necessary to use this library, at least for the main use cases.
 
 ## Using an AI Assistant
 
@@ -359,7 +360,7 @@ If you want to use traditional Django templates, you can try using HTMX to avoid
 ### Using other AI models
 
 By default the supported models are OpenAI ones,
-but you can use [any chat model from Langchain that supports Tool Calling](https://python.langchain.com/v0.2/docs/integrations/chat/#advanced-features) by overriding `get_llm`:
+but you can use [any chat model from LangChain that supports Tool Calling](https://python.langchain.com/docs/integrations/chat/#featured-providers) by overriding `get_llm`:
 
 ```python title="myapp/ai_assistants.py"
 from django_ai_assistant import AIAssistant
@@ -421,7 +422,7 @@ thereby improving the quality of the response by avoiding generic or off-topic a
 For this to work, your must do the following in your AI Assistant:
 
 1. Add `has_rag = True` as a class attribute;
-2. Override the `get_retriever` method to return a [Langchain Retriever](https://python.langchain.com/v0.2/docs/how_to/#retrievers).
+2. Override the `get_retriever` method to return a [LangChain Retriever](https://python.langchain.com/v0.3/docs/how_to/#retrievers).
 
 For example:
 
@@ -439,7 +440,7 @@ class DocsAssistant(AIAssistant):
     has_rag = True
 
     def get_retriever(self) -> BaseRetriever:
-        return ...  # use a Langchain Retriever here
+        return ...  # use a LangChain Retriever here
 ```
 
 The `rag/ai_assistants.py` file in the [example project](https://github.com/vintasoftware/django-ai-assistant/tree/main/example#readme)