✨ feat(langflow): add new chains to config.yaml and custom chains to interface/chains/custom.py
The following chains were added to the config.yaml file: RetrievalQA, RetrievalQAWithSourcesChain, QAWithSourcesChain, ConversationalRetrievalChain, and CombineDocsChain. These chains were added to improve the functionality of the application and provide more options for users.
In addition, custom chains were added to the interface/chains/custom.py file. The CombineDocsChain was added to allow users to combine multiple documents into a single document for use in the question answering chains. The QA_CHAIN_TYPES constant was also added to the frontend_node/constants.py file to provide a list of available question answering chain types.
🔀 refactor(custom_lists.py): merge CUSTOM_AGENTS and CUSTOM_CHAINS into CUSTOM_NODES
🔀 refactor(loading.py): add instantiate_chains function to instantiate chains
🔀 refactor(base.py): add CustomChain class
The changes add support for a new chain called load_qa_chain to CUSTOM_NODES. CUSTOM_AGENTS and CUSTOM_CHAINS are merged into CUSTOM_NODES. A new function called instantiate_chains is added to instantiate chains. A new class called CustomChain is added to the base.py file. This class is used to define a custom chain.
✨ feat(frontend_node): add extra fields to MemoryFrontendNode and ChainFrontendNode
The unused code in custom.py has been removed. The MemoryFrontendNode and ChainFrontendNode classes have been updated to include additional fields that are required for their respective templates. The MemoryFrontendNode now has a return_messages field, and the ChainFrontendNode now has a memory field. These fields are optional and can be toggled on or off as required.
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.
✨ 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.
🧪 test(test_database.py): add test case for creating flows without a flow
The flow field is now optional to allow creation of flows without a flow. This is useful when creating a flow that will be populated later. A test case was added to ensure that flows can be created without a flow.
🐛 fix(llms.py): add name check before checking if "azure" is in name.lower()
🔨 refactor(test_database.py): rename updated_flow_style variable to to_update_flow_style for clarity
The update_settings function now has an optional database_url parameter to allow for more flexibility in updating settings. The llms.py file now checks if the name variable is not None before checking if "azure" is in name.lower(). In test_database.py, the updated_flow_style variable is renamed to to_update_flow_style for clarity.
The FlowListRead schema is added to support reading a list of flows with their styles. The SQLModelSerializable base model is added to support serialization of SQLModel objects to JSON using orjson. This improves performance and reduces memory usage.
🐛 fix(flow.py): add optional style relationship to Flow model
The style relationship is now optional to allow for flows without styles. This is achieved by setting the uselist parameter of the sa_relationship_kwargs to False.
✨ feat(flow.py): add FlowReadWithStyle and FlowUpdate models
The FlowReadWithStyle model is added to support reading a flow with its style. The FlowUpdate model is added to support updating a flow.
The FlowStyle model is added to the project, which represents the style of a flow. It has a color and an emoji field, and a foreign key to the Flow model. The CRUD classes are also added to the file, which are FlowStyleCreate, FlowStyleRead, and FlowStyleUpdate. These classes are used to create, read, and update FlowStyle instances respectively.
The imports for the deleted FlowStyle model are removed from flow_styles.py. The comments for the FlowStyleCreate class are updated to reflect the fields it contains.
✨ feat(router.py): add new routers for flows and flow styles
🔧 refactor(__init__.py): add new routers to __all__ list
🔧 refactor(conftest.py): update import statement for get_session function
The unused code and endpoints related to flows have been removed from the database.py file. New routers for flows and flow styles have been added to the router.py file. The __all__ list in the __init__.py file has been updated to include the new routers. The import statement for the get_session function in the conftest.py file has been updated to reflect the new location of the function.
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.
🐛 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.
