🐛 fix(constants.tsx): refactor getCurlCode and getPythonCode to use buildTweakObject function for generating tweak object
🐛 fix(ApiModal): update logic for opening accordions based on tweak.current length and closeEdit value
🔒 chore(poetry.lock): update joblib version to 1.3.1
The versions of the anthropic, google-api-python-client, and joblib packages have been updated to their latest versions. This ensures that the application is using the most recent bug fixes, improvements, and features provided by these packages.
- Simplify class names in the SingleAlert component and update color variables.
- Update class names and color variables in the Dialog component.
- Update color variables in the constants file.
- Update color variables and class names in the index.css file.
- Update class names and color variables in the PageComponent file.
- Add a color variable for shared blur styles.
- Update Tailwind CSS configuration to include new color variables.
Note: These changes improve code readability and maintain color consistency throughout the application.
The code now catches an additional exception when the object does not have a 'conn' field. This improves the error handling by providing a more specific error message when building a connection to the database fails.
The 'from_method_nodes' dictionary in the 'MemoryCreator' class in 'base.py' has been updated to include the 'SQLiteEntityStore' class and its '__init__' method. This change allows the 'SQLiteEntityStore' class to be used as a memory node in the LangFlow application.
✨ feat(memories.py): add show attribute to entity_store field in MemoryFrontendNode class
The condition to add extra fields in the MemoryFrontendNode class has been fixed to correctly check if any of the base classes are in a list of base message classes. Additionally, the show attribute has been added to the entity_store field in the MemoryFrontendNode class to control its visibility.
The "kwargs" and "args" parameters are now excluded from the generated template dictionary. This ensures that only the relevant parameters are included in the template, improving the accuracy and usefulness of the generated template.
When processing the data graph, if the langchain_object is of an unknown type, a ValueError is now raised. This provides better error handling and helps identify any unexpected langchain_object types that may be encountered during processing.
✨ feat(base.py): add support for creating MemoryFrontendNode from a specific method in ZepChatMessageHistory class
The `MemoryCreator` class now supports creating `MemoryFrontendNode` from a specific method in the `ZepChatMessageHistory` class. This is achieved by adding the `from_method_nodes` dictionary with the method name as the key and the class name as the value. The `build_template_from_method` function is used to create the `MemoryFrontendNode` from the specified method. This enhancement allows for more flexibility in creating `MemoryFrontendNode` instances.
✨ feat(base.py): add support for ZepRetriever in from_method_nodes dictionary
The build_template_from_method call for the MultiQueryRetriever in the get_signature method was using the incorrect method name "from_llm". It has been fixed to use the correct method name "from_method_nodes". Additionally, support for the ZepRetriever has been added to the from_method_nodes dictionary to allow for its usage in the RetrieverCreator class.
The 'url' field in the MemoryFrontendNode class is now set to be shown in the frontend. This change allows the 'url' field to be visible and accessible to users in the frontend interface.
The "self" parameter is now excluded from the template building process. This change improves the accuracy of the generated template by removing unnecessary information.
The unused integrations in the memories and retrievers sections have been commented out to improve code readability and remove unnecessary clutter. These integrations are not currently being used in the application and can be safely ignored.
- Update CSS classes in codeAreaComponent, floatComponent, intComponent, and dialog components to improve styling and maintain consistency.
- Replace specific color values with Tailwind CSS utility classes.
- Refactor input styles and focus styles for better user experience.
- Remove unused CSS classes from index.css.
The instantiate_memory function now includes error handling for a specific AttributeError that occurs when the object does not have a 'cursor' attribute. If this error occurs, an AttributeError is raised with a specific error message indicating a failure to build a connection to the database. This change improves the error handling and provides more informative error messages for debugging purposes.
🐛 fix(base.py): call the method to remove unwanted base classes before converting the frontend node to a dict
The `process_base_classes` method is added to the `FrontendNode` class to remove unwanted base classes from the list of base classes. This method iterates over the base classes and filters out any classes that are present in the `CLASSES_TO_REMOVE` list. The method is then called before converting the frontend node to a dictionary representation in the `to_dict` method to ensure that the unwanted base classes are not included in the final output.
The configurations for the ChatPromptTemplate, SystemMessagePromptTemplate, and HumanMessagePromptTemplate wrappers have been commented out as they are currently not being used. This helps to improve code readability and reduces unnecessary clutter in the configuration file.
The config.yaml file now includes an output_parsers section with the StructuredOutputParser and ResponseSchema as the specified parsers. This change was made to enhance the functionality of the backend by enabling the parsing of structured output and response schemas.
🔧 fix(settings.py): add "output_parsers" attribute to the Settings class
The "input_types" list in the template_field of the post_validate_prompt function in validate.py was missing the "BaseOutputParser" type. This fix adds it to the list to ensure proper validation.
The Settings class in settings.py was missing the "output_parsers" attribute. This fix adds the attribute to the class to ensure that the list of output parsers can be properly configured and accessed.
The import statements for vertex types in constants.py have been refactored to import all types from langflow.graph.vertex.types. This improves code readability and maintainability by reducing the number of import statements and consolidating them into a single import.
The OutputParserVertex class is added to the types.py file. This class extends the Vertex class and is used to represent a vertex in the graph that performs output parsing. It takes a data parameter of type Dict in its constructor and calls the super constructor with the data and a base_type of "output_parsers". This change is made to support the implementation of output parsing functionality in the graph.
🔧 fix(loading.py): add import statement for output_parser_creator
🔧 fix(listing.py): add import statement for output_parser_creator
🔧 fix(types.py): add import statement for output_parser_creator
The import statements for the `output_parsers` module were missing in the respective files. This fix adds the necessary import statements to ensure that the `output_parser_creator` is accessible and can be used in the code.
🚀 feat(output_parsers): add OutputParserCreator class to handle creation and loading of output parsers
🚀 feat(output_parsers): add OutputParserFrontendNode class to handle formatting of output parser fields
The commit adds the following changes:
- A new file `__init__.py` is added to the `output_parsers` module.
- A new file `base.py` is added to the `output_parsers` module.
- A new file `output_parsers.py` is added to the `frontend_node` module.
The `__init__.py` file initializes the `OutputParserCreator` class, which is responsible for creating and loading output parsers. It also defines a method to get the signature of an output parser.
The `base.py` file contains the base class `OutputParserCreator` which is a subclass of `LangChainTypeCreator`. It defines the type name as "output_parsers" and provides methods to get the frontend node class and the type to loader dictionary. It also defines a method to convert the output parsers to a list.
The `output_parsers.py` file contains the `OutputParserFrontendNode` class, which is a subclass of `FrontendNode`. It provides a method to format the field of an output parser.
These changes are done to add support for output parsers in the application. The `OutputParserCreator` class allows for dynamic creation and loading of output parsers, while the `OutputParserFrontendNode` class provides a way to format the fields of an output parser.
🚀 feat(utils.ts): add support for output_parsers node name and icon
The `nodeColors` object now includes a color for the `output_parsers` node type, allowing it to be visually distinguished. Similarly, the `nodeNames` object now includes a name for the `output_parsers` node type. Additionally, the `nodeIconsLucide` object now includes an icon for the `output_parsers` node type. These changes improve the consistency and completeness of the node types in the application.
The instantiate_prompt function now properly formats the input variables before passing them to the prompt object. If the input variable is a string, it is passed as is. If the input variable is a list of Document objects, it is formatted to contain the page_content and metadata as one string separated by a newline. This ensures that the prompt object receives the correct input format.
The `add_extra_base_classes` method is added to the `LLMFrontendNode` class. This method checks if the "BaseLLM" class is already present in the `base_classes` list and appends it if not. This change allows for easier extensibility by providing a way to add additional base classes to the `LLMFrontendNode` class.
The `from_method_nodes` dictionary is added to the `RetrieverCreator` class in order to map the "MultiQueryRetriever" node to the "from_llm" method. This refactor improves the readability and maintainability of the code by explicitly defining the mapping between nodes and methods.
🚀 feat(loading.py): implement the ability to instantiate retrievers based on node type and class object
The `instantiate_based_on_type` function now includes a new condition to handle the instantiation of retrievers. The `instantiate_retriever` function is introduced to handle the specific logic for creating retrievers. This change allows for the dynamic creation of retrievers based on the provided node type and class object.
✨ feat(utils.py): add support for importing retriever module based on retriever name
The `import_retriever` function is added to the `utils.py` module to allow importing the retriever module based on the retriever name. This function uses the `import_module` function from the `importlib` module to dynamically import the retriever module from the `langchain.retrievers` package. This enhances the modularity and extensibility of the codebase by providing a convenient way to import retrievers based on their names.
The RetrieverVertex class is added to the list of available vertex types in the langflow.graph.vertex.types module. This allows for the creation of vertices that represent retrievers in the graph.
The import statements for vertex types in the constants.py file have been refactored to import from langflow.graph.vertex.types as types. This change improves the readability and maintainability of the code by making it clear where the vertex types are coming from.
🐛 fix(chat.py): fix incorrect key used to retrieve graph_data from flow_data_store
The BuildStatus.IN_PROGRESS constant is changed to BuildStatus.STARTED to provide a more accurate representation of the flow status. Additionally, the incorrect key "data" is replaced with "graph_data" when retrieving the graph data from the flow_data_store dictionary.
✨ feat(AccordionComponent): add support for multiple open accordions by changing the open prop from boolean to string array
🐛 fix(constants.tsx): remove escape characters from the JSON stringified tweaks to fix parsing issues
✨ feat(constants.tsx): add support for tweaks dictionary in the getCurlCode and getPythonCode functions
🐛 fix(ApiModal): fix the logic to set the initial value of the openAccordion state based on the getTweak array
✨ feat(ApiModal): add support for opening accordions based on the getTweak array when switching to the Tweak tab
🐛 fix(ApiModal): fix the logic to filter and remove empty tweaks from the tweak.current array
✨ feat(ApiModal): add support for adding new tweaks to the tweak.current array and generating Python API code
🐛 fix(ApiModal): fix the logic to get the value of a specific tweak template based on the node id and template name
✨ feat(ApiModal): add support for opening accordions based on the tweak.current array when switching to the Tweak tab
🐛 fix(types): add missing newline at the end of the file
✨ feat(chatMessage): improve rendering of multi-line messages
The `convert` variable is now imported before being used to convert ANSI to HTML. Additionally, the `DOMPurify` library is imported and used to sanitize the HTML content before rendering it, preventing potential XSS attacks. The rendering of multi-line messages has been improved by splitting the message by newline characters and rendering each line separately with a line break.
The dompurify package is added as a dependency to the frontend package.json file. This package is needed for sanitizing HTML to prevent cross-site scripting (XSS) attacks.
The Search icon has been renamed to FileSearch to provide a more accurate representation of its purpose. This change improves clarity and ensures that the icon name aligns with its actual functionality.
🐛 fix(util.py): handle non-string types in format_dict function
The base retriever class is added to provide a common interface for all retrievers in the language chain. The frontend node class for retrievers is also added to handle the formatting of fields specific to retrievers.
In the util.py file, a fix is made to handle non-string types in the format_dict function. Previously, if the type of a field was not a string, an error would occur. This fix ensures that the type is converted to a string before further processing.
The __init__.py file is added to the retrievers directory to make it a Python package. This allows the directory to be recognized as a module and enables importing of modules and submodules within the retrievers directory.
The type dictionary in the listing.py file now includes a "retrievers" key, which contains a list of retriever creators. This allows for the inclusion of retrievers as a supported type in the application.
