7d183ff57e
feat(chat.py, chat_manager.py): add error handling for async_get_result_and_steps feat(chat.py): add client_id to websocket endpoint feat(schemas.py): add data_type field to ChatResponse schema refactor(run.py): memoize build_langchain_object_with_caching function with maxsize of 10 |
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| .devcontainer | ||
| .github | ||
| docker_example | ||
| img | ||
| src | ||
| tests | ||
| .gitignore | ||
| CODE_OF_CONDUCT.md | ||
| CONTRIBUTING.md | ||
| dev.Dockerfile | ||
| docker-compose.debug.yml | ||
| docker-compose.yml | ||
| Dockerfile | ||
| LICENSE | ||
| Makefile | ||
| poetry.lock | ||
| pyproject.toml | ||
| README.md | ||
⛓️ LangFlow
~ A User Interface For LangChain ~
LangFlow is a GUI for LangChain, designed with react-flow to provide an effortless way to experiment and prototype flows with drag-and-drop components and a chat box.
📦 Installation
You can install LangFlow from pip:
pip install langflow
Next, run:
langflow
🎨 Creating Flows
Creating flows with LangFlow is easy. Simply drag sidebar components onto the canvas and connect them together to create your pipeline. LangFlow provides a range of LangChain components to choose from, including LLMs, prompt serializers, agents, and chains.
Explore by editing prompt parameters, link chains and agents, track an agent's thought process, and export your flow.
Once you're done, you can export your flow as a JSON file to use with LangChain. To do so, click the "Export" button in the top right corner of the canvas, then in Python, you can load the flow with:
from langflow import load_flow_from_json
flow = load_flow_from_json("path/to/flow.json")
# Now you can use it like any chain
flow("Hey, have you heard of LangFlow?")
👋 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.
📄 License
LangFlow is released under the MIT License. See the LICENSE file for details.