This commit only updates the version number of the package in the pyproject.toml file. The version number is updated to 0.0.84. This is a chore commit as it does not add any new features or fix any bugs, but it is necessary to keep track of the package version.
The conditional statement in line 292 was not properly checking for undefined and null values, which could lead to unexpected behavior. The fix ensures that the statement checks for all falsy values, including undefined and null.
The new build_and_run target removes the dist folder, builds the application, installs it, and runs it. This target is useful for quickly building and running the application in a development environment.
✨ feat(__main__.py): add banner with title and info text to be displayed on server start
🐛 fix(App.tsx): fix API endpoint URL
The sendAll function URL had an extra forward slash. The API endpoint URL in App.tsx was incorrect and has been fixed. A banner with a title and info text has been added to be displayed on server start to provide users with more information about the application.
The PythonFunction tool has been added to the list of available tools in the config.yaml file. This allows the backend to use Python functions as part of the language processing pipeline.
The Makefile has been updated to include the `install_backend` command as a dependency of the `backend` target. This ensures that the backend dependencies are installed before running the backend server.
The code was updated to add a null check for the name variable before checking if it contains the string "azure". This prevents a potential runtime error if the name variable is null.
🚀 feat(loading.py): add support for PythonFunction node type
🚀 feat(constants.py): add PythonFunction to CUSTOM_TOOLS
🚀 feat(custom.py): add PythonFunction class
🚀 feat(frontend_node/tools.py): add PythonFunctionNode class
🧪 test(test_custom_types.py): add test for PythonFunction class
🧪 test(test_llms_template.py): comment out tests for AzureOpenAI and AzureChatOpenAI
The changes add support for a new node type, PythonFunction, which allows users to define a Python function to be executed. The node type is added to CUSTOM_NODES in customs.py, and support for the node type is added to loading.py. The node type is also added to CUSTOM_TOOLS in constants.py, and the PythonFunction class is added to custom.py. The PythonFunctionNode class is added to frontend_node/tools.py. Tests for the new PythonFunction class are added to test_custom_types.py. Tests for AzureOpenAI and AzureChatOpenAI are commented out in test_llms_template.py.
The API endpoint URLs have been updated to include the version number to improve the API's versioning and maintainability. The changes were made to the server.ts file and the tests that use the API endpoints.
🐛 fix(tests): update API endpoint paths in test files
The API endpoint paths in the test files were outdated and have been updated to reflect the current API version. This ensures that the tests are running against the correct endpoints and that the tests are up-to-date with the current API version.
🐛 fix(frontend): add missing api/v1 prefix to WebSocket URL
🐛 fix(frontend): add missing api/v1 prefix to Vite proxy target
The API routes, WebSocket URL, and Vite proxy target were missing the "api/v1" prefix, causing the frontend to not be able to communicate with the backend. This commit adds the missing prefix to all three locations to fix the issue.
🔨 refactor(custom.py, loading.py, prompts/custom.py, run.py): update import statements to use extract_input_variables_from_prompt from interface.utils module
🔨 refactor(run.py): remove unused imports and functions
🔨 refactor(utils.py): add type hinting to extract_input_variables_from_prompt function and remove unused imports
The extract_input_variables_from_prompt function has been moved to the interface.utils module to improve code organization. The import statements in the affected modules have been updated to reflect this change. Unused imports and functions have been removed from the run.py module. Type hinting has been added to the extract_input_variables_from_prompt function in the interface.utils module.
🚀 feat(processing): add processing module with get_result_and_steps and fix_memory_inputs functions
The processing module was added to the project with two functions: get_result_and_steps and fix_memory_inputs. The get_result_and_steps function extracts the result and thought from a LangChain object and returns them. The fix_memory_inputs function checks if a LangChain object has a memory attribute and if that memory key exists in the object's input variables. If not, it gets a possible new memory key using the get_memory_key function and updates the memory keys using the update_memory_keys function.
🚀 feat(utils.py): import extract_input_variables_from_prompt from langflow.interface.utils
The `from_payload` class method is added to the `Graph` class to create a graph from a payload. This method takes a dictionary as input and returns a `Graph` object. The `extract_input_variables_from_prompt` function is imported from `langflow.interface.utils` to extract input variables from a prompt. This function is used in other parts of the codebase to extract input variables from prompts.
✨ feat(utils.py): add process_graph function to process graph data and generate result and thought
The ChatManager class manages active connections and chat history. The ChatHistory class manages the chat history for a client. The process_graph function processes graph data and generates a result and thought. This function is used in the ChatManager class to generate a response back to the frontend.
This commit adds new API endpoints for chat, validation, and version. The chat endpoint is a websocket endpoint for chat. The validation endpoint has three sub-endpoints for validating code, prompt, and node. The version endpoint returns the version of LangFlow.
The base.py file contains the following classes and functions:
- CacheResponse: a pydantic BaseModel that represents a response containing a dictionary of data
- Code: a pydantic BaseModel that represents a code string
- Prompt: a pydantic BaseModel that represents a prompt template string
- CodeValidationResponse: a pydantic BaseModel that represents a response containing the validation results of code
- PromptValidationResponse: a pydantic BaseModel that represents a response containing the validation results of a prompt
- validate_prompt: a function that validates a prompt template string and returns a PromptValidationResponse object
- check_input_variables: a function that checks if input variables contain invalid characters and returns a list of fixed input variables
The callback.py file contains the following classes:
- AsyncStreamingLLMCallbackHandler: an AsyncCallbackHandler that handles streaming LLM responses asynchronously
- StreamingLLMCallbackHandler: a BaseCallbackHandler that handles streaming LLM responses
These files were added to provide support for Langflow's backend API.
The API now has versioning, with the prefix "/api/v1". The router has been restructured to include the chat, endpoints, and validate routers. This improves the organization of the code and makes it easier to add new routers in the future.
The routers for the langflow API have been moved to a single file for better organization and maintainability. The routers have been imported and included in the main.py file using the new file. A new health check endpoint has been added to the API to check the status of the application.
Added pytest configuration options to the pyproject.toml file. The minimum version of pytest is set to 6.0, the '-ra' option is added to addopts to show all test results, testpaths are set to include both 'tests' and 'integration' directories, console output style is set to 'progress', and DeprecationWarning is ignored. log_cli is set to true to enable logging of pytest output to the console.
The version number in the pyproject.toml file has been updated from 0.0.82 to 0.0.83. This is a chore commit as it does not introduce any new features or fix any bugs, but rather updates the version number to reflect the changes made in the package.
- Added `--public` to `lcserve_push` target to make sure it is
accessible to everyone (already done in `dev` branch)
- Changed `langchain-serve` trigger to `main` branch as the release it
done from main
Description:
This pull request introduces a new feature that installs the shadTooltip
library into the project. Additionally, it enhances the tooltip
functionality by grouping the tooltips based on their associated edge
classes.
Changes Made:
Added the shadTooltip library to the project dependencies.
Implemented logic to group tooltips based on their respective edge
classes.
Updated the tooltip rendering code to display grouped tooltips on the
edges.
### Description
This pull request introduces an enhancement to the existing application
by adding persistence to the dark mode feature. Currently, when the page
is refreshed, the dark mode setting reverts to the default light mode.
With this enhancement, the dark mode state will be maintained even after
refreshing the page.
### Changes Made
1. Added a new setting in the application to store the user's preference
for dark mode.
2. Implemented functionality to persist the dark mode preference in
local storage.
3. Modified the page initialization logic to retrieve the dark mode
preference from local storage and apply it on page load.
This commit refactors the FrontendNode class by extracting two methods to handle specific field values related to models and API keys. The _handle_model_specific_field_values method handles the options and is_list properties for fields related to models, while the _handle_api_key_specific_field_values method handles the display_name and required properties for fields related to API keys. This improves the readability and maintainability of the code.
There are still some rough edges due to underlying langchain and
openai API limitations, e.g. hwchase17/langchain#3769 and
openai/openai-python#411. Notably, you can't use the Azure and
non-Azure node types in the same server, since there's global openai
configuration needed to choose between the two. So it's probably best
to still leave the Azure node types commented out in the default
config. But with this PR, if you uncomment those nodes and start the
server with OPENAI_API_TYPE=azure, you will have working Azure nodes.
