diff --git a/.gitignore b/.gitignore index b026948c..409b49b2 100644 --- a/.gitignore +++ b/.gitignore @@ -25,13 +25,19 @@ cache ### IDE .vscode +.idea *.nogit.* *.pdf +*.yaml src/data neo4j* */package-lock.json web/package-lock.json saves notebooks -*.yaml \ No newline at end of file +local_neo4j/data +local_neo4j/logs +local_neo4j/import +local_neo4j/plugins +local_neo4j/conf \ No newline at end of file diff --git a/README.md b/README.md index a391ec99..f4c8443e 100644 --- a/README.md +++ b/README.md @@ -3,22 +3,42 @@ -### 准备 +## 准备 1. 提供 API 服务商的 API_KEY，并放置在 `src/.env` 文件中，参考 `src/.env.template`。默认使用的是智谱AI。 -2. 配置 python 环境 `pip install -r src/requirements.txt` +2. 配置 python 环境 `pip install -r requirements.txt` +**如果不启用知识库，可以仅安装下面的依赖** -### 启动命令行模式 - -```bash -python -m src.cli +``` +FlagEmbedding==1.2.10 +Flask==3.0.3 +Flask_Cors==4.0.1 +openai==1.35.10 +python-dotenv==1.0.1 +PyYAML==6.0.1 +zhipuai ``` -### 启动网页模式 +### 【可选】配置图数据库 neo4j + +使用 docker 部署 neo4j 服务，配置文件见 [local_neo4j/docker-compose.yml](local_neo4j/docker-compose.yml). +默认账号密码见最后一行，可以使用 `http://localhost:7474/` 在浏览器可视化访问。 ```bash -python -m src.api +cd local_neo4j +docker compose up -d +``` + +可以使用 `python test_neo4j.py` 来测试是否正常启动。使用 `docker compose down` 可停止服务。 +如果想要管理 neo4j，也可以使用 `docker ps` 查看容器 id，然后使用 `docker exec -it /bin/bash` 进入容器。 +如果想要删除数据库中的文件，可以进入容器并停止 neo4j 后，执行 `rm -rf /data/databases`。 + + +## 启动 + +```bash +python -m src.api cd web npm install diff --git a/local_neo4j/docker-compose.yml b/local_neo4j/docker-compose.yml new file mode 100644 index 00000000..aff1e9b2 --- /dev/null +++ b/local_neo4j/docker-compose.yml @@ -0,0 +1,17 @@ +version: '3.9' +services: + + neo4j: + image: neo4j:latest + volumes: + - ./conf:/var/lib/neo4j/conf + - ./import:/var/lib/neo4j/import + - ./plugins:/plugins + - ./data:/data + - ./logs:/var/lib/neo4j/logs + restart: always + ports: + - 7474:7474 + - 7687:7687 + environment: + - NEO4J_AUTH=neo4j/0123456789 diff --git a/local_neo4j/test_neo4j.py b/local_neo4j/test_neo4j.py new file mode 100644 index 00000000..a293e4fc --- /dev/null +++ b/local_neo4j/test_neo4j.py @@ -0,0 +1,34 @@ +from neo4j import GraphDatabase +from neo4j.exceptions import ServiceUnavailable, AuthError + +def check_neo4j_status(uri="bolt://localhost:7687", username="neo4j", password="0123456789"): + """ + 检查 Neo4j 数据库是否可以连接并正常工作。 + + 参数: + uri (str): Neo4j 的 URI，默认为 "bolt://localhost:7687" + username (str): 数据库用户名，默认为 "neo4j" + password (str): 数据库密码，默认为 "0123456789" + + 返回: + str: "OK" 表示连接成功，"UNAVAILABLE" 表示服务不可用，"AUTH_FAILED" 表示认证失败。 + """ + try: + driver = GraphDatabase.driver(uri, auth=(username, password)) + with driver.session() as session: + # 简单的查询来测试连接 + result = session.run("RETURN 1") + if result.single()[0] == 1: + return "OK" + except ServiceUnavailable: + return "UNAVAILABLE" + except AuthError: + return "AUTH_FAILED" + finally: + # 确保关闭驱动 + driver.close() + +# 测试函数 +status = check_neo4j_status() +print(f"Neo4j status: {status}") + diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 00000000..0cbf80b3 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,18 @@ +dashscope==1.20.5 +FlagEmbedding==1.2.11 +Flask==3.0.3 +Flask_Cors==4.0.1 +llama_index==0.11.1 +neo4j==5.23.1 +openai==1.42.0 +paddleocr==2.8.1 +pymilvus==2.4.5 +python-dotenv==1.0.1 +PyYAML==6.0.2 +qianfan==0.4.6 +torch==2.4.0 +tqdm==4.66.5 +zhipuai==2.1.4.20230814 +PyMuPDF +llama-index-readers-file +peft \ No newline at end of file diff --git a/src/config/__init__.py b/src/config/__init__.py index b98038c4..50ae6ce7 100644 --- a/src/config/__init__.py +++ b/src/config/__init__.py @@ -38,12 +38,12 @@ class Config(SimpleConfig): ### >>> 默认配置 # 可以在 config/base.yaml 中覆盖 - self.add_item("mode", default="cli", des="运行模式", choices=["cli", "api"]) self.add_item("stream", default=True, des="是否开启流式输出") self.add_item("save_dir", default="saves", des="保存目录") # 功能选项 self.add_item("enable_reranker", default=False, des="是否开启重排序") self.add_item("enable_knowledge_base", default=False, des="是否开启知识库") + self.add_item("enable_knowledge_graph", default=False, des="是否开启知识图谱") self.add_item("enable_search_engine", default=False, des="是否开启搜索引擎") # 模型配置 @@ -70,6 +70,13 @@ class Config(SimpleConfig): "choices": choices } + def __dict__(self): + blocklist = [ + "_config_items", + "model_names", + ] + return {k: v for k, v in self.items() if k not in blocklist} + def handle_self(self): ### handle local model model_root_dir = os.getenv("MODEL_ROOT_DIR", "pretrained_models") @@ -98,7 +105,6 @@ class Config(SimpleConfig): content = f.read() if content: local_config = json.loads(content) - local_config.pop("_config_items") self.update(local_config) else: print(f"{self.filename} is empty.") @@ -108,7 +114,6 @@ class Config(SimpleConfig): content = f.read() if content: local_config = yaml.safe_load(content) - local_config.pop("_config_items") self.update(local_config) else: print(f"{self.filename} is empty.") diff --git a/src/core/database.py b/src/core/database.py index 05b7b432..3966b6dc 100644 --- a/src/core/database.py +++ b/src/core/database.py @@ -8,46 +8,6 @@ from src.models.embedding import get_embedding_model logger = setup_logger("DataBaseManager") -class DataBaseLite: - def __init__(self, name, description, db_type, dimension=None, **kwargs) -> None: - self.name = name - self.description = description - self.db_type = db_type - self.dimension = dimension - self.db_id = kwargs.get("db_id", hashstr(name)) - self.metaname = kwargs.get("metaname", f"{db_type[:1]}{hashstr(name)}") - self.metadata = kwargs.get("metaname", {}) - self.files = kwargs.get("files", []) - self.embed_model = kwargs.get("embed_model", None) - - def id2file(self, file_id): - for f in self.files: - if f["file_id"] == file_id: - return f - return None - - def update(self, metadata): - self.metadata = metadata - - def to_dict(self): - return { - "name": self.name, - "description": self.description, - "db_type": self.db_type, - "db_id": self.db_id, - "embed_model": self.embed_model, - "metaname": self.metaname, - "metadata": self.metadata, - "files": self.files, - "dimension": self.dimension - } - - def to_json(self): - return json.dumps(self.to_dict(), ensure_ascii=False) - - def __str__(self): - return self.to_json() - class DataBaseManager: def __init__(self, config=None) -> None: @@ -111,13 +71,16 @@ class DataBaseManager: return {"databases": [db.to_dict() for db in self.data["databases"]]} def get_graph(self): - if self.config.enable_graph_base: + if self.config.enable_knowledge_graph: self.data["graph"].update(self.graph_base.get_database_info("neo4j")) return {"graph": self.data["graph"]} else: return {"message": "Graph base not enabled", "graph": {}} def create_database(self, database_name, description, db_type, dimension): + from src.config import EMBED_MODEL_INFO + dimension = dimension or EMBED_MODEL_INFO[self.config.embed_model]["dimension"] + new_database = DataBaseLite(database_name, description, db_type, @@ -134,7 +97,7 @@ class DataBaseManager: if db.embed_model != self.config.embed_model: logger.error(f"Embed model not match, {db.embed_model} != {self.config.embed_model}") - return {"message": "Embed model not match", "status": "failed"} + return {"message": f"Embed model not match, cur: {self.config.embed_model}", "status": "failed"} new_files = [] for file in files: @@ -208,7 +171,6 @@ class DataBaseManager: logger.error(f"File format not supported, only support {support_format}") raise Exception(f"File format not supported, only support {support_format}") - def delete_file(self, db_id, file_id): db = self.get_kb_by_id(db_id) file_idx_to_delete = [idx for idx, f in enumerate(db.files) if f["file_id"] == file_id][0] @@ -252,4 +214,45 @@ class DataBaseManager: for db in self.data["databases"]: if db.db_id == db_id: return db - return None \ No newline at end of file + return None + + +class DataBaseLite: + def __init__(self, name, description, db_type, dimension=None, **kwargs) -> None: + self.name = name + self.description = description + self.db_type = db_type + self.dimension = dimension + self.db_id = kwargs.get("db_id", hashstr(name)) + self.metaname = kwargs.get("metaname", f"{db_type[:1]}{hashstr(name)}") + self.metadata = kwargs.get("metaname", {}) + self.files = kwargs.get("files", []) + self.embed_model = kwargs.get("embed_model", None) + + def id2file(self, file_id): + for f in self.files: + if f["file_id"] == file_id: + return f + return None + + def update(self, metadata): + self.metadata = metadata + + def to_dict(self): + return { + "name": self.name, + "description": self.description, + "db_type": self.db_type, + "db_id": self.db_id, + "embed_model": self.embed_model, + "metaname": self.metaname, + "metadata": self.metadata, + "files": self.files, + "dimension": self.dimension + } + + def to_json(self): + return json.dumps(self.to_dict(), ensure_ascii=False) + + def __str__(self): + return self.to_json() \ No newline at end of file diff --git a/src/core/graphbase.py b/src/core/graphbase.py index 70d97255..664b4729 100644 --- a/src/core/graphbase.py +++ b/src/core/graphbase.py @@ -9,11 +9,12 @@ import warnings from src.plugins import pdf2txt from src.plugins.oneke import OneKE +from src.utils import setup_logger + +logger = setup_logger("server-graphbase") warnings.filterwarnings("ignore", category=UserWarning) - - UIE_MODEL = None class GraphDatabase: @@ -36,6 +37,16 @@ class GraphDatabase: """关闭数据库连接""" self.driver.close() + def get_sample_nodes(self, kgdb_name='neo4j', num=50): + """获取指定数据库的前 num 个节点信息""" + self.use_database(kgdb_name) + def query(tx, num): + result = tx.run("MATCH (n)-[r]->(m) RETURN n, r, m LIMIT $num", num=int(num)) + return result.values() + + with self.driver.session() as session: + return session.execute_read(query, num) + def create_graph_database(self, kgdb_name): """创建新的数据库，如果已存在则返回已有数据库的名称""" with self.driver.session() as session: @@ -116,21 +127,25 @@ class GraphDatabase: MERGE (t:Entity {name: $t}) MERGE (h)-[r:RELATION {type: $r}]->(t) """, h=entry['h'], t=entry['t'], r=entry['r']) - def _create_vector_index(tx): - index_name = "entity-embeddings" + def _create_vector_index(tx, dim): + index_name = "entityEmbeddings" if not _index_exists(tx, index_name): tx.run(f""" CREATE VECTOR INDEX {index_name} FOR (n: Entity) ON (n.embedding) OPTIONS {{indexConfig: {{ - `vector.dimensions`: 1024, + `vector.dimensions`: {dim}, `vector.similarity_function`: 'cosine' }} }}; """) + + from src.config import EMBED_MODEL_INFO + embed_info = EMBED_MODEL_INFO[self.config.embed_model] with self.driver.session() as session: session.execute_write(_create_graph, triples) - session.execute_write(_create_vector_index) - for entry in triples: + session.execute_write(_create_vector_index, embed_info.dimension) + for i, entry in enumerate(triples): + logger.info(f"Adding entity {i+1}/{len(triples)}") embedding_h = self.get_embedding(entry['h']) session.execute_write(self.set_embedding, entry['h'], embedding_h) @@ -148,37 +163,39 @@ class GraphDatabase: triples = list(read_triples(file_path)) - def batch_create(tx, triples): - query = """ - UNWIND $triples AS triple - MERGE (a:Entity {name: triple.h}) - MERGE (b:Entity {name: triple.t}) - MERGE (a)-[r:RELATION {type: triple.r}]->(b) - """ - tx.run(query, triples=triples) + self.txt_add_vector_entity(triples, kgdb_name) - def batch_add_embeddings(tx, embeddings): - query = """ - UNWIND $embeddings AS embedding - MATCH (e:Entity {name: embedding.name}) - SET e.embedding = embedding.vector - """ - tx.run(query, embeddings=embeddings) - - with self.driver.session() as session: - session.execute_write(batch_create, triples) - - # 获取embedding并批量添加 - embeddings = [] - for triple in triples: - h = triple['h'] - t = triple['t'] - embedding_h = self.get_embedding(h) - embedding_t = self.get_embedding(t) - embeddings.append({"name": h, "vector": embedding_h}) - embeddings.append({"name": t, "vector": embedding_t}) - - session.execute_write(batch_add_embeddings, embeddings) + # def batch_create(tx, triples): + # query = """ + # UNWIND $triples AS triple + # MERGE (a:Entity {name: triple.h}) + # MERGE (b:Entity {name: triple.t}) + # MERGE (a)-[r:RELATION {type: triple.r}]->(b) + # """ + # tx.run(query, triples=triples) + # + # def batch_add_embeddings(tx, embeddings): + # query = """ + # UNWIND $embeddings AS embedding + # MATCH (e:Entity {name: embedding.name}) + # SET e.embedding = embedding.vector + # """ + # tx.run(query, embeddings=embeddings) + # + # with self.driver.session() as session: + # session.execute_write(batch_create, triples) + # + # # 获取embedding并批量添加 + # embeddings = [] + # for triple in triples: + # h = triple['h'] + # t = triple['t'] + # embedding_h = self.get_embedding(h) + # embedding_t = self.get_embedding(t) + # embeddings.append({"name": h, "vector": embedding_h}) + # embeddings.append({"name": t, "vector": embedding_t}) + # + # session.execute_write(batch_add_embeddings, embeddings) self.status = "open" return kgdb_name @@ -260,13 +277,21 @@ class GraphDatabase: with self.driver.session() as session: return session.execute_read(query, keyword, hops) + def query_node(self, entity_name, args): + # TODO 添加判断节点数量为 0 停止检索 + + if args.get("exact_match"): + raise NotImplemented("not implement for `exact_match`") + else: + return self.query_by_vector(entity_name, kgdb_name=args.get("kgdb_name"), hops=args.get("hops")) + def query_by_vector_tep(self, keyword, kgdb_name='neo4j'): """向量查询""" self.use_database(kgdb_name) def query(tx, text): embedding = self.get_embedding(text) result = tx.run(""" - CALL db.index.vector.queryNodes('entity-embeddings', 10, $embedding) + CALL db.index.vector.queryNodes('entityEmbeddings', 10, $embedding) YIELD node AS similarEntity, score RETURN similarEntity.name AS name, score """, embedding=embedding) @@ -277,7 +302,7 @@ class GraphDatabase: with self.driver.session() as session: return session.execute_read(query, keyword) - def query_by_vector(self, entity_name, threshold=0.9,kgdb_name='neo4j', hops=2, num_of_res=2): + def query_by_vector(self, entity_name, threshold=0.9, kgdb_name='neo4j', hops=2, num_of_res=2): self.use_database(kgdb_name) result = self.query_by_vector_tep(entity_name) querys = [] diff --git a/src/core/retriever.py b/src/core/retriever.py index 2025f3a2..2071f3cb 100644 --- a/src/core/retriever.py +++ b/src/core/retriever.py @@ -83,7 +83,7 @@ class Retriever: r["file"] = kb.id2file(r["entity"]["file_id"]) if self.config.enable_reranker: - RERANK_THRESHOLD = 0.1 + RERANK_THRESHOLD = 0.001 for r in kb_res: r["rerank_score"] = self.reranker.compute_score([query, r["entity"]["text"]], normalize=True) kb_res.sort(key=lambda x: x["rerank_score"], reverse=True) @@ -124,7 +124,46 @@ class Retriever: return entities + def foramt_general_results(self, results): + logger.debug(f"Formatting general results: {results}") + formatted_results = {"nodes": [], "edges": []} + + for item in results: + relationship = item[1] + rel_id = relationship.element_id + nodes = relationship.nodes + if len(nodes) != 2: + continue + + source, target = nodes + + source_id = source.element_id + target_id = target.element_id + source_name = source._properties.get('name', 'unknown') + target_name = target._properties.get('name', 'unknown') + + if source_id not in formatted_results["nodes"]: + formatted_results["nodes"].append({"id": source_id, "name": source_name}) + if target_id not in formatted_results["nodes"]: + formatted_results["nodes"].append({"id": target_id, "name": target_name}) + + relationship_type = relationship._properties.get('type', 'unknown') + if relationship_type == 'unknown': + relationship_type = relationship.type + + formatted_results["edges"].append({ + "id": rel_id, + "type": relationship_type, + "source_id": source_id, + "target_id": target_id, + "source_name": source_name, + "target_name": target_name + }) + + return formatted_results + def format_query_results(self, results): + logger.debug(f"Formatting query results: {results}") formatted_results = {"nodes": [], "edges": []} node_dict = {} diff --git a/src/models/embedding.py b/src/models/embedding.py index e7c482d9..9eb81ee2 100644 --- a/src/models/embedding.py +++ b/src/models/embedding.py @@ -45,10 +45,11 @@ class ZhipuEmbedding: self.query_instruction_for_retrieval = "为这个句子生成表示以用于检索相关文章：" def predict(self, message): - data = [] for i in range(0, len(message), 10): + if len(message) > 10: + logger.info(f"Encoding {i} to {i+10} with {len(message)} messages") group_msg = message[i:i+10] response = self.client.embeddings.create( model=self.model_info.default_path, diff --git a/src/requirements.txt b/src/requirements.txt deleted file mode 100644 index 29577253..00000000 --- a/src/requirements.txt +++ /dev/null @@ -1,6 +0,0 @@ -FlagEmbedding==1.2.10 -Flask==3.0.3 -Flask_Cors==4.0.1 -openai==1.35.10 -python-dotenv==1.0.1 -PyYAML==6.0.1 diff --git a/src/views/database_view.py b/src/views/database_view.py index 43bf1b75..98adcfae 100644 --- a/src/views/database_view.py +++ b/src/views/database_view.py @@ -123,9 +123,20 @@ def get_graph_node(): return jsonify({'message': 'entity_name and kgdb_name are required'}), 400 logger.debug(f"Get graph node {entity_name} in {kgdb_name} with {hops} hops") - result = startup.dbm.graph_base.query_by_vector(entity_name, kgdb_name=kgdb_name, hops=hops) + result = startup.dbm.graph_base.query_node(entity_name, request.args) return jsonify({'result': startup.retriever.format_query_results(result), 'message': 'success'}), 200 +@db.route('/graph/nodes', methods=['GET']) +def get_graph_nodes(): + kgdb_name = request.args.get('kgdb_name') + num = request.args.get('num') + if not kgdb_name: + return jsonify({'message': 'kgdb_name is required'}), 400 + + logger.debug(f"Get graph nodes in {kgdb_name} with {num} nodes") + result = startup.dbm.graph_base.get_sample_nodes(kgdb_name, num) + return jsonify({'result': startup.retriever.foramt_general_results(result), 'message': 'success'}), 200 + @db.route('/graph/add', methods=['POST']) def add_graph_entity(): data = json.loads(request.data) diff --git a/web/index.html b/web/index.html index 251b07a4..e61e329a 100644 --- a/web/index.html +++ b/web/index.html @@ -1,5 +1,5 @@ - + diff --git a/web/package.json b/web/package.json index 765b9d14..e95d555b 100644 --- a/web/package.json +++ b/web/package.json @@ -12,7 +12,7 @@ }, "dependencies": { "@ant-design/icons-vue": "^6.1.0", - "@antv/g6": "^5.0.9", + "@antv/g6": "^5.0.17", "@vueuse/core": "^10.11.0", "ant-design-vue": "^4.2.3", "axios": "^1.3.4", diff --git a/web/src/assets/base.css b/web/src/assets/base.css index dbc51192..fcc4b5d6 100644 --- a/web/src/assets/base.css +++ b/web/src/assets/base.css @@ -11,6 +11,7 @@ --main-100: #ABE0F7; --main-50: #CDF5FF; --main-25: #E6FAFF; + --main-10: #F5FDFF; --c-white: #ffffff; --c-white-soft: #f8f8f8; diff --git a/web/src/assets/main.css b/web/src/assets/main.css index 9005a704..2deb3a7b 100644 --- a/web/src/assets/main.css +++ b/web/src/assets/main.css @@ -2,4 +2,16 @@ :root { --header-height: 60px; +} + +/* layout */ + +.layout-container { + width: 100%; + padding: 0px 30px; + background-color: #FCFEFF; + + h2 { + margin: 20px 0 10px 0; + } } \ No newline at end of file diff --git a/web/src/components/ChatComponent.vue b/web/src/components/ChatComponent.vue index 93f51c8a..3b72d028 100644 --- a/web/src/components/ChatComponent.vue +++ b/web/src/components/ChatComponent.vue @@ -14,7 +14,7 @@ class="newchat nav-btn" @click="$emit('newconv')" > - 新对话 {{ configStore.config?.model_name }} + 新对话：{{ configStore.config?.model_name }}
@@ -107,11 +107,12 @@ :class="message.role" >

{{ message.text }}

-
+
+
请求错误，请重试

{ return acc; }, {}) } + scrollToBottom() } const simpleCall = (message) => { @@ -403,6 +406,11 @@ const sendMessage = () => { } return readChunk() }) + .catch((error) => { + console.error(error) + updateStatus(cur_res_id, "error") + isStreaming.value = false + }) } else { console.log('请输入消息') } @@ -573,7 +581,7 @@ watch( max-width: 900px; margin: 0 auto; flex-grow: 1; - padding: 1rem; + padding: 1rem 2rem; display: flex; flex-direction: column; @@ -591,6 +599,15 @@ watch( color: black; /* box-shadow: 0px 0.3px 0.9px rgba(0, 0, 0, 0.12), 0px 1.6px 3.6px rgba(0, 0, 0, 0.16); */ /* animation: slideInUp 0.1s ease-in; */ + + .err-msg { + color: red; + border: 1px solid red; + padding: 0.2rem 1rem; + border-radius: 8px; + text-align: center; + background: #FFEBEE; + } } .message-box.sent { @@ -623,8 +640,6 @@ watch( word-wrap: break-word; margin-bottom: 0; } - - } @@ -773,7 +788,7 @@ button:disabled { @keyframes loading {0%,80%,100%{transform:scale(0.5);}40%{transform:scale(1);}} .slide-out-left{-webkit-animation:slide-out-left .2s cubic-bezier(.55,.085,.68,.53) both;animation:slide-out-left .5s cubic-bezier(.55,.085,.68,.53) both} -.swing-in-top-fwd {-webkit-animation: swing-in-top-fwd 0.2s cubic-bezier(0.175, 0.885, 0.320, 1.275) both;animation: swing-in-top-fwd 0.5s cubic-bezier(0.175, 0.885, 0.320, 1.275) both;} +.swing-in-top-fwd{-webkit-animation:swing-in-top-fwd .2s ease-out both;animation:swing-in-top-fwd .2s ease-out both} @-webkit-keyframes swing-in-top-fwd{0%{-webkit-transform:rotateX(-100deg);transform:rotateX(-100deg);-webkit-transform-origin:top;transform-origin:top;opacity:0}100%{-webkit-transform:rotateX(0deg);transform:rotateX(0deg);-webkit-transform-origin:top;transform-origin:top;opacity:1}}@keyframes swing-in-top-fwd{0%{-webkit-transform:rotateX(-100deg);transform:rotateX(-100deg);-webkit-transform-origin:top;transform-origin:top;opacity:0}100%{-webkit-transform:rotateX(0deg);transform:rotateX(0deg);-webkit-transform-origin:top;transform-origin:top;opacity:1}} @-webkit-keyframes slide-out-left{0%{-webkit-transform:translateX(0);transform:translateX(0);opacity:1}100%{-webkit-transform:translateX(-1000px);transform:translateX(-1000px);opacity:0}}@keyframes slide-out-left{0%{-webkit-transform:translateX(0);transform:translateX(0);opacity:1}100%{-webkit-transform:translateX(-1000px);transform:translateX(-1000px);opacity:0}} diff --git a/web/src/components/RefsComponent.vue b/web/src/components/RefsComponent.vue index 3e83fae8..895411f9 100644 --- a/web/src/components/RefsComponent.vue +++ b/web/src/components/RefsComponent.vue @@ -1,8 +1,8 @@