The missing return statement caused the function to not return any value when the node_type is not "VertexAI". Adding the return statement ensures that the function returns the instantiated class object with the provided parameters in all cases.
The VertexAIIcon component is added to the icons directory, allowing the usage of the Vertex AI icon in the application. The nodeIconsLucide object in the utils.ts file is updated to include the VertexAI and ChatVertexAI icons, enabling their usage in the application. This addition enhances the visual representation of the application by providing new icons related to Vertex AI functionality.
🔧 fix(loading.py): remove empty lines
The type error for the service_account import in llm.py is ignored to prevent a linting error. In loading.py, empty lines were removed for code cleanliness.
The ChatVertexAI integration is temporarily commented out due to a bug that causes an AttributeError. This bug needs to be resolved before the integration can be activated again.
🔀 chore(custom_lists.py): add ChatVertexAI to the import statements for better modularity and extensibility
🔀 chore(custom_lists.py): add ChatVertexAI to the llm_type_to_cls_dict for better compatibility and flexibility
🔀 chore(llms.py): change required field for credentials to be optional for better user experience
🔀 chore(llms.py): add advanced and show fields for specific fields related to VertexAI for better configurability
The import statements in `custom_lists.py` have been reformatted to improve readability and maintainability. The `ChatVertexAI` class has been added to the import statements to enhance modularity and extensibility.
The `ChatVertexAI` class has been added to the `llm_type_to_cls_dict` dictionary in `custom_lists.py` to improve compatibility and flexibility.
In `llms.py`, the `required` field for the `credentials` field has been changed to be optional for a better user experience.
The `advanced` and `show` fields have been added to specific fields related to VertexAI in `llms.py` to provide better configurability.
The logic to find the matched_type in the Edge class has been simplified by removing unnecessary nested loops and using a single generator expression. This improves the readability and efficiency of the code.
🔧 chore(loading.py): call initialize_vertexai function when node_type is "VertexAI"
The `llm.py` file now includes a new function `initialize_vertexai` that initializes the VertexAI credentials if a `credentials` parameter is provided. This allows for the usage of VertexAI credentials in the application. In `loading.py`, the `initialize_vertexai` function is called when the `node_type` is "VertexAI", ensuring that the VertexAI credentials are properly initialized for that specific node type.
The condition for showing fields in the VertexAI class has been simplified to exclude specific field names. This improves readability and maintainability of the code.
⬆️ feat(pyproject.toml): add google-cloud-aiplatform dependency to enable integration with Google Cloud AI Platform
The python dependency version has been updated to >=3.9,<3.11 to ensure compatibility with the project. Additionally, the google-cloud-aiplatform dependency has been added to enable integration with Google Cloud AI Platform services.
📝 chore(llms.py): improve field configuration for VertexAI template and modify field type for "credentials"
The `add_extra_fields` method is modified to add an additional field called "credentials" for the VertexAI template. The field is of type "file" and is required. It allows the user to upload a JSON file as credentials. The `format_openai_field` method is also updated to handle the new "credentials" field.
- PostgresChatMessageHistory added to allow loading memory from external
sources
- MultiQueryRetriever added which builds better queries to
Retrievers/VectorStores
- XSS protection in the chat that uses markdown
- FrontendNode refactor start
- Fix Serializable nodes that were exposing wrong base classes on
Components
- ConversationEntityMemory as the first memory with EntityStore (entity
stores coming very soon)
The strenum package has been updated from version 0.4.12 to version 0.4.15. This update may include bug fixes, performance improvements, or new features.
The base class "Serializable" has been renamed to "BaseLLM" to provide a more descriptive and consistent name for the class. This change improves clarity and maintainability of the code.
🐛 fix(codeAreaComponent): fix formatting and add missing semicolons to improve code readability and maintainability
🐛 fix(dropdownComponent): fix formatting and add missing semicolons to improve code readability and maintainability
🐛 fix(floatComponent): fix formatting and add missing semicolons to improve code readability and maintainability
🐛 fix(inputFileComponent): fix formatting and add missing semicolons to improve code readability and maintainability
🐛 fix(inputListComponent): fix formatting and add missing semicolons to improve code readability and maintainability
🐛 fix(intComponent): fix formatting and add missing semicolons to improve code readability and maintainability
🐛 fix(promptComponent): fix formatting and add missing semicolons to improve code readability and maintainability
🐛 fix(textAreaComponent): fix formatting and add missing semicolons to improve code readability and maintainability
🐛 fix(ui/accordion.tsx): fix formatting and add missing semicolons to improve code readability and maintainability
The changes were made to fix formatting issues and add missing semicolons in the code. This improves code readability and maintainability, making it easier for developers to understand and work with the code.
🐛 fix(constants.tsx): fix formatting and indentation issues in the buildTweakObject function
🐛 fix(popUpContext.tsx): remove unnecessary empty line
🐛 fix(tabsContext.tsx): remove unnecessary empty line
🐛 fix(ApiModal/index.tsx): fix formatting and indentation issues
🐛 fix(codeAreaModal/index.tsx): fix formatting and indentation issues
🐛 fix(types/components/index.ts): remove unnecessary empty line
The changes in constants.tsx fix formatting and indentation issues in the buildTweakObject function to improve code readability. The changes in popUpContext.tsx, tabsContext.tsx, ApiModal/index.tsx, codeAreaModal/index.tsx, and types/components/index.ts fix formatting and indentation issues to ensure consistent code style.
🐛 fix(base.py): add optional 'name' parameter to the format method in FieldFormatter
In loading.py, the error message when failing to build a connection to the database is now formatted in a more readable way. This improves the clarity of the error message and makes it easier to identify the cause of the issue.
In base.py, the format method in FieldFormatter now accepts an optional 'name' parameter. This allows for more flexibility when formatting the field and provides the ability to include the field name in the formatting process if needed.
The color code for the "retrievers" category in the nodeColors object has been updated from "#F5B85A" to "#e6b25a". This change was made to ensure consistency and improve the visual representation of the "retrievers" category.
The ConversationEntityMemory integration is now documented in the config.yaml file. This provides a link to the documentation for using the ConversationEntityMemory module with SQLite as the underlying storage. This change improves the clarity and accessibility of the documentation for this integration.
🐛 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.
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.
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 `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.
