修改并适配硅基流动模型
This commit is contained in:
Wenjie Zhang
parent
78811d2601
commit
b2dbc17fec
@ -59,10 +59,10 @@ class Config(SimpleConfig):
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# 模型配置
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## 注意这里是模型名，而不是具体的模型路径，默认使用 HuggingFace 的路径
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## 如果需要自定义本地模型路径，则在 src/.env 中配置 MODEL_DIR
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self.add_item("model_provider", default="zhipu", des="模型提供商", choices=list(MODEL_NAMES.keys()))
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self.add_item("model_name", default=None, des="模型名称")
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self.add_item("embed_model", default="zhipu-embedding-3", des="Embedding 模型", choices=list(EMBED_MODEL_INFO.keys()))
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self.add_item("reranker", default="bge-reranker-v2-m3", des="Re-Ranker 模型", choices=list(RERANKER_LIST.keys()))
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self.add_item("model_provider", default="siliconflow", des="模型提供商", choices=list(MODEL_NAMES.keys()))
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self.add_item("model_name", default="Qwen/Qwen2.5-7B-Instruct", des="模型名称")
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self.add_item("embed_model", default="siliconflow/BAAI/bge-m3", des="Embedding 模型", choices=list(EMBED_MODEL_INFO.keys()))
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self.add_item("reranker", default="siliconflow/BAAI/bge-reranker-v2-m3", des="Re-Ranker 模型", choices=list(RERANKER_LIST.keys()))
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self.add_item("model_local_paths", default={}, des="本地模型路径")
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self.add_item("use_rewrite_query", default="off", des="重写查询", choices=["off", "on", "hyde"])
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### <<< 默认配置结束
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@ -1,6 +1,5 @@
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import os
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from src.models.embedding import EmbeddingModel
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from pymilvus import MilvusClient, MilvusException
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from src.utils import setup_logger, hashstr
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logger = setup_logger("KnowledgeBase")
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@ -83,7 +82,7 @@ class KnowledgeBase:
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def search(self, query, collection_name, limit=3):
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query_vectors = self.embed_model.encode_queries([query])
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query_vectors = self.embed_model.batch_encode([query])
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return self.search_by_vector(query_vectors[0], collection_name, limit)
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def search_by_vector(self, vector, collection_name, limit=3):
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@ -1,4 +1,4 @@
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from src.models.embedding import Reranker
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from src.models.rerank_model import get_reranker
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from src.utils.logging_config import setup_logger
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logger = setup_logger("server-common")
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@ -12,7 +12,7 @@ class Retriever:
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self.model = model
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if self.config.enable_reranker:
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self.reranker = Reranker(config)
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self.reranker = get_reranker(config)
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if self.config.enable_web_search:
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from src.utils.web_search import WebSearcher
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@ -110,15 +110,13 @@ class Retriever:
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for r in all_kb_res:
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r["file"] = kb.id2file(r["entity"]["file_id"])
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# use distance threshold to filter results
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if meta.get("mode") == "search":
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kb_res = all_kb_res
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else:
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kb_res = [r for r in all_kb_res if r["distance"] > distance_threshold]
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kb_res = [r for r in all_kb_res if r["distance"] > distance_threshold]
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if self.config.enable_reranker:
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for r in kb_res:
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r["rerank_score"] = self.reranker.compute_score([rw_query, r["entity"]["text"]], normalize=True)[0]
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if self.config.enable_reranker and len(kb_res) > 0:
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texts = [r["entity"]["text"] for r in kb_res]
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rerank_scores = self.reranker.compute_score([rw_query, texts], normalize=True)
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for i, r in enumerate(kb_res):
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r["rerank_score"] = rerank_scores[i]
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kb_res.sort(key=lambda x: x["rerank_score"], reverse=True)
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kb_res = [_res for _res in kb_res if _res["rerank_score"] > rerank_threshold]
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@ -1,105 +1,125 @@
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import os
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from FlagEmbedding import FlagModel, FlagReranker
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import json
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import requests
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from FlagEmbedding import FlagModel
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from src.config import EMBED_MODEL_INFO, RERANKER_LIST
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from src.config import EMBED_MODEL_INFO
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from src.utils.logging_config import setup_logger
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from src.utils import hashstr
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logger = setup_logger("EmbeddingModel")
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GLOBAL_EMBED_STATE = {}
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class EmbeddingModel(FlagModel):
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def __init__(self, model_info, config, **kwargs):
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self.info = model_info
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model_name_or_path = config.model_local_paths.get(model_info["name"], model_info.get("default_path"))
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logger.info(f"Loading embedding model {model_info['name']} from {model_name_or_path}")
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class LocalEmbeddingModel(FlagModel):
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def __init__(self, config, **kwargs):
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info = EMBED_MODEL_INFO[config.embed_model]
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model_name_or_path = config.model_local_paths.get(info["name"], info.get("default_path"))
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logger.info(f"Loading embedding model {info['name']} from {model_name_or_path}")
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super().__init__(model_name_or_path,
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query_instruction_for_retrieval=model_info.get("query_instruction", None),
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query_instruction_for_retrieval=info.get("query_instruction", None),
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use_fp16=False, **kwargs)
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logger.info(f"Embedding model {model_info['name']} loaded")
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logger.info(f"Embedding model {info['name']} loaded")
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class Reranker(FlagReranker):
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def __init__(self, config, **kwargs):
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assert config.reranker in RERANKER_LIST.keys(), f"Unsupported Reranker: {config.reranker}, only support {RERANKER_LIST.keys()}"
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model_info = RERANKER_LIST[config.reranker]
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model_name_or_path = config.model_local_paths.get(model_info["name"], model_info.get("default_path"))
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logger.info(f"Loading Reranker model {config.reranker} from {model_name_or_path}")
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super().__init__(model_name_or_path, use_fp16=True, **kwargs)
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logger.info(f"Reranker model {config.reranker} loaded")
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from zhipuai import ZhipuAI
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class ZhipuEmbedding:
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def __init__(self, model_info, config) -> None:
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self.config = config
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self.model_info = model_info
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self.client = ZhipuAI(api_key=os.getenv("ZHIPUAI_API_KEY"))
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logger.info("Zhipu Embedding model loaded")
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self.query_instruction_for_retrieval = "为这个句子生成表示以用于检索相关文章："
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class RemoteEmbeddingModel:
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embed_state = {}
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def predict(self, message):
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def batch_encode(self, messages, batch_size=20):
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data = []
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batch_size = 20
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if len(message) > batch_size:
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global GLOBAL_EMBED_STATE
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task_id = hashstr(message)
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logger.info(f"Creating new state for process {task_id}")
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GLOBAL_EMBED_STATE[task_id] = {
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if len(messages) > batch_size:
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task_id = hashstr(messages)
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self.embed_state[task_id] = {
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'status': 'in-progress',
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'total': len(message),
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'total': len(messages),
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'progress': 0
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}
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for i in range(0, len(message), batch_size):
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if len(message) > batch_size:
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logger.info(f"Encoding {i} to {i+batch_size} with {len(message)} messages")
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GLOBAL_EMBED_STATE[task_id]['progress'] = i
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for i in range(0, len(messages), batch_size):
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group_msg = messages[i:i+batch_size]
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logger.info(f"Encoding {i} to {i+batch_size} with {len(messages)} messages")
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response = self.encode_queries(group_msg)
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data.extend(response)
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group_msg = message[i:i+batch_size]
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response = self.client.embeddings.create(
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model=self.model_info.get("default_path", None),
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input=group_msg,
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)
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if len(messages) > batch_size:
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self.embed_state[task_id]['progress'] = len(messages)
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self.embed_state[task_id]['status'] = 'completed'
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data.extend([a.embedding for a in response.data])
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return data
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if len(message) > batch_size:
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GLOBAL_EMBED_STATE[task_id]['progress'] = len(message)
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GLOBAL_EMBED_STATE[task_id]['status'] = 'completed'
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class ZhipuEmbedding(RemoteEmbeddingModel):
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def __init__(self, config) -> None:
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self.config = config
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self.model = EMBED_MODEL_INFO[config.embed_model]["name"]
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self.client = ZhipuAI(api_key=os.getenv("ZHIPUAI_API_KEY"))
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def predict(self, message):
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response = self.client.embeddings.create(
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model=self.model,
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input=message,
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)
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data = [a.embedding for a in response.data]
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return data
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def encode(self, message):
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return self.predict(message)
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def encode_queries(self, queries):
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# queries = [self.query_instruction_for_retrieval + query for query in queries]
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return self.predict(queries)
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class SiliconFlowEmbedding(RemoteEmbeddingModel):
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def __init__(self, config) -> None:
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self.url = "https://api.siliconflow.cn/v1/embeddings"
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self.model = EMBED_MODEL_INFO[config.embed_model]["name"]
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api_key = os.getenv("SILICONFLOW_API_KEY")
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assert api_key, "SILICONFLOW_API_KEY is required"
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self.headers = {
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"Authorization": f"Bearer {api_key}",
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"Content-Type": "application/json"
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}
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def encode(self, message):
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payload = self.build_payload(message)
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response = requests.request("POST", self.url, json=payload, headers=self.headers)
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response = json.loads(response.text)
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# logger.debug(f"SiliconFlow Embedding response: {response}")
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assert response["data"], f"SiliconFlow Embedding failed: {response}"
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data = [a["embedding"] for a in response["data"]]
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return data
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def encode_queries(self, queries):
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return self.encode(queries)
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def build_payload(self, message):
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return {
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"model": self.model,
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"input": message,
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}
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def get_embedding_model(config):
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if not config.enable_knowledge_base:
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return None
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provider, model_name = config.embed_model.split('/', 1)
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assert config.embed_model in EMBED_MODEL_INFO.keys(), f"Unsupported embed model: {config.embed_model}, only support {EMBED_MODEL_INFO.keys()}"
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logger.debug(f"Loading embedding model {config.embed_model}")
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if provider == "local":
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model = LocalEmbeddingModel(config)
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if config.embed_model in ["bge-large-zh-v1.5"]:
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model = EmbeddingModel(EMBED_MODEL_INFO[config.embed_model], config)
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if provider == "zhipu":
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model = ZhipuEmbedding(config)
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if config.embed_model in ["zhipu-embedding-2", "zhipu-embedding-3"]:
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model = ZhipuEmbedding(EMBED_MODEL_INFO[config.embed_model], config)
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if provider == "siliconflow":
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model = SiliconFlowEmbedding(config)
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return model
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70
src/models/rerank_model.py
Normal file
70
src/models/rerank_model.py
Normal file
@ -0,0 +1,70 @@
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import os
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import json
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import requests
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import numpy as np
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from FlagEmbedding import FlagReranker
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from src.config import RERANKER_LIST
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from src.utils.logging_config import setup_logger
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logger = setup_logger("RerankModel")
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class LocalReranker(FlagReranker):
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def __init__(self, config, **kwargs):
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model_info = RERANKER_LIST[config.reranker]
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model_name_or_path = config.model_local_paths.get(model_info["name"], model_info.get("default_path"))
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logger.info(f"Loading Reranker model {config.reranker} from {model_name_or_path}")
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super().__init__(model_name_or_path, use_fp16=True, **kwargs)
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logger.info(f"Reranker model {config.reranker} loaded")
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def sigmoid(x):
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return 1 / (1 + np.exp(-x))
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class SilconFlowReranker():
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def __init__(self, config, **kwargs):
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self.url = "https://api.siliconflow.cn/v1/rerank"
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self.model = RERANKER_LIST[config.reranker]["name"]
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api_key = os.getenv("SILICONFLOW_API_KEY")
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assert api_key, "SILICONFLOW_API_KEY is required"
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self.headers = {
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"Authorization": f"Bearer {api_key}",
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"Content-Type": "application/json"
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}
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def compute_score(self, sentence_pairs, batch_size = 256, max_length = 512, normalize = False):
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# TODO 还没实现 batch_size
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query, sentences = sentence_pairs[0], sentence_pairs[1]
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payload = self.build_payload(query, sentences, max_length)
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response = requests.request("POST", self.url, json=payload, headers=self.headers)
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response = json.loads(response.text)
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logger.debug(f"SiliconFlow Reranker response: {response}")
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results = sorted(response["results"], key=lambda x: x["index"])
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all_scores = [result["relevance_score"] for result in results]
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if normalize:
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all_scores = [sigmoid(score) for score in all_scores]
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return all_scores
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def build_payload(self, query, sentences, max_length = 512):
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return {
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"model": self.model,
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"query": query,
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"documents": sentences,
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"max_chunks_per_doc": max_length,
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}
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def get_reranker(config):
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assert config.reranker in RERANKER_LIST.keys(), f"Unsupported Reranker: {config.reranker}, only support {RERANKER_LIST.keys()}"
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provider, model_name = config.reranker.split('/', 1)
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if provider == "local":
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return LocalReranker(config)
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elif provider == "siliconflow":
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return SilconFlowReranker(config)
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@ -68,30 +68,33 @@ MODEL_NAMES:
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siliconflow:
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name: SiliconFlow
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url: https://cloud.siliconflow.cn/models
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default: meta-llama/Meta-Llama-3.1-8B-Instruct
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default: Qwen/Qwen2.5-7B-Instruct
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env:
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- SILICONFLOW_API_KEY
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models:
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- meta-llama/Meta-Llama-3.1-8B-Instruct
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- meta-llama/Meta-Llama-3.1-70B-Instruct
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- meta-llama/Meta-Llama-3.1-405B-Instruct
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- Qwen/Qwen2.5-7B-Instruct
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- deepseek-ai/DeepSeek-R1
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- deepseek-ai/DeepSeek-V3
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EMBED_MODEL_INFO:
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bge-m3:
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local/BAAI/bge-m3:
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name: BAAI/bge-m3
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default_path: BAAI/bge-m3
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dimension: 1024
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zhipu-embedding-2:
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name: zhipu-embedding-2
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default_path: embedding-2
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zhipu/zhipu-embedding-2:
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name: embedding-2
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dimension: 1024
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zhipu-embedding-3:
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name: zhipu-embedding-3
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default_path: embedding-3
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zhipu/zhipu-embedding-3:
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name: embedding-3
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dimension: 2048
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siliconflow/BAAI/bge-m3:
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name: BAAI/bge-m3
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dimension: 1024
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RERANKER_LIST:
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bge-reranker-v2-m3:
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local/BAAI/bge-reranker-v2-m3:
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name: BAAI/bge-reranker-v2-m3
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default_path: BAAI/bge-reranker-v2-m3
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siliconflow/BAAI/bge-reranker-v2-m3:
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name: BAAI/bge-reranker-v2-m3
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@ -46,7 +46,7 @@
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<div class="flex-center" @click="meta.summary_title = !meta.summary_title">
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总结对话标题 <div @click.stop><a-switch v-model:checked="meta.summary_title" /></div>
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</div>
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<div class="flex-center" @click="meta.enable_retrieval = !meta.enable_retrieval">
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<div class="flex-center" @click="meta.enable_retrieval = !meta.enable_retrieval" v-if="configStore.config.enable_knowledge_base">
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启用检索 <div @click.stop><a-switch v-model:checked="meta.enable_retrieval" /></div>
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</div>
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<div class="flex-center">
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@ -117,13 +117,13 @@
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<div></div>
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</div>
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<div v-else-if="message.status == 'searching' && isStreaming" class="searching-msg"><i>正在检索……</i></div>
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<div v-else-if="message.status == 'reasoning' && isStreaming" class="searching-msg"><i>正在思考…… {{ message.reasoning }}</i></div>
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<div v-else-if="message.status == 'reasoning' && isStreaming" class="searching-msg"><i>正在思考…… {{ message.reasoning_content }}</i></div>
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<div
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v-else-if="message.text.length == 0 || message.status == 'error' || (message.status != 'finished' && !isStreaming)"
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class="err-msg"
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@click="retryMessage(message.id)"
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>
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请求错误，请重试
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请求错误，请重试。{{ message.message }}
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</div>
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<div v-else
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v-html="renderMarkdown(message)"
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@ -234,15 +234,15 @@ const marked = new Marked(
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})
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);
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||||
|
||||
const consoleMsg = (message) => console.log(message)
|
||||
const consoleMsg = (msg) => console.log(msg)
|
||||
onClickOutside(panel, () => setTimeout(() => opts.showPanel = false, 30))
|
||||
onClickOutside(modelCard, () => setTimeout(() => opts.showModelCard = false, 30))
|
||||
|
||||
const renderMarkdown = (message) => {
|
||||
if (message.status === 'loading') {
|
||||
return marked.parse(message.text + '🟢')
|
||||
const renderMarkdown = (msg) => {
|
||||
if (msg.status === 'loading') {
|
||||
return marked.parse(msg.text + '🟢')
|
||||
} else {
|
||||
return marked.parse(message.text)
|
||||
return marked.parse(msg.text)
|
||||
}
|
||||
}
|
||||
|
||||
@ -306,11 +306,11 @@ const generateRandomHash = (length) => {
|
||||
return hash;
|
||||
}
|
||||
|
||||
const appendUserMessage = (message) => {
|
||||
const appendUserMessage = (msg) => {
|
||||
conv.value.messages.push({
|
||||
id: generateRandomHash(16),
|
||||
role: 'sent',
|
||||
text: message
|
||||
text: msg
|
||||
})
|
||||
scrollToBottom()
|
||||
}
|
||||
@ -320,7 +320,7 @@ const appendAiMessage = (text, refs=null) => {
|
||||
id: generateRandomHash(16),
|
||||
role: 'received',
|
||||
text: text,
|
||||
reasoning: '',
|
||||
reasoning_content: '',
|
||||
refs,
|
||||
status: "init",
|
||||
meta: {},
|
||||
@ -329,40 +329,44 @@ const appendAiMessage = (text, refs=null) => {
|
||||
}
|
||||
|
||||
const updateMessage = (info) => {
|
||||
const message = conv.value.messages.find((message) => message.id === info.id);
|
||||
if (message) {
|
||||
const msg = conv.value.messages.find((msg) => msg.id === info.id);
|
||||
if (msg) {
|
||||
try {
|
||||
// 只有在 text 不为空时更新
|
||||
if (info.text !== null && info.text !== undefined && info.text !== '') {
|
||||
message.text = info.text;
|
||||
msg.text = info.text;
|
||||
}
|
||||
|
||||
if (info.reasoning !== null && info.reasoning !== undefined && info.reasoning !== '') {
|
||||
message.reasoning = info.reasoning;
|
||||
if (info.reasoning_content !== null && info.reasoning_content !== undefined && info.reasoning_content !== '') {
|
||||
msg.reasoning_content = info.reasoning_content;
|
||||
}
|
||||
|
||||
// 只有在 refs 不为空时更新
|
||||
if (info.refs !== null && info.refs !== undefined) {
|
||||
message.refs = info.refs;
|
||||
msg.refs = info.refs;
|
||||
}
|
||||
|
||||
if (info.model_name !== null && info.model_name !== undefined && info.model_name !== '') {
|
||||
message.model_name = info.model_name;
|
||||
msg.model_name = info.model_name;
|
||||
}
|
||||
|
||||
// 只有在 status 不为空时更新
|
||||
// 只有在 status 不为空时更新
|
||||
if (info.status !== null && info.status !== undefined && info.status !== '') {
|
||||
message.status = info.status;
|
||||
msg.status = info.status;
|
||||
}
|
||||
|
||||
if (info.meta !== null && info.meta !== undefined) {
|
||||
message.meta = info.meta;
|
||||
msg.meta = info.meta;
|
||||
}
|
||||
|
||||
if (info.message !== null && info.message !== undefined) {
|
||||
msg.message = info.message;
|
||||
}
|
||||
scrollToBottom();
|
||||
} catch (error) {
|
||||
console.error('Error updating message:', error);
|
||||
message.status = 'error';
|
||||
message.text = '消息更新失败';
|
||||
msg.status = 'error';
|
||||
msg.text = '消息更新失败';
|
||||
}
|
||||
} else {
|
||||
console.error('Message not found:', info.id);
|
||||
@ -371,9 +375,9 @@ const updateMessage = (info) => {
|
||||
|
||||
|
||||
const groupRefs = (id) => {
|
||||
const message = conv.value.messages.find((message) => message.id === id)
|
||||
if (message.refs && message.refs.knowledge_base.results.length > 0) {
|
||||
message.groupedResults = message.refs.knowledge_base.results
|
||||
const msg = conv.value.messages.find((msg) => msg.id === id)
|
||||
if (msg.refs && msg.refs.knowledge_base.results.length > 0) {
|
||||
msg.groupedResults = msg.refs.knowledge_base.results
|
||||
.filter(result => result.file && result.file.filename)
|
||||
.reduce((acc, result) => {
|
||||
const { filename } = result.file;
|
||||
@ -387,11 +391,11 @@ const groupRefs = (id) => {
|
||||
scrollToBottom()
|
||||
}
|
||||
|
||||
const simpleCall = (message) => {
|
||||
const simpleCall = (msg) => {
|
||||
return new Promise((resolve, reject) => {
|
||||
fetch('/api/chat/call_lite', {
|
||||
method: 'POST',
|
||||
body: JSON.stringify({ query: message, }),
|
||||
body: JSON.stringify({ query: msg, }),
|
||||
headers: { 'Content-Type': 'application/json' }
|
||||
})
|
||||
.then((response) => response.json())
|
||||
@ -432,24 +436,11 @@ const fetchChatResponse = (user_input, cur_res_id) => {
|
||||
const readChunk = () => {
|
||||
return reader.read().then(({ done, value }) => {
|
||||
if (done) {
|
||||
const message = conv.value.messages.find((message) => message.id === cur_res_id)
|
||||
console.log(message)
|
||||
if (message.meta.enable_retrieval) {
|
||||
const msg = conv.value.messages.find((msg) => msg.id === cur_res_id)
|
||||
console.log(msg)
|
||||
if (msg.meta.enable_retrieval) {
|
||||
console.log("fetching refs")
|
||||
fetchRefs(cur_res_id).then((data) => {
|
||||
console.log(data)
|
||||
updateMessage({
|
||||
id: cur_res_id,
|
||||
refs: data,
|
||||
status: "finished",
|
||||
});
|
||||
groupRefs(cur_res_id);
|
||||
})
|
||||
} else {
|
||||
updateMessage({
|
||||
id: cur_res_id,
|
||||
status: "finished",
|
||||
});
|
||||
groupRefs(cur_res_id);
|
||||
}
|
||||
isStreaming.value = false;
|
||||
if (conv.value.messages.length === 2) { renameTitle(); }
|
||||
@ -468,12 +459,11 @@ const fetchChatResponse = (user_input, cur_res_id) => {
|
||||
updateMessage({
|
||||
id: cur_res_id,
|
||||
text: data.response,
|
||||
reasoning: data.reasoning_response,
|
||||
model_name: data.model_name,
|
||||
reasoning_content: data.reasoning_content,
|
||||
status: data.status,
|
||||
meta: data.meta,
|
||||
...data,
|
||||
});
|
||||
// console.log(data)
|
||||
// console.log("Last message", conv.value.messages[conv.value.messages.length - 1].text)
|
||||
// console.log("Last message", conv.value.messages[conv.value.messages.length - 1].status)
|
||||
|
||||
@ -541,7 +531,7 @@ const sendMessage = () => {
|
||||
|
||||
const retryMessage = (id) => {
|
||||
// 找到 id 对应的 message，然后删除包含 message 在内以及后面所有的 message
|
||||
const index = conv.value.messages.findIndex(message => message.id === id);
|
||||
const index = conv.value.messages.findIndex(msg => msg.id === id);
|
||||
const pastMessage = conv.value.messages[index-1]
|
||||
console.log("retryMessage", id, pastMessage)
|
||||
conv.value.inputText = pastMessage.text
|
||||
@ -552,8 +542,8 @@ const retryMessage = (id) => {
|
||||
sendMessage();
|
||||
}
|
||||
|
||||
const autoSend = (message) => {
|
||||
conv.value.inputText = message
|
||||
const autoSend = (msg) => {
|
||||
conv.value.inputText = msg
|
||||
sendMessage()
|
||||
}
|
||||
|
||||
@ -750,12 +740,12 @@ watch(
|
||||
/* animation: slideInUp 0.1s ease-in; */
|
||||
|
||||
.err-msg {
|
||||
color: #FF6B6B;
|
||||
border: 1px solid #FF6B6B;
|
||||
padding: 0.2rem 1rem;
|
||||
color: #eb8080;
|
||||
border: 1px solid #eb8080;
|
||||
padding: 0.5rem 1rem;
|
||||
border-radius: 8px;
|
||||
text-align: center;
|
||||
background: #FFF0F0;
|
||||
text-align: left;
|
||||
background: #FFF5F5;
|
||||
margin-bottom: 10px;
|
||||
cursor: pointer;
|
||||
}
|
||||
|
||||
@ -1,10 +1,10 @@
|
||||
<template>
|
||||
<div class="refs" v-if="showRefs">
|
||||
<div class="tags">
|
||||
<span class="item btn" @click="copyText(msg.text)"><CopyOutlined /></span>
|
||||
<!-- <span class="item btn" @click="likeThisResponse(msg)"><LikeOutlined /></span> -->
|
||||
<!-- <span class="item btn" @click="dislikeThisResponse(msg)"><DislikeOutlined /></span> -->
|
||||
<span class="item"><GlobalOutlined /> {{ msg.model_name }}</span>
|
||||
<span class="item btn" @click="copyText(msg.text)"><CopyOutlined /></span>
|
||||
<span
|
||||
class="item btn"
|
||||
@click="openSubGraph(msg)"
|
||||
@ -165,15 +165,15 @@ const getPercent = (value) => {
|
||||
display: flex;
|
||||
margin-bottom: 20px;
|
||||
color: var(--gray-500);
|
||||
font-size: 14px;
|
||||
font-size: 13px;
|
||||
gap: 10px;
|
||||
|
||||
.item {
|
||||
background: var(--gray-100);
|
||||
color: var(--gray-800);
|
||||
color: var(--gray-700);
|
||||
padding: 2px 8px;
|
||||
border-radius: 8px;
|
||||
font-size: 14px;
|
||||
font-size: 13px;
|
||||
user-select: none;
|
||||
|
||||
&.btn {
|
||||
|
||||
Loading…
Reference in New Issue
Block a user