Gabriel Luiz Freitas Almeida 4ebfd59b1b chore: remove unused files and update imports (#1967)
* chore(pyproject.toml): update vulture dependency to version 2.11

* chore: Remove unused files and imports

* Refactor legacy_custom/customs.py by removing unused nodes and chains

* Refactor langflow.interface.custom.base.py by removing unused code

* Refactor test_custom_component.py to import CustomComponent from langflow.custom

* refactor(agents): remove AgentInitializer and OpenAIConversationalAgent components as they are no longer needed
refactor(embeddings): remove client parameter from OpenAIEmbeddingsComponent as it is not used
refactor(memories): change search_scope and search_type parameters in ZepMessageReaderComponent to be of type str
refactor(model_specs): remove examples parameter from ChatVertexAIComponent as it is not used
refactor(models): change metadata parameter type in OllamaModel to Dict for consistency

refactor(VertexAiModel.py): remove examples parameter from ChatVertexAIComponent constructor to simplify the class structure
refactor(MultiQueryRetriever.py): change prompt parameter type to Text for better consistency and readability
refactor(JsonToolkit.py): update build method to handle both json and yaml file types for JsonToolkit creation
refactor(OpenAPIToolkit.py): update build method to handle both json and yaml file types for JsonSpec creation and improve parameter naming for clarity

* Format json

* refactor(langflow.custom): update imports in code files to use the new langflow.custom module

* chore(settings.py): remove unused settings file and related imports and classes from the project.

* refactor(langflow): optimize imports in graph/__init__.py and graph/graph/base.py
refactor(langflow): remove unused code and simplify logic in vertex/base.py

refactor(types.py): remove unused imports and classes, clean up commented out code, and improve code readability by removing unnecessary methods and attributes

refactor(utils.py): remove unused functions is_basic_type, invoke_lc_runnable, generate_result
feat(load): add new functionality to load flow from JSON file or object and run flow from JSON file or object
feat(load): add new modules load.py and __init__.py for loading and running flow from JSON
feat(processing): remove unused functions get_result_and_steps, flush_langfuse_callback_if_present

refactor(process.py): remove unused functions and imports to clean up the codebase
feat(utils.py): remove unused file utils.py to declutter the project and improve maintainability
test(test_loading.py): update import paths after restructuring the project folders
2024-05-24 13:13:38 -07:00
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Langflow

Langflow is a new, visual way to build, iterate and deploy AI apps.

Documentation and Community

📦 Installation

You can install Langflow with pip:

# Make sure you have Python 3.10 installed on your system.
# Install the pre-release version
python -m pip install langflow --pre --force-reinstall

# or stable version
python -m pip install langflow -U

Then, run Langflow with:

python -m langflow run

You can also preview Langflow in HuggingFace Spaces. Clone the space using this link, to create your own Langflow workspace in minutes.

🎨 Creating Flows

Creating flows with Langflow is easy. Simply drag components from the sidebar onto the canvas and connect them to start building your application.

Explore by editing prompt parameters, grouping components into a single high-level component, and building your own Custom Components.

Once youre done, you can export your flow as a JSON file.

Load the flow with:

from langflow.load import run_flow_from_json

results = run_flow_from_json("path/to/flow.json", input_value="Hello, World!")

🖥️ Command Line Interface (CLI)

Langflow provides a command-line interface (CLI) for easy management and configuration.

Usage

You can run the Langflow using the following command:

langflow run [OPTIONS]

Each option is detailed below:

  • --help: Displays all available options.
  • --host: Defines the host to bind the server to. Can be set using the LANGFLOW_HOST environment variable. The default is 127.0.0.1.
  • --workers: Sets the number of worker processes. Can be set using the LANGFLOW_WORKERS environment variable. The default is 1.
  • --timeout: Sets the worker timeout in seconds. The default is 60.
  • --port: Sets the port to listen on. Can be set using the LANGFLOW_PORT environment variable. The default is 7860.
  • --config: Defines the path to the configuration file. The default is config.yaml.
  • --env-file: Specifies the path to the .env file containing environment variables. The default is .env.
  • --log-level: Defines the logging level. Can be set using the LANGFLOW_LOG_LEVEL environment variable. The default is critical.
  • --components-path: Specifies the path to the directory containing custom components. Can be set using the LANGFLOW_COMPONENTS_PATH environment variable. The default is langflow/components.
  • --log-file: Specifies the path to the log file. Can be set using the LANGFLOW_LOG_FILE environment variable. The default is logs/langflow.log.
  • --cache: Selects the type of cache to use. Options are InMemoryCache and SQLiteCache. Can be set using the LANGFLOW_LANGCHAIN_CACHE environment variable. The default is SQLiteCache.
  • --dev/--no-dev: Toggles the development mode. The default is no-dev.
  • --path: Specifies the path to the frontend directory containing build files. This option is for development purposes only. Can be set using the LANGFLOW_FRONTEND_PATH environment variable.
  • --open-browser/--no-open-browser: Toggles the option to open the browser after starting the server. Can be set using the LANGFLOW_OPEN_BROWSER environment variable. The default is open-browser.
  • --remove-api-keys/--no-remove-api-keys: Toggles the option to remove API keys from the projects saved in the database. Can be set using the LANGFLOW_REMOVE_API_KEYS environment variable. The default is no-remove-api-keys.
  • --install-completion [bash|zsh|fish|powershell|pwsh]: Installs completion for the specified shell.
  • --show-completion [bash|zsh|fish|powershell|pwsh]: Shows completion for the specified shell, allowing you to copy it or customize the installation.
  • --backend-only: This parameter, with a default value of False, allows running only the backend server without the frontend. It can also be set using the LANGFLOW_BACKEND_ONLY environment variable.
  • --store: This parameter, with a default value of True, enables the store features, use --no-store to deactivate it. It can be configured using the LANGFLOW_STORE environment variable.

These parameters are important for users who need to customize the behavior of Langflow, especially in development or specialized deployment scenarios.

Environment Variables

You can configure many of the CLI options using environment variables. These can be exported in your operating system or added to a .env file and loaded using the --env-file option.

A sample .env file named .env.example is included with the project. Copy this file to a new file named .env and replace the example values with your actual settings. If you're setting values in both your OS and the .env file, the .env settings will take precedence.

Deployment

Deploy Langflow on Google Cloud Platform

Follow our step-by-step guide to deploy Langflow on Google Cloud Platform (GCP) using Google Cloud Shell. The guide is available in the Langflow in Google Cloud Platform document.

Alternatively, click the "Open in Cloud Shell" button below to launch Google Cloud Shell, clone the Langflow repository, and start an interactive tutorial that will guide you through the process of setting up the necessary resources and deploying Langflow on your GCP project.

Open in Cloud Shell

Deploy on Railway

Use this template to deploy Langflow 1.0 Preview on Railway:

Deploy 1.0 Preview on Railway

Or this one to deploy Langflow 0.6.x:

Deploy on Railway

Deploy on Render

Deploy to Render

👋 Contributing

We welcome contributions from developers of all levels to our open-source project on GitHub. If you'd like to contribute, please check our contributing guidelines and help make Langflow more accessible.


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🌟 Contributors

langflow contributors

📄 License

Langflow is released under the MIT License. See the LICENSE file for details.

Description
Langflow is a powerful tool for building and deploying AI-powered agents and workflows.
Readme MIT 2.3 GiB
Languages
Python 64.5%
TypeScript 23.4%
JavaScript 11.4%
CSS 0.3%
Makefile 0.2%
Other 0.1%