FAQs

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How does Zentry work?

What are the key features of Zentry?

How Zentry is different from traditional RAG?

What are the common use-cases of Zentry?

Why aren't my memories being created?

Zentry uses a sophisticated classification system to determine which parts of text should be extracted as memories. Not all text content will generate memories, as the system is designed to identify specific types of memorable information. There are several scenarios where Zentry may return an empty list of memories:

When users input definitional questions (e.g., “What is backpropagation?”)
For general concept explanations that don’t contain personal or experiential information
Technical definitions and theoretical explanations
General knowledge statements without personal context
Abstract or theoretical content

Example Scenarios

Input: "What is machine learning?"
No memories extracted - Content is definitional and does not meet memory classification criteria.

Input: "Yesterday I learned about machine learning in class"
Memory extracted - Contains personal experience and temporal context.

Best PracticesTo ensure successful memory extraction:

Include temporal markers (when events occurred)
Add personal context or experiences
Frame information in terms of real-world applications or experiences
Include specific examples or cases rather than general definitions

How do I configure Zentry for AWS Lambda?

When deploying Zentry on AWS Lambda, you’ll need to modify the storage directory configuration due to Lambda’s file system restrictions. By default, Lambda only allows writing to the /tmp directory.To configure Zentry for AWS Lambda, set the Zentry_DIR environment variable to point to a writable directory in /tmp:

Zentry_DIR=/tmp/.Zentry

If you’re not using environment variables, you’ll need to modify the storage path in your code:

# Change from
home_dir = os.path.expanduser("~")
Zentry_dir = os.environ.get("Zentry_DIR") or os.path.join(home_dir, ".Zentry")

# To
Zentry_dir = os.environ.get("Zentry_DIR", "/tmp/.Zentry")

Note that the /tmp directory in Lambda has a size limit of 512MB and its contents are not persistent between function invocations.

How can I use metadata with Zentry?

How do I disable telemetry in Zentry?

To disable telemetry in Zentry, you can set the Zentry_TELEMETRY environment variable to False:

Zentry_TELEMETRY=False

You can also disable telemetry programmatically in your code:

import os
os.environ["Zentry_TELEMETRY"] = "False"

Setting this environment variable will prevent Zentry from collecting and sending any usage data, ensuring complete privacy for your application.

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