Description

Welcome to the WhyHow Knowledge Graph Studio! This platform makes it easy to create and manage RAG-native knowledge graphs and offers features like rule-based entity resolution, modular graph construction, flexible data ingestion, and an API-first design with a supporting SDK (check out our code examples). Whether you’re working with structured or unstructured data, building exploratory graphs or highly schema-constrained graphs, this platform is built for scalability and flexibility, enabling you to build dynamic graph-enabled AI workflows, ideal for both experimentation and large-scale use.

This platform is built on top of a NoSQL database. NoSQL data stores like MongoDB are a powerful choice for building knowledge graphs, offering a flexible, scalable storage layer that enable fast data retrieval, easy traversal of complex relationships, and a familiar interface for developers.

We are aiming to be database agnostic and also working with a number of other partners to bring similar capabilities to other relational and graph databases.

Check out our case studies and articles on the WhyHow Blog. (Here's a link to the open sourcing announcement.)

We also have a parallel open-source triple creation tool, Knowledge Table to check out

Demo

demo-vid.mov

Check out the graph here. We built this demo using an Amazon 10k.

Installation

To install the package you can first clone the repo

This client requires Python version 3.10 or higher.

$ git clone [email protected]:whyhow-ai/knowledge-graph-studio.git
$ cd knowledge-graph-studio
$ pip install .

If you are a developer you probably want to use an editable install. Additionally, you need to install development and documentation dependencies.

$ pip install -e .[dev,docs]

Quickstart

1. Pre-requisites

In order to get started with the WhyHow API with this quickstart, you will need the following:

• OpenAI API key
• MongoDB account
  • You must create a project and cluster in MongoDB Atlas (dedicated M10+ recommended for best performance)

2. Configuration

Environment Variables Copy the .env.example file to .env and update the values per your environment. To get started with this version, you need to provide values for mongodb, openai.

$ cp .env.sample .env

To get started, you must configure, at minimum, the following enviroinment variables:

WHYHOW__EMBEDDING__OPENAI__API_KEY=<your openai api key>
WHYHOW__GENERATIVE__OPENAI__API_KEY=<your openai api key - can be the same>
WHYHOW__MONGODB__USERNAME=<your altas database username>
WHYHOW__MONGODB__PASSWORD=<your altas database password>
WHYHOW__MONGODB__DATABASE_NAME=main
WHYHOW__MONGODB__HOST=<your altas host i.e. 'xxx.xxx.mongodb.net'>

Create Collections

Once you have configured your environment variables, you must create the database, collections, and indexes in your Atlas cluster. To simplify this, we have included a cli script in src/whyhow_api/cli/. To set this up, run the following:

$ cd src/whyhow_api/cli/
$ python admin.py setup-collections --config-file collection_index_config.json

This script will create 11 collections: chunk, document, graph, node, query, rule, schema, task, triple, user, and workspace. To verify, browse your collections in your MongoDB Atlas browser, or MongoDB Compass.

Create User

Important

Once you create the user below, copy the API Key as you will need this to communicate with the backend via the SDK.

Once the collections have been configured, you must create a user and an API key. We have included a cli script for this as well. To create the user, run the following from src/whyhow_api/cli/:

$ python admin.py create-user --email <your email address> --openai-key <your openai api key>

# User created with email: <email>, API Key: <whyhow api key>

Once the user creation completes successfully, you should see a message that includes your email address and WhyHow API key. You should copy this key and use this to configure the SDK.

3. Launching the API

One the configuration is complete, you can start the API server by running the following:

$ uvicorn src.whyhow_api.main:app

Note that there is a utility script whyhow-locate that will generate the full path.

$ uvicorn $(whyhow-locate)

You can then navigate to http://localhost:8000/docs to see the Swagger UI.

4. Test Locally

Install Python SDK

$ pip install whyhow

Configure and run

Important

Configure your WhyHow client using the API Key you created in step 2.

from whyhow import WhyHow, Triple, Node, Chunk, Relation

# Configure WhyHow client
client = WhyHow(api_key='<your whyhow api key>', base_url="http://localhost:8000")

# Create workspace
workspace = client.workspaces.create(name="Demo Workspace")

# Create chunk(s)
chunk = client.chunks.create(
    workspace_id=workspace.workspace_id,
    chunks=[Chunk(
        content="preneur and visionary, Sam Altman serves as the CEO of OpenAI, leading advancements in artifici"
    )]
)

# Create triple(s)
triples = [
    Triple(
        head=Node(
            name="Sam Altman",
            label="Person",
            properties={"title": "CEO"}
        ),
        relation=Relation(
            name="runs",
        ),
        tail=Node(
            name="OpenAI",
            label="Business",
            properties={"market cap": "$157 Billion"}
        ),
        chunk_ids=[c.chunk_id for c in chunk]
    )
]

# Create graph
graph = client.graphs.create_graph_from_triples(
    name="Demo Graph",
    workspace_id=workspace.workspace_id,
    triples=triples
)

# Query graph
query = client.graphs.query_unstructured(
    graph_id=graph.graph_id,
    query="Who runs OpenAI?"
)

Docker

You can also run the server using Docker. Once you have completed steps 1 and 2 of the Quickstart, you can build and run the Knowlwedge Graph Studio backend using Docker.

Building the image

We assume that the image tag is v1 (modify based on your needs)

$ docker build --platform=linux/amd64 -t kg_engine:v1 .

Running the image

$ OUTSIDE_PORT=1234
$ docker run -it --rm -p $OUTSIDE_PORT:8000 kg_engine:v1