32 lines
1.4 KiB
Python
32 lines
1.4 KiB
Python
# Copyright 2026 zhaoxi826
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import List, Dict, Any
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from sqlmodel import select
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# Assuming MemoryRecord is accessible or passed, simulating direct pgvector call
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class RAGTool:
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def __init__(self, async_session_maker):
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self.async_session_maker = async_session_maker
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async def get_embedding(self, query: str) -> List[float]:
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# Simulated embedding logic; in reality, this would call an embedding API
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return [0.1] * 1536
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async def retrieve(self, query: str, limit: int = 5) -> List[Dict[str, Any]]:
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embedding = await self.get_embedding(query)
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# We simulate the retrieve_memory call logic from MemoryRAG here
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# Normally you would inject MemoryRAG or a repository, doing a simplistic return here
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return [{"query": query, "simulated_results": f"Found results for {query} with vector {embedding[:2]}..."}]
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