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import random
import uuid
from tqdm import tqdm
from typing import Union
import argilla as rg
import gradio as gr
import nltk
import pandas as pd
from datasets import (
Dataset,
get_dataset_config_names,
get_dataset_split_names,
load_dataset,
)
from distilabel.distiset import Distiset
from gradio.oauth import OAuthToken
from gradio_huggingfacehub_search import HuggingfaceHubSearch
from huggingface_hub import HfApi
from unstructured.chunking.title import chunk_by_title
from unstructured.partition.auto import partition
from synthetic_dataset_generator.apps.base import (
combine_datasets,
get_iframe,
hide_success_message,
push_pipeline_code_to_hub,
show_success_message,
test_max_num_rows,
validate_argilla_user_workspace_dataset,
validate_push_to_hub,
)
from synthetic_dataset_generator.constants import DEFAULT_BATCH_SIZE
from synthetic_dataset_generator.pipelines.base import get_rewritten_prompts
from synthetic_dataset_generator.pipelines.embeddings import (
get_embeddings,
get_sentence_embedding_dimensions,
)
from synthetic_dataset_generator.pipelines.rag import (
DEFAULT_DATASET_DESCRIPTIONS,
get_chunks_generator,
get_prompt_generator,
generate_pipeline_code,
get_sentence_pair_generator,
get_response_generator,
)
from synthetic_dataset_generator.utils import (
column_to_list,
get_argilla_client,
get_org_dropdown,
get_random_repo_name,
swap_visibility,
)
nltk.download("punkt_tab")
nltk.download("averaged_perceptron_tagger_eng")
def _get_valid_columns(dataframe: pd.DataFrame):
doc_valid_columns = []
for col in dataframe.columns:
sample_val = dataframe[col].iloc[0]
if isinstance(sample_val, str):
doc_valid_columns.append(col)
return doc_valid_columns
def _load_dataset_from_hub(
repo_id: str,
num_rows: int = 10,
token: Union[OAuthToken, None] = None,
progress=gr.Progress(track_tqdm=True),
):
if not repo_id:
raise gr.Error("Hub repo id is required")
subsets = get_dataset_config_names(repo_id, token=token)
splits = get_dataset_split_names(repo_id, subsets[0], token=token)
ds = load_dataset(repo_id, subsets[0], split=splits[0], token=token, streaming=True)
rows = []
for idx, row in enumerate(tqdm(ds, desc="Loading the dataset", total=num_rows)):
rows.append(row)
if idx == num_rows:
break
ds = Dataset.from_list(rows)
dataframe = ds.to_pandas()
doc_valid_columns = _get_valid_columns(dataframe)
col_doc = doc_valid_columns[0] if doc_valid_columns else ""
return (
dataframe,
gr.Dropdown(
choices=doc_valid_columns,
label="Documents column",
value=col_doc,
interactive=(False if col_doc == "" else True),
multiselect=False,
),
)
def _preprocess_input_data(file_paths, num_rows, progress=gr.Progress(track_tqdm=True)):
data = {}
total_chunks = 0
for file_path in tqdm(file_paths, desc="Processing files", total=len(file_paths)):
partitioned_file = partition(filename=file_path)
chunks = [str(chunk) for chunk in chunk_by_title(partitioned_file)]
data[file_path] = chunks
total_chunks += len(chunks)
if total_chunks >= num_rows:
break
dataframe = pd.DataFrame.from_records(
[(k, v) for k, values in data.items() for v in values],
columns=["filename", "chunks"],
)
col_doc = "chunks"
return (
dataframe,
gr.Dropdown(
choices=["chunks"],
label="Documents column",
value=col_doc,
interactive=(False if col_doc == "" else True),
multiselect=False,
),
)
def generate_system_prompt(dataset_description, progress=gr.Progress()):
progress(0.1, desc="Initializing")
generate_description = get_prompt_generator()
progress(0.5, desc="Generating")
result = next(
generate_description.process(
[
{
"instruction": dataset_description,
}
]
)
)[0]["generation"]
progress(1.0, desc="Prompt generated")
return result
def load_dataset_file(
repo_id: str,
file_paths: list[str],
input_type: str,
num_rows: int = 10,
token: Union[OAuthToken, None] = None,
progress=gr.Progress(),
):
progress(0.1, desc="Loading the source data")
if input_type == "dataset-input":
return _load_dataset_from_hub(repo_id, num_rows, token)
else:
return _preprocess_input_data(file_paths, num_rows)
def generate_dataset(
input_type: str,
dataframe: pd.DataFrame,
system_prompt: str,
document_column: str,
retrieval: bool = False,
reranking: bool = False,
num_rows: int = 10,
temperature: float = 0.7,
is_sample: bool = False,
progress=gr.Progress(),
):
num_rows = test_max_num_rows(num_rows)
progress(0.0, desc="Initializing dataset generation")
if input_type == "prompt-input":
chunk_generator = get_chunks_generator(
temperature=temperature, is_sample=is_sample
)
else:
document_data = column_to_list(dataframe, document_column)
if len(document_data) < num_rows:
document_data += random.choices(
document_data, k=num_rows - len(document_data)
)
retrieval_generator = get_sentence_pair_generator(
action="query",
triplet=True if retrieval else False,
temperature=temperature,
is_sample=is_sample,
)
response_generator = get_response_generator(
temperature=temperature, is_sample=is_sample
)
if reranking:
reranking_generator = get_sentence_pair_generator(
action="semantically-similar",
triplet=True,
temperature=temperature,
is_sample=is_sample,
)
steps = 2 + sum([1 if reranking else 0, 1 if input_type == "prompt-type" else 0])
total_steps: int = num_rows * steps
step_progress = round(1 / steps, 2)
batch_size = DEFAULT_BATCH_SIZE
# generate chunks
if input_type == "prompt-input":
n_processed = 0
chunk_results = []
rewritten_system_prompts = get_rewritten_prompts(system_prompt, num_rows)
while n_processed < num_rows:
progress(
step_progress * n_processed / num_rows,
total=total_steps,
desc="Generating chunks",
)
remaining_rows = num_rows - n_processed
batch_size = min(batch_size, remaining_rows)
inputs = [
{"task": random.choice(rewritten_system_prompts)}
for _ in range(batch_size)
]
chunks = list(chunk_generator.process(inputs=inputs))
chunk_results.extend(chunks[0])
n_processed += batch_size
random.seed(a=random.randint(0, 2**32 - 1))
document_data = [chunk["generation"] for chunk in chunk_results]
progress(step_progress, desc="Generating chunks")
# generate questions
n_processed = 0
retrieval_results = []
while n_processed < num_rows:
progress(
step_progress * n_processed / num_rows,
total=total_steps,
desc="Generating questions",
)
remaining_rows = num_rows - n_processed
batch_size = min(batch_size, remaining_rows)
inputs = [
{"anchor": document}
for document in document_data[n_processed : n_processed + batch_size]
]
questions = list(retrieval_generator.process(inputs=inputs))
retrieval_results.extend(questions[0])
n_processed += batch_size
for result in retrieval_results:
result["context"] = result["anchor"]
if retrieval:
result["question"] = result["positive"]
result["positive_retrieval"] = result.pop("positive")
result["negative_retrieval"] = result.pop("negative")
else:
result["question"] = result.pop("positive")
progress(step_progress, desc="Generating questions")
# generate responses
n_processed = 0
response_results = []
while n_processed < num_rows:
progress(
step_progress + step_progress * n_processed / num_rows,
total=total_steps,
desc="Generating responses",
)
batch = retrieval_results[n_processed : n_processed + batch_size]
responses = list(response_generator.process(inputs=batch))
response_results.extend(responses[0])
n_processed += batch_size
for result in response_results:
result["response"] = result["generation"]
progress(step_progress, desc="Generating responses")
# generate reranking
if reranking:
n_processed = 0
reranking_results = []
while n_processed < num_rows:
progress(
step_progress * n_processed / num_rows,
total=total_steps,
desc="Generating reranking data",
)
batch = response_results[n_processed : n_processed + batch_size]
batch = list(reranking_generator.process(inputs=batch))
reranking_results.extend(batch[0])
n_processed += batch_size
for result in reranking_results:
result["positive_reranking"] = result.pop("positive")
result["negative_reranking"] = result.pop("negative")
progress(
1,
total=total_steps,
desc="Creating dataset",
)
# create distiset
distiset_results = []
source_results = reranking_results if reranking else response_results
base_keys = ["context", "question", "response"]
retrieval_keys = ["positive_retrieval", "negative_retrieval"] if retrieval else []
reranking_keys = ["positive_reranking", "negative_reranking"] if reranking else []
relevant_keys = base_keys + retrieval_keys + reranking_keys
for result in source_results:
record = {key: result.get(key) for key in relevant_keys if key in result}
distiset_results.append(record)
dataframe = pd.DataFrame(distiset_results)
progress(1.0, desc="Dataset generation completed")
return dataframe
def generate_sample_dataset(
repo_id: str,
file_paths: list[str],
input_type: str,
system_prompt: str,
document_column: str,
retrieval_reranking: list[str],
num_rows: str,
oauth_token: Union[OAuthToken, None],
progress=gr.Progress(),
):
retrieval = "Retrieval" in retrieval_reranking
reranking = "Reranking" in retrieval_reranking
if input_type == "prompt-input":
dataframe = pd.DataFrame(columns=["context", "question", "response"])
else:
dataframe, _ = load_dataset_file(
repo_id=repo_id,
file_paths=file_paths,
input_type=input_type,
num_rows=num_rows,
token=oauth_token,
)
progress(0.5, desc="Generating dataset")
dataframe = generate_dataset(
input_type=input_type,
dataframe=dataframe,
system_prompt=system_prompt,
document_column=document_column,
retrieval=retrieval,
reranking=reranking,
num_rows=10,
is_sample=True,
)
return dataframe
def push_dataset_to_hub(
dataframe: pd.DataFrame,
org_name: str,
repo_name: str,
oauth_token: Union[gr.OAuthToken, None],
private: bool,
pipeline_code: str,
progress=gr.Progress(),
):
progress(0.0, desc="Validating")
repo_id = validate_push_to_hub(org_name, repo_name)
progress(0.5, desc="Creating dataset")
dataset = Dataset.from_pandas(dataframe)
dataset = combine_datasets(repo_id, dataset, oauth_token)
distiset = Distiset({"default": dataset})
progress(0.9, desc="Pushing dataset")
distiset.push_to_hub(
repo_id=repo_id,
private=private,
include_script=False,
token=oauth_token.token,
create_pr=False,
)
push_pipeline_code_to_hub(pipeline_code, org_name, repo_name, oauth_token)
progress(1.0, desc="Dataset pushed")
return dataframe
def push_dataset(
org_name: str,
repo_name: str,
private: bool,
original_repo_id: str,
file_paths: list[str],
input_type: str,
system_prompt: str,
document_column: str,
retrieval_reranking: list[str],
num_rows: int,
temperature: float,
pipeline_code: str,
oauth_token: Union[gr.OAuthToken, None] = None,
progress=gr.Progress(),
) -> pd.DataFrame:
retrieval = "Retrieval" in retrieval_reranking
reranking = "Reranking" in retrieval_reranking
if input_type == "prompt-input":
dataframe = pd.DataFrame(columns=["context", "question", "response"])
else:
dataframe, _ = load_dataset_file(
repo_id=original_repo_id,
file_paths=file_paths,
input_type=input_type,
num_rows=num_rows,
token=oauth_token,
)
progress(0.5, desc="Generating dataset")
dataframe = generate_dataset(
input_type=input_type,
dataframe=dataframe,
system_prompt=system_prompt,
document_column=document_column,
retrieval=retrieval,
reranking=reranking,
num_rows=num_rows,
temperature=temperature,
is_sample=True,
)
push_dataset_to_hub(
dataframe, org_name, repo_name, oauth_token, private, pipeline_code
)
dataframe = dataframe[
dataframe.applymap(
lambda x: str(x).strip() if pd.notna(x) else x
).apply(
lambda row: row.notna().all() and (row != "").all(), axis=1
)
]
try:
progress(0.1, desc="Setting up user and workspace")
hf_user = HfApi().whoami(token=oauth_token.token)["name"]
client = get_argilla_client()
if client is None:
return ""
progress(0.5, desc="Creating dataset in Argilla")
fields = [
rg.TextField(
name="context",
title="Context",
description="Context for the generation",
),
rg.ChatField(
name="chat",
title="Chat",
description="User and assistant conversation based on the context",
),
]
for item in ["positive", "negative"]:
if retrieval:
fields.append(
rg.TextField(
name=f"{item}_retrieval",
title=f"{item.capitalize()} retrieval",
description=f"The {item} query for retrieval",
)
)
if reranking:
fields.append(
rg.TextField(
name=f"{item}_reranking",
title=f"{item.capitalize()} reranking",
description=f"The {item} query for reranking",
)
)
questions = [
rg.LabelQuestion(
name="relevant",
title="Are the question and response relevant to the given context?",
labels=["yes", "no"],
),
rg.LabelQuestion(
name="is_response_correct",
title="Is the response correct?",
labels=["yes", "no"],
),
]
for item in ["positive", "negative"]:
if retrieval:
questions.append(
rg.LabelQuestion(
name=f"is_{item}_retrieval_relevant",
title=f"Is the {item} retrieval relevant?",
labels=["yes", "no"],
required=False,
)
)
if reranking:
questions.append(
rg.LabelQuestion(
name=f"is_{item}_reranking_relevant",
title=f"Is the {item} reranking relevant?",
labels=["yes", "no"],
required=False,
)
)
metadata = [
rg.IntegerMetadataProperty(
name=f"{item}_length", title=f"{item.capitalize()} length"
)
for item in ["context", "question", "response"]
]
vectors = [
rg.VectorField(
name=f"{item}_embeddings",
dimensions=get_sentence_embedding_dimensions(),
)
for item in ["context", "question", "response"]
]
settings = rg.Settings(
fields=fields,
questions=questions,
metadata=metadata,
vectors=vectors,
guidelines="Please review the conversation and provide an evaluation.",
)
dataframe["chat"] = dataframe.apply(
lambda row: [
{"role": "user", "content": row["question"]},
{"role": "assistant", "content": row["response"]},
],
axis=1,
)
for item in ["context", "question", "response"]:
dataframe[f"{item}_length"] = dataframe[item].apply(
lambda x: len(x) if x is not None else 0
)
dataframe[f"{item}_embeddings"] = get_embeddings(
dataframe[item].apply(lambda x: x if x is not None else "").to_list()
)
rg_dataset = client.datasets(name=repo_name, workspace=hf_user)
if rg_dataset is None:
rg_dataset = rg.Dataset(
name=repo_name,
workspace=hf_user,
settings=settings,
client=client,
)
rg_dataset = rg_dataset.create()
progress(0.7, desc="Pushing dataset to Argilla")
hf_dataset = Dataset.from_pandas(dataframe)
rg_dataset.records.log(records=hf_dataset)
progress(1.0, desc="Dataset pushed to Argilla")
except Exception as e:
raise gr.Error(f"Error pushing dataset to Argilla: {e}")
return ""
def show_system_prompt_visibility():
return {system_prompt: gr.Textbox(visible=True)}
def hide_system_prompt_visibility():
return {system_prompt: gr.Textbox(visible=False)}
def show_document_column_visibility():
return {document_column: gr.Dropdown(visible=True)}
def hide_document_column_visibility():
return {
document_column: gr.Dropdown(
choices=["Load your data first in step 1."],
value="Load your data first in step 1.",
visible=False,
)
}
def show_pipeline_code_visibility():
return {pipeline_code_ui: gr.Accordion(visible=True)}
def hide_pipeline_code_visibility():
return {pipeline_code_ui: gr.Accordion(visible=False)}
######################
# Gradio UI
######################
with gr.Blocks() as app:
with gr.Column() as main_ui:
gr.Markdown("## 1. Select your input")
with gr.Row(equal_height=False):
with gr.Column(scale=2):
input_type = gr.Dropdown(
label="Input type",
choices=["dataset-input", "file-input", "prompt-input"],
value="dataset-input",
multiselect=False,
visible=False,
)
with gr.Tab("Load from Hub") as tab_dataset_input:
with gr.Row(equal_height=False):
with gr.Column(scale=2):
search_in = HuggingfaceHubSearch(
label="Search",
placeholder="Search for a dataset",
search_type="dataset",
sumbit_on_select=True,
)
with gr.Row():
clear_dataset_btn_part = gr.Button(
"Clear", variant="secondary"
)
load_dataset_btn = gr.Button("Load", variant="primary")
with gr.Column(scale=3):
examples = gr.Examples(
examples=[
"charris/wikipedia_sample",
"plaguss/argilla_sdk_docs_raw_unstructured",
"BeIR/hotpotqa-generated-queries",
],
label="Example datasets",
fn=lambda x: x,
inputs=[search_in],
run_on_click=True,
)
search_out = gr.HTML(label="Dataset preview", visible=False)
with gr.Tab("Load your file") as tab_file_input:
with gr.Row(equal_height=False):
with gr.Column(scale=2):
file_in = gr.File(
label="Upload your file. Supported formats: .md, .txt, .docx, .pdf",
file_count="multiple",
file_types=[".md", ".txt", ".docx", ".pdf"],
)
with gr.Row():
clear_file_btn_part = gr.Button(
"Clear", variant="secondary"
)
load_file_btn = gr.Button("Load", variant="primary")
with gr.Column(scale=3):
file_out = gr.HTML(label="Dataset preview", visible=False)
with gr.Tab("Generate from prompt") as tab_prompt_input:
with gr.Row(equal_height=False):
with gr.Column(scale=2):
dataset_description = gr.Textbox(
label="Dataset description",
placeholder="Give a precise description of your desired dataset.",
)
with gr.Row():
clear_prompt_btn_part = gr.Button(
"Clear", variant="secondary"
)
load_prompt_btn = gr.Button("Create", variant="primary")
with gr.Column(scale=3):
examples = gr.Examples(
examples=DEFAULT_DATASET_DESCRIPTIONS,
inputs=[dataset_description],
cache_examples=False,
label="Examples",
)
gr.HTML(value="<hr>")
gr.Markdown(value="## 2. Configure your task")
with gr.Row(equal_height=True):
with gr.Row(equal_height=False):
with gr.Column(scale=2):
system_prompt = gr.Textbox(
label="System prompt",
placeholder="You are a helpful assistant.",
visible=False,
)
document_column = gr.Dropdown(
label="Document Column",
info="Select the document column to generate the RAG dataset",
choices=["Load your data first in step 1."],
value="Load your data first in step 1.",
interactive=False,
multiselect=False,
allow_custom_value=False,
)
retrieval_reranking = gr.CheckboxGroup(
choices=[("Retrieval", "Retrieval"), ("Reranking", "Reranking")],
type="value",
label="Data for RAG",
info="Indicate the additional data you want to generate for RAG.",
)
with gr.Row():
clear_btn_full = gr.Button("Clear", variant="secondary")
btn_apply_to_sample_dataset = gr.Button(
"Save", variant="primary"
)
with gr.Column(scale=3):
dataframe = gr.Dataframe(
headers=["context", "question", "response"],
wrap=True,
interactive=False,
)
gr.HTML(value="<hr>")
gr.Markdown(value="## 3. Generate your dataset")
with gr.Row(equal_height=False):
with gr.Column(scale=2):
org_name = get_org_dropdown()
repo_name = gr.Textbox(
label="Repo name",
placeholder="dataset_name",
value=f"my-distiset-{str(uuid.uuid4())[:8]}",
interactive=True,
)
num_rows = gr.Number(
label="Number of rows",
value=10,
interactive=True,
scale=1,
)
temperature = gr.Slider(
label="Temperature",
minimum=0.1,
maximum=1,
value=0.7,
step=0.1,
interactive=True,
)
private = gr.Checkbox(
label="Private dataset",
value=False,
interactive=True,
scale=1,
)
btn_push_to_hub = gr.Button("Push to Hub", variant="primary", scale=2)
with gr.Column(scale=3):
success_message = gr.Markdown(
visible=True,
min_height=100, # don't remove this otherwise progress is not visible
)
with gr.Accordion(
"Customize your pipeline with distilabel",
open=False,
visible=False,
) as pipeline_code_ui:
code = generate_pipeline_code(
repo_id=search_in.value,
file_paths=file_in.value,
input_type=input_type.value,
system_prompt=system_prompt.value,
document_column=document_column.value,
retrieval_reranking=retrieval_reranking.value,
num_rows=num_rows.value,
)
pipeline_code = gr.Code(
value=code,
language="python",
label="Distilabel Pipeline Code",
)
tab_dataset_input.select(
fn=lambda: "dataset-input",
inputs=[],
outputs=[input_type],
).then(fn=hide_system_prompt_visibility, inputs=[], outputs=[system_prompt]).then(
fn=show_document_column_visibility, inputs=[], outputs=[document_column]
)
tab_file_input.select(
fn=lambda: "file-input",
inputs=[],
outputs=[input_type],
).then(fn=hide_system_prompt_visibility, inputs=[], outputs=[system_prompt]).then(
fn=show_document_column_visibility, inputs=[], outputs=[document_column]
)
tab_prompt_input.select(
fn=lambda: "prompt-input",
inputs=[],
outputs=[input_type],
).then(fn=show_system_prompt_visibility, inputs=[], outputs=[system_prompt]).then(
fn=hide_document_column_visibility, inputs=[], outputs=[document_column]
)
search_in.submit(fn=get_iframe, inputs=search_in, outputs=search_out).then(
fn=lambda df: pd.DataFrame(columns=df.columns),
inputs=[dataframe],
outputs=[dataframe],
)
load_dataset_btn.click(
fn=load_dataset_file,
inputs=[search_in, file_in, input_type],
outputs=[
dataframe,
document_column,
],
)
load_file_btn.click(
fn=load_dataset_file,
inputs=[search_in, file_in, input_type],
outputs=[
dataframe,
document_column,
],
)
load_prompt_btn.click(
fn=generate_system_prompt,
inputs=[dataset_description],
outputs=[system_prompt],
show_progress=True,
).success(
fn=generate_sample_dataset,
inputs=[
search_in,
file_in,
input_type,
system_prompt,
document_column,
retrieval_reranking,
num_rows,
],
outputs=dataframe,
)
btn_apply_to_sample_dataset.click(
fn=generate_sample_dataset,
inputs=[
search_in,
file_in,
input_type,
system_prompt,
document_column,
retrieval_reranking,
num_rows,
],
outputs=dataframe,
)
btn_push_to_hub.click(
fn=validate_argilla_user_workspace_dataset,
inputs=[repo_name],
outputs=[success_message],
show_progress=True,
).then(
fn=validate_push_to_hub,
inputs=[org_name, repo_name],
outputs=[success_message],
show_progress=True,
).success(
fn=hide_success_message,
outputs=[success_message],
show_progress=True,
).success(
fn=hide_pipeline_code_visibility,
inputs=[],
outputs=[pipeline_code_ui],
).success(
fn=push_dataset,
inputs=[
org_name,
repo_name,
private,
search_in,
file_in,
input_type,
system_prompt,
document_column,
retrieval_reranking,
num_rows,
temperature,
pipeline_code,
],
outputs=[success_message],
show_progress=True,
).success(
fn=show_success_message,
inputs=[org_name, repo_name],
outputs=[success_message],
).success(
fn=generate_pipeline_code,
inputs=[
search_in,
file_in,
input_type,
system_prompt,
document_column,
retrieval_reranking,
num_rows,
],
outputs=[pipeline_code],
).success(
fn=show_pipeline_code_visibility,
inputs=[],
outputs=[pipeline_code_ui],
)
clear_dataset_btn_part.click(fn=lambda : "", inputs=[], outputs=[search_in])
clear_file_btn_part.click(fn=lambda: None, inputs=[], outputs=[file_in])
clear_prompt_btn_part.click(
fn=lambda : "", inputs=[], outputs=[dataset_description]
)
clear_btn_full.click(
fn=lambda df: ("", [], pd.DataFrame(columns=df.columns)),
inputs=[dataframe],
outputs=[document_column, retrieval_reranking, dataframe],
)
app.load(fn=swap_visibility, outputs=main_ui)
app.load(fn=get_org_dropdown, outputs=[org_name])
app.load(fn=get_random_repo_name, outputs=[repo_name])
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