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from enum import Enum
from typing import List, Optional, Dict, Any
from pydantic import BaseModel, Field
class TableType(str, Enum):
"""
Enum representing the default table types for the database.
Each value corresponds to a specific table name used for storing different types of data.
"""
store_agent = "agent" # Table for agent data
store_workflow = "workflow" # Table for workflow data
store_memory = "memory" # Table for memory data
store_history = "history" # Table for history data
store_indexing = "indexing" # Table for indexing data
class MemoryStore(BaseModel):
"""
Stores memory-related metadata with optional fields for keywords, entities, and embeddings.
"""
memory_id: str = Field(..., description="Unique identifier for the memory")
content: str = Field(..., description="Main content of the memory")
date: str = Field(..., description="Date associated with the memory")
key_words: Optional[List] = Field([], description="Optional list of keywords associated with the memory")
entity_content: Optional[Dict[str, Any]] = Field({}, description="Optional dictionary of entity-related content")
embedding: Optional[List] = Field([], description="Optional list of embedding vectors")
# Pydantic model for workflow data storage
class WorkflowStore(BaseModel):
"""
Stores workflow metadata with a unique name and content dictionary.
"""
name: str = Field(..., description="Unique workflow identifier")
content: Dict[str, Any] = Field(..., description="Dictionary containing workflow details")
date: Optional[str] = Field("", description="Date associated with the workflow")
# Pydantic model for agent data storage
class AgentStore(BaseModel):
"""
Stores agent metadata with a unique name and content dictionary.
"""
name: str = Field(..., description="Unique agent identifier")
content: Dict[str, Any] = Field(..., description="Dictionary containing agent details")
date: Optional[str] = Field("", description="Date associated with the agent")
# Pydantic model for history data storage
class HistoryStore(BaseModel):
"""
Stores changes to memory with event details and timestamps.
"""
memory_id: str = Field(..., description="Identifier of the memory being modified")
old_memory: str = Field(..., description="Original memory content before change")
new_memory: str = Field(..., description="Updated memory content after change")
event: str = Field(..., description="Description of the event causing the change")
created_at: Optional[str] = Field("", description="Optional timestamp for creation")
updated_at: Optional[str] = Field("", description="Optional timestamp for last update")
# Pydantic model for index data storage
class IndexStore(BaseModel):
"""
Stores indexing metadata with a unique id and basic chunk/node attribute.
"""
corpus_id: str = Field(..., description="Identifier for the associated corpus")
content: Dict[str, Any] = Field(..., description="Serialized index content (e.g., LlamaIndex JSON)")
date: Optional[str] = Field(default="", description="Creation or last update date")
metadata: Dict[str, Any] = Field(default_factory=dict, description="Additional metadata, e.g., vector store collection name")
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