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rill-flow
Rill Flow is a high-performance, scalable workflow orchestration engine for distributed workloads and LLMs
Stars: 306
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Rill Flow is a high-performance, scalable distributed workflow orchestration service that supports the execution of tens of millions of tasks per day with task execution latency less than 100ms. It is distributed and supports the orchestration and scheduling of heterogeneous distributed systems. Rill Flow is easy to use, supporting visual process orchestration and plug-in access. It is cloud native, allowing for cloud native container deployment and cloud native function orchestration. Additionally, Rill Flow supports rapid integration of LLM model services.
README:
Rill Flow is a high-performance, scalable distributed workflow orchestration service with the following core features:
- High performance: Supports the execution of tens of millions of tasks per day, with task execution latency less than 100ms
- Distributed: Supports the orchestration and scheduling of heterogeneous distributed systems
- Ease to use: supports visual process orchestration and plug-in access
- Cloud native: Supports cloud native container deployment and cloud native function orchestration
- AIGC: supports rapid integration of LLM model services
Live Demo (sandbox/sandbox)
Before you begin, ensure that the following tools are installed:
- Environment suitable for OSX/Linux
- Docker
- Docker-Compose
Install Rill Flow services on your local environment using Docker-Compose:
git clone https://github.com/weibocom/rill-flow.git
Enter the Docker directory of the Rill-Flow source code and execute the one-click start command:
cd rill-flow/docker
docker-compose up -d
If your system has Docker Compose V2 installed instead of V1, please use docker compose instead of docker-compose. Check if this is the case by running docker compose version. Read more information here.
To check the status of Rill Flow, please execute the following command:
docker-compose ps
Here is the expected output:
Name Command State Ports
------------------------------------------------------------------------------------------------------------------------------------------------------------
rill-flow-mysql docker-entrypoint.sh --bin ... Up 0.0.0.0:3306->3306/tcp, 33060/tcp
rillflow_cache_1 docker-entrypoint.sh redis ... Up 6379/tcp
rillflow_jaeger_1 /go/bin/all-in-one-linux Up 14250/tcp, 14268/tcp, 0.0.0.0:16686->16686/tcp, 5775/udp, 5778/tcp, 6831/udp, 6832/udp
rillflow_rill-flow_1 catalina.sh run Up 0.0.0.0:8080->8080/tcp
rillflow_sample-executor_1 uvicorn main:app --host 0. ... Up
rillflow_ui_1 /docker-entrypoint.sh /bin ... Up 0.0.0.0:80->80/tcp
If your actual output matches the expected output, it means that Rill Flow has been successfully installed.
After the command is successfully executed, you can access the Rill Flow management background at http://localhost (admin/admin). If the server is deployed, use the server IP address for access (port 80 by default).
- Step 1: Open the Rill Flow management background, click the 'Flow Definition' menu, enter the 'Flow Definition List' page, click the 'Create' button
- Step 2: After entering the 'Flow Graph Edit' page, open the 'one-click import' switch, copy the following yaml file content into the text box, click the 'Submit' button, you can submit a simple flowchart.
version: 1.0.0
workspace: rillFlowSimple
dagName: greet
alias: release
type: flow
inputSchema: >-
[{"required":true,"name":"Bob","type":"String"},{"required":true,"name":"Alice","type":"String"}]
tasks:
- category: function
name: Bob
resourceName: http://sample-executor:8000/greet.json?user=Bob
pattern: task_sync
tolerance: false
next: Alice
inputMappings:
- source: "$.context.Bob"
target: "$.input.Bob"
- category: function
name: Alice
resourceName: http://sample-executor:8000/greet.json?user=Alice
pattern: task_sync
tolerance: false
inputMappings:
- source: "$.context.Alice"
target: "$.input.Alice"
- Step 3: Submit the flow graph to execute the task
Click the 'Test' button, fill in the required parameters, and click the 'Submit' button.
- Step 4: Viewing the execution Result Click the 'Submit' button in the previous step and you will automatically jump to the execution details page. You can view the execution status and details by clicking the 'Execution Records' button.
More instructions on viewing results can be found in Execution Status
The following are contributors to the project along with their GitHub links:
- axb (@qdaxb) Maintainer
- techlog (@techloghub) Maintainer
- ch15084 (@ch15084) Maintainer
- Ocean (@hhh041)
- xilong-t (@xilong-t)
- qfl (@qiaofenlin)
- Kylen (@Kylen)
- zzfzzf (@zzfzzf)
- feifei (@feifei325)
- moqimoqidea (@moqimoqidea)
- Guo, Jiansheng (@guojiansheng0925)
Rill Flow is an open-source project under the Apache License 2.0.
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ai-guide
This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.
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classifai
Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.
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chatbot-ui
Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.
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BricksLLM
BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students
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uAgents
uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.
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griptape
Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.