from quality_prompts.prompt import QualityPrompt

directive = "You are given a document and your task..."
additional_information = "In the knowledge graph, ..."
output_formatting = "You will respond with a knowledge graph in..."

prompt = QualityPrompt(
                        directive,
                        additional_information,
                        output_formatting,
                        exemplar_store
                       )

input_text = "list the disorders included in cvd"
prompt.few_shot(input_text=input_text, n_shots=1)

prompt.system2attention(input_text)
print(prompt.compile())

>> You are given a document and your task is to create a knowledge graph from it.
        
In the knowledge graph, entities such as people, places, objects, institutions, topics, ideas, etc. are represented as nodes.
Whereas the relationships and actions between them are represented as edges.

Example input: Cardiovascular disease (CVD) encompasses a spectrum of...
Example output: [{'entity': 'cardiovascular disease (cvd)', 'connections': ...

You will respond with a knowledge graph in the given JSON format:

[
    {"entity" : "Entity_name", "connections" : [
        {"entity" : "Connected_entity_1", "relationship" : "Relationship_with_connected_entity_1},
        {"entity" : "Connected_entity_2", "relationship" : "Relationship_with_connected_entity_2},
        ]
    },
]

prompt.tabular_chain_of_thought_prompting(input_text=input_text)
print(prompt.compile())

Solve the given math problem.
Think through the problem step by step to solve it.
At each step, you have to figure out:
- the step number,
- the sub-question to be answered in that step,
- the thought process of solving that step, and
- the result of solving that step.
Respond in the following markdown table format for each step:
|step|subquestion|process|result|