Langflow is a powerful tool for building and deploying AI-powered agents and workflows.
http://www.langflow.org
5b5eea9895
The changes include: - Moved the `Edge` class to a new `edge` package - Moved the `Graph` class to a new `graph` package - Moved the `Node` class to a new `node` package - Moved the `VectorStoreNode` class to the `node/types.py` module - Moved the `Edge`, `Graph`, and `Node` classes to their respective `base.py` modules - Added an `__init__.py` file to each package to allow for importing of classes - Added a `constants.py` module to the `graph` package to store constants used in the `Graph` class - Refactored the `Graph` class to use the new `Node` and `Edge` classes - Refactored the `Graph` class to use a dictionary to map node types to their respective classes - Refactored the `Graph` class to remove invalid nodes from the graph - Refactored the `Graph` class to handle the LLM node within the graph - Refactored the `Graph` class to build the nodes before building the edges - Refactored the `Graph` class to use the `get_node` method to find nodes by id - Refactored the `Graph` class to use the `get_node_neighbors` method to find the neighbors of a node - Refactored the `Graph` class to use the `get_children_by_node_type` method to find the children of a node based on the node type These changes improve the modularity and maintainability of the `langflow` package by separating the classes into their respective packages and modules. The changes also make it easier to add new node types to the `Graph` class by using a dictionary to map node types to their respective classes. 🚀 feat(node): add Node class to represent a node in the graph 🚀 feat(constants.py): add DIRECT_TYPES constant to represent direct types in a node's template The Node class represents a node in the graph and is responsible for parsing the data and building the module. The DIRECT_TYPES constant is a list of direct types in a node's template. 🚧 chore(types.py): add import statements for typing and Node classes This commit adds import statements for the typing module and the Node class to the types.py file. This is necessary for the code to run properly as it uses these classes and modules. 🚧 chore(loading.py): remove unnecessary import statement This commit removes an unnecessary import statement from the loading.py file. The import statement was causing a circular import error and was not needed for the code to run properly. 🚧 chore(run.py): update import statement for Graph class This commit updates the import statement for the Graph class in the run.py file. The import statement was outdated and was causing an import error. 🚧 chore(conftest.py): update import statement for Graph class This commit updates the import statement for the Graph class in the conftest.py file. The import statement was outdated and was causing an import error. 🚧 chore(test_graph.py): update import statements for Node and Edge classes This commit updates the import statements for the Node and Edge classes in the test_graph.py file. The import statements were outdated and were causing import errors. |
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⛓️ 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
Locally
You can install LangFlow from pip:
pip install langflow
Next, run:
python -m langflow
or
langflow
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.
Deploy Langflow on Jina AI Cloud
Langflow integrates with langchain-serve to provide a one-command deployment to Jina AI Cloud.
Start by installing langchain-serve with
pip install -U langchain-serve
Then, run:
langflow --jcloud
🎉 Langflow server successfully deployed on Jina AI Cloud 🎉
🔗 Click on the link to open the server (please allow ~1-2 minutes for the server to startup): https://<your-app>.wolf.jina.ai/
📖 Read more about managing the server: https://github.com/jina-ai/langchain-serve
Show complete (example) output
🚀 Deploying Langflow server on Jina AI Cloud
╭───────────────────────── 🎉 Flow is available! ──────────────────────────╮
│ │
│ ID langflow-e3dd8820ec │
│ Gateway (Websocket) wss://langflow-e3dd8820ec.wolf.jina.ai │
│ Dashboard https://dashboard.wolf.jina.ai/flow/e3dd8820ec │
│ │
╰──────────────────────────────────────────────────────────────────────────╯
╭──────────────┬──────────────────────────────────────────────────────────────────────────────╮
│ App ID │ langflow-e3dd8820ec │
├──────────────┼──────────────────────────────────────────────────────────────────────────────┤
│ Phase │ Serving │
├──────────────┼──────────────────────────────────────────────────────────────────────────────┤
│ Endpoint │ wss://langflow-e3dd8820ec.wolf.jina.ai │
├──────────────┼──────────────────────────────────────────────────────────────────────────────┤
│ App logs │ dashboards.wolf.jina.ai │
├──────────────┼──────────────────────────────────────────────────────────────────────────────┤
│ Swagger UI │ https://langflow-e3dd8820ec.wolf.jina.ai/docs │
├──────────────┼──────────────────────────────────────────────────────────────────────────────┤
│ OpenAPI JSON │ https://langflow-e3dd8820ec.wolf.jina.ai/openapi.json │
╰──────────────┴──────────────────────────────────────────────────────────────────────────────╯
🎉 Langflow server successfully deployed on Jina AI Cloud 🎉
🔗 Click on the link to open the server (please allow ~1-2 minutes for the server to startup): https://langflow-e3dd8820ec.wolf.jina.ai/
📖 Read more about managing the server: https://github.com/jina-ai/langchain-serve
API Usage
You can use Langflow directly on your browser, or use the API endpoints on Jina AI Cloud to interact with the server.
Show API usage (with python)
import json
import requests
FLOW_PATH = "Time_traveller.json"
# HOST = 'http://localhost:7860'
HOST = 'https://langflow-f1ed20e309.wolf.jina.ai'
API_URL = f'{HOST}/predict'
def predict(message):
with open(FLOW_PATH, "r") as f:
json_data = json.load(f)
payload = {'exported_flow': json_data, 'message': message}
response = requests.post(API_URL, json=payload)
return response.json()
predict('Take me to 1920s Bangalore')
{
"result": "Great choice! Bangalore in the 1920s was a vibrant city with a rich cultural and political scene. Here are some suggestions for things to see and do:\n\n1. Visit the Bangalore Palace - built in 1887, this stunning palace is a perfect example of Tudor-style architecture. It was home to the Maharaja of Mysore and is now open to the public.\n\n2. Attend a performance at the Ravindra Kalakshetra - this cultural center was built in the 1920s and is still a popular venue for music and dance performances.\n\n3. Explore the neighborhoods of Basavanagudi and Malleswaram - both of these areas have retained much of their old-world charm and are great places to walk around and soak up the atmosphere.\n\n4. Check out the Bangalore Club - founded in 1868, this exclusive social club was a favorite haunt of the British expat community in the 1920s.\n\n5. Attend a meeting of the Indian National Congress - founded in 1885, the INC was a major force in the Indian independence movement and held many meetings and rallies in Bangalore in the 1920s.\n\nHope you enjoy your trip to 1920s Bangalore!"
}
Read more about resource customization, cost, and management of Langflow apps on Jina AI Cloud in the langchain-serve repository.
🎨 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.