# Create a local environment$ poetry config virtualenvs.create false --local# Install dependencies.$ poetry install

ontology = Ontology(# labels of the entities to be extracted. Can be a string or an object, like the following.labels=[{"Person": "Person name without any adjectives, Remember a person may be references by their name or using a pronoun"},{"Object": "Do not add the definite article 'the' in the object name"},{"Event": "Event event involving multiple people. Do not include qualifiers or verbs like gives, leaves, works etc."},"Place","Document","Organisation","Action",{"Miscellanous": "Any important concept can not be categorised with any other given label"},],# Relationships that are important for your application.# These are more like instructions for the LLM to nudge it to focus on specific relationships.# There is no guarentee that only these relationships will be extracted, but some models do a good job overall at sticking to these relations.relationships=["Relation between any pair of Entities",],)

## Pydantic document modelclass Document(BaseModel):text: strmetadata: dict

from graph_maker import GraphMaker, Ontology, GroqClientfrom graph_maker import Document
## Select a groq supported model## model = "mixtral-8x7b-32768"model ="llama3-8b-8192"## model = "llama3-70b-8192"## model="gemma-7b-it" ## This is probably the fastest of all models, though a tad inaccurate.
llm = GroqClient(model=model, temperature=0.1, top_p=0.5)graph_maker = GraphMaker(ontology=ontology, llm_client=llm, verbose=False)
## create a graph out of a list of Documents.graph = graph_maker.from_documents(list(docs),delay_s_between=10 ## delay_s_between because otherwise groq api maxes out pretty fast.)## result -> a list of Edges.print("Total number of Edges", len(graph))## 1503

class Node(BaseModel):label: strname: str
class Edge(BaseModel):node_1: Nodenode_2: Noderelationship: strmetadata: dict = {}order: Union[int, None] = None