Structured generation with instructor¶
Answer instructions with knowledge graphs defined as pydantic.BaseModel objects using instructor in distilabel.
This script makes use of MistralLLM and the structured output capabilities thanks to instructor to generate knowledge graphs from complex topics.
This example is translated from this awesome example from instructor cookbook.
structured_generation_with_instructor.py
# Copyright 2023-present, Argilla, Inc.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
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from typing import List
from pydantic import BaseModel, Field
from distilabel.llms import MistralLLM
from distilabel.pipeline import Pipeline
from distilabel.steps import LoadDataFromDicts
from distilabel.steps.tasks import TextGeneration
class Node(BaseModel):
id: int
label: str
color: str
class Edge(BaseModel):
source: int
target: int
label: str
color: str = "black"
class KnowledgeGraph(BaseModel):
nodes: List[Node] = Field(..., default_factory=list)
edges: List[Edge] = Field(..., default_factory=list)
with Pipeline(
name="Knowledge-Graphs",
description=(
"Generate knowledge graphs to answer questions, this type of dataset can be used to "
"steer a model to answer questions with a knowledge graph."
),
) as pipeline:
sample_questions = [
"Teach me about quantum mechanics",
"Who is who in The Simpsons family?",
"Tell me about the evolution of programming languages",
]
load_dataset = LoadDataFromDicts(
name="load_instructions",
data=[
{
"system_prompt": "You are a knowledge graph expert generator. Help me understand by describing everything as a detailed knowledge graph.",
"instruction": f"{question}",
}
for question in sample_questions
],
)
text_generation = TextGeneration(
name="knowledge_graph_generation",
llm=MistralLLM(
model="open-mixtral-8x22b", structured_output={"schema": KnowledgeGraph}
),
input_batch_size=8,
output_mappings={"model_name": "generation_model"},
)
load_dataset >> text_generation
if __name__ == "__main__":
distiset = pipeline.run(
parameters={
text_generation.name: {
"llm": {"generation_kwargs": {"max_new_tokens": 2048}}
}
},
use_cache=False,
)
distiset.push_to_hub("distilabel-internal-testing/knowledge_graphs")
Visualizing the graphs
Want to see how to visualize the graphs? You can test it using the following script. Generate some samples on your own and take a look:
Note
This example uses graphviz to render the graph, you can install with pip in the following way:
