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memo/proxy/main.py
It seems that when answering questions in memo/proxy/main.py, the recall mechanism doesn't utilize the dictionary information from the graph database.
Current code: memories_text = "\n".join(memory["memory"] for memory in relevant_memories["results"])
In memo/memory/main.py, the memory search actually includes parallel searches in both vector storage and graph storage. The code returns: return {"results": original_memories, "relations": graph_entities}
However, here only original_memories from vector storage recall is handled, and the graph database entities (graph_entities) are not used in constructing the memory recall.
How should we handle the recall of entities from the graph database?
For instance, data structured as follows is not being utilized: search_results.append({"source": item[0], "relationship": item[1], "target": item[2]})
Example:
🐛 Describe the bug
memo/proxy/main.py
It seems that when answering questions in memo/proxy/main.py, the recall mechanism doesn't utilize the dictionary information from the graph database.
Current code:
memories_text = "\n".join(memory["memory"] for memory in relevant_memories["results"])
In
memo/memory/main.py
, the memory search actually includes parallel searches in both vector storage and graph storage. The code returns:return {"results": original_memories, "relations": graph_entities}
However, here only original_memories from vector storage recall is handled, and the graph database entities (graph_entities) are not used in constructing the memory recall.
How should we handle the recall of entities from the graph database?
For instance, data structured as follows is not being utilized:
search_results.append({"source": item[0], "relationship": item[1], "target": item[2]})
Example:
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