OrbisDB Python Starter

This repository is a modified version of the Index Network Ceramic-Python library that includes examples and slight alterations to be compatible with OrbisDB.

Overview

This demo repository is structured as a simple Flask server that exposes a class called CeramicActions found in examples.py. This example architecture emulates an AI agent environment and assigns three imaginary agents individual Ceramic private keys to author data to the network.

Additionally, for no reason in particular, the demo uses a pageview data model. This has already been deployed to Ceramic, and is therefore provided in the example .env file as the default table value.

Finally, this demo only uses raw SQL queries to read data. However, OrbisDB also exposes a GraphQL endpoint for read capabilities (that you can easily incorporate into your code if preferred).

Getting Started

1. Create a copy of the example env file:

cp .env.example .env

2. OrbisDB Setup

To make things simple, we will use the hosted OrbisDB Studio and the shared node instance it provides for this demo, but keep in mind that you can set up your separate instance whenever you want (more details at OrbisDB).

First, sign in with your wallet.

Once signed in, the studio will default to the Contexts tab at the top. On the right-hand side, you will see the shared node endpoints (already provided for you in your env file), as well as your environment ID. Go ahead and assign that value to ENV_ID in your new .env file.

Next, set up a context. These help developers segment their data models and usage based on the applications they are meant for. Create a new context (call it whatever you like), and assign the resulting string to CONTEXT_ID in your .env file.

3. Create your virtual environment and install dependencies:

python3 -m venv myenv
source myenv/bin/activate
pip3 install -r requirements.txt

4. Create and assign private Ceramic seeds to each of the agents in your .env file:

python3 seeds.py

Copy the strings corresponding to each agent from your terminal log and assign to each agent.

5. Finally, start your server:

python3 server-example.py

Your server will now be running on http://127.0.0.1:5000/

Reading and Creating Data

You can reference the pseudocode provided in the server-example.py file. For example, creating a document:

curl -X POST "http://127.0.0.1:5000/create_document?agent=agent_three" \
     -H "Content-Type: application/json" \
     -d '{"page": "/home", "address": "0x8071f6F971B438f7c0EA72C950430EE7655faBCe", "customer_user_id": 3}'