Long-Term Knowledge Storage and Retrieval › Practical Considerations
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As you build long-term memory into your assistant, keep these points in mind:
Storage limits: Vector databases can grow large. Consider setting limits on how many facts to store, or implementing a way to archive or remove old information.
Privacy: Long-term memory means persistent data. Be thoughtful about what you store and how you protect it. Never store sensitive information like passwords or financial details without proper encryption.
Retrieval quality: The quality of your retrieval depends on your embedding model. Better embeddings lead to better semantic search. The example above uses a small, fast model, but you might want a more powerful one for production use.
Context window limits: Even with long-term memory, you can only include so many retrieved facts in each request to the language model. Prioritize the most relevant information.
Updating facts: What happens when information changes? You might need a way to update or delete facts. For example, if the user moves to a new city, you want to update that fact rather than having two conflicting locations stored.
Here's a simple update mechanism:
def update_fact(self, old_fact, new_fact):
"""Replace an old fact with updated information"""
# Remove old fact
if old_fact in self.memory.facts:
idx = self.memory.facts.index(old_fact)
self.memory.facts.pop(idx)
self.memory.vectors.pop(idx)
# Add new fact
self.memory.add_fact(new_fact)
return f"Updated: {new_fact}"