Pretor/pretor/tool_plugin/rag/rag.py

18 lines
851 B
Python

from typing import List, Dict, Any
from sqlmodel import select
# Assuming MemoryRecord is accessible or passed, simulating direct pgvector call
class RAGTool:
def __init__(self, async_session_maker):
self.async_session_maker = async_session_maker
async def get_embedding(self, query: str) -> List[float]:
# Simulated embedding logic; in reality, this would call an embedding API
return [0.1] * 1536
async def retrieve(self, query: str, limit: int = 5) -> List[Dict[str, Any]]:
embedding = await self.get_embedding(query)
# We simulate the retrieve_memory call logic from MemoryRAG here
# Normally you would inject MemoryRAG or a repository, doing a simplistic return here
return [{"query": query, "simulated_results": f"Found results for {query} with vector {embedding[:2]}..."}]