Langflow is a powerful tool for building and deploying AI-powered agents and workflows.
http://www.langflow.org
349f3441c8* feat: improve model input fields for huggingface component 1. Make model_id, inference_endpoint and api_token fields required 2. Convert temperature to SliderInput with range 0-2 3. Update temperature info to match HuggingFace's description Co-authored-by: Vinícios Batista da Silva <vinicios.batsi@gmail.com> * feat(huggingface): update model selection and temperature input This commit enhances the HuggingFace component by implementing a fixed list of large language models in the dropdown selection and improving the temperature input with a slider control. Key changes in src/backend/base/langflow/components/models/huggingface.py: - Add DEFAULT_MODEL constant set to "meta-llama/Llama-3.3-70B-Instruct" - Replace model_id StrInput with DropdownInput containing pre-selected models: * meta-llama/Llama-3.3-70B-Instruct (default) * mistralai/Mixtral-8x7B-Instruct-v0.1 * mistralai/Mistral-7B-Instruct-v0.3 * meta-llama/Llama-3.1-8B-Instruct * Qwen/Qwen2.5-Coder-32B-Instruct * Qwen/QwQ-32B-Preview * openai-community/gpt2 * custom option - Add real_time_refresh to model_id dropdown for dynamic updates - Implement custom model input field that shows/hides based on selection - Replace temperature FloatInput with SliderInput for better UX: * Added RangeSpec with min=0, max=2, step=0.01 * Maintains default value of 0.8 - Add build_config update logic to handle custom model visibility - Update API URL generation to support custom model IDs - Import RangeSpec and SliderInput from langflow packages Co-authored-by: Vinícios Batista da Silva <vinicios.batsi@gmail.com> * [autofix.ci] apply automated fixes * test(huggingface): update tests for enhanced model selection and UI controls This commit updates the test suite to verify the new features and changes in the HuggingFace component, ensuring proper functionality of the model selection dropdown and improved temperature control. Key changes in src/backend/tests/unit/components/models/test_huggingface.py: - Update test_huggingface_inputs to verify new DropdownInput for model_id: * Check DEFAULT_MODEL as default value * Verify presence of 'custom' option * Validate required and real_time_refresh settings - Add verification for custom_model field: * Confirm initial custom_model hidden state * Verify required flag - Add specific checks for temperature SliderInput: * Validate default value of 0.8 * Verify RangeSpec configuration (min=0, max=2, step=0.01) - Improve test structure with detailed assertions for field configurations - Update imports to include DEFAULT_MODEL constant Related to previous commit that enhanced the HuggingFace component with fixed model list and slider controls. Co-authored-by: Vinícios Batista da Silva <vinicios.batsi@gmail.com> --------- Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com> |
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Langflow is a low-code app builder for RAG and multi-agent AI applications. It’s Python-based and agnostic to any model, API, or database.
Docs - Free Cloud Service - Self Managed
✨ Core features
- Python-based and agnostic to models, APIs, data sources, or databases.
- Visual IDE for drag-and-drop building and testing of workflows.
- Playground to immediately test and iterate workflows with step-by-step control.
- Multi-agent orchestration and conversation management and retrieval.
- Free cloud service to get started in minutes with no setup.
- Publish as an API or export as a Python application.
- Observability with LangSmith, LangFuse, or LangWatch integration.
- Enterprise-grade security and scalability with free DataStax Langflow cloud service.
- Customize workflows or create flows entirely just using Python.
- Ecosystem integrations as reusable components for any model, API or database.
📦 Quickstart
- Install with uv (recommended) (Python 3.10 to 3.12):
uv pip install langflow
- Install with pip (Python 3.10 to 3.12):
pip install langflow
- Cloud: DataStax Langflow is a hosted environment with zero setup. Sign up for a free account.
- Self-managed: Run Langflow in your environment. Install Langflow to run a local Langflow server, and then use the Quickstart guide to create and execute a flow.
- Hugging Face: Clone the space using this link to create a Langflow workspace.
⭐ Stay up-to-date
Star Langflow on GitHub to be instantly notified of new releases.
👋 Contribute
We welcome contributions from developers of all levels. If you'd like to contribute, please check our contributing guidelines and help make Langflow more accessible.