Memory Export

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Overview

The Memory Export feature allows you to create structured exports of memories using customizable Pydantic schemas. This process enables you to transform your stored memories into specific data formats that match your needs. You can apply various filters to narrow down which memories to export and define exactly how the data should be structured.

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Creating a Memory Export

To create a memory export, you’ll need to:

1. Define your schema structure
2. Submit an export job
3. Retrieve the exported data

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Define Schema

Here’s an example schema for extracting professional profile information:

{
    "$defs": {
        "EducationLevel": {
            "enum": ["high_school", "bachelors", "masters"],
            "title": "EducationLevel",
            "type": "string"
        },
        "EmploymentStatus": {
            "enum": ["full_time", "part_time", "student"],
            "title": "EmploymentStatus", 
            "type": "string"
        }
    },
    "properties": {
        "full_name": {
            "anyOf": [
                {
                    "maxLength": 100,
                    "minLength": 2,
                    "type": "string"
                },
                {
                    "type": "null"
                }
            ],
            "default": null,
            "description": "The professional's full name",
            "title": "Full Name"
        },
        "current_role": {
            "anyOf": [
                {
                    "type": "string"
                },
                {
                    "type": "null"
                }
            ],
            "default": null,
            "description": "Current job title or role",
            "title": "Current Role"
        }
    },
    "title": "ProfessionalProfile",
    "type": "object"
}

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Submit Export Job

You can optionally provide additional instructions to guide how memories are processed and structured during export using the export_instructions parameter.

# Basic export request
response = client.create_memory_export(
    schema=json_schema,
    user_id="alice"
)

# Export with custom instructions
export_instructions = """
1. Create a comprehensive profile with detailed information in each category
2. Only mark fields as "None" when absolutely no relevant information exists
3. Base all information directly on the user's memories
4. When contradictions exist, prioritize the most recent information
5. Clearly distinguish between factual statements and inferences
"""

# For create operation, using only user_id filter as requested
filters = {
    "AND": [
        {"user_id": "alex"}
    ]
}

response = client.create_memory_export(
    schema=json_schema,
    filters=filters,
    export_instructions=export_instructions  # Optional
)

print(response)

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Retrieve Export

Once the export job is complete, you can retrieve the structured data:

# Corrected date range (assuming you meant July 10 to July 20)
filters = {
    "AND": [
        {"created_at": {"gte": "2024-07-10", "lte": "2024-07-20"}},
        {"user_id": "alex"}
    ]
}

response = client.get_memory_export(filters=filters)
print(response)

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Available Filters

You can apply various filters to customize which memories are included in the export:

• user_id: Filter memories by specific user
• agent_id: Filter memories by specific agent
• run_id: Filter memories by specific run
• session_id: Filter memories by specific session
• created_at: Filter memories by date

If you have any questions, please feel free to reach out to us using one of the following methods: