🔒 chore(auth.py): add refresh token functionality with expiration time of 180 minutes
🔒 chore(login.py): change token endpoint URL from /token to /login for better semantics
🔒 chore(login.py): add refresh token creation to login endpoint to provide a refresh token along with the access token
🐛 fix(base.py): change the error message when _built_object is None to provide more specific information and handle the case when _built_object is an instance of UnbuiltObject
🔧 chore(frontend): add SelectTrigger component to handle select triggers in UI components
🔧 chore(frontend): add Select component to handle select inputs in UI components
🔧 chore(frontend): add EditNodeModal component to handle editing of node data in modals
The changes were made to add support for the @radix-ui/react-select package to the frontend package.json dependencies. Additionally, the SelectTrigger and Select components were added to handle select triggers and select inputs in the UI components. Finally, the EditNodeModal component was added to handle the editing of node data in modals.
🐛 fix(nodeToolbarComponent): fix import statements and add missing dependencies
✨ feat(nodeToolbarComponent): add select-trigger component and implement functionality to handle select change and show/hide modal based on selection
🐛 fix(directory_reader.py): return False if code is not valid Python to prevent false positives
🐛 fix(directory_reader.py): fix method name from is_type_hint_used_but_not_imported to _is_type_hint_used_in_args for consistency
🐛 fix(directory_reader.py): fix method name from is_type_hint_imported to _is_type_hint_imported for consistency
🐛 fix(directory_reader.py): fix return value of _is_type_hint_used_in_args method to return False if type hint is used but not imported
✨ feat(OpenAIConversationalAgent.py): add support for return_intermediate_steps parameter in AgentExecutor constructor to enable returning intermediate steps during conversation
This commit adds a new file `ConversationalAgent.py` to the `src/backend/langflow/components/agents` directory. The `ConversationalAgent` class is a custom component that represents a conversational agent capable of using OpenAI's function calling API.
The `ConversationalAgent` class has the following features:
- It inherits from the `CustomComponent` class.
- It has a `display_name` attribute set to "OpenaAI Conversational Agent".
- It has a `description` attribute set to "Conversational Agent that can use OpenAI's function calling API".
- It implements the `build_config` method to define the configuration options for the agent.
- It implements the `build` method to create an instance of the `AgentExecutor` class, which represents the agent's execution environment.
- The `build` method takes several parameters, including `model_name`, `tools`, `memory`, `system_message`, and `max_token_limit`.
- It uses the `ChatOpenAI` class from the `langchain.chat_models` module to create an instance of the OpenAI language model.
- It uses the `ConversationTokenBufferMemory` class from the `langchain.memory.token_buffer` module to handle conversation history and token buffering.
- It uses the `OpenAIFunctionsAgent` class from the `langchain.agents.openai_functions_agent.base` module to create an instance of the OpenAI functions agent.
- It returns an instance of the `AgentExecutor` class with the agent, tools, memory, verbose, and return_intermediate_steps parameters set.
📝 feat(__init__.py): add empty __init__.py file to the agents directory
This commit adds an empty `__init__.py` file to the `src/backend/langflow/components/agents` directory. The `__init__.py` file is necessary to make the `agents` directory a Python package.
📝 WHY: The addition of BaseMemory to LANGCHAIN_BASE_TYPES allows for the customization of the memory component in the Langchain interface. This enables users to implement their own memory functionality according to their specific needs.
