Prompts for entity extraction from MSFT GraphRAG project

extract_graph.py

GRAPH_EXTRACTION_PROMPT = """
-Goal-
Given a text document that is potentially relevant to this activity and a list of entity types, identify all entities of those types from the text and all relationships among the identified entities.

-Steps-
1. Identify all entities. For each identified entity, extract the following information:
- entity_name: Name of the entity, capitalized
- entity_type: One of the following types: [{entity_types}]
- entity_description: Comprehensive description of the entity's attributes and activities
Format each entity as ("entity"{{tuple_delimiter}}<entity_name>{{tuple_delimiter}}<entity_type>{{tuple_delimiter}}<entity_description>)

2. From the entities identified in step 1, identify all pairs of (source_entity, target_entity) that are *clearly related* to each other.
For each pair of related entities, extract the following information:
- source_entity: name of the source entity, as identified in step 1
- target_entity: name of the target entity, as identified in step 1
- relationship_description: explanation as to why you think the source entity and the target entity are related to each other
- relationship_strength: an integer score between 1 to 10, indicating strength of the relationship between the source entity and target entity
Format each relationship as ("relationship"{{tuple_delimiter}}<source_entity>{{tuple_delimiter}}<target_entity>{{tuple_delimiter}}<relationship_description>{{tuple_delimiter}}<relationship_strength>)

3. Return output in {language} as a single list of all the entities and relationships identified in steps 1 and 2. Use **{{record_delimiter}}** as the list delimiter.

4. If you have to translate into {language}, just translate the descriptions, nothing else!

5. When finished, output {{completion_delimiter}}.

-Examples-
######################
{examples}

-Real Data-
######################
entity_types: [{entity_types}]
text: {{input_text}}
######################
output:"""