The noopener and noreferrer attributes were added to the documentation link to improve security by preventing the linked page from having access to the window.opener object and to prevent the referrer header from being sent to the linked page, respectively.
🐛 fix(nodeToolbarComponent): fix documentation link not working when no documentation is provided
The button colors and icons have been updated to improve visibility and consistency. The delete and copy buttons now have a darker background color when hovered over. The edit button now has a rounded right corner and a lighter background color when there are no nodes present. The documentation button now shows a "Coming Soon" tooltip when no documentation is provided and has a muted color. The documentation link now works correctly even when no documentation is provided.
The delayDuration prop was removed from all ShadTooltip components in the ExtraSidebar component. This improves the user experience by removing the delay before the tooltip is displayed.
The required indicator is now next to the title, which improves readability and makes it easier to see which parameters are required. The documentation tooltip has been simplified to only show the documentation link, which improves the user experience by reducing clutter. Unused imports have been removed to improve code quality.
🔨 refactor(parameterComponent): move required indicator next to title
The ShadTooltipComponent has been refactored to destructure the props and add types to improve readability and maintainability. The ShadTooltipProps type has been added to the types/components/index.ts file to define the expected props for the ShadTooltipComponent. The delayDuration, side, content, and children props are now destructured from the props object and have their respective types defined.
🔥 refactor(manager.py): remove unnecessary blank line at the end of the file
The package version has been updated to 0.2.3 in the pyproject.toml file. This is a chore as it does not affect the functionality of the package. The blank line at the end of the manager.py file has been removed as it is unnecessary and does not add any value to the code. This is a refactor as it improves the code readability.
The variable name static_files_dir was changed to improve semantics. It is now more clear that it is a directory path. An optional static_files_dir parameter was added to the setup_app function to allow for a directory path to be passed in. This allows for more flexibility in serving static files.
🐛 fix(__main__.py): fix static_files_dir variable name to improve semantics
The first change removes an extra blank line in the ChatManager class. The second change updates the try_setting_streaming_options function to use the ChatConfig class to set the streaming option instead of hardcoding it. This makes the code more modular and easier to maintain.
This commit adds a new function `instantiate_llm` to handle LLM (Language Model) instantiation. It also sets the `ChatConfig.streaming` attribute based on the `openai_api_base` parameter. This is a workaround to ensure that JinaChat works until streaming is implemented.
The ChatConfig class is added to the project with a single attribute, streaming, set to True. This attribute is used to determine whether the chatbot should use streaming or request-response communication with the client.
✨ feat(__main__.py, main.py): add support for a custom static files directory to be passed as an argument to the app
The `setup_static_files` function has been moved from `__main__.py` to `main.py` to improve code organization. The function has also been renamed to `setup_app` to better reflect its purpose. The `create_app` function has been renamed to `setup_app` to follow the naming convention of the new function. The `setup_app` function now accepts an optional argument `static_files_dir` which allows the user to specify a custom directory for static files. This improves the flexibility of the app as it can now be run with a custom frontend.
This commit updates the documentation in the constants.py file to include additional API options that can be used instead of the default OpenAI API. The new options are JinaChat, LocalAI, and Prem. This change provides more information to the user and allows them to make an informed decision when choosing an API to use.
The `info` field is added to the `TemplateField` class to provide additional information about the field. The `OPENAI_API_BASE_INFO` constant is added to the `constants.py` file to provide information about the base URL of the OpenAI API and how it can be changed to use other APIs like Prem and LocalAI. The `info` field is set to `OPENAI_API_BASE_INFO` for the `openai_api_base` field in the `LLMFrontendNode` class in `llms.py`.
The ParameterComponent now has an info icon and tooltip to show additional information about the parameter. The GenericNode component now passes the info prop to the ParameterComponent to show the additional information. The ParameterComponentType has been refactored to include the info property.
🎨 style(parameterComponent): add info icon and tooltip to show additional information
🚀 feat(GenericNode): pass info prop to ParameterComponent to show additional information
Added documentation links for various document loaders, embeddings, and llms to improve the readability and usability of the config.yaml file. These links provide a quick reference to the documentation for each of the modules, making it easier for developers to understand and use them.
🔧 refactor(tabsContext.tsx): add missing type annotations and improve code readability
🔧 refactor(ApiModal): improve code readability by adding a new line to a JSX element
🔧 refactor(EditNodeModal): remove unnecessary blank line in a JSX element
The package version has been updated to 0.2.2 to reflect the changes made to the package. This is a chore commit as it does not include any functional changes to the package.
The `AgentType` enum is added to the `langchain.agents.custom` module to improve readability and type safety. The `InitializeAgent` class now uses the `AgentType` enum to ensure that the `agent` parameter is a valid value from the enum.
The import of RecursiveCharacterTextSplitter was removed as it was not being used in the code. The instantiation of TextSplitter was fixed by removing the unnecessary check for RecursiveCharacterTextSplitter and simplifying the code.
🔥 refactor(loading.py): remove unused import of RecursiveCharacterTextSplitter
The commit changes the comparison operator from '==' to 'is' to compare object types. This is because 'is' compares the object identity while '==' compares the object value. In this case, we want to compare the object identity, so 'is' is the correct operator to use.
The tooltip now includes a link to the documentation of the node, which makes it more accessible and user-friendly. The link is now wrapped in the tooltip title, which improves the semantics of the code.
🔼 chore(pyproject.toml): bump package version to 0.2.1
The langchain dependency has been updated to version 0.0.215, which includes bug fixes and performance improvements. The package version has been bumped to 0.2.1 to reflect the changes made.
The import statement for the MidjourneyIcon was misspelled as MidjorneyIcon, which caused a runtime error. This commit fixes the typo by changing the import statement to MidjourneyIcon.
The import of Boxes and LayoutDashboard were removed as they were not being used in the file. New icons were added to nodeIconsLucide to improve the variety of icons available for use. The new icons added are MongoDBAtlasVectorSearch, Pinecone, and SupabaseVectorStore.
🔥 chore(utils.ts): remove unused import of Boxes and LayoutDashboard
🐛 fix(loading.py): fix type hinting in instantiate_embedding function
🔨 refactor(loading.py): add type hinting to instantiate_textsplitter function
The changes in this commit add type hinting to the `instantiate_agent`, `instantiate_embedding`, and `instantiate_textsplitter` functions to improve code readability and maintainability. The `instantiate_embedding` function had a bug in its type hinting which has been fixed.
Added documentation links to the vectorstores integrations in the config.yaml file. This will make it easier for developers to access the documentation for each integration.
The RecursiveCharacterTextSplitter class in textsplitters.py now has a new field called separator_type. This field is used to specify the type of separator to be used in the splitter. The separator_type field is a string and can take any value from the Language enum or "Text". This change was made to improve the flexibility of the RecursiveCharacterTextSplitter class.
This commit adds type hints to the function parameters and return types in the loading.py file. This improves the readability and maintainability of the codebase by making it easier to understand the expected types of the parameters and return values of the functions.
Added documentation links for new integrations and memories to improve the documentation of the project. The new integrations are Cohere and HuggingFaceHub, and the new memories are ConversationBufferWindowMemory and VectorStoreRetrieverMemory.
The VectorStoreFrontendNode class now has VectorStoreRetriever as an extra base class in addition to BaseRetriever. This change was made to improve the functionality of the class by allowing it to inherit from VectorStoreRetriever.
The `update_settings` function now accepts a `cache` parameter that allows the user to specify the type of cache to use. The `cache` parameter is set to a default value of `SQLiteCache` and can be overridden by setting the `LANGCHAIN_CACHE` environment variable. This feature improves the flexibility of the application as it allows the user to choose the type of cache that best suits their needs.
The cache configuration option has been added to the settings file with a default value of "InMemoryCache". This allows the user to choose the cache implementation they want to use.
This commit adds support for configurable LLM caching. The `setup_llm_caching` function now imports the cache class from the `langchain.cache` module based on the `settings.cache` value. If the import is successful, the `langchain.llm_cache` is set to an instance of the cache class. If the import fails, a warning is logged. If an exception is raised during the setup, a warning is logged with the error message.
