Merge branch 'rc/new-plugin' into refactor/new-plugin-system

This commit is contained in:
Junyan Qin
2025-08-24 21:40:02 +08:00
232 changed files with 11998 additions and 1440 deletions

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@@ -4,7 +4,7 @@ from .base import Base
class Bot(Base):
"""机器人"""
"""Bot"""
__tablename__ = 'bots'

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@@ -12,7 +12,7 @@ initial_metadata = [
class Metadata(Base):
"""数据库元数据"""
"""Database metadata"""
__tablename__ = 'metadata'

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@@ -4,7 +4,7 @@ from .base import Base
class LLMModel(Base):
"""LLM 模型"""
"""LLM model"""
__tablename__ = 'llm_models'
@@ -23,3 +23,24 @@ class LLMModel(Base):
server_default=sqlalchemy.func.now(),
onupdate=sqlalchemy.func.now(),
)
class EmbeddingModel(Base):
"""Embedding 模型"""
__tablename__ = 'embedding_models'
uuid = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True, unique=True)
name = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
description = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
requester = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
requester_config = sqlalchemy.Column(sqlalchemy.JSON, nullable=False, default={})
api_keys = sqlalchemy.Column(sqlalchemy.JSON, nullable=False)
extra_args = sqlalchemy.Column(sqlalchemy.JSON, nullable=False, default={})
created_at = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, server_default=sqlalchemy.func.now())
updated_at = sqlalchemy.Column(
sqlalchemy.DateTime,
nullable=False,
server_default=sqlalchemy.func.now(),
onupdate=sqlalchemy.func.now(),
)

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@@ -4,7 +4,7 @@ from .base import Base
class LegacyPipeline(Base):
"""旧版流水线"""
"""Legacy pipeline"""
__tablename__ = 'legacy_pipelines'
@@ -20,13 +20,12 @@ class LegacyPipeline(Base):
)
for_version = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
is_default = sqlalchemy.Column(sqlalchemy.Boolean, nullable=False, default=False)
stages = sqlalchemy.Column(sqlalchemy.JSON, nullable=False)
config = sqlalchemy.Column(sqlalchemy.JSON, nullable=False)
class PipelineRunRecord(Base):
"""流水线运行记录"""
"""Pipeline run record"""
__tablename__ = 'pipeline_run_records'
@@ -43,3 +42,4 @@ class PipelineRunRecord(Base):
started_at = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False)
finished_at = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False)
result = sqlalchemy.Column(sqlalchemy.JSON, nullable=False)
knowledge_base_uuid = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)

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@@ -4,7 +4,7 @@ from .base import Base
class PluginSetting(Base):
"""插件配置"""
"""Plugin setting"""
__tablename__ = 'plugin_settings'

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@@ -0,0 +1,50 @@
import sqlalchemy
from .base import Base
# Base = declarative_base()
# DATABASE_URL = os.getenv('DATABASE_URL', 'sqlite:///./rag_knowledge.db')
# print("Using database URL:", DATABASE_URL)
# engine = create_engine(DATABASE_URL, connect_args={'check_same_thread': False})
# SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
# def create_db_and_tables():
# """Creates all database tables defined in the Base."""
# Base.metadata.create_all(bind=engine)
# print('Database tables created or already exist.')
class KnowledgeBase(Base):
__tablename__ = 'knowledge_bases'
uuid = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True, unique=True)
name = sqlalchemy.Column(sqlalchemy.String, index=True)
description = sqlalchemy.Column(sqlalchemy.Text)
created_at = sqlalchemy.Column(sqlalchemy.DateTime, default=sqlalchemy.func.now())
embedding_model_uuid = sqlalchemy.Column(sqlalchemy.String, default='')
top_k = sqlalchemy.Column(sqlalchemy.Integer, default=5)
class File(Base):
__tablename__ = 'knowledge_base_files'
uuid = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True, unique=True)
kb_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
file_name = sqlalchemy.Column(sqlalchemy.String)
extension = sqlalchemy.Column(sqlalchemy.String)
created_at = sqlalchemy.Column(sqlalchemy.DateTime, default=sqlalchemy.func.now())
status = sqlalchemy.Column(sqlalchemy.String, default='pending') # pending, processing, completed, failed
class Chunk(Base):
__tablename__ = 'knowledge_base_chunks'
uuid = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True, unique=True)
file_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
text = sqlalchemy.Column(sqlalchemy.Text)
# class Vector(Base):
# __tablename__ = 'knowledge_base_vectors'
# uuid = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True, unique=True)
# chunk_id = sqlalchemy.Column(sqlalchemy.String, nullable=True)
# embedding = sqlalchemy.Column(sqlalchemy.LargeBinary)

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@@ -0,0 +1,13 @@
from sqlalchemy import Column, Integer, ForeignKey, LargeBinary
from sqlalchemy.orm import declarative_base, relationship
Base = declarative_base()
class Vector(Base):
__tablename__ = 'vectors'
id = Column(Integer, primary_key=True, index=True)
chunk_id = Column(Integer, ForeignKey('chunks.id'), unique=True)
embedding = Column(LargeBinary) # Store embeddings as binary
chunk = relationship('Chunk', back_populates='vector')

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@@ -0,0 +1,13 @@
from __future__ import annotations
import pydantic
from typing import Any
class RetrieveResultEntry(pydantic.BaseModel):
id: str
metadata: dict[str, Any]
distance: float