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import subprocess
import sys
import time
from typing import List
from distilabel.steps.generators.data import LoadDataFromDicts
from distilabel.steps.expand import ExpandColumns
from distilabel.steps.keep import KeepColumns
from distilabel.steps.tasks.self_instruct import SelfInstruct
from distilabel.steps.tasks.evol_instruct.base import EvolInstruct
from distilabel.llms.huggingface import InferenceEndpointsLLM
from distilabel.pipeline import Pipeline
from distilabel.steps import TextGenerationToArgilla
from dotenv import load_dotenv
from domain import (
DomainExpert,
CleanNumberedList,
create_topics,
create_examples_template,
APPLICATION_DESCRIPTION,
)
load_dotenv()
def define_pipeline(
argilla_api_key: str,
argilla_api_url: str,
argilla_dataset_name: str,
topics: List[str],
perspectives: List[str],
domain_expert_prompt: str,
examples: List[dict],
hub_token: str,
endpoint_base_url: str,
):
"""Define the pipeline for the specific domain."""
terms = create_topics(topics, perspectives)
template = create_examples_template(examples)
with Pipeline("farming") as pipeline:
load_data = LoadDataFromDicts(
name="load_data",
data=[{"input": term} for term in terms],
batch_size=64,
)
llm = InferenceEndpointsLLM(
base_url=endpoint_base_url,
api_key=hub_token,
)
self_instruct = SelfInstruct(
name="self-instruct",
application_description=APPLICATION_DESCRIPTION,
num_instructions=5,
input_batch_size=8,
llm=llm,
)
evol_instruction_complexity = EvolInstruct(
name="evol_instruction_complexity",
llm=llm,
num_evolutions=2,
store_evolutions=True,
input_batch_size=8,
include_original_instruction=True,
input_mappings={"instruction": "question"},
)
expand_instructions = ExpandColumns(
name="expand_columns", columns={"instructions": "question"}
)
cleaner = CleanNumberedList(name="clean_numbered_list")
expand_evolutions = ExpandColumns(
name="expand_columns_evolved",
columns={"evolved_instructions": "evolved_questions"},
)
domain_expert = DomainExpert(
name="domain_expert",
llm=llm,
input_batch_size=8,
input_mappings={"instruction": "evolved_questions"},
output_mappings={"generation": "domain_expert_answer"},
)
domain_expert._system_prompt = domain_expert_prompt
domain_expert._template = template
keep_columns = KeepColumns(
name="keep_columns",
columns=["model_name", "evolved_questions", "domain_expert_answer"],
)
to_argilla = TextGenerationToArgilla(
name="text_generation_to_argilla",
dataset_name=argilla_dataset_name,
dataset_workspace="admin",
api_url=argilla_api_url,
api_key=argilla_api_key,
input_mappings={
"instruction": "evolved_questions",
"generation": "domain_expert_answer",
},
)
load_data.connect(self_instruct)
self_instruct.connect(expand_instructions)
expand_instructions.connect(cleaner)
cleaner.connect(evol_instruction_complexity)
evol_instruction_complexity.connect(expand_evolutions)
expand_evolutions.connect(domain_expert)
domain_expert.connect(keep_columns)
keep_columns.connect(to_argilla)
return pipeline
def serialize_pipeline(
argilla_api_key: str,
argilla_api_url: str,
argilla_dataset_name: str,
topics: List[str],
perspectives: List[str],
domain_expert_prompt: str,
hub_token: str,
endpoint_base_url: str,
pipeline_config_path: str = "pipeline.yaml",
examples: List[dict] = [],
):
"""Serialize the pipeline to a yaml file."""
pipeline = define_pipeline(
argilla_api_key=argilla_api_key,
argilla_api_url=argilla_api_url,
argilla_dataset_name=argilla_dataset_name,
topics=topics,
perspectives=perspectives,
domain_expert_prompt=domain_expert_prompt,
hub_token=hub_token,
endpoint_base_url=endpoint_base_url,
examples=examples,
)
pipeline.save(path=pipeline_config_path, overwrite=True, format="yaml")
def create_pipelines_run_command(
hub_token: str,
argilla_api_key: str,
argilla_api_url: str,
pipeline_config_path: str = "pipeline.yaml",
argilla_dataset_name: str = "domain_specific_datasets",
):
"""Create the command to run the pipeline."""
command_to_run = [
sys.executable,
"-m",
"distilabel",
"pipeline",
"run",
"--config",
pipeline_config_path,
"--param",
f"text_generation_to_argilla.dataset_name={argilla_dataset_name}",
"--param",
f"text_generation_to_argilla.api_key={argilla_api_key}",
"--param",
f"text_generation_to_argilla.api_url={argilla_api_url}",
"--param",
f"self-instruct.llm.api_key={hub_token}",
"--param",
f"evol_instruction_complexity.llm.api_key={hub_token}",
"--param",
f"domain_expert.llm.api_key={hub_token}",
"--ignore-cache",
]
return command_to_run
def run_pipeline(
hub_token: str,
argilla_api_key: str,
argilla_api_url: str,
pipeline_config_path: str = "pipeline.yaml",
argilla_dataset_name: str = "domain_specific_datasets",
):
"""Run the pipeline and yield the output as a generator of logs."""
command_to_run = create_pipelines_run_command(
hub_token=hub_token,
pipeline_config_path=pipeline_config_path,
argilla_dataset_name=argilla_dataset_name,
argilla_api_key=argilla_api_key,
argilla_api_url=argilla_api_url,
)
# Run the script file
process = subprocess.Popen(
args=command_to_run,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
env={"HF_TOKEN": hub_token},
)
while process.stdout and process.stdout.readable():
time.sleep(0.2)
line = process.stdout.readline()
if not line:
break
yield line.decode("utf-8")
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