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Zentry extends its capabilities beyond text by supporting multimodal data, including images and documents. With this feature, users can seamlessly integrate visual and document content into their interactions—allowing Zentry to extract relevant information from various media types and enrich the memory system.

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How It Works

When a user submits an image or document, Zentry processes it to extract textual information and other pertinent details. These details are then added to the user’s memory, enhancing the system’s ability to understand and recall multimodal inputs.

import os
from Zentry import MemoryClient

os.environ["Zentry_API_KEY"] = "your-api-key"

client = MemoryClient()

messages = [
    {
        "role": "user",
        "content": "Hi, my name is Alice."
    },
    {
        "role": "assistant",
        "content": "Nice to meet you, Alice! What do you like to eat?"
    },
    {
        "role": "user",
        "content": {
            "type": "image_url",
            "image_url": {
                "url": "https://www.superhealthykids.com/wp-content/uploads/2021/10/best-veggie-pizza-featured-image-square-2.jpg"
            }
        }
    },
]

# Calling the add method to ingest messages into the memory system
client.add(messages, user_id="alice")

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Supported Media Types

Zentry currently supports the following media types:

  1. Images - JPG, PNG, and other common image formats
  2. Documents - MDX, TXT, and PDF files

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Integration Methods

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1. Images

You can include an image by providing its direct URL. This method is simple and efficient for online images.

# Define the image URL
image_url = "https://www.superhealthykids.com/wp-content/uploads/2021/10/best-veggie-pizza-featured-image-square-2.jpg"

# Create the message dictionary with the image URL
image_message = {
    "role": "user",
    "content": {
        "type": "image_url",
        "image_url": {
            "url": image_url
        }
    }
}
client.add([image_message], user_id="alice")

​
Using Base64 Image Encoding for Local Files

For local images—or when embedding the image directly is preferable—you can use a Base64-encoded string.

import base64

# Path to the image file
image_path = "path/to/your/image.jpg"

# Encode the image in Base64
with open(image_path, "rb") as image_file:
    base64_image = base64.b64encode(image_file.read()).decode("utf-8")

# Create the message dictionary with the Base64-encoded image
image_message = {
    "role": "user",
    "content": {
        "type": "image_url",
        "image_url": {
            "url": f"data:image/jpeg;base64,{base64_image}"
        }
    }
}
client.add([image_message], user_id="alice")

​
2. Text Documents (MDX/TXT)

Zentry supports both online and local text documents in MDX or TXT format.

​
Using a Document URL

# Define the document URL
document_url = "https://www.w3.org/TR/2003/REC-PNG-20031110/iso_8859-1.txt"

# Create the message dictionary with the document URL
document_message = {
    "role": "user",
    "content": {
        "type": "mdx_url",
        "mdx_url": {
            "url": document_url
        }
    }
}
client.add([document_message], user_id="alice")

​
Using Base64 Encoding for Local Documents

import base64

# Path to the document file
document_path = "path/to/your/document.txt"

# Function to convert file to Base64
def file_to_base64(file_path):
    with open(file_path, "rb") as file:
        return base64.b64encode(file.read()).decode('utf-8')

# Encode the document in Base64
base64_document = file_to_base64(document_path)

# Create the message dictionary with the Base64-encoded document
document_message = {
    "role": "user",
    "content": {
        "type": "mdx_url",
        "mdx_url": {
            "url": base64_document
        }
    }
}
client.add([document_message], user_id="alice")

​
3. PDF Documents

Zentry supports PDF documents via URL.

# Define the PDF URL
pdf_url = "https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf"

# Create the message dictionary with the PDF URL
pdf_message = {
    "role": "user",
    "content": {
        "type": "pdf_url",
        "pdf_url": {
            "url": pdf_url
        }
    }
}
client.add([pdf_message], user_id="alice")

​
Complete Example with Multiple File Types

Here’s a comprehensive example showing how to work with different file types:

import base64
from Zentry import MemoryClient

client = MemoryClient()

def file_to_base64(file_path):
    with open(file_path, "rb") as file:
        return base64.b64encode(file.read()).decode('utf-8')

# Example 1: Using an image URL
image_message = {
    "role": "user",
    "content": {
        "type": "image_url",
        "image_url": {
            "url": "https://example.com/sample-image.jpg"
        }
    }
}

# Example 2: Using a text document URL
text_message = {
    "role": "user",
    "content": {
        "type": "mdx_url",
        "mdx_url": {
            "url": "https://www.w3.org/TR/2003/REC-PNG-20031110/iso_8859-1.txt"
        }
    }
}

# Example 3: Using a PDF URL
pdf_message = {
    "role": "user",
    "content": {
        "type": "pdf_url",
        "pdf_url": {
            "url": "https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf"
        }
    }
}

# Add each message to the memory system
client.add([image_message], user_id="alice")
client.add([text_message], user_id="alice")
client.add([pdf_message], user_id="alice")

Using these methods, you can seamlessly incorporate various media types into your interactions, further enhancing Zentry’s multimodal capabilities.

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

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