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from sqlalchemy import Column, Integer, String, Text, DateTime, ForeignKey, Index
from sqlalchemy.sql import func
from sqlalchemy.orm import relationship, mapped_column
from sqlalchemy.dialects.postgresql import TSVECTOR
from pgvector.sqlalchemy import Vector
from core.database import Base
class DocumentChunk(Base):
"""Document chunk with embedding for RAG retrieval"""
__tablename__ = "document_chunks"
id = Column(Integer, primary_key=True, index=True)
agent_id = Column(Integer, ForeignKey("agents.id", ondelete="CASCADE"), nullable=False)
content = Column(Text, nullable=False)
source = Column(String, nullable=True)
chunk_index = Column(Integer, nullable=True)
section_title = Column(String, nullable=True)
token_count = Column(Integer, nullable=True)
# 384 dimensions for bge-small-en-v1.5 (unifying for MEXAR Ultimate)
embedding = mapped_column(Vector(384))
# Full-text search column
content_tsvector = Column(TSVECTOR)
created_at = Column(DateTime(timezone=True), server_default=func.now())
agent = relationship("Agent", back_populates="chunks")
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