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llm-app: AI Pipelines Made Easy

Get ready-to-run cloud templates for AI pipelines and enterprise search. For startup founders in need of machine learning solutions.
intermediate⏱ 1-2 hours💵 Free
59,070 stars1,432 forksJupyter NotebookQuality 8/10Updated 7/5/2026100% free · open source
What it is

Create ready-to-run cloud templates for AI pipelines and enterprise search with this tool.

What you can make with it

Automations like: when a new document is uploaded to Sharepoint, index it in your enterprise search and alert relevant teams via a chatbot.

How it helps

This tool helps by providing pre-built templates for AI pipelines, saving you time and effort in setting up and deploying your machine learning solutions, and ensuring your data is always in sync across different platforms.

Real use case example

"A founder of a startup needs to quickly set up an enterprise search for their team, so they use this tool to create a ready-to-run cloud template, connect it to their Sharepoint and Google Drive accounts, and then customize the search functionality to meet their specific needs, resulting in a fully functional search system in a short amount of time."

If you're new

A beginner should pick this up when they need to deploy their first AI pipeline and want a simple, guided process.

If you're senior

A senior engineer would reach for this when they need to quickly spin up a new AI pipeline for a proof-of-concept or a small project and don't want to spend time setting up the infrastructure from scratch.

Common confusion cleared up

One common confusion about this tool is that it is not a full-fledged AI model, but rather a template and pipeline tool that helps you deploy and manage your own AI models and data.

Pairs with
Hugging Face modelsDockerKafka
Why we list it on WorkflowStacks: This tool is included in the marketplace because it provides free and open-source templates that can save costs compared to paid alternatives and simplify the process of building and deploying AI pipelines.
What it does

Llm-app provides ready-to-deploy cloud templates for building real-time AI search and data integration across business systems, automating information workflows.

Install / run
git clone https://github.com/pathwaycom/llm-app.git
When to use it
  • •When you need to integrate data from multiple sources like Sharepoint, Google Drive, or PostgreSQL into a unified search system
  • •When automating workflows that require real-time data updates from APIs or event streams like Kafka
  • •When building AI-powered search functionality into your application with live data synchronization
Quick start
  1. 1Navigate into the cloned repository with 'cd llm-app'
  2. 2Pull the Docker image with 'docker pull pathwaycom/llm-app'
  3. 3Run the Docker container with 'docker run -p 8000:8000 pathwaycom/llm-app'
  4. 4Access the application at 'http://localhost:8000' to start configuring your AI search and data integration
  5. 5Modify the configuration files, such as those in the 'config' directory, to connect to your specific data sources
Ready-to-paste prompt
docker run -p 8000:8000 -v $(pwd)/config:/app/config pathwaycom/llm-app --sync-sharepoint --sync-postgres
Heads up: Ensure you have Docker installed and running on your system, as llm-app is designed to be Docker-friendly and requires it to run the application
Saves to your device

Topics

chatbot
hugging-face
llm
llm-local
llm-prompting
llm-security
llmops
machine-learning
open-ai
pathway
rag
real-time
retrieval-augmented-generation
vector-database
vector-index
What's inside — free to inspect
No purchase needed

Read the entire source before you build — unlike paid marketplaces that hide it behind a buy button.

7
top-level files
5
folders
61.1M
repo size
MIT
license
Key files
README.md
File tree
.github/
.vscode/
assets/
cookbooks/
templates/
.gitignore
CODE_OF_CONDUCT.md
CONTRIBUTING.md
LICENSE
pyproject.toml
README.md
setup.cfg
Quick Actions
Details
Creator
pathwaycom
Language
Jupyter Notebook
Category
support
Published
7/19/2023

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