#  Copyright 2026 zhaoxi826
#
#  Licensed under the Apache License, Version 2.0 (the "License");
#  you may not use this file except in compliance with the License.
#  You may obtain a copy of the License at
#
#       http://www.apache.org/licenses/LICENSE-2.0
#
#  Unless required by applicable law or agreed to in writing, software
#  distributed under the License is distributed on an "AS IS" BASIS,
#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#  See the License for the specific language governing permissions and
#  limitations under the License.

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]}..."}]