Caution
This repo has been deprecated and is no longer supported. Instead please see the Knowledge Graph Studio. We are focusing on KG orchestration and management through our KG Studio. The Knowledge Graph Studio has been open-sourced here - https://github.com/whyhow-ai/knowledge-graph-studio.
The WhyHow Knowledge Graph Creation SDK enables you to quickly and easily build automated knowledge graphs tailored to your unique worldview. Instantly build, extend, and query well-scoped KGs with your data.
- Python 3.10 or higher
- OpenAI API key
- Pinecone API key
- Neo4j credentials (username, password, and URL)
You can install the SDK directly from PyPI using pip:
pip install whyhow
# For OpenAI
export OPENAI_API_KEY=<your openai api key>
# For Azure OpenAI
export AZURE_OPENAI_API_KEY=<your azure openai api key>
export AZURE_OPENAI_API_VERSION=<your azure openai api version>
export AZURE_OPENAI_ENDPOINT=<your azure openai api endpoint>
export AZURE_OPENAI_MODEL_NAME=<your azure model name>
export AZURE_OPENAI_EMBEDDING_NAME=<your azure embedding name>
export PINECONE_API_KEY=<your pinecone api key>
export NEO4J_URL=<your neo4j url>
export NEO4J_USER=<your neo4j username>
export NEO4J_PASSWORD=<your neo4j password>Alternatively, you can clone the repo and install the package
git clone [email protected]:whyhow-ai/whyhow.git
cd whyhow
pip install .
# For OpenAI
export OPENAI_API_KEY=<your openai api key>
# For Azure OpenAI
export AZURE_OPENAI_API_KEY=<your azure openai api key>
export AZURE_OPENAI_API_VERSION=<your azure openai api version>
export AZURE_OPENAI_ENDPOINT=<your azure openai api endpoint>
export PINECONE_API_KEY=<your pinecone api key>
export NEO4J_URL=<your neo4j url>
export NEO4J_USER=<your neo4j username>
export NEO4J_PASSWORD=<your neo4j password>Navigate to the examples/.
Import the SDK and initialize the client using your WhyHow API key.
from whyhow import WhyHow
client = WhyHow(api_key=<your whyhow api key>)For Azure Open AI:
from whyhow import WhyHow
client = WhyHow(api_key=<your whyhow api key>, use_azure=True)For an alternative model (for example, healthcare for text extraction):
from whyhow import WhyHow
client = WhyHow(api_key=<your whyhow api key>, model_type='health')Your namespace is a logical grouping of the raw data you upload, the seed concepts you define, and the graphs you create. Namespaces are meant to be tightly scoped to your use case. You can create as many namespaces as you want.
namespace = "harry-potter"
documents = ["files/harry_potter_and_the_philosophers_stone.pdf","files/harry_potter_and_the_chamber_of_secrets.pdf"]
documents_response = client.graph.add_documents(namespace, documents)
print(documents_response)
# Adding your documents
You can create a graph in three different ways. First, you can create a graph using a user-defined schema, giving you complete control over the types of entities and relationships that are extracted and used to build the graph. You can also create a graph using a set of seed questions. In this case, WhyHow will automatically extract entities and relationships that are most applicable to the things you want to know, and construct a graph from these concepts. Or, you can fully deterministically create a graph from structured context in the form of a CSV.
Create graph with schema if...
- Your graph must adhere to a consistent structure.
- You are very familiar with the structure of your raw documents.
- You need comprehensive extraction of concepts across the entire document.
Create graph with seed questions if...
- You are unsure as to which relationships and patterns you'd like to build into your graph.
- You want to build your graph with only the most semantically similar raw data.
Create graph with csv if...
- You alrady know the structure of your data.
- You already have data stored in a table format.
Tell the WhyHow SDK exactly which entities, relationships, and patterns you'd like to extract and build into your graph by defining them in a JSON-based schema.
#schema.json
{
"entities": [
{
"name": "character",
"description": "A person appearing in the book, e.g., Harry Potter, Ron Weasley, Hermione Granger, Albus Dumbledore."
},
{
"name": "object",
"description": "Inanimate items that characters use or interact with, e.g., wand, Philosopher's Stone, Invisibility Cloak, broomstick."
}
...
],
"relations": [
{
"name": "friends with",
"description": "Denotes a friendly relationship between characters."
},
{
"name": "interacts with",
"description": "Describes a scenario in which a character engages with another character, creature, or object."
},
...
],
"patterns": [
{
"head": "character",
"relation": "friends with",
"tail": "character",
"description": "One character is friends with another, e.g., Harry Potter is friends with Ron Weasley."
},
{
"head": "character",
"relation": "interacts with",
"tail": "object",
"description": "A character interacting with an object, e.g., Harry Potter interacts with the Invisibility Cloak."
}
]
}
Using this schema, we extract relevant concepts from your raw data, construct triples, and generate a graph according to the patterns you define.
# Create graph from schema
schema = "files/schema.json"
create_graph_with_schema_response = client.graph.create_graph_from_schema(namespace, schema)
print(create_graph_with_schema_response)
# Creating your graph
Tell the WhyHow SDK what you care about by providing a list of concepts in the form of natural language questions. Using these questions, we create a small ontology to guide extraction of entities and relationships that are most relevant to your use case, then construct a graph.
questions = ["What does Harry wear?","Who is Harry friends with?"]
create_graph_response = client.graph.create_graph(namespace, questions)
print(create_graph_response)
# Creating your graph
Provide a CSV and a schema (or automatically generate one using the generate_schema method) to create a graph. WhyHow will automatically extract entities and relationships from your CSV headers and data.
namespace = "specialists"
documents = ["../examples/assets/specialists.csv"]
schema_file = "../examples/assets/specialists.json"
# Automatically generate a schema
schema = client.graph.generate_schema(documents=documents)
# Create a graph from a CSV and the schema you bring/build
csv_graph = client.graph.create_graph_from_csv(
namespace=namespace, schema_file=schema_file
)
# Query the graph created from csv using specific entities and relations
query = "Who speaks English and live in Houston?"
entities = ["English","Houston"]
relations = ["SPEAKS","LIVE_IN"]
specific_query_response = client.graph.query_graph_specific(
namespace=namespace,
query=query,
entities=entities,
relations=relations,
include_triples=False,
include_chunks=False,
)
WhyHow.AI is building tools to help developers bring more determinism and control to their RAG pipelines using graph structures. If you're thinking about, in the process of, or have already incorporated knowledge graphs in RAG, we’d love to chat at [email protected], or follow our newsletter at WhyHow.AI. Join our discussions about rules, determinism and knowledge graphs in RAG on our Discord.
We appreciate your interest.