From 16d736e3aa871b22894ae8f18a2f2b6276853c2d Mon Sep 17 00:00:00 2001 From: "qinyong@9artedu.com" Date: Mon, 15 Jun 2026 09:27:28 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=9D=E5=A7=8B=E5=8C=96=E9=A1=B9=E7=9B=AE?= =?UTF-8?q?=EF=BC=8C=E5=AE=8C=E6=95=B4=E4=B8=9A=E5=8A=A1=E4=BB=A3=E7=A0=81?= =?UTF-8?q?=E6=8F=90=E4=BA=A4?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .gitignore | 3 + .idea/.gitignore | 8 + .../inspectionProfiles/profiles_settings.xml | 6 + .idea/misc.xml | 7 + .idea/modules.xml | 8 + .idea/sales-assistant-py.iml | 10 + .idea/vcs.xml | 6 + app/__init__.py | 0 app/__pycache__/__init__.cpython-312.pyc | Bin 0 -> 139 bytes app/__pycache__/llm.cpython-312.pyc | Bin 0 -> 610 bytes app/aa.md | 1 + app/client/__init__.py | 0 .../__pycache__/__init__.cpython-312.pyc | Bin 0 -> 146 bytes .../embedding_client_manager.cpython-312.pyc | Bin 0 -> 4264 bytes .../milvus_client_manager.cpython-312.pyc | Bin 0 -> 3104 bytes 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...rchive_messages_repository.cpython-312.pyc | Bin 0 -> 3979 bytes app/repository/archive_messages_repository.py | 72 + app/repository/milvus/__init__.py | 0 .../__pycache__/__init__.cpython-312.pyc | Bin 0 -> 157 bytes .../summary_repository.cpython-312.pyc | Bin 0 -> 7654 bytes app/repository/milvus/summary_repository.py | 156 + app/summary/__init__.py | 4 + app/summary/router.py | 26 + app/summary/service.py | 180 + app/summary/task.py | 88 + conf/app_config.yaml | 35 + conf/sql.sql | 0 docker/docker-compose.yml | 80 + .../bge-large-zh-v1.5/.gitattributes | 35 + .../bge-large-zh-v1.5/1_Pooling/config.json | 7 + docker/embedding/bge-large-zh-v1.5/README.md | 427 + .../embedding/bge-large-zh-v1.5/config.json | 40 + .../config_sentence_transformers.json | 7 + .../embedding/bge-large-zh-v1.5/modules.json | 20 + .../bge-large-zh-v1.5/pytorch_model.bin | 3 + .../sentence_bert_config.json | 4 + .../bge-large-zh-v1.5/special_tokens_map.json | 7 + .../bge-large-zh-v1.5/tokenizer.json | 21278 ++++++++++++++++ .../bge-large-zh-v1.5/tokenizer_config.json | 15 + docker/embedding/bge-large-zh-v1.5/vocab.txt | 21128 +++++++++++++++ main.py | 28 + pyproject.toml | 23 + uv.lock | 3051 +++ 64 files changed, 47090 insertions(+) create mode 100644 .gitignore create mode 100644 .idea/.gitignore create mode 100644 .idea/inspectionProfiles/profiles_settings.xml create mode 100644 .idea/misc.xml create mode 100644 .idea/modules.xml create mode 100644 .idea/sales-assistant-py.iml create mode 100644 .idea/vcs.xml create mode 100644 app/__init__.py create mode 100644 app/__pycache__/__init__.cpython-312.pyc create mode 100644 app/__pycache__/llm.cpython-312.pyc create mode 100644 app/aa.md create mode 100644 app/client/__init__.py create mode 100644 app/client/__pycache__/__init__.cpython-312.pyc create mode 100644 app/client/__pycache__/embedding_client_manager.cpython-312.pyc create mode 100644 app/client/__pycache__/milvus_client_manager.cpython-312.pyc create mode 100644 app/client/__pycache__/mysql_client_manager.cpython-312.pyc create mode 100644 app/client/embedding_client_manager.py create mode 100644 app/client/milvus_client_manager.py create mode 100644 app/client/mysql_client_manager.py create mode 100644 app/conf/__init__.py create mode 100644 app/conf/__pycache__/__init__.cpython-312.pyc create mode 100644 app/conf/__pycache__/app_config.cpython-312.pyc create mode 100644 app/conf/app_config.py create mode 100644 app/dto/__init__.py create mode 100644 app/dto/__pycache__/__init__.cpython-312.pyc create mode 100644 app/dto/__pycache__/summary_dto.cpython-312.pyc create mode 100644 app/dto/summary_dto.py create mode 100644 app/llm.py create mode 100644 app/models/__init__.py create mode 100644 app/models/__pycache__/__init__.cpython-312.pyc create mode 100644 app/models/mysql/__init__.py create mode 100644 app/models/mysql/__pycache__/__init__.cpython-312.pyc create mode 100644 app/models/mysql/__pycache__/archive_messages.cpython-312.pyc create mode 100644 app/models/mysql/archive_messages.py create mode 100644 app/repository/__init__.py create mode 100644 app/repository/__pycache__/__init__.cpython-312.pyc create mode 100644 app/repository/__pycache__/archive_messages_repository.cpython-312.pyc create mode 100644 app/repository/archive_messages_repository.py create mode 100644 app/repository/milvus/__init__.py create mode 100644 app/repository/milvus/__pycache__/__init__.cpython-312.pyc create mode 100644 app/repository/milvus/__pycache__/summary_repository.cpython-312.pyc create mode 100644 app/repository/milvus/summary_repository.py create mode 100644 app/summary/__init__.py create mode 100644 app/summary/router.py create mode 100644 app/summary/service.py create mode 100644 app/summary/task.py create mode 100644 conf/app_config.yaml create mode 100644 conf/sql.sql create mode 100644 docker/docker-compose.yml create mode 100644 docker/embedding/bge-large-zh-v1.5/.gitattributes create mode 100644 docker/embedding/bge-large-zh-v1.5/1_Pooling/config.json create mode 100644 docker/embedding/bge-large-zh-v1.5/README.md create mode 100644 docker/embedding/bge-large-zh-v1.5/config.json create mode 100644 docker/embedding/bge-large-zh-v1.5/config_sentence_transformers.json create mode 100644 docker/embedding/bge-large-zh-v1.5/modules.json create mode 100644 docker/embedding/bge-large-zh-v1.5/pytorch_model.bin create mode 100644 docker/embedding/bge-large-zh-v1.5/sentence_bert_config.json create mode 100644 docker/embedding/bge-large-zh-v1.5/special_tokens_map.json create mode 100644 docker/embedding/bge-large-zh-v1.5/tokenizer.json create mode 100644 docker/embedding/bge-large-zh-v1.5/tokenizer_config.json create mode 100644 docker/embedding/bge-large-zh-v1.5/vocab.txt create mode 100644 main.py create mode 100644 pyproject.toml create mode 100644 uv.lock diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..162a70d --- /dev/null +++ b/.gitignore @@ -0,0 +1,3 @@ +# 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result.get("embeddings", []) + + async def aembed_query(self, text: str) -> list: + embeddings = await self.aembed_documents([text]) + return embeddings[0] if embeddings else [] + + +embedding_client = EmbeddingClientManager(app_config.embedding) + +if __name__ == "__main__": + print("Testing EmbeddingClientManager...") + + async def test(): + try: + embedding_client.init(wait_for_ready=True) + print("Initialization successful") + + text = "What is Deep Learning?" + print(f"Test text: {text}") + + result = await embedding_client.aembed_query(text) + print(f"Embedding successful") + print(f"Vector length: {len(result)}") + print(f"First 10 values: {result[:10]}") + + await embedding_client.close() + except Exception as e: + print(f"Test failed: {str(e)}") + + asyncio.run(test()) \ No newline at end of file diff --git a/app/client/milvus_client_manager.py b/app/client/milvus_client_manager.py new file mode 100644 index 0000000..505fbb1 --- /dev/null +++ b/app/client/milvus_client_manager.py @@ -0,0 +1,59 @@ +from typing import Optional + +from pymilvus import MilvusClient as PyMilvusClient + +from app.conf.app_config import MilvusConfig, app_config + + +class MilvusClientWrapper: + """Milvus 客户端单例包装器""" + _instance = None + _initialized = False + + def __new__(cls, milvus_config: MilvusConfig = None): + if cls._instance is None: + cls._instance = super(MilvusClientWrapper, cls).__new__(cls) + return cls._instance + + def __init__(self, milvus_config: MilvusConfig = None): + if self._initialized: + return + + if milvus_config is None: + milvus_config = app_config.milvus + + self.milvus_config = milvus_config + self.client: Optional[PyMilvusClient] = None + self._initialized = True + + def init(self): + if self.client is not None: + return + + uri = f"http://{self.milvus_config.host}:{self.milvus_config.port}" + self.client = PyMilvusClient( + uri=uri, + user=self.milvus_config.user, + password=self.milvus_config.password + ) + + def close(self): + if self.client is not None: + self.client.close() + self.client = None + + def has_collection(self, collection_name: str) -> bool: + self._ensure_connection() + return self.client.has_collection(collection_name) + + def drop_collection(self, collection_name: str): + self._ensure_connection() + if self.client.has_collection(collection_name): + self.client.drop_collection(collection_name) + + def _ensure_connection(self): + if self.client is None: + self.init() + + +milvus_client = MilvusClientWrapper() \ No newline at end of file diff --git a/app/client/mysql_client_manager.py b/app/client/mysql_client_manager.py new file mode 100644 index 0000000..75d2aec --- /dev/null +++ b/app/client/mysql_client_manager.py @@ -0,0 +1,45 @@ +import asyncio +from typing import Optional + +from sqlalchemy import text +from sqlalchemy.ext.asyncio import create_async_engine, AsyncEngine, async_sessionmaker + +from app.conf.app_config import DBConfig, app_config + + +class MysqlClientManager: + def __init__(self, db_config: DBConfig): + self.db_config = db_config + self.engine: Optional[AsyncEngine] = None + self.session_factory = None + + def _get_url(self): + return f"mysql+asyncmy://{self.db_config.user}:{self.db_config.password}@{self.db_config.host}:{self.db_config.port}/{self.db_config.database}?charset=utf8mb4" + + def init(self): + self.engine = create_async_engine(url=self._get_url(), + pool_size=10, + pool_pre_ping=True) + self.session_factory = async_sessionmaker(db_assistant_mysql_client_manager.engine, + autoflush=True, + expire_on_commit=False) + + async def close(self): + await self.engine.dispose() + + +db_assistant_mysql_client_manager = MysqlClientManager(app_config.db_assistant) + +if __name__ == '__main__': + db_assistant_mysql_client_manager.init() + + + async def test(): + async with db_assistant_mysql_client_manager.session_factory() as session: + result = await session.execute(text("select * from archive_messages limit 10")) + rows = result.mappings().fetchall() + print(type(rows[0])) + print(rows) + + + asyncio.run(test()) diff --git a/app/conf/__init__.py b/app/conf/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/app/conf/__pycache__/__init__.cpython-312.pyc b/app/conf/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..9730bdd6a6d5e718cee1ac2e62ebe75fa538fb7a GIT binary patch literal 144 zcmX@j%ge<81l&#PSs?l`h(HIQS%4zb87dhx8U0o=6fpsLpFwJV>APCRB7%Q6rT0&+TQhV9H+5U(h}6Bb<{cj`J_d(G^o z;G7~QNI@Ln)}9c@Ry}a#$ejx&K`|qNDj^QN8RdZB#Cx;rbs8I5iIw)-H}k!D``)~_ zZ}$6CN+)nFel@rHmqN&IIOt!|K-fPUA>=-}Ni1SXHfcx|NkTbdM;dZP7P4$B4Yi_5 zgh~-|omk2ivDC+~4zI`lgH)mc8wEBt#KrZIiWKc!!?zDE*i7u&_s)KgR`NnEo`&#c;}>%u^J-}LU_$Yc|SB!}w^;v;#(2aU7dx48v?T11l~l z$*#v9k$sHe@7Jzgx>a>7ddoF!>Yjt>>#k=uy>qShEwj}Eu6Y{;1BH6+e5=i3uuec6 z1otSwr{qa8ySdm+q&IJL_0;Awc(--KS45rv=}v0VsnzPu8kR=A#+BQcz!+>2`dBVS zSsMMwAY>85rsYr)4jyuP3JHWsxLphjoYrwSQ~6yb?|Y5>Xs_pidEO?9%Ji!cYt*ko 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File: + enable: bool + level: str + path: str + rotation: str + retention: str + + +@dataclass +class Console: + enable: bool + level: str + + +@dataclass +class LoggingConfig: + file: File + console: Console + +# 数据库配置 +@dataclass +class DBConfig: + host: str + port: int + user: str + password: str + database: str + +@dataclass +class EmbeddingConfig: + host: str + port: int + model: str + +@dataclass +class MilvusConfig: + host: str + port: int + user: str + password: str + embedding_size: int + +@dataclass +class LLMConfig: + model_name: str + api_key: str + base_url: str + +@dataclass +class AppConfig: + logging: LoggingConfig + db_assistant: DBConfig + embedding: EmbeddingConfig + llm: LLMConfig + milvus: MilvusConfig + + +config_file = Path(__file__).parents[2] / 'conf' / 'app_config.yaml' +context = OmegaConf.load(config_file) +schema = OmegaConf.structured(AppConfig) +app_config: AppConfig = OmegaConf.to_object(OmegaConf.merge(schema, context)) + +if __name__ == '__main__': + print(app_config.db_assistant.host) diff --git a/app/dto/__init__.py b/app/dto/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/app/dto/__pycache__/__init__.cpython-312.pyc b/app/dto/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..830bd6015a8f6eabdd2f7bc94f10eeccf7c45567 GIT binary patch literal 143 zcmX@j%ge<81e24rvOx4>5P=Rpvj9b=GgLBYGWxA#C}INgK7-W!(sQ+nNzPA6jVVsd zNiEh*EH2J0E=kNQ(JiQqNh~OcNh!&XiI30B%PfhH*DI*J#bJ}1pHiBWYFESx)XoUR Q#UREIYBv>gnF^$-Sg<;5y64^`KnJuF2assvW;*K$Z}1t@D9?T@kf5&!uLe|G=? literal 0 HcmV?d00001 diff --git a/app/dto/summary_dto.py b/app/dto/summary_dto.py new file mode 100644 index 0000000..3f5bc71 --- /dev/null +++ b/app/dto/summary_dto.py @@ -0,0 +1,9 @@ +from dataclasses import dataclass + + +@dataclass +class SummaryDto: + msg_date: str + from_user: str + to_user: str + summary: str \ No newline at end of file diff --git a/app/llm.py b/app/llm.py new file mode 100644 index 0000000..6498535 --- /dev/null +++ b/app/llm.py @@ -0,0 +1,5 @@ +from langchain.chat_models import init_chat_model + +from app.conf.app_config import app_config + +llm = init_chat_model(model=app_config.llm.model_name, api_key=app_config.llm.api_key, base_url=app_config.llm.base_url ,temperature=1,extra_body={"thinking": {"type": "disabled"}},) diff --git a/app/models/__init__.py b/app/models/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/app/models/__pycache__/__init__.cpython-312.pyc b/app/models/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3c5c6ab64a8ddb54b15157d495e82cc06cbee75c GIT binary patch literal 146 zcmX@j%ge<81UAZASs?l`h(HIQS%4zb87dhx8U0o=6fpsLpFwJV8M<1{x7 literal 0 HcmV?d00001 diff --git a/app/models/mysql/__init__.py b/app/models/mysql/__init__.py new file mode 100644 index 0000000..ca4cf59 --- /dev/null +++ b/app/models/mysql/__init__.py @@ -0,0 +1,3 @@ +from .archive_messages import ArchiveMessages + +__all__ = ["ArchiveMessages"] diff --git a/app/models/mysql/__pycache__/__init__.cpython-312.pyc b/app/models/mysql/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..6ddba71e797b39f0b4448885e0279456c8ece35a GIT binary patch literal 239 zcmX@j%ge<81i6LUS-wE}F^B^LOi;#WF(6|)LkdF*V-7a44*;P z{4#g7ib>8-NsTE^%tlWlSO7?TU}j`wyvv~bfLrDQmuw?@5j#*20LU9Z;Q#;t literal 0 HcmV?d00001 diff --git a/app/models/mysql/__pycache__/archive_messages.cpython-312.pyc b/app/models/mysql/__pycache__/archive_messages.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..314e78e91f80b7d88eeb9d486e47ca0954847a2d GIT binary patch literal 4451 zcma)9YjhLW5#H5XvL(MU7;FqU5QT@rs|h5;fHBk%J8f)wg5pNq=&p_IT}j-PNbS-d zxdCEm6WhFOtGoJvx_0-!wj+T_RB`iw>M9fT0v^# z^D@%>F4k?IMU^HuE87Sc;UfW&cbjF$_CR|>Fv7R7+#&yX;g#p?kB4OwHJ8(E!n>F}c9`*2ljT5{bgghdE*EUmHg#iqPs@e5yIi*VL!!tB 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mapped_column(BigInteger, primary_key=True, autoincrement=True, comment='自增主键') + msgid = mapped_column(String(255), nullable=False, comment='企微消息唯一ID') + seq = mapped_column(BigInteger, nullable=False, comment='存档序列号，用于增量拉取') + corp_id = mapped_column(String(64), nullable=False, comment='企业ID') + action = mapped_column(String(32), default='send', comment='send/recall/agree/disagree') + from_user = mapped_column(String(128), nullable=False, comment='发送者userid') + from_role = mapped_column(String(32), nullable=False, comment='发送者角色: INTERNAL-企业内部成员 EXTERNAL-外部联系人 SYSTEM-系统') + to_user = mapped_column(String(128), nullable=True, comment='接收者userid（单聊）') + tolist = mapped_column(Text, nullable=True, comment='接收者列表（群聊），JSON数组字符串') + roomid = mapped_column(String(255), nullable=True, comment='群聊ID（群聊时有值）') + msgtype = mapped_column(String(64), nullable=False, comment='text/image/voice/video/file/link/location/weapp/chatrecord/voip') + msgtime = mapped_column(BigInteger, nullable=False, comment='消息发送时间，UTC毫秒时间戳') + content = mapped_column(Text, nullable=True, comment='文本消息内容（长文本使用LONGTEXT）') + media_data = mapped_column(Text, nullable=True, comment='媒体消息元数据（sdkfileid/文件大小/时长等），JSON字符串') + session_id = mapped_column(String(128), nullable=True, comment='关联的会话ID（业务生成）') + decrypt_status = mapped_column(Integer, default=1, comment='解密状态: 1成功 2失败') + decrypt_error = mapped_column(String(512), nullable=True, comment='解密失败原因') + created_at = mapped_column(TIMESTAMP, nullable=False, default=datetime.now, comment='创建时间') + updated_at = mapped_column(TIMESTAMP, nullable=False, default=datetime.now, onupdate=datetime.now, comment='更新时间') + + def to_dict(self): + return { + 'id': self.id, + 'msgid': self.msgid, + 'seq': self.seq, + 'corp_id': self.corp_id, + 'action': self.action, + 'from_user': self.from_user, + 'from_role': self.from_role, + 'to_user': self.to_user, + 'tolist': self.tolist, + 'roomid': self.roomid, + 'msgtype': self.msgtype, + 'msgtime': self.msgtime, + 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z9Z%yeYVKpxyK0XOuGb3QG36kvi List[ArchiveMessages]: + """ + 根据日期范围获取消息记录 + + Args: + start_date: 开始日期 (格式: YYYY-MM-DD) + end_date: 结束日期 (格式: YYYY-MM-DD) + + Returns: + ArchiveMessages 对象列表 + """ + base_query = """ + SELECT DATE(created_at) AS created_at, from_user + FROM archive_messages + """ + conditions = [] + params = {} + + if start_date: + conditions.append(" and DATE(created_at) >= :start_date") + params["start_date"] = start_date + + if end_date: + conditions.append("DATE(created_at) <= :end_date") + params["end_date"] = end_date + base_query += " WHERE 1=1 and roomid='' and msgtype='text' " + #base_query += " WHERE (from_user='wmI1AkDQAA3h0jxrRpeaHZhMKeHExA4w' )or( from_user='LiHeYi' and to_user='[\"wmI1AkDQAA3h0jxrRpeaHZhMKeHExA4w\"]') and roomid='' and msgtype='text' " + if conditions: + base_query += " AND ".join(conditions)+" " + base_query += " GROUP BY DATE(created_at), from_user ORDER BY created_at DESC, from_user;" + + query = text(base_query) + result = await self.session.execute(query, params) + return [ArchiveMessages(**dict(row)) for row in result.mappings().fetchall()] + + + async def get_message_statistics_by_date_user(self, day: str = None, from_user: str = None, to_user: str = None) -> List[ArchiveMessages]: + """ + 根据日期+发送人+接收人查询记录 + Returns: + ArchiveMessages 对象列表 + """ + if to_user: + sql = """ + SELECT * FROM archive_messages + WHERE DATE(created_at) = :day + AND from_user = :from_user + AND to_user = :to_user and msgtype='text' + ORDER BY created_at DESC + """ + result = await self.session.execute(text(sql), {"day": day, "from_user": from_user, "to_user": to_user}) + else : + sql = """ + SELECT from_user,to_user FROM archive_messages + WHERE DATE(created_at) = :day + AND from_user = :from_user and msgtype='text' + group by to_user + """ + result = await self.session.execute(text(sql), {"day": day, "from_user": from_user}) + return [ArchiveMessages(**dict(row)) for row in result.mappings().fetchall()] \ No newline at end of file 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milvus_client.init() + + def create_collection(self, dim: int = None) -> bool: + """ + 创建消息摘要集合 + + Args: + dim: 向量维度，默认从配置读取 + + Returns: + 是否成功创建（已存在返回False） + """ + if dim is not None: + self.dim = dim + + if milvus_client.has_collection(self.collection_name): + return False + + schema = milvus_client.client.create_schema( + auto_id=True, + enable_dynamic_field=True, + description="消息摘要向量集合" + ) + + schema.add_field(field_name="id", datatype=DataType.INT64, is_primary=True, auto_id=True) + schema.add_field(field_name="msg_time", datatype=DataType.VARCHAR, max_length=100) + schema.add_field(field_name="msg_type", datatype=DataType.INT8) + schema.add_field(field_name="from_user", datatype=DataType.VARCHAR, max_length=150) + schema.add_field(field_name="to_user", datatype=DataType.VARCHAR, max_length=150) + schema.add_field(field_name="room_id", datatype=DataType.VARCHAR, max_length=150) + schema.add_field(field_name="message_context", datatype=DataType.VARCHAR, max_length=65535) + schema.add_field(field_name="summary", datatype=DataType.VARCHAR, max_length=65535) + schema.add_field(field_name="message_dense_vector", datatype=DataType.FLOAT_VECTOR, dim=self.dim) + schema.add_field(field_name="summary_dense_vector", datatype=DataType.FLOAT_VECTOR, dim=self.dim) + schema.add_field(field_name="created_at", datatype=DataType.INT64) + + index_params = milvus_client.client.prepare_index_params() + index_params.add_index( + field_name="message_dense_vector", + index_type="HNSW", + metric_type="COSINE", + params={"M": 32, "efConstruction": 300} + ) + index_params.add_index( + field_name="summary_dense_vector", + index_type="HNSW", + metric_type="COSINE", + params={"M": 32, "efConstruction": 300} + ) + milvus_client.client.create_collection( + collection_name=self.collection_name, + schema=schema, + index_params=index_params + ) + + return True + + def insert(self, msg_time: List[str], msg_type: List[int], from_user: List[str], + to_user: List[str], room_id: List[str],message_context: List[str], summary: List[str], message_dense_vector: List[List[float]], + summary_dense_vector: List[List[float]]) -> List[int]: + """ + 插入消息摘要数据 + + Args: + msg_time: 消息时间列表 + msg_type: 消息类型列表 + from_user: 发送者列表 + to_user: 接收者列表 + room_id: 群聊ID列表 + summary: 消息摘要列表 + dense_vector: 向量列表 + + Returns: + 插入的数据ID列表 + """ + self._ensure_connection() + self.create_collection() + import time + created_at = [int(time.time() * 1000)] * len(msg_time) + + data = [] + for i in range(len(msg_time)): + data.append({ + "msg_time": msg_time[i], + "msg_type": msg_type[i], + "from_user": from_user[i], + "to_user": to_user[i], + "room_id": room_id[i], + "message_context": message_context[i], + "summary": summary[i], + "message_dense_vector": message_dense_vector[i], + "summary_dense_vector": summary_dense_vector[i], + "created_at": created_at[i] + }) + + result = milvus_client.client.insert( + collection_name=self.collection_name, + data=data + ) + + milvus_client.client.flush(self.collection_name) + return result.get("ids", []) + + def delete(self, expr: str): + """ + 删除数据 + + Args: + expr: 删除条件表达式 + """ + self._ensure_connection() + + milvus_client.client.delete( + collection_name=self.collection_name, + filter=expr + ) + milvus_client.client.flush(self.collection_name) + + def drop_collection(self): + """删除集合""" + milvus_client.drop_collection(self.collection_name) + + def get_collection_stats(self) -> Dict[str, Any]: + """获取集合统计信息""" + self._ensure_connection() + + try: + result = milvus_client.client.get_collection_stats(collection_name=self.collection_name) + return { + "num_entities": result.get("row_count", 0), + "collection_name": self.collection_name + } + except Exception as e: + print(f"获取统计信息失败: {e}") + return {"num_entities": 0, "collection_name": self.collection_name} + + def close(self): + """关闭连接""" + milvus_client.close() diff --git a/app/summary/__init__.py b/app/summary/__init__.py new file mode 100644 index 0000000..1f0dc31 --- /dev/null +++ b/app/summary/__init__.py @@ -0,0 +1,4 @@ +from .router import router as summary_router +from .service import SummaryService + +__all__ = ["summary_router", "SummaryService"] diff --git a/app/summary/router.py b/app/summary/router.py new file mode 100644 index 0000000..3a8bfe5 --- /dev/null +++ b/app/summary/router.py @@ -0,0 +1,26 @@ +from typing import List, Dict, Any, Optional + +from fastapi import APIRouter, Query + +from app.summary.service import SummaryService + +router = APIRouter(prefix="/summary", tags=["summary"]) + + +@router.get("/message-statistics", response_model=List[Dict[str, Any]]) +async def get_message_statistics( + start_date: Optional[str] = Query(None, description="开始日期 (格式: YYYY-MM-DD)"), + end_date: Optional[str] = Query(None, description="结束日期 (格式: YYYY-MM-DD)") +): + """ + 获取按日期和发送者分组的消息统计数据 + + SQL逻辑: + SELECT DATE(created_at) AS created_at, from_user + FROM archive_messages + GROUP BY DATE(created_at), from_user + ORDER BY created_at DESC; + """ + if start_date or end_date: + return await SummaryService.get_message_statistics_by_date(start_date, end_date) + return await SummaryService.get_message_statistics() diff --git a/app/summary/service.py b/app/summary/service.py new file mode 100644 index 0000000..e0a030b --- /dev/null +++ b/app/summary/service.py @@ -0,0 +1,180 @@ +import asyncio +import time +from typing import List + +from langchain_core.prompts import PromptTemplate + +from app.client.embedding_client_manager import EmbeddingClientManager, embedding_client +from app.client.milvus_client_manager import milvus_client +from app.client.mysql_client_manager import db_assistant_mysql_client_manager +from app.dto.summary_dto import SummaryDto +from app.llm import llm +from app.models.mysql import ArchiveMessages +from app.repository.archive_messages_repository import ArchiveMessagesRepository +from app.repository.milvus.summary_repository import SummaryRepository + + +class SummaryService: + + def __init__(self, archive_messages_repository: ArchiveMessagesRepository, + embedding_client: EmbeddingClientManager, + summary_repository: SummaryRepository): + self.archive_messages_repository = archive_messages_repository + self.embedding_client = embedding_client + self.summary_repository = summary_repository + + async def get_message_summary(self, date: str = None): + stime = time.time() + if date: + day_ArchiveMessages = await self.archive_messages_repository.get_message_statistics_by_date(start_date=date, + end_date=date) + print(f"{date}共产生了{len(day_ArchiveMessages)}条会话") + else: + day_ArchiveMessages = await self.archive_messages_repository.get_message_statistics_by_date() + + # 改成map + date_user_message_a_set = set() + for idx, day_message in enumerate(day_ArchiveMessages, 1): #打印计数 + print(f"处理第{idx}会话") + day_date = str(day_message.created_at) + user_a = day_message.from_user + # 找出发送人相关的接收人信息 + date_user_message_a = await self.archive_messages_repository.get_message_statistics_by_date_user(day_date, + user_a) + + coro_list = [] + # 找出发送人与接收人 相关的所有会话信息 + for date_a in date_user_message_a: + + date_user_b = date_a.to_user + # if date_user_b != "[\"wmI1AkDQAA3h0jxrRpeaHZhMKeHExA4w\"]": + # continue + + date_user_a = date_a.from_user + date_user_b_clean = date_user_b.replace("[\"", "").replace("\"]", "") + date_user_a_append = "[\"" + date_user_a + "\"]" + # 判断只要存在就跳过 + if date_user_a + date_user_b in date_user_message_a_set or date_user_b_clean + date_user_a_append in date_user_message_a_set: + continue + print(f"查询{date_user_a}和{date_user_b}对话开始") + list1 = await self.archive_messages_repository.get_message_statistics_by_date_user(day_date, + date_user_a, + date_user_b) + + list2 = await self.archive_messages_repository.get_message_statistics_by_date_user(day_date, + date_user_b_clean, + date_user_a_append) + # 查询过的聊天账号，后续不再查询 + date_user_message_a_set.add(date_user_a + date_user_b) + date_user_message_a_set.add(date_user_b_clean + date_user_a_append) + coro_list = list1 + list2 + coro_list.sort(key=lambda x: x.created_at, reverse=False) + print(f"查询{date_user_a}和{date_user_b}对话共：{len(coro_list)}") + # 保留语义骨架，去除闲聊填充 + filter_gossip_message_res = await self.filter_gossip_message(coro_list) + filter_gossip_message_text = str(filter_gossip_message_res.content) if hasattr( + filter_gossip_message_res, 'content') else str(filter_gossip_message_res) + print(f"查询{date_user_a}和{date_user_b}对话进行保留语义骨架，去除闲聊填充") + # 摘要 + summary_res = await self.date_message_summary(coro_list) + summary_text = str(summary_res.content) if hasattr(summary_res, 'content') else str(summary_res) + print(f"查询{date_user_a}和{date_user_b}对话进行摘要") + # 摘要向量 + batch_embeddings = await self.embedding_client.aembed_documents( + [summary_text, filter_gossip_message_text]) + + # + try: + self.summary_repository.insert( + msg_time=[day_date], + msg_type=[0], + from_user=[date_a.from_user], + to_user=[date_user_b_clean], + room_id=[date_a.roomid or ""], + message_context=[filter_gossip_message_text], + summary=[summary_text], + message_dense_vector=[batch_embeddings[1]], + summary_dense_vector=[batch_embeddings[0]] + ) + print(f"查询{date_user_a}和{date_user_b}对话成功入 库") + except Exception as e: + print(e) + print(f"运行结束：{(time.time()-stime)}") + + async def filter_gossip_message(self, coro_list: List[ArchiveMessages]) -> str: + prompt = """ + 保留语义骨架，去除闲聊填充 + # 历史对话 + {message_str} + 其中INTERNAL表示销售，EXTERNAL表示用户 + # 例如 + 原文： + 用户：在吗？ + 销售：在的，您好，请问有什么可以帮您？ + 用户：我想问一下，那个，我的订单怎么还没发货啊？ + 销售：好的，请问您的订单号是多少呢？ + 用户：订单号是 12345，我昨天就下单了。 + 销售：我查一下，稍等... 您的订单正在打包中，预计今天发出。 + + 结构化提取后： + [用户] 咨询订单发货状态，订单号 12345，昨日下单。 + [销售] 查询后告知正在打包，预计今日发出。 + """ + lines = [f"{msg.created_at} {msg.from_role}：{msg.content}" for msg in coro_list] + message_str = "\n".join(lines) + + prompt_template = PromptTemplate(template=prompt, input_variables=["message_str"]) + chain = prompt_template | llm + result = await chain.ainvoke({"message_str": message_str}) + print(result) + return result + + async def date_message_summary(self, coro_list: List[ArchiveMessages]) -> str: + prompt = """ + # 聊天历史轻量化摘要任务 + 任务：生成极简轮次摘要，用于替换原始对话节省上下文窗口 + # 历史对话 + {message_str} + ## 说明 + 其中INTERNAL表示销售，EXTERNAL表示客户 + 输出格式示例（无多余文字）： + 【日期】 202X年X月X日 + ** 核心诉求** + 用户反馈订单 #12345 物流停滞超7天未更新。 + 询问是否支持跨店满减叠加优惠券。 + ** 处理结果** + 已联系快递网点核实，包裹因暴雨滞留，预计明日送达。（已安抚情绪并补偿10元无门槛券） + 明确告知当前活动规则不支持叠加，引导领取店铺专属券。 + ** 待办事项 ** + [明日 10:00] 跟进物流签收状态并回访用户。 + ** 关键标签**：#物流异常#优惠规则咨询#情绪安抚 + """ + lines = [f"{msg.created_at} {msg.from_role}：{msg.content}" for msg in coro_list] + message_str = "\n".join(lines) + + prompt_template = PromptTemplate(template=prompt, input_variables=["message_str"]) + chain = prompt_template | llm + result = await chain.ainvoke({"message_str": message_str}) + print(result) + return result + + +async def build(): + db_assistant_mysql_client_manager.init() + embedding_client.init() + milvus_client.init() + try: + async with db_assistant_mysql_client_manager.session_factory() as db_assistant: + archive_messages_repository = ArchiveMessagesRepository(db_assistant) + summary_repository = SummaryRepository() + service = SummaryService(archive_messages_repository, embedding_client, summary_repository) + await service.get_message_summary("2026-6-11") + finally: + await embedding_client.close() + await db_assistant_mysql_client_manager.close() + milvus_client.close() + + +if __name__ == '__main__': + asyncio.run(build()) + diff --git a/app/summary/task.py b/app/summary/task.py new file mode 100644 index 0000000..dac475e --- /dev/null +++ b/app/summary/task.py @@ -0,0 +1,88 @@ +import asyncio +import logging +from datetime import datetime, timedelta +from typing import Optional + +from apscheduler.schedulers.asyncio import AsyncIOScheduler + +from app.client.mysql_client_manager import db_assistant_mysql_client_manager +from app.repository.archive_messages_repository import ArchiveMessagesRepository +from app.summary.service import SummaryService + + +logging.basicConfig( + level=logging.INFO, + format='%(asctime)s - %(levelname)s - %(message)s', + handlers=[logging.FileHandler('summary_task.log'), logging.StreamHandler()] +) +logger = logging.getLogger(__name__) + + +async def generate_daily_summary(date: Optional[str] = None): + """ + 生成指定日期的消息摘要，如果不传日期则生成前一天的摘要 + + Args: + date: 日期字符串，格式 YYYY-MM-DD，不传则默认前一天 + """ + if not date: + yesterday = datetime.now() - timedelta(days=1) + date = yesterday.strftime("%Y-%m-%d") + + logger.info(f"开始生成 {date} 的消息摘要...") + + try: + db_assistant_mysql_client_manager.init() + async with db_assistant_mysql_client_manager.session_factory() as db_assistant: + archive_messages_repository = ArchiveMessagesRepository(db_assistant) + service = SummaryService(archive_messages_repository) + summaries = await service.get_message_summary(date) + + logger.info(f"生成完成，共生成 {len(summaries)} 条摘要") + for summary in summaries: + logger.info(f"日期: {summary.date}, 发送人: {summary.from_user}, 接收人: {summary.to_user}") + + return summaries + except Exception as e: + logger.error(f"生成摘要失败: {str(e)}", exc_info=True) + raise + + +def start_scheduler(): + """ + 启动定时任务调度器，每天凌晨2点执行 + """ + scheduler = AsyncIOScheduler(timezone='Asia/Shanghai') + + scheduler.add_job( + generate_daily_summary, + trigger='cron', + hour=2, + minute=0, + second=0, + id='daily_summary_task', + name='每日消息摘要任务', + replace_existing=True + ) + + logger.info("定时任务调度器已启动，每天凌晨2:00执行消息摘要任务") + + scheduler.start() + + try: + asyncio.get_event_loop().run_forever() + except (KeyboardInterrupt, SystemExit): + logger.info("正在关闭定时任务调度器...") + scheduler.shutdown() + + +if __name__ == '__main__': + import sys + + if len(sys.argv) > 1: + # 如果传入了日期参数，直接生成指定日期的摘要 + target_date = sys.argv[1] + asyncio.run(generate_daily_summary(target_date)) + else: + # 否则启动定时任务调度器 + start_scheduler() \ No newline at end of file diff --git a/conf/app_config.yaml b/conf/app_config.yaml new file mode 100644 index 0000000..beef847 --- /dev/null +++ b/conf/app_config.yaml @@ -0,0 +1,35 @@ +logging: + file: + enable: true + level: INFO + path: logs + rotation: "10 MB" + retention: "7 days" + console: + enable: true + level: INFO + +db_assistant: + host: sh-cdb-6fzlwnms.sql.tencentcdb.com + port: 63912 + user: assistant_prod + password: U%$4Tu_C3+4 + database: ai_assistant + +embedding: + host: localhost + port: 8081 + model: BAAI/bge-large-zh-v1.5 + + +milvus: + host: localhost + port: 19530 + user: root + password: Milvus + embedding_size: 1024 + +llm: + model_name: deepseek-v4-flash + api_key: sk-8edc9f25d59643b3b08efea5d81c55a9 + base_url: https://api.deepseek.com diff --git a/conf/sql.sql b/conf/sql.sql new file mode 100644 index 0000000..e69de29 diff --git a/docker/docker-compose.yml b/docker/docker-compose.yml new file mode 100644 index 0000000..468cad9 --- /dev/null +++ b/docker/docker-compose.yml @@ -0,0 +1,80 @@ +services: + etcd: + container_name: milvus-etcd + image: quay.io/coreos/etcd:v3.5.18 + environment: + - ETCD_AUTO_COMPACTION_MODE=revision + - ETCD_AUTO_COMPACTION_RETENTION=1000 + - ETCD_QUOTA_BACKEND_BYTES=4294967296 + - ETCD_SNAPSHOT_COUNT=50000 + volumes: + - ${DOCKER_VOLUME_DIRECTORY:-.}/volumes/etcd:/etcd + # 核心修改：127.0.0.1 → etcd + command: etcd -advertise-client-urls=http://etcd:2379 -listen-client-urls http://0.0.0.0:2379 --data-dir /etcd + healthcheck: + test: ["CMD", "etcdctl", "endpoint", "health"] + interval: 30s + timeout: 20s + retries: 3 + + minio: + container_name: milvus-minio + image: minio/minio:RELEASE.2023-03-20T20-16-18Z + environment: + MINIO_ACCESS_KEY: minioadmin + MINIO_SECRET_KEY: minioadmin + ports: + - "9001:9001" + - "9000:9000" + volumes: + - ${DOCKER_VOLUME_DIRECTORY:-.}/volumes/minio:/minio_data + command: minio server /minio_data --console-address ":9001" + healthcheck: + test: ["CMD", "curl", "-f", "http://localhost:9000/minio/health/live"] + interval: 30s + timeout: 20s + retries: 3 + + standalone: + container_name: milvus-standalone + # 修正镜像tag，删除多余v + image: milvusdb/milvus:v2.5.5 + command: ["milvus", "run", "standalone"] + security_opt: + - seccomp:unconfined + environment: + ETCD_ENDPOINTS: etcd:2379 + MINIO_ADDRESS: minio:9000 + volumes: + - ${DOCKER_VOLUME_DIRECTORY:-.}/volumes/milvus:/var/lib/milvus + healthcheck: + test: ["CMD", "curl", "-f", "http://localhost:9091/healthz"] + interval: 30s + start_period: 90s + timeout: 20s + retries: 3 + ports: + - "19530:19530" + - "9091:9091" + depends_on: + - "etcd" + - "minio" + +# Hugging Face TEI 引擎 用 Rust + C 张量库 推理 + embedding: + image: ghcr.io/huggingface/text-embeddings-inference:cpu-1.8 + container_name: embedding + restart: unless-stopped + ports: + - "8081:80" + environment: + MODEL_ID: /models/bge-large-zh-v1.5 + MAX_CONCURRENT_REQUESTS: "16" + MAX_BATCH_TOKENS: "16384" + volumes: + - ./embedding/bge-large-zh-v1.5:/models/bge-large-zh-v1.5 + + +networks: + default: + name: milvus \ No newline at end of file diff --git a/docker/embedding/bge-large-zh-v1.5/.gitattributes b/docker/embedding/bge-large-zh-v1.5/.gitattributes new file mode 100644 index 0000000..a6344aa --- /dev/null +++ b/docker/embedding/bge-large-zh-v1.5/.gitattributes @@ -0,0 +1,35 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text diff --git a/docker/embedding/bge-large-zh-v1.5/1_Pooling/config.json b/docker/embedding/bge-large-zh-v1.5/1_Pooling/config.json new file mode 100644 index 0000000..9bd8592 --- /dev/null +++ b/docker/embedding/bge-large-zh-v1.5/1_Pooling/config.json @@ -0,0 +1,7 @@ +{ + "word_embedding_dimension": 1024, + "pooling_mode_cls_token": true, + "pooling_mode_mean_tokens": false, + "pooling_mode_max_tokens": false, + "pooling_mode_mean_sqrt_len_tokens": false +} \ No newline at end of file diff --git a/docker/embedding/bge-large-zh-v1.5/README.md b/docker/embedding/bge-large-zh-v1.5/README.md new file mode 100644 index 0000000..8ff3287 --- /dev/null +++ b/docker/embedding/bge-large-zh-v1.5/README.md @@ -0,0 +1,427 @@ +--- +license: mit +language: +- zh +tags: + - sentence-transformers + - feature-extraction + - sentence-similarity + - transformers +--- + + +

FlagEmbedding

+ + +

+

+ Model List | + FAQ | + Usage | + Evaluation | + Train | + Contact | + Citation | + License +

+

+ +For more details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding). + +If you are looking for a model that supports more languages, longer texts, and other retrieval methods, you can try using [bge-m3](https://huggingface.co/BAAI/bge-m3). + + +[English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md) + +FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following projects currently: + +- **Long-Context LLM**: [Activation Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon) +- **Fine-tuning of LM** : [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail) +- **Dense Retrieval**: [BGE-M3](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3), [LLM Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), [BGE Embedding](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/baai_general_embedding) +- **Reranker Model**: [BGE Reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker) +- **Benchmark**: [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) + +## News +- 1/30/2024: Release **BGE-M3**, a new member to BGE model series! M3 stands for **M**ulti-linguality (100+ languages), **M**ulti-granularities (input length up to 8192), **M**ulti-Functionality (unification of dense, lexical, multi-vec/colbert retrieval). +It is the first embedding model which supports all three retrieval methods, achieving new SOTA on multi-lingual (MIRACL) and cross-lingual (MKQA) benchmarks. +[Technical Report](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/BGE_M3/BGE_M3.pdf) and [Code](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3). :fire: +- 1/9/2024: Release [Activation-Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon), an effective, efficient, compatible, and low-cost (training) method to extend the context length of LLM. [Technical Report](https://arxiv.org/abs/2401.03462) :fire: +- 12/24/2023: Release **LLaRA**, a LLaMA-7B based dense retriever, leading to state-of-the-art performances on MS MARCO and BEIR. Model and code will be open-sourced. Please stay tuned. [Technical Report](https://arxiv.org/abs/2312.15503) :fire: +- 11/23/2023: Release [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail), a method to maintain general capabilities during fine-tuning by merging multiple language models. [Technical Report](https://arxiv.org/abs/2311.13534) :fire: +- 10/12/2023: Release [LLM-Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), a unified embedding model to support diverse retrieval augmentation needs for LLMs. [Technical Report](https://arxiv.org/pdf/2310.07554.pdf) +- 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) and [massive training data](https://data.baai.ac.cn/details/BAAI-MTP) of BGE has been released +- 09/12/2023: New models: + - **New reranker model**: release cross-encoder models `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models. + - **update embedding model**: release `bge-*-v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction. + + +
+ More + + +- 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md): Add script to mine hard negatives and support adding instruction during fine-tuning. +- 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [this](#using-langchain); C-MTEB **leaderboard** is [available](https://huggingface.co/spaces/mteb/leaderboard). +- 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗** +- 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada: +- 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (**C-MTEB**), consisting of 31 test dataset. + +
+ + +## Model List + +`bge` is short for `BAAI general embedding`. + +| Model | Language | | Description | query instruction for retrieval [1] | +|:-------------------------------|:--------:| :--------:| :--------:|:--------:| +| [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) | Multilingual | [Inference](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3#usage) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3) | Multi-Functionality(dense retrieval, sparse retrieval, multi-vector(colbert)), Multi-Linguality, and Multi-Granularity(8192 tokens) | | +| [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder) | English | [Inference](./FlagEmbedding/llm_embedder/README.md) [Fine-tune](./FlagEmbedding/llm_embedder/README.md) | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See [README](./FlagEmbedding/llm_embedder/README.md) | +| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | | +| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | | +| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | +| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | +| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | +| [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` | +| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` | +| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` | +| [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` | +| [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: ` | +| [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) |a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` | +| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：` | +| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：` | +| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：` | + +[1\]: If you need to search the relevant passages to a query, we suggest to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, **no instruction** needs to be added to passages. + +[2\]: Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models. +For examples, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 document to get the final top-3 results. + +All models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI. +If you cannot open the Huggingface Hub, you also can download the models at https://model.baai.ac.cn/models . + + +## Frequently asked questions + +
+ 1. How to fine-tune bge embedding model? + + +Following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model. +Some suggestions: +- Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives), which can improve the retrieval performance. +- If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity. +- If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker. + + +
+ +
+ 2. The similarity score between two dissimilar sentences is higher than 0.5 + + +**Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.** + +Since we finetune the models by contrastive learning with a temperature of 0.01, +the similarity distribution of the current BGE model is about in the interval \[0.6, 1\]. +So a similarity score greater than 0.5 does not indicate that the two sentences are similar. + +For downstream tasks, such as passage retrieval or semantic similarity, +**what matters is the relative order of the scores, not the absolute value.** +If you need to filter similar sentences based on a similarity threshold, +please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9). + +
+ +
+ 3. When does the query instruction need to be used + + + +For the `bge-*-v1.5`, we improve its retrieval ability when not using instruction. +No instruction only has a slight degradation in retrieval performance compared with using instruction. +So you can generate embedding without instruction in all cases for convenience. + +For a retrieval task that uses short queries to find long related documents, +it is recommended to add instructions for these short queries. +**The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.** +In all cases, the documents/passages do not need to add the instruction. + +
+ + +## Usage + +### Usage for Embedding Model + +Here are some examples for using `bge` models with +[FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers). + +#### Using FlagEmbedding +``` +pip install -U FlagEmbedding +``` +If it doesn't work for you, you can see [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding. + +```python +from FlagEmbedding import FlagModel +sentences_1 = ["样例数据-1", "样例数据-2"] +sentences_2 = ["样例数据-3", "样例数据-4"] +model = FlagModel('BAAI/bge-large-zh-v1.5', + query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：", + use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation +embeddings_1 = model.encode(sentences_1) +embeddings_2 = model.encode(sentences_2) +similarity = embeddings_1 @ embeddings_2.T +print(similarity) + +# for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query +# corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction +queries = ['query_1', 'query_2'] +passages = ["样例文档-1", "样例文档-2"] +q_embeddings = model.encode_queries(queries) +p_embeddings = model.encode(passages) +scores = q_embeddings @ p_embeddings.T +``` +For the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list). + +By default, FlagModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs. +You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable. + + +#### Using Sentence-Transformers + +You can also use the `bge` models with [sentence-transformers](https://www.SBERT.net): + +``` +pip install -U sentence-transformers +``` +```python +from sentence_transformers import SentenceTransformer +sentences_1 = ["样例数据-1", "样例数据-2"] +sentences_2 = ["样例数据-3", "样例数据-4"] +model = SentenceTransformer('BAAI/bge-large-zh-v1.5') +embeddings_1 = model.encode(sentences_1, normalize_embeddings=True) +embeddings_2 = model.encode(sentences_2, normalize_embeddings=True) +similarity = embeddings_1 @ embeddings_2.T +print(similarity) +``` +For s2p(short query to long passage) retrieval task, +each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)). +But the instruction is not needed for passages. +```python +from sentence_transformers import SentenceTransformer +queries = ['query_1', 'query_2'] +passages = ["样例文档-1", "样例文档-2"] +instruction = "为这个句子生成表示以用于检索相关文章：" + +model = SentenceTransformer('BAAI/bge-large-zh-v1.5') +q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True) +p_embeddings = model.encode(passages, normalize_embeddings=True) +scores = q_embeddings @ p_embeddings.T +``` + +#### Using Langchain + +You can use `bge` in langchain like this: +```python +from langchain.embeddings import HuggingFaceBgeEmbeddings +model_name = "BAAI/bge-large-en-v1.5" +model_kwargs = {'device': 'cuda'} +encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity +model = HuggingFaceBgeEmbeddings( + model_name=model_name, + model_kwargs=model_kwargs, + encode_kwargs=encode_kwargs, + query_instruction="为这个句子生成表示以用于检索相关文章：" +) +model.query_instruction = "为这个句子生成表示以用于检索相关文章：" +``` + + +#### Using HuggingFace Transformers + +With the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding. + +```python +from transformers import AutoTokenizer, AutoModel +import torch +# Sentences we want sentence embeddings for +sentences = ["样例数据-1", "样例数据-2"] + +# Load model from HuggingFace Hub +tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh-v1.5') +model = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5') +model.eval() + +# Tokenize sentences +encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') +# for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages) +# encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt') + +# Compute token embeddings +with torch.no_grad(): + model_output = model(**encoded_input) + # Perform pooling. In this case, cls pooling. + sentence_embeddings = model_output[0][:, 0] +# normalize embeddings +sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1) +print("Sentence embeddings:", sentence_embeddings) +``` + +### Usage for Reranker + +Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. +You can get a relevance score by inputting query and passage to the reranker. +The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range. + + +#### Using FlagEmbedding +``` +pip install -U FlagEmbedding +``` + +Get relevance scores (higher scores indicate more relevance): +```python +from FlagEmbedding import FlagReranker +reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation + +score = reranker.compute_score(['query', 'passage']) +print(score) + +scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]) +print(scores) +``` + + +#### Using Huggingface transformers + +```python +import torch +from transformers import AutoModelForSequenceClassification, AutoTokenizer + +tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large') +model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large') +model.eval() + +pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']] +with torch.no_grad(): + inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512) + scores = model(**inputs, return_dict=True).logits.view(-1, ).float() + print(scores) +``` + +## Evaluation + +`baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!** +For more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md). + +- **MTEB**: + +| Model Name | Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) | STS (10) | Summarization (1) | Classification (12) | +|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| +| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 | **64.23** | **54.29** | 46.08 | 87.12 | 60.03 | 83.11 | 31.61 | 75.97 | +| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 768 | 512 | 63.55 | 53.25 | 45.77 | 86.55 | 58.86 | 82.4 | 31.07 | 75.53 | +| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | 384 | 512 | 62.17 |51.68 | 43.82 | 84.92 | 58.36 | 81.59 | 30.12 | 74.14 | +| [bge-large-en](https://huggingface.co/BAAI/bge-large-en) | 1024 | 512 | 63.98 | 53.9 | 46.98 | 85.8 | 59.48 | 81.56 | 32.06 | 76.21 | +| [bge-base-en](https://huggingface.co/BAAI/bge-base-en) | 768 | 512 | 63.36 | 53.0 | 46.32 | 85.86 | 58.7 | 81.84 | 29.27 | 75.27 | +| [gte-large](https://huggingface.co/thenlper/gte-large) | 1024 | 512 | 63.13 | 52.22 | 46.84 | 85.00 | 59.13 | 83.35 | 31.66 | 73.33 | +| [gte-base](https://huggingface.co/thenlper/gte-base) | 768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 | +| [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1024| 512 | 62.25 | 50.56 | 44.49 | 86.03 | 56.61 | 82.05 | 30.19 | 75.24 | +| [bge-small-en](https://huggingface.co/BAAI/bge-small-en) | 384 | 512 | 62.11 | 51.82 | 44.31 | 83.78 | 57.97 | 80.72 | 30.53 | 74.37 | +| [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) | 768 | 512 | 61.79 | 49.26 | 44.74 | 86.62 | 57.29 | 83.06 | 32.32 | 61.79 | +| [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 768 | 512 | 61.5 | 50.29 | 43.80 | 85.73 | 55.91 | 81.05 | 30.28 | 73.84 | +| [gte-small](https://huggingface.co/thenlper/gte-small) | 384 | 512 | 61.36 | 49.46 | 44.89 | 83.54 | 57.7 | 82.07 | 30.42 | 72.31 | +| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 | 60.99 | 49.25 | 45.9 | 84.89 | 56.32 | 80.97 | 30.8 | 70.93 | +| [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 384 | 512 | 59.93 | 49.04 | 39.92 | 84.67 | 54.32 | 80.39 | 31.16 | 72.94 | +| [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 768 | 512 | 59.51 | 42.24 | 43.72 | 85.06 | 56.42 | 82.63 | 30.08 | 73.42 | +| [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 768 | 514 | 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 | +| [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 | + + + +- **C-MTEB**: +We create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks. +Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction. + +| Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering | +|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| +| [**BAAI/bge-large-zh-v1.5**](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 1024 | **64.53** | 70.46 | 56.25 | 81.6 | 69.13 | 65.84 | 48.99 | +| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | 768 | 63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 | +| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | 512 | 57.82 | 61.77 | 49.11 | 70.41 | 63.96 | 60.92 | 44.18 | +| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | 1024 | 64.20 | 71.53 | 54.98 | 78.94 | 68.32 | 65.11 | 48.39 | +| [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 53 | 76.77 | 68.58 | 64.91 | 50.01 | +| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 54.12 | 77.5 | 67.07 | 64.91 | 47.63 | +| [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 1024 | 58.79 | 63.66 | 48.44 | 69.89 | 67.34 | 56.00 | 48.23 | +| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 | 63.07 | 49.45 | 70.35 | 63.64 | 61.48 | 45.09 | +| [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 | +| [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 | 57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 | +| [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 768 | 55.48 | 61.63 | 46.49 | 67.07 | 65.35 | 54.35 | 40.68 | +| [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | 384 | 55.38 | 59.95 | 45.27 | 66.45 | 65.85 | 53.86 | 45.26 | +| [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 | 53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 | +| [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 | +| [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 | +| [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 | + + +- **Reranking**: +See [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for evaluation script. + +| Model | T2Reranking | T2RerankingZh2En\* | T2RerankingEn2Zh\* | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg | +|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| +| text2vec-base-multilingual | 64.66 | 62.94 | 62.51 | 14.37 | 48.46 | 48.6 | 50.26 | +| multilingual-e5-small | 65.62 | 60.94 | 56.41 | 29.91 | 67.26 | 66.54 | 57.78 | +| multilingual-e5-large | 64.55 | 61.61 | 54.28 | 28.6 | 67.42 | 67.92 | 57.4 | +| multilingual-e5-base | 64.21 | 62.13 | 54.68 | 29.5 | 66.23 | 66.98 | 57.29 | +| m3e-base | 66.03 | 62.74 | 56.07 | 17.51 | 77.05 | 76.76 | 59.36 | +| m3e-large | 66.13 | 62.72 | 56.1 | 16.46 | 77.76 | 78.27 | 59.57 | +| bge-base-zh-v1.5 | 66.49 | 63.25 | 57.02 | 29.74 | 80.47 | 84.88 | 63.64 | +| bge-large-zh-v1.5 | 65.74 | 63.39 | 57.03 | 28.74 | 83.45 | 85.44 | 63.97 | +| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | 67.28 | 63.95 | 60.45 | 35.46 | 81.26 | 84.1 | 65.42 | +| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | 67.6 | 64.03 | 61.44 | 37.16 | 82.15 | 84.18 | 66.09 | + +\* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval tasks + +## Train + +### BAAI Embedding + +We pre-train the models using [retromae](https://github.com/staoxiao/RetroMAE) and train them on large-scale pairs data using contrastive learning. +**You can fine-tune the embedding model on your data following our [examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).** +We also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain). +Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned. +More training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md). + + + +### BGE Reranker + +Cross-encoder will perform full-attention over the input pair, +which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model. +Therefore, it can be used to re-rank the top-k documents returned by embedding model. +We train the cross-encoder on a multilingual pair data, +The data format is the same as embedding model, so you can fine-tune it easily following our [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker). +More details please refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker) + + +## Contact +If you have any question or suggestion related to this project, feel free to open an issue or pull request. +You also can email Shitao Xiao(stxiao@baai.ac.cn) and Zheng Liu(liuzheng@baai.ac.cn). + + +## Citation + +If you find this repository useful, please consider giving a star :star: and citation + +``` +@misc{bge_embedding, + title={C-Pack: Packaged Resources To Advance General Chinese Embedding}, + author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff}, + year={2023}, + eprint={2309.07597}, + archivePrefix={arXiv}, + primaryClass={cs.CL} +} +``` + +## License +FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). 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"识": 6399, + "诈": 6400, + "诉": 6401, + "诊": 6402, + "诋": 6403, + "词": 6404, + "诏": 6405, + "译": 6406, + "试": 6407, + "诗": 6408, + "诘": 6409, + "诙": 6410, + "诚": 6411, + "诛": 6412, + "话": 6413, + "诞": 6414, + "诟": 6415, + "诠": 6416, + "诡": 6417, + "询": 6418, + "诣": 6419, + "诤": 6420, + "该": 6421, + "详": 6422, + "诧": 6423, + "诩": 6424, + "诫": 6425, + "诬": 6426, + "语": 6427, + "误": 6428, + "诰": 6429, + "诱": 6430, + "诲": 6431, + "说": 6432, + "诵": 6433, + "诶": 6434, + "请": 6435, + "诸": 6436, + "诺": 6437, + "读": 6438, + "诽": 6439, + "课": 6440, + "诿": 6441, + "谀": 6442, + "谁": 6443, + "调": 6444, + "谄": 6445, + "谅": 6446, + "谆": 6447, + "谈": 6448, + "谊": 6449, + "谋": 6450, + "谌": 6451, + "谍": 6452, + "谎": 6453, + "谏": 6454, + "谐": 6455, + "谑": 6456, + "谒": 6457, + "谓": 6458, + "谔": 6459, + "谕": 6460, + "谗": 6461, + "谘": 6462, + "谙": 6463, + "谚": 6464, + "谛": 6465, + "谜": 6466, + "谟": 6467, + "谢": 6468, + "谣": 6469, + "谤": 6470, + "谥": 6471, + "谦": 6472, + "谧": 6473, + "谨": 6474, + "谩": 6475, + "谪": 6476, + "谬": 6477, + "谭": 6478, + "谯": 6479, + "谱": 6480, + "谲": 6481, + "谴": 6482, + "谶": 6483, + "谷": 6484, + "豁": 6485, + "豆": 6486, + "豇": 6487, + "豈": 6488, + "豉": 6489, + "豊": 6490, + "豌": 6491, + "豎": 6492, + "豐": 6493, + "豔": 6494, + "豚": 6495, + "象": 6496, + "豢": 6497, + "豪": 6498, + "豫": 6499, + "豬": 6500, + "豹": 6501, + "豺": 6502, + "貂": 6503, + "貅": 6504, + "貌": 6505, + "貓": 6506, + "貔": 6507, + "貘": 6508, + "貝": 6509, + "貞": 6510, + "負": 6511, + "財": 6512, + "貢": 6513, + "貧": 6514, + "貨": 6515, + "販": 6516, + "貪": 6517, + "貫": 6518, + "責": 6519, + "貯": 6520, + "貰": 6521, + "貳": 6522, + "貴": 6523, + "貶": 6524, + "買": 6525, + "貸": 6526, + "費": 6527, + "貼": 6528, + "貽": 6529, + "貿": 6530, + "賀": 6531, + "賁": 6532, + "賂": 6533, + "賃": 6534, + "賄": 6535, + "資": 6536, + "賈": 6537, + "賊": 6538, + "賑": 6539, + "賓": 6540, + "賜": 6541, + "賞": 6542, + "賠": 6543, + "賡": 6544, + "賢": 6545, + "賣": 6546, + "賤": 6547, + "賦": 6548, + "質": 6549, + "賬": 6550, + "賭": 6551, + "賴": 6552, + "賺": 6553, + "購": 6554, + "賽": 6555, + "贅": 6556, + "贈": 6557, + "贊": 6558, + "贍": 6559, + "贏": 6560, + "贓": 6561, + "贖": 6562, + "贛": 6563, + "贝": 6564, + "贞": 6565, + "负": 6566, + "贡": 6567, + "财": 6568, + "责": 6569, + "贤": 6570, + "败": 6571, + "账": 6572, + "货": 6573, + "质": 6574, + "贩": 6575, + "贪": 6576, + "贫": 6577, + "贬": 6578, + "购": 6579, + "贮": 6580, + "贯": 6581, + "贰": 6582, + "贱": 6583, + "贲": 6584, + "贴": 6585, + "贵": 6586, + "贷": 6587, + "贸": 6588, + "费": 6589, + "贺": 6590, + "贻": 6591, + "贼": 6592, + "贾": 6593, + "贿": 6594, + "赁": 6595, + "赂": 6596, + "赃": 6597, + "资": 6598, + "赅": 6599, + "赈": 6600, + "赊": 6601, + "赋": 6602, + "赌": 6603, + "赎": 6604, + "赏": 6605, + "赐": 6606, + "赓": 6607, + "赔": 6608, + "赖": 6609, + "赘": 6610, + "赚": 6611, + "赛": 6612, + "赝": 6613, + "赞": 6614, + "赠": 6615, + "赡": 6616, + "赢": 6617, + "赣": 6618, + "赤": 6619, + "赦": 6620, + "赧": 6621, + "赫": 6622, + "赭": 6623, + "走": 6624, + "赳": 6625, + "赴": 6626, + "赵": 6627, + "赶": 6628, + "起": 6629, + "趁": 6630, + "超": 6631, + "越": 6632, + "趋": 6633, + "趕": 6634, + "趙": 6635, + "趟": 6636, + "趣": 6637, + "趨": 6638, + "足": 6639, + "趴": 6640, + "趵": 6641, + "趸": 6642, + "趺": 6643, + "趾": 6644, + "跃": 6645, + "跄": 6646, + "跆": 6647, + "跋": 6648, + "跌": 6649, + "跎": 6650, + "跑": 6651, + "跖": 6652, + "跚": 6653, + "跛": 6654, + "距": 6655, + "跟": 6656, + "跡": 6657, + "跤": 6658, + "跨": 6659, + "跩": 6660, + "跪": 6661, + "路": 6662, + "跳": 6663, + "践": 6664, + "跷": 6665, + "跹": 6666, + "跺": 6667, + "跻": 6668, + "踉": 6669, + "踊": 6670, + "踌": 6671, + "踏": 6672, + "踐": 6673, + "踝": 6674, + "踞": 6675, + "踟": 6676, + "踢": 6677, + "踩": 6678, + "踪": 6679, + "踮": 6680, + "踱": 6681, + "踴": 6682, + "踵": 6683, + "踹": 6684, + "蹂": 6685, + "蹄": 6686, + "蹇": 6687, + "蹈": 6688, + "蹉": 6689, + "蹊": 6690, + "蹋": 6691, + "蹑": 6692, + "蹒": 6693, + "蹙": 6694, + "蹟": 6695, + "蹣": 6696, + "蹤": 6697, + "蹦": 6698, + "蹩": 6699, + "蹬": 6700, + "蹭": 6701, + "蹲": 6702, + "蹴": 6703, + "蹶": 6704, + "蹺": 6705, + "蹼": 6706, + 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"辕": 6784, + "辖": 6785, + "辗": 6786, + "辘": 6787, + "辙": 6788, + "辛": 6789, + "辜": 6790, + "辞": 6791, + "辟": 6792, + "辣": 6793, + "辦": 6794, + "辨": 6795, + "辩": 6796, + "辫": 6797, + "辭": 6798, + "辮": 6799, + "辯": 6800, + "辰": 6801, + "辱": 6802, + "農": 6803, + "边": 6804, + "辺": 6805, + "辻": 6806, + "込": 6807, + "辽": 6808, + "达": 6809, + "迁": 6810, + "迂": 6811, + "迄": 6812, + "迅": 6813, + "过": 6814, + "迈": 6815, + "迎": 6816, + "运": 6817, + "近": 6818, + "返": 6819, + "还": 6820, + "这": 6821, + "进": 6822, + "远": 6823, + "违": 6824, + "连": 6825, + "迟": 6826, + "迢": 6827, + "迤": 6828, + "迥": 6829, + "迦": 6830, + "迩": 6831, + "迪": 6832, + "迫": 6833, + "迭": 6834, + "述": 6835, + "迴": 6836, + "迷": 6837, + "迸": 6838, + "迹": 6839, + "迺": 6840, + "追": 6841, + "退": 6842, + "送": 6843, + "适": 6844, + "逃": 6845, + "逅": 6846, + "逆": 6847, + "选": 6848, + "逊": 6849, + "逍": 6850, + "透": 6851, + "逐": 6852, + "递": 6853, + "途": 6854, + "逕": 6855, + "逗": 6856, + "這": 6857, + "通": 6858, + "逛": 6859, + "逝": 6860, + 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+##｡ +##｢ +##｣ +##､ +##･ +##ｯ +##ｰ +##ｲ +##ｸ +##ｼ +##ｽ +##ﾄ +##ﾉ +##ﾌ +##ﾗ +##ﾙ +##ﾝ +##ﾞ +##ﾟ +##￣ +##￥ +##👍 +##🔥 +##😂 +##😎 diff --git a/main.py b/main.py new file mode 100644 index 0000000..fce03e5 --- /dev/null +++ b/main.py @@ -0,0 +1,28 @@ +from fastapi import FastAPI + +from app.client.mysql_client_manager import db_assistant_mysql_client_manager +from app.summary import summary_router + +app = FastAPI(title="Sales Assistant API", version="1.0.0") + +app.include_router(summary_router) + + +@app.on_event("startup") +async def startup(): + db_assistant_mysql_client_manager.init() + + +@app.on_event("shutdown") +async def shutdown(): + await db_assistant_mysql_client_manager.close() + + +@app.get("/") +async def root(): + return {"message": "Sales Assistant API is running"} + + +if __name__ == "__main__": + import uvicorn + uvicorn.run(app, host="0.0.0.0", port=8000) diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..9a9ab94 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,23 @@ +[project] +name = "sales-assistant-py" +version = "0.1.0" +description = "Add your description here" +requires-python = ">=3.12" +dependencies = [ + "aiohttp>=3.14.1", + "apscheduler>=3.11.2", + "asyncmy>=0.2.11", + "cryptography>=48.0.1", + "fastapi[standard]>=0.136.3", + "huggingface-hub>=1.18.0", + "jieba>=0.42.1", + "langchain>=1.3.7", + "langchain-deepseek>=1.1.0", + "langchain-huggingface>=1.2.2", + "langgraph>=1.2.4", + "loguru>=0.7.3", + "omegaconf>=2.3.0", + "pymilvus>=3.0.0", + "pyyaml>=6.0.3", + "sqlalchemy>=2.0.50", +] diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..bf4892b --- /dev/null +++ b/uv.lock @@ -0,0 +1,3051 @@ +version = 1 +revision = 3 +requires-python = ">=3.12" +resolution-markers = [ + "python_full_version >= '3.14' and sys_platform == 'win32'", + "python_full_version >= '3.14' and sys_platform == 'emscripten'", + "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", 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