OrbisDB, Lit Protocol, and Langchain Starter

This is a basic starter project for building with the following tools and APIs:

• Next.js
• LangchainJS
• GPT3
• OrbisDB
• Lit Protocol

What we're building

We are building an app that takes text (text files), embeds them into vectors, stores them into OrbisDB, and allows semantic searching of the data.

We've also enabled data privacy using Lit Protocol to encrypt the corresponding text for each embedding, and programmatically decrypt based on specified access control conditions.

Running the app

In this section I will walk you through how to deploy and run this app.

Prerequisites

To run this app, you need the following:

1. An OpenAI API key
2. A modified [OrbisDB] instance (outlined below)
3. Docker
4. A Lit token ID (also shown below)

Initial Setup

To run the app locally, follow these steps:

1. Clone this repo and install the dependencies

git clone https://github.com/ceramicstudio/orbis-lit-langchain
cd orbis-lit-langchain
yarn install

2. In a separate terminal, clone this modified version of OrbisDB and install the dependencies

git clone https://github.com/mzkrasner/orbisdb
cd orbisdb
npm install

3. In your orbisdb terminal, start the database process

# Ensure that you have your Docker Daemon running in the background first
npm run dev

Your OrbisDB instance will need to initially be configured using the GUI running on localhost:7008. Navigate to this address in your browser and follow these steps:

a. For "Ceramic node URL" enter the following value: https://ceramic-orbisdb-mainnet-direct.hirenodes.io/

This is the default public Ceramic node that the hosted Orbis Studio uses. We will leverage this to avoid the setup of a local Ceramic node

b. For "Ceramic Seed" simply click "generate a new one" and go to the next page

c. For "Database configuration" enter the following:

User=postgres
Database=postgres
Password=postgres
Host=localhost
Port=5432

These are the default configurations your Docker image is using. Entering them here enables you to configure your OrbisDB instance to index Ceramic stream data using your dockerized Postgres instance.

Go to the next page

d. Click next on the presets page (do not select anything)

e. Connect with your Metamask account and click "Get started". Keep the Orbis Studio UI in your browser as we will navigate back to it later

4. Go to your orbis-lit-langchain terminal and copy the example env file

cp .env.example.local .env.local

5. Navigate to your browser running the OrbisDB UI and create a new context. You can call this anything you want. Once saved, click into your new context and copy the value prefixed with "k" into your .env.local file

CONTEXT_ID="<your-context-id>"

Contexts allow developers to organize their data across different applications or projects. When this application uses the Orbis SDK to write embeddings, it will leverage this context when making the write request

6. Next, we will create an OrbisDB seed to self-authenticate onto the Ceramic Network using the Orbis SDK

yarn gen-seed

This is the seed the Orbis SDK will use to self-authenticate your OrbisDB client onto the Ceramic Network in order to perform writes.

Copy only the array of numbers into your .env.local file

# enter as a string like "[2, 19, 140, 10...]"
ORBIS_SEED="your-array-here"

Make sure the final number in your array does not contain a trailing comma

7. Copy an active and funded OpenAI API key into your .env.local file next to OPENAI_API_KEY

8. Choose or create a dummy Metamask address and claim Lit Protocol Testnet tokens using that address by visiting https://chronicle-yellowstone-faucet.getlit.dev/

9. Navigate to https://explorer.litprotocol.com/ in your browser and sign in with the same dummy address as the previous step. Once signed in, click "Mint a new PKP". After minting, copy the value under "Token ID" into your .env.local file

LIT_TOKEN_ID="<your-token-id>"

10. Grab the private key from your dummy Metamask wallet (used in the two steps above) and enter it into your .env.local file

ETHEREUM_PRIVATE_KEY="<your-private-key>"

11. Finally, deploy your OrbisDB data model we will use to create and query via vector search

yarn deploy-model

This create a new "table" in your OrbisDB instance by creating a Ceramic model stream using the model definition found in scripts/deploy-model.mjs on line 15.

Copy the value prefixed with "k" into your .env.local file

TABLE_ID="<your-table-id>"

Running the Application

Now that our environment is configured, run the following to start the application from within your orbis-lit-langchain terminal

yarn dev

Make sure that OrbisDB is still running in your other terminal.

Navigate to localhost:3000 in your browser.

Create embeddings

This repository contains a small portion of the Ceramic Developer Docs (specifically information on Decentralized Identifiers) that the application will use to create encrypted embeddings. Feel free to replace this with other documentation if you wish

Click on "Create index and embeddings" and observe your terminal logs in both your orbisdb and orbis-lit-langchain terminals.

Once finished, your browser console will notify you that the data has been successfully created and loaded into OrbisDB.

Run a query

Since the dataset is limited to special knowledge about DIDs, try the following query

tell me about decentralized identifiers in ceramic

Since this is knowledge contained in the embeddings we just created, your LLM's response will find these embeddings based on cosine similarity search and use it as context in the response (after decrypting the values). You can observe your terminal's logs to see what decrypted context it's using.

Ensure that the dummy wallet you spun up contains 0.000001 ETH or more

Access control

At the moment, very simple access control conditions are being leveraged based on whether the wallet trying to read the data contains >=0.000001 ETH (found in utils)

const accessControlConditions = [
  {
    contractAddress: "",
    standardContractType: "",
    chain: "ethereum",
    method: "eth_getBalance",
    parameters: [":userAddress", "latest"],
    returnValueTest: {
      comparator: ">=",
      value: "1000000000000", // 0.000001 ETH
    },
  },
];

There is a wide array of access control conditions you can use or create. For more information, visit Lit's Access Control documentation.