Spaces:
Running
Running
File size: 4,079 Bytes
acd7cf4 3a3b216 d02622b acd7cf4 3a3b216 acd7cf4 d02622b 3a3b216 acd7cf4 3a3b216 acd7cf4 3a3b216 acd7cf4 3a3b216 acd7cf4 3a3b216 acd7cf4 3a3b216 acd7cf4 3a3b216 acd7cf4 3a3b216 acd7cf4 3a3b216 acd7cf4 3a3b216 acd7cf4 3a3b216 acd7cf4 3a3b216 acd7cf4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 |
import asyncio
from collections import defaultdict
from tqdm.asyncio import tqdm as tqdm_async
from graphgen.bases import BaseLLMWrapper
from graphgen.models import JsonKVStorage, NetworkXStorage
from graphgen.templates import DESCRIPTION_REPHRASING_PROMPT
from graphgen.utils import detect_main_language, logger
async def quiz(
synth_llm_client: BaseLLMWrapper,
graph_storage: NetworkXStorage,
rephrase_storage: JsonKVStorage,
max_samples: int = 1,
max_concurrent: int = 1000,
) -> JsonKVStorage:
"""
Get all edges and quiz them
:param synth_llm_client: generate statements
:param graph_storage: graph storage instance
:param rephrase_storage: rephrase storage instance
:param max_samples: max samples for each edge
:param max_concurrent: max concurrent
:return:
"""
semaphore = asyncio.Semaphore(max_concurrent)
async def _process_single_quiz(des: str, prompt: str, gt: str):
async with semaphore:
try:
# 如果在rephrase_storage中已经存在，直接取出
descriptions = await rephrase_storage.get_by_id(des)
if descriptions:
return None
new_description = await synth_llm_client.generate_answer(
prompt, temperature=1
)
return {des: [(new_description, gt)]}
except Exception as e: # pylint: disable=broad-except
logger.error("Error when quizzing description %s: %s", des, e)
return None
edges = await graph_storage.get_all_edges()
nodes = await graph_storage.get_all_nodes()
results = defaultdict(list)
tasks = []
for edge in edges:
edge_data = edge[2]
description = edge_data["description"]
language = "English" if detect_main_language(description) == "en" else "Chinese"
results[description] = [(description, "yes")]
for i in range(max_samples):
if i > 0:
tasks.append(
_process_single_quiz(
description,
DESCRIPTION_REPHRASING_PROMPT[language]["TEMPLATE"].format(
input_sentence=description
),
"yes",
)
)
tasks.append(
_process_single_quiz(
description,
DESCRIPTION_REPHRASING_PROMPT[language]["ANTI_TEMPLATE"].format(
input_sentence=description
),
"no",
)
)
for node in nodes:
node_data = node[1]
description = node_data["description"]
language = "English" if detect_main_language(description) == "en" else "Chinese"
results[description] = [(description, "yes")]
for i in range(max_samples):
if i > 0:
tasks.append(
_process_single_quiz(
description,
DESCRIPTION_REPHRASING_PROMPT[language]["TEMPLATE"].format(
input_sentence=description
),
"yes",
)
)
tasks.append(
_process_single_quiz(
description,
DESCRIPTION_REPHRASING_PROMPT[language]["ANTI_TEMPLATE"].format(
input_sentence=description
),
"no",
)
)
for result in tqdm_async(
asyncio.as_completed(tasks), total=len(tasks), desc="Quizzing descriptions"
):
new_result = await result
if new_result:
for key, value in new_result.items():
results[key].extend(value)
for key, value in results.items():
results[key] = list(set(value))
await rephrase_storage.upsert({key: results[key]})
return rephrase_storage
|