9dba69cffc
📝 docs(README.md): add a section about joining the Discord server to encourage community engagement and collaboration
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Markdown
289 lines
15 KiB
Markdown
<!-- Title -->
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# ⛓️ Langflow
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~ An effortless way to experiment and prototype [LangChain](https://github.com/hwchase17/langchain) pipelines ~
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<p>
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<img alt="GitHub Contributors" src="https://img.shields.io/github/contributors/logspace-ai/langflow" />
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<img alt="GitHub Last Commit" src="https://img.shields.io/github/last-commit/logspace-ai/langflow" />
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<img alt="" src="https://img.shields.io/github/repo-size/logspace-ai/langflow" />
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<img alt="GitHub Issues" src="https://img.shields.io/github/issues/logspace-ai/langflow" />
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<img alt="GitHub Pull Requests" src="https://img.shields.io/github/issues-pr/logspace-ai/langflow" />
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<img alt="Github License" src="https://img.shields.io/github/license/logspace-ai/langflow" />
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</p>
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<p>
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<a href="https://discord.gg/EqksyE2EX9"><img alt="Discord Server" src="https://dcbadge.vercel.app/api/server/EqksyE2EX9?compact=true&style=flat"/></a>
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<a href="https://huggingface.co/spaces/Logspace/Langflow"><img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="HuggingFace Spaces"></a>
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</p>
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<a href="https://github.com/logspace-ai/langflow">
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<img width="100%" src="https://github.com/logspace-ai/langflow/blob/main/img/langflow-demo.gif?raw=true"></a>
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<p>
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</p>
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# Table of Contents
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- [⛓️ Langflow](#️-langflow)
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- [Table of Contents](#table-of-contents)
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- [📦 Installation](#-installation)
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- [Locally](#locally)
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- [HuggingFace Spaces](#huggingface-spaces)
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- [🖥️ Command Line Interface (CLI)](#️-command-line-interface-cli)
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- [Usage](#usage)
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- [Environment Variables](#environment-variables)
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- [Deployment](#deployment)
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- [Deploy Langflow on Google Cloud Platform](#deploy-langflow-on-google-cloud-platform)
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- [Deploy Langflow on Jina AI Cloud](#deploy-langflow-on-jina-ai-cloud)
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- [API Usage](#api-usage)
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- [Deploy on Railway](#deploy-on-railway)
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- [Deploy on Render](#deploy-on-render)
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- [🎨 Creating Flows](#-creating-flows)
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- [👋 Contributing](#-contributing)
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- [📄 License](#-license)
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# 📦 Installation
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### <b>Locally</b>
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You can install Langflow from pip:
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```shell
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# This installs the package without dependencies for local models
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pip install langflow
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```
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To use local models (e.g llama-cpp-python) run:
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```shell
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pip install langflow[local]
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```
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This will install the following dependencies:
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- [CTransformers](https://github.com/marella/ctransformers)
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- [llama-cpp-python](https://github.com/abetlen/llama-cpp-python)
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- [sentence-transformers](https://github.com/UKPLab/sentence-transformers)
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You can still use models from projects like LocalAI
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Next, run:
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```shell
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python -m langflow
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```
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or
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```shell
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langflow # or langflow --help
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```
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### HuggingFace Spaces
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You can also check it out on [HuggingFace Spaces](https://huggingface.co/spaces/Logspace/Langflow) and run it in your browser! You can even clone it and have your own copy of Langflow to play with.
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# 🖥️ Command Line Interface (CLI)
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Langflow provides a command-line interface (CLI) for easy management and configuration.
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## Usage
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You can run the Langflow using the following command:
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```shell
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langflow [OPTIONS]
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```
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Each option is detailed below:
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- `--help`: Displays all available options.
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- `--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`.
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- `--workers`: Sets the number of worker processes. Can be set using the `LANGFLOW_WORKERS` environment variable. The default is `1`.
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- `--timeout`: Sets the worker timeout in seconds. The default is `60`.
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- `--port`: Sets the port to listen on. Can be set using the `LANGFLOW_PORT` environment variable. The default is `7860`.
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- `--config`: Defines the path to the configuration file. The default is `config.yaml`.
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- `--env-file`: Specifies the path to the .env file containing environment variables. The default is `.env`.
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- `--log-level`: Defines the logging level. Can be set using the `LANGFLOW_LOG_LEVEL` environment variable. The default is `critical`.
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- `--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`.
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- `--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`.
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- `--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`.
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- `--jcloud/--no-jcloud`: Toggles the option to deploy on Jina AI Cloud. The default is `no-jcloud`.
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- `--dev/--no-dev`: Toggles the development mode. The default is `no-dev`.
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- `--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.
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- `--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`.
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- `--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`.
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- `--install-completion [bash|zsh|fish|powershell|pwsh]`: Installs completion for the specified shell.
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- `--show-completion [bash|zsh|fish|powershell|pwsh]`: Shows completion for the specified shell, allowing you to copy it or customize the installation.
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### Environment Variables
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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.
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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.
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# Deployment
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## Deploy Langflow on Google Cloud Platform
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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**](GCP_DEPLOYMENT.md) document.
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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.
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[](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/logspace-ai/langflow&working_dir=scripts&shellonly=true&tutorial=walkthroughtutorial_spot.md)
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## Deploy Langflow on [Jina AI Cloud](https://github.com/jina-ai/langchain-serve)
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Langflow integrates with langchain-serve to provide a one-command deployment to Jina AI Cloud.
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Start by installing `langchain-serve` with
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```bash
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pip install langflow[deploy]
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# or
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pip install -U langchain-serve
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```
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Then, run:
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```bash
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langflow --jcloud
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```
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```text
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🎉 Langflow server successfully deployed on Jina AI Cloud 🎉
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🔗 Click on the link to open the server (please allow ~1-2 minutes for the server to startup): https://<your-app>.wolf.jina.ai/
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📖 Read more about managing the server: https://github.com/jina-ai/langchain-serve
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```
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<details>
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<summary>Show complete (example) output</summary>
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```text
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🚀 Deploying Langflow server on Jina AI Cloud
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╭───────────────────────── 🎉 Flow is available! ──────────────────────────╮
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│ │
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│ ID langflow-e3dd8820ec │
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│ Gateway (Websocket) wss://langflow-e3dd8820ec.wolf.jina.ai │
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│ Dashboard https://dashboard.wolf.jina.ai/flow/e3dd8820ec │
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│ │
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╰──────────────────────────────────────────────────────────────────────────╯
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╭──────────────┬──────────────────────────────────────────────────────────────────────────────╮
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│ App ID │ langflow-e3dd8820ec │
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├──────────────┼──────────────────────────────────────────────────────────────────────────────┤
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│ Phase │ Serving │
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├──────────────┼──────────────────────────────────────────────────────────────────────────────┤
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│ Endpoint │ wss://langflow-e3dd8820ec.wolf.jina.ai │
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├──────────────┼──────────────────────────────────────────────────────────────────────────────┤
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│ App logs │ dashboards.wolf.jina.ai │
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├──────────────┼──────────────────────────────────────────────────────────────────────────────┤
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│ Swagger UI │ https://langflow-e3dd8820ec.wolf.jina.ai/docs │
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├──────────────┼──────────────────────────────────────────────────────────────────────────────┤
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│ OpenAPI JSON │ https://langflow-e3dd8820ec.wolf.jina.ai/openapi.json │
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╰──────────────┴──────────────────────────────────────────────────────────────────────────────╯
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🎉 Langflow server successfully deployed on Jina AI Cloud 🎉
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🔗 Click on the link to open the server (please allow ~1-2 minutes for the server to startup): https://langflow-e3dd8820ec.wolf.jina.ai/
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📖 Read more about managing the server: https://github.com/jina-ai/langchain-serve
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```
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</details>
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#### API Usage
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You can use Langflow directly on your browser, or use the API endpoints on Jina AI Cloud to interact with the server.
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<details>
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<summary>Show API usage (with python)</summary>
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```python
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import requests
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BASE_API_URL = "https://langflow-e3dd8820ec.wolf.jina.ai/api/v1/predict"
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FLOW_ID = "864c4f98-2e59-468b-8e13-79cd8da07468"
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# You can tweak the flow by adding a tweaks dictionary
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# e.g {"OpenAI-XXXXX": {"model_name": "gpt-4"}}
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TWEAKS = {
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"ChatOpenAI-g4jEr": {},
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"ConversationChain-UidfJ": {}
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}
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def run_flow(message: str, flow_id: str, tweaks: dict = None) -> dict:
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"""
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Run a flow with a given message and optional tweaks.
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:param message: The message to send to the flow
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:param flow_id: The ID of the flow to run
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:param tweaks: Optional tweaks to customize the flow
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:return: The JSON response from the flow
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"""
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api_url = f"{BASE_API_URL}/{flow_id}"
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payload = {"message": message}
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if tweaks:
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payload["tweaks"] = tweaks
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response = requests.post(api_url, json=payload)
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return response.json()
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# Setup any tweaks you want to apply to the flow
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print(run_flow("Your message", flow_id=FLOW_ID, tweaks=TWEAKS))
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```
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```json
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{
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"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!"
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}
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```
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</details>
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> Read more about resource customization, cost, and management of Langflow apps on Jina AI Cloud in the **[langchain-serve](https://github.com/jina-ai/langchain-serve)** repository.
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## Deploy on Railway
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[](https://railway.app/template/Emy2sU?referralCode=MnPSdg)
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## Deploy on Render
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<a href="https://render.com/deploy?repo=https://github.com/logspace-ai/langflow/tree/main">
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<img src="https://render.com/images/deploy-to-render-button.svg" alt="Deploy to Render" />
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</a>
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# 🎨 Creating Flows
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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](https://langchain.readthedocs.io/en/latest/reference.html) to choose from, including LLMs, prompt serializers, agents, and chains.
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Explore by editing prompt parameters, link chains and agents, track an agent's thought process, and export your flow.
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Once you're done, you can export your flow as a JSON file to use with LangChain.
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To do so, click the "Export" button in the top right corner of the canvas, then
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in Python, you can load the flow with:
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```python
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from langflow import load_flow_from_json
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flow = load_flow_from_json("path/to/flow.json")
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# Now you can use it like any chain
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flow("Hey, have you heard of Langflow?")
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```
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# 👋 Contributing
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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](./CONTRIBUTING.md) and help make Langflow more accessible.
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---
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Join our [Discord](https://discord.com/invite/EqksyE2EX9) server to ask questions, make suggestions and showcase your projects! 🦾
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<p>
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</p>
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[](https://star-history.com/#logspace-ai/langflow&Date)
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# 📄 License
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Langflow is released under the MIT License. See the LICENSE file for details.
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