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import re
from typing import Any
from graphgen.bases import BaseGenerator
from graphgen.templates import COT_GENERATION_PROMPT
from graphgen.utils import compute_content_hash, detect_main_language, logger
class CoTGenerator(BaseGenerator):
@staticmethod
def build_prompt(
batch: tuple[list[tuple[str, dict]], list[tuple[Any, Any, dict]]]
) -> str:
"""
Build prompts for COT Template Design.
:param batch:
:return:
"""
nodes, edges = batch
entities_str = "\n".join(
[
f"{index + 1}. {node[0]}: {node[1]['description']}"
for index, node in enumerate(nodes)
]
)
relationships_str = "\n".join(
[
f"{index + 1}. {edge[0]} -- {edge[1]}: {edge[2]['description']}"
for index, edge in enumerate(edges)
]
)
language = detect_main_language(entities_str + relationships_str)
prompt = COT_GENERATION_PROMPT[language]["COT_TEMPLATE_DESIGN"].format(
entities=entities_str, relationships=relationships_str
)
return prompt
@staticmethod
def build_prompt_for_cot_generation(
batch: tuple[list[tuple[str, dict]], list[tuple[Any, Any, dict]]],
question: str,
reasoning_path: str,
) -> str:
"""
Build prompts for COT Generation.
"""
nodes, edges = batch
entities_str = "\n".join(
[
f"{index + 1}. {node[0]}: {node[1]['description']}"
for index, node in enumerate(nodes)
]
)
relationships_str = "\n".join(
[
f"{index + 1}. {edge[0]} -- {edge[1]}: {edge[2]['description']}"
for index, edge in enumerate(edges)
]
)
language = detect_main_language(entities_str + relationships_str)
prompt = COT_GENERATION_PROMPT[language]["COT_GENERATION"].format(
entities=entities_str,
relationships=relationships_str,
question=question,
reasoning_template=reasoning_path,
)
return prompt
@staticmethod
def parse_response(response: str) -> dict:
"""
Parse CoT template from response.
:param response:
:return: dict with question and reasoning_path
"""
question_match = re.search(r"<question>(.*?)</question>", response, re.DOTALL)
reasoning_path_match = re.search(
r"<reasoning_path>(.*?)</reasoning_path>", response, re.DOTALL
)
if question_match and reasoning_path_match:
question = question_match.group(1).strip()
reasoning_path = reasoning_path_match.group(1).strip()
else:
logger.warning("Failed to parse response: %s", response)
return {}
question = question.strip('"').strip("'")
reasoning_path = reasoning_path.strip('"').strip("'")
logger.debug("CoT Question: %s", question)
logger.debug("CoT Reasoning Path: %s", reasoning_path)
return {
"question": question,
"reasoning_path": reasoning_path,
}
async def generate(
self,
batch: tuple[
list[tuple[str, dict]], list[tuple[Any, Any, dict] | tuple[Any, Any, Any]]
],
) -> dict[str, Any]:
"""
Generate QAs based on a given batch.
:param batch
:return: QA pairs
"""
result = {}
prompt = self.build_prompt(batch)
response = await self.llm_client.generate_answer(prompt)
response = self.parse_response(response)
if not response:
return result
question, reasoning_path = response["question"], response["reasoning_path"]
prompt = self.build_prompt_for_cot_generation(batch, question, reasoning_path)
cot_answer = await self.llm_client.generate_answer(prompt)
logger.debug("CoT Answer: %s", cot_answer)
qa_pairs = {
compute_content_hash(question): {
"question": question,
"answer": cot_answer,
"reasoning_path": reasoning_path,
}
}
result.update(qa_pairs)
return result
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