What could be a sample python code for feeding KG to LLM?

Feeding a knowledge graph directly to an LLM like a large language model (LLM) isn't the most straightforward approach. LLMs typically work with textual prompts and instructions. However, you can leverage the knowledge graph to inform the prompts you provide to the LLM:

Here's a code example (illustrative, specific libraries may vary):

Python

import networkx as nx

def get_entity_descriptions(knowledge_graph, entity):

  """

  Extracts information about a specific entity from the knowledge graph.

  This can involve descriptions, properties, or relationships with other entities.

  """

  # Modify this function based on how your knowledge graph stores information

  description = knowledge_graph.nodes[entity].get("description", "")

  related_entities = [n for n in knowledge_graph.neighbors(entity)]

  relations = [knowledge_graph.edges[entity, neighbor]["label"] for neighbor in related_entities]

  related_info = ", ".join([f"{n} ({rel})" for n, rel in zip(related_entities, relations)])

  return f"{entity}: {description} (Related to: {related_info})"

def generate_prompt_with_kg(knowledge_graph, entity, task):

  """

  Generates a prompt for the LLM based on the knowledge graph and the desired task.

  """

  entity_info = get_entity_descriptions(knowledge_graph, entity)

  # Define different prompt templates based on the task (e.g., question answering, summarization)

  if task == "question_answering":

    prompt = f"Given the information: {entity_info}. Answer the following question: {{your_question}}"

  elif task == "summarization":

    prompt = f"Write a concise summary of: {entity_info}"

  # Add your LLM specific call here (replace with actual LLM library call)

  # response = llm_call(prompt)

  # Replace with your LLM specific response processing

  response = "LLM response will be placed here"

  return prompt, response

# Example usage

knowledge_graph = # Load your knowledge graph (replace with your loading method)

entity = "Paris"

task = "question_answering"

question = "What country is Paris located in?"

prompt, response = generate_prompt_with_kg(knowledge_graph, entity, task)

print("Prompt:", prompt)

print("LLM Response:", response)

references:

Gemini