Mflow Starter Kit

Welcome to the m_flow Starter Repo! This repository is designed to help you get started quickly by providing a structured dataset and pre-built data pipelines using m_flow to build powerful knowledge graphs.

You can use this repo to ingest, process, and visualize data in minutes.

By following this guide, you will:

• Load structured company and employee data
• Utilize pre-built pipelines for data processing
• Perform graph-based search and query operations
• Visualize entity relationships effortlessly on a graph

How to Use This Repo 🛠

Install uv if you don't have it on your system

pip install uv

Install dependencies

uv sync

Setup LLM

Add environment variables to .env file. In case you choose to use OpenAI provider, add just the model and api_key.

LLM_PROVIDER=""
LLM_MODEL=""
LLM_ENDPOINT=""
LLM_API_KEY=""
LLM_API_VERSION=""

EMBEDDING_PROVIDER=""
EMBEDDING_MODEL=""
EMBEDDING_ENDPOINT=""
EMBEDDING_API_KEY=""
EMBEDDING_API_VERSION=""

Activate the Python environment:

source .venv/bin/activate

Run the Default Pipeline

This script runs the memorize pipeline with default settings. It ingests text data, builds a knowledge graph, and allows you to run search queries.

python src/pipelines/default.py

Run the Low-Level Pipeline

This script implements its own pipeline with custom ingestion task. It processes the given JSON data about companies and employees, making it searchable via a graph.

python src/pipelines/low_level.py

Run the Custom Model Pipeline

Custom model uses custom pydantic model for graph extraction. This script categorizes programming languages as an example and visualizes relationships.

python src/pipelines/custom-model.py

Graph Preview

Use the M-Flow web UI to visualize the knowledge graph interactively:

mflow -ui          # Launch the web console at http://localhost:3000

Navigate to the Knowledge Graph page to explore entity relationships and episode structures.

What will you build with m_flow?

• Expand the dataset by adding more structured/unstructured data
• Customize the data model to fit your use case
• Use the search API to build an intelligent assistant
• Visualize knowledge graphs for better insights