🔧 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.
This pull request introduces a new admin login page to the application,
enhancing the user management experience by implementing a user table
with pagination. This will allow administrators to efficiently manage
users, edit passwords, delete accounts, and apply filters through a
user-friendly table interface.
/login/admin - Just click login
✨ feat(PaginatorComponent): add support for custom icons using IconComponent for better customization options
🔧 fix(UserManagementModal): remove unused imports and fix import order for better organization
✨ feat(UserManagementModal): add password confirmation validation and reset form on modal open for better user experience
🔧 fix(UserManagementModal): fix modal size to be full height for better visibility and usability
✨ feat(UserManagementModal): add form fields for username and password with validation for better user input handling
🔧 fix(UserManagementModal): fix form field labels and error messages for better clarity and user guidance
✨ feat(UserManagementModal): add form submission and confirmation handling for better user interaction and data management
🔧 fix(UserManagementModal): fix cancel button styling for better consistency and user experience
🔧 fix(baseModal): add missing size options to the size prop to support additional modal sizes
✨ feat(baseModal): add support for "small-h-full" and "medium-h-full" sizes to allow modals with smaller height and full width
🔧 fix(AdminPage): import missing IconComponent and ShadTooltip components
✨ feat(AdminPage): add functionality to handle editing and adding new users
🔧 fix(applies.css): add missing semicolon to .field-invalid and .label-invalid classes
🔧 fix(styleUtils.ts): import missing Pencil, ChevronsRight, and ChevronsLeft icons
🐛 fix(test_custom_component.py): update assertion to expect return_type as a list instead of a string
🐛 fix(test_vectorstore_template.py): update assertion to check if all vectorstores in settings are present in the response
🔧 chore(Metaphor.py): update search method to use the provided parameters for use_autoprompt and search_num_results
🔧 chore(Metaphor.py): update find_similar method to use the provided parameter for similar_num_results
🐛 fix(Metaphor.py): ignore type error for returning a list with mixed types
🐛 fix(Vectara.py): add condition to check if documents and embedding are not None before creating Vectara instance
🐛 fix(CustomComponent.py): change return type of get_function_entrypoint_return_type to List[str] to match the actual return value
The MetaphorToolkit component is added to the langflow toolkit. It provides functionality for searching metaphors using the Metaphor API. The component includes three tools: search, get_contents, and find_similar. The search tool allows users to search for metaphors using a query. The get_contents tool retrieves the contents of a webpage based on the ids returned from the search tool. The find_similar tool finds search results similar to a given URL returned from the search tool.
The MetaphorToolkit component is still in beta and requires a Metaphor API key to function. The API key is stored securely and can be configured in the field_config of the component. For more information, refer to the documentation: [Metaphor Toolkit Documentation](https://python.langchain.com/docs/integrations/tools/metaphor_search)
