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pipeline.py
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pipeline.py
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from pathlib import Path
import sys
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import os
import rdflib
import re
import json
import string
import torch
import time
import yaml
class DatasetGenerator():
QUESTIONS_ONLY_PATH="01-questions_only"
QUESIONS_WITH_ANSWERS_PATH="02-answered_questions"
QUESIONS_WITH_QUERIES_PATH="03-answers_and_queries"
ENRICHED_WITH_GPT_PATH="04-enriched_with_gpt"
EXECUTED_QUERIES_PATH="05-sparql_queries_executed"
small_models = [
"microsoft/Phi-3-mini-128k-instruct",
"microsoft/Phi-3-medium-4k-instruct",
"openchat/openchat-3.6-8b-20240522",
"google/gemma-7b-it",
"mistralai/Mistral-7B-Instruct-v0.3",
"Qwen/Qwen1.5-7B-Chat",
"Qwen/Qwen2-7B-Instruct",
"occiglot/occiglot-7b-eu5-instruct",
"01-ai/Yi-1.5-9B-Chat-16K",
]
medium_models = [
"01-ai/Yi-1.5-34B-Chat-16K",
"google/gemma-2-27b-it",
"internlm/internlm2_5-20b-chat",
"jpacifico/Chocolatine-14B-Instruct-4k-DPO",
"Azure99/blossom-v5.1-34b",
"mistralai/Mistral-Nemo-Instruct-2407"
]
gpt_models = [
"gpt-4o-2024-05-13",
"gpt-4o-mini-2024-07-18",
"gpt-4-turbo-2024-04-09",
"gpt-3.5-turbo-0125"
]
def restore_from_json(filepath):
with open(filepath, "r") as fp:
data = json.load(fp)
dg = DatasetGenerator(**data["meta"]["init_args"])
dg._result_dict["data"] = data["data"]
return dg
def __init__(self,
list_of_model_checkpoints,
path_to_ttl,
number_of_questions_per_model = 5,
gpt_versions = None,
feedback_dialog = True,
max_rounds_of_feedback = 3
):
init_args = locals()
del(init_args["self"])
self.model_checkpoints = list_of_model_checkpoints
self.feedback_dialog = feedback_dialog
self.max_rounds_of_feedback = max_rounds_of_feedback
self.graph_path = path_to_ttl
with open(path_to_ttl, "r") as fp:
self.graph_ttl = fp.read()
self.bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="fp4",
bnb_4bit_compute_dtype=torch.float16
)
self.gpt_versions = gpt_versions
self.n_questions = number_of_questions_per_model
self._result_dict = {
"meta": {
"kg": self.graph_ttl,
"init_args": init_args,
},
"data": []
}
def __repr__(self):
return f"""DatasetGenerator based on LLMs
----------------------------------------------
Model checkpoints: {len(self.model_checkpoints)}
{self.model_checkpoints}
----------------------------------------------
Questions per Model:
{self.n_questions}
----------------------------------------------
TTL file:
{self.graph_path}
----------------------------------------------
GPT versions for data enrichment:
{self.gpt_versions}
"""
def run(self):
if len(self._result_dict["data"]) == 0:
self.generate_questions()
ref_item = self._result_dict["data"][0]
if "generated_answers" not in ref_item.keys():
self.generate_answers()
if "generated_queries" not in ref_item.keys():
self.generate_queries()
if self.gpt_models is not None and self.gpt_models[0] not in ref_item["generated_queries"].keys():
self.generate_gpt_queries()
if "sparql_result_sets" not in ref_item.keys():
self.execute_queries_and_store_results()
def _send_prompt_and_parse_questions(self, m, t, msg, missing_questions):
input_ids = t.apply_chat_template(msg, tokenize=True, add_generation_prompt=True, return_tensors="pt").to('cuda')
generated_ids = m.generate(input_ids, max_new_tokens=1024)
# Cutting off because generated_ids contains the input ids
generated_ids = [ generated_ids[0][len(input_ids[0]):] ]
response = t.batch_decode(generated_ids, skip_special_tokens=True)[0]
questions = response.split("\n")
parsed_questions = []
counter = 0
for q in questions:
counter += 1
if counter > missing_questions:
break
q = q.strip().replace("<|im_end|>", "")
if not q.endswith("?"):
counter -= 1
else:
q = re.sub(r"^[^ ]*[0-9]+\.[^ ]* ", "", q)
parsed_questions.append(q)
return parsed_questions, response
def update_meta(self):
self._result_dict["meta"]["timestamp_unix"] = time.time()
self._result_dict["meta"]["timestamp_pretty"] = time.strftime("%Y-%m-%d %H:%M:%S")
def generate_questions(self):
# .--..--..--..--..--..--..--..--..--..--..--..--..--..--..--..--.
# / .. \.. \.. \.. \.. \.. \.. \.. \.. \.. \.. \.. \.. \.. \.. \.. \
# \ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/ /
# \/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /
# / /\/ /`' /`' /`' /`' /`' /`' /`' /`' /`' /`' /`' /`' /`' /\/ /\
# / /\ \/`--'`--'`--'`--'`--'`--'`--'`--'`--'`--'`--'`--'`--'\ \/\ \
# \ \/\ \ /\ \/ /
# \/ /\ \ / /\/ /
# / /\/ / \ \/ /\
# / /\ \/ Generating natural language \ \/\ \
# \ \/\ \ questions /\ \/ /
# \/ /\ \ / /\/ /
# / /\/ / \ \/ /\
# / /\ \/ \ \/\ \
# \ \/\ \.--..--..--..--..--..--..--..--..--..--..--..--..--./\ \/ /
# \/ /\/ ../ ../ ../ ../ ../ ../ ../ ../ ../ ../ ../ ../ ../ /\/ /
# / /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\
# / /\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \
# \ `'\ `'\ `'\ `'\ `'\ `'\ `'\ `'\ `'\ `'\ `'\ `'\ `'\ `'\ `'\ `' /
# `--'`--'`--'`--'`--'`--'`--'`--'`--'`--'`--'`--'`--'`--'`--'`--'
print("=================================")
print(" Generating questions")
print("=================================")
Path(DatasetGenerator.QUESTIONS_ONLY_PATH).mkdir(exist_ok=True)
for cp in self.model_checkpoints:
print("===============================================")
print(f" {cp}")
print("===============================================")
prompt = f"""Generate {self.n_questions} questions that fit the following knowledge graph in ttl format:
{self.graph_ttl}
One question per line. No additional line breaks. No enumeration."""
model = AutoModelForCausalLM.from_pretrained(
cp,
device_map="auto",
quantization_config = self.bnb_config,
trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(cp, trust_remote_code=True)
messages = [
{ "role": "user", "content": prompt }
]
questions = []
# Some LLMS (I'm looking at you gemma!) tend to provide less questions
# than we asked for. This is why we have to provide a feedback loop to
# allow those LLMs to fix their mistake
feedback_count = 0
missing_questions = self.n_questions
while len(questions) < self.n_questions:
new_questions, raw_response = self._send_prompt_and_parse_questions(model, tokenizer, messages, missing_questions)
questions += [ { "question": q, "generated_by": cp, "feedback_count": feedback_count } for q in new_questions ]
missing_questions = self.n_questions - len(questions)
messages.append( { "role": "assistant", "content": raw_response } )
messages.append( {"role": "user", "content": f"Please generate {missing_questions} more questions." } )
feedback_count += 1
if not self.feedback_dialog or feedback_count > self.max_rounds_of_feedback:
break
self._result_dict["data"] += questions
for idx in range(len(self._result_dict["data"])):
self._result_dict["data"][idx]["index"] = idx+1
self.update_meta()
with open(f"{DatasetGenerator.QUESTIONS_ONLY_PATH}/merged.json", "w") as fp:
json.dump(self._result_dict, fp, indent=2)
def generate_answers(self):
# .--..--..--..--..--..--..--..--..--..--..--..--..--..--..--..--.
# / .. \.. \.. \.. \.. \.. \.. \.. \.. \.. \.. \.. \.. \.. \.. \.. \
# \ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/ /
# \/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /
# / /\/ /`' /`' /`' /`' /`' /`' /`' /`' /`' /`' /`' /`' /`' /\/ /\
# / /\ \/`--'`--'`--'`--'`--'`--'`--'`--'`--'`--'`--'`--'`--'\ \/\ \
# \ \/\ \ /\ \/ /
# \/ /\ \ / /\/ /
# / /\/ / \ \/ /\
# / /\ \/ Generating answers via LLMs \ \/\ \
# \ \/\ \ /\ \/ /
# \/ /\ \ / /\/ /
# / /\/ / \ \/ /\
# / /\ \/ \ \/\ \
# \ \/\ \.--..--..--..--..--..--..--..--..--..--..--..--..--./\ \/ /
# \/ /\/ ../ ../ ../ ../ ../ ../ ../ ../ ../ ../ ../ ../ ../ /\/ /
# / /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\
# / /\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \
# \ `'\ `'\ `'\ `'\ `'\ `'\ `'\ `'\ `'\ `'\ `'\ `'\ `'\ `'\ `'\ `' /
# `--'`--'`--'`--'`--'`--'`--'`--'`--'`--'`--'`--'`--'`--'`--'`--'
print("=================================")
print(" Generating answers")
print("=================================")
Path(DatasetGenerator.QUESIONS_WITH_ANSWERS_PATH).mkdir(exist_ok=True)
self.prompt = string.Template("""You are given the following knowledge graph in ttl format:
${graph_ttl}
${question}
Answer as short as possible. Give only facts, no full sentences.""")
for idx in range(len(self._result_dict["data"])):
if "generated_answers" not in self._result_dict["data"][idx].keys():
self._result_dict["data"][idx]["generated_answers"] = {}
for cp in self.model_checkpoints:
print("===============================================")
print(f" {cp}")
print("===============================================")
model = AutoModelForCausalLM.from_pretrained(cp, device_map="auto", quantization_config = self.bnb_config, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(cp, trust_remote_code=True)
for q in self._result_dict["data"]:
filled_prompt = self.prompt.substitute(graph_ttl=self.graph_ttl, question=q["question"])
messages = [
{ "role": "user", "content": filled_prompt }
]
input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to('cuda')
generated_ids = model.generate(input_ids, max_new_tokens=128)
generated_ids = [ generated_ids[0][len(input_ids[0]):] ]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
q["generated_answers"][cp] = response
self.update_meta()
with open(f"{DatasetGenerator.QUESIONS_WITH_ANSWERS_PATH}/merged.json", "w") as fp:
json.dump(self._result_dict, fp, indent=2)
def generate_queries(self):
# .--..--..--..--..--..--..--..--..--..--..--..--..--..--..--..--.
# / .. \.. \.. \.. \.. \.. \.. \.. \.. \.. \.. \.. \.. \.. \.. \.. \
# \ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/ /
# \/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /
# / /\/ /`' /`' /`' /`' /`' /`' /`' /`' /`' /`' /`' /`' /`' /\/ /\
# / /\ \/`--'`--'`--'`--'`--'`--'`--'`--'`--'`--'`--'`--'`--'\ \/\ \
# \ \/\ \ /\ \/ /
# \/ /\ \ / /\/ /
# / /\/ / \ \/ /\
# / /\ \/ Generating SPARQL queries \ \/\ \
# \ \/\ \ /\ \/ /
# \/ /\ \ / /\/ /
# / /\/ / \ \/ /\
# / /\ \/ \ \/\ \
# \ \/\ \.--..--..--..--..--..--..--..--..--..--..--..--..--./\ \/ /
# \/ /\/ ../ ../ ../ ../ ../ ../ ../ ../ ../ ../ ../ ../ ../ /\/ /
# / /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\
# / /\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \
# \ `'\ `'\ `'\ `'\ `'\ `'\ `'\ `'\ `'\ `'\ `'\ `'\ `'\ `'\ `'\ `' /
# `--'`--'`--'`--'`--'`--'`--'`--'`--'`--'`--'`--'`--'`--'`--'`--'
print("=================================")
print(" Generating queries")
print("=================================")
Path(DatasetGenerator.QUESIONS_WITH_QUERIES_PATH).mkdir(exist_ok=True)
self.prompt = string.Template("""
You are given the following knowledge graph in ttl format:
${graph_ttl}
Create a SPARQL query to answer the following question: ${question}
Give only the query. Do not generate any other text. Wrap the query in code tags: ```
""")
for idx in range(len(self._result_dict["data"])):
if "generated_queries" not in self._result_dict["data"][idx].keys():
self._result_dict["data"][idx]["generated_queries"] = {}
for cp in self.model_checkpoints:
print("===============================================")
print(f" {cp}")
print("===============================================")
model = AutoModelForCausalLM.from_pretrained(cp, device_map="auto", quantization_config = self.bnb_config, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(cp, trust_remote_code=True)
for q in self._result_dict["data"]:
filled_prompt = self.prompt.substitute(graph_ttl=self.graph_ttl, question=q["question"])
messages = [
{ "role": "user", "content": filled_prompt }
]
input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to('cuda')
generated_ids = model.generate(input_ids, max_new_tokens=128)
generated_ids = [ generated_ids[0][len(input_ids[0]):] ]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
q["generated_queries"][cp] = response
self.update_meta()
with open(f"{DatasetGenerator.QUESIONS_WITH_QUERIES_PATH}/merged.json", "w") as fp:
json.dump(self._result_dict, fp, indent=2)
def generate_gpt_queries(self):
# .--..--..--..--..--..--..--..--..--..--..--..--..--..--..--..--.
# / .. \.. \.. \.. \.. \.. \.. \.. \.. \.. \.. \.. \.. \.. \.. \.. \
# \ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/ /
# \/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /
# / /\/ /`' /`' /`' /`' /`' /`' /`' /`' /`' /`' /`' /`' /`' /\/ /\
# / /\ \/`--'`--'`--'`--'`--'`--'`--'`--'`--'`--'`--'`--'`--'\ \/\ \
# \ \/\ \ /\ \/ /
# \/ /\ \ / /\/ /
# / /\/ / \ \/ /\
# / /\ \/ Generating reference Queries \ \/\ \
# \ \/\ \ via GPT /\ \/ /
# \/ /\ \ / /\/ /
# / /\/ / \ \/ /\
# / /\ \/ \ \/\ \
# \ \/\ \.--..--..--..--..--..--..--..--..--..--..--..--..--./\ \/ /
# \/ /\/ ../ ../ ../ ../ ../ ../ ../ ../ ../ ../ ../ ../ ../ /\/ /
# / /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\
# / /\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \
# \ `'\ `'\ `'\ `'\ `'\ `'\ `'\ `'\ `'\ `'\ `'\ `'\ `'\ `'\ `'\ `' /
# `--'`--'`--'`--'`--'`--'`--'`--'`--'`--'`--'`--'`--'`--'`--'`--'
if self.gpt_versions:
print("==========================================")
print(" Generating reference queries via GPT")
print("==========================================")
Path(self.ENRICHED_WITH_GPT_PATH).mkdir(exist_ok=True)
from openai import OpenAI
prompt_template = string.Template("""
You are given the following knowledge graph in ttl format:
${graph_ttl}
Create a SPARQL query to answer the following question: ${question}
Give only the query. Do not generate any other text.
""")
cl = OpenAI(
api_key=os.environ["OPENAI_API_KEY"]
)
for entry in self._result_dict["data"]:
question = entry["question"]
prompt = prompt_template.substitute(question=question, graph_ttl=self.graph_ttl)
for gpt_version in self.gpt_versions:
chat_completion = cl.chat.completions.create(
messages = [
{
"role": "user",
"content": prompt
}
],
model=gpt_version
)
entry["generated_queries"][gpt_version] = chat_completion.choices[0].message.content
self.update_meta()
with open(f"{self.ENRICHED_WITH_GPT_PATH}/merged.json", "w") as fp:
json.dump(self._result_dict, fp, indent=2)
def execute_queries_and_store_results(self):
# .--..--..--..--..--..--..--..--..--..--..--..--..--..--..--..--.
# / .. \.. \.. \.. \.. \.. \.. \.. \.. \.. \.. \.. \.. \.. \.. \.. \
# \ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/ /
# \/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /
# / /\/ /`' /`' /`' /`' /`' /`' /`' /`' /`' /`' /`' /`' /`' /\/ /\
# / /\ \/`--'`--'`--'`--'`--'`--'`--'`--'`--'`--'`--'`--'`--'\ \/\ \
# \ \/\ \ /\ \/ /
# \/ /\ \ / /\/ /
# / /\/ / \ \/ /\
# / /\ \/ Executing queries and saving \ \/\ \
# \ \/\ \ the results /\ \/ /
# \/ /\ \ / /\/ /
# / /\/ / \ \/ /\
# / /\ \/ \ \/\ \
# \ \/\ \.--..--..--..--..--..--..--..--..--..--..--..--..--./\ \/ /
# \/ /\/ ../ ../ ../ ../ ../ ../ ../ ../ ../ ../ ../ ../ ../ /\/ /
# / /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\/ /\
# / /\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \/\ \
# \ `'\ `'\ `'\ `'\ `'\ `'\ `'\ `'\ `'\ `'\ `'\ `'\ `'\ `'\ `'\ `' /
# `--'`--'`--'`--'`--'`--'`--'`--'`--'`--'`--'`--'`--'`--'`--'`--'
print("==========================================")
print(" Executing queries and saving the results")
print("==========================================")
Path(DatasetGenerator.EXECUTED_QUERIES_PATH).mkdir(exist_ok=True)
g = rdflib.Graph()
g.parse(self.graph_path)
def prep_query(query):
candidate = re.findall(r"```.*```", query, re.DOTALL | re.IGNORECASE)
candidate = " ".join(candidate).replace("`", "").replace("\"", "'").replace("sparql", "").replace("SPARQL", "").replace("sql", "").replace("?", " ?")
return re.sub(r"prefix.*","",candidate, 0, re.IGNORECASE)
for item in self._result_dict["data"]:
question = item["question"]
queries = item["generated_queries"]
if "sparql_result_sets" not in item.keys():
item["sparql_result_sets"] = {}
for k,v in queries.items():
query = prep_query(v)
try:
item["sparql_result_sets"][k] = {
"cleaned_query": query,
"result": list(g.query(query))
}
except Exception as e:
item["sparql_result_sets"][k] = {
"cleaned_query": query,
"result": None,
"error": str(e)
}
self.update_meta()
with open(f"{DatasetGenerator.EXECUTED_QUERIES_PATH}/merged.json", "w") as fp:
json.dump(self._result_dict, fp, indent=2)
if __name__ == "__main__":
try:
conf_file = sys.argv[1]
except:
conf_file = "config.yaml"
with open(conf_file, "r") as fp:
conf_dict = yaml.load(fp, Loader=yaml.Loader)
if "run" in conf_dict.keys():
if not "gpt_versions" in conf_dict["run"].keys():
conf_dict["run"]["gpt_versions"] = []
dg = DatasetGenerator(
conf_dict["run"]["models"],
conf_dict["run"]["ttl_path"],
conf_dict["run"]["number_of_questions"],
gpt_versions=conf_dict["run"]["gpt_versions"]
)
elif "resume" in conf_dict.keys():
dg = DatasetGenerator.restore_from_json(conf_dict["resume"]["load_file"])
else:
print("Malformed config file")
print(dg)
dg.run()