使用 instructor 进行结构化生成¶
使用 distilabel 中的 instructor，用定义为 pydantic.BaseModel 对象的知识图回答指令。
此脚本使用 MistralLLM 和结构化输出功能，这要归功于 instructor，从复杂主题生成知识图。
此示例从 instructor 食谱中的这个 精彩示例 翻译而来。
structured_generation_with_instructor.py
# Copyright 2023-present, Argilla, Inc.
#
# 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
#
# https://apache.ac.cn/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import List
from pydantic import BaseModel, Field
from distilabel.models 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")
