Vision Models

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Prerequisites

1. Create an API key for authentication.
2. Save the API key to an environment variable:
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   export NEBIUS_API_KEY=<API_key>
   

3. Install LlamaIndex packages:
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   pip3 install llama-index-multi-modal-llms-nebius \
      llama-index matplotlib
   

   Since Nebius AI Studio supports an OpenAI-compatible API, the llama-index-multi-modal-llms-nebius package includes the llama-index-multi-modal-llms-openai package.

   Show If you get error: externally_managed_ environment

   Create a virtual Python environment. You can install packages there that are isolated from the basic environment.To prepare a virtual environment:

   1. Create it:
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      python3 -m venv <environment_name>
      

   2. Activate the environment:
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      source <environment_name>/bin/activate
      

      A directory with the environment name is created.

   Now, you can install required Python packages. When you no longer need the created virtual environment, run the deactivate command and delete the environment directory.

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Prepare a script

1. Set up the Nebius AI Studio environment:
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   import os
   from llama_index.multi_modal_llms.nebius import NebiusMultiModal
   from PIL import Image
   import matplotlib.pyplot as plt
   
   # Take the API key from the environment variable
   NEBIUS_API_KEY = os.getenv("NEBIUS_API_KEY")
   
   # Load a model
   mm_llm = NebiusMultiModal(
       model="Qwen/Qwen2-VL-72B-Instruct",
       api_key=NEBIUS_API_KEY,
       max_new_tokens=300,
   )
   

2. Load a local image or an image accessible by a URL:
   • Local
   • From URL

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   from llama_index.core import SimpleDirectoryReader
   
   # Enter the path to the required image
   image_documents = SimpleDirectoryReader(
       input_files=["<path/to/image>"]
   ).load_data()
   img = Image.open("<path/to/image>")
   plt.imshow(img)
   

3. Add one of the following methods to the script, depending on your use case:

   Use case	Description	How to implement
   Prompt	Ask a question as a prompt.	Call the following method: mm_llm.complete( prompt="Describe the image as an alternative text", image_documents=image_documents, )
   Streaming output	Output is printed out word by word. This can be helpful for chats, so the user can watch how the answer is being typed gradually.	Call the mm_llm.stream_complete() method and put in it prompt and image_documents as well. Next, print out the response: for r in response: print(r.delta, end="")
   Multi-message request	Include system prompts and a chat history to your request, so Nebius AI Studio returns more precise output.	Make an array of messages and then pass it along in the mm_llm.chat() method. For more details, see Examples.
   Multi-message request with streaming output	Add system prompts and a chat history and receive the streaming output.	Make an array of messages and then pass it along in the mm_llm.stream_chat() method.
   Asynchronous request	Call a method asynchronously, so the next methods do not wait until it is finished.	Call the await mm_llm.acomplete() method and put in it prompt and image_documents as for a regular prompt.
   Asynchronous request with streaming output	Call a method asynchronously and have output typed word by word.	Call the await mm_llm.astream_complete() method with prompt and image_documents within it. Next, print out the response with async. For more details, see Examples.
   Asynchronous request with a multi-message request	Call a method asynchronously and add system prompts and a chat history.	Make an array of messages and then pass it along in the await mm_llm.achat() method.
   Asynchronous request with a multi-message request and streaming output	Combine asynchronous behavior, system prompts, a chat history and streaming output.	Make an array of messages and then pass it along in the await mm_llm.astream_chat() method. Next, print out the response with async.

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Examples

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Multi-message request

from llama_index.multi_modal_llms.openai.utils import (
    generate_openai_multi_modal_chat_message,
)

# Create a chat as a list of messages
chat_msg_1 = generate_openai_multi_modal_chat_message(
    prompt="Describe the images as an alternative text",
    role="user",
    image_documents=image_documents,
)

chat_msg_2 = generate_openai_multi_modal_chat_message(
    prompt="The image is a graph showing the surge in US mortgage rates. It is a visual representation of data, with a title at the top and labels for the x and y-axes. Unfortunately, without seeing the image, I cannot provide specific details about the data or the exact design of the graph.",
    role="assistant",
)

chat_msg_3 = generate_openai_multi_modal_chat_message(
    prompt="can I know more?",
    role="user",
)

chat_messages = [chat_msg_1, chat_msg_2, chat_msg_3]
chat_response = mm_llm.chat(
    messages=chat_messages,
)

# Get a reply to the last message
for msg in chat_messages:
    print(msg.role, msg.content)
print("Response:")
print(chat_response)

MessageRole.USER Describe the images as an alternative text
MessageRole.ASSISTANT The image is a graph showing the surge in US mortgage rates. It is a visual representation of data, with a title at the top and labels for the x and y-axes. Unfortunately, without seeing the image, I cannot provide specific details about the data or the exact design of the graph.
MessageRole.USER can I know more?
Response:
assistant: The image is a graph that displays the increase in US mortgage rates. It is a visual representation of data, with a title at the top and labels for the x and y-axes. The x-axis likely represents time, while the y-axis represents the mortgage rates. The graph may include lines or bars to show the trend over time. Without seeing the image, I cannot provide specific details about the data or the exact design of the graph.

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Asynchronous request with streaming output

import asyncio

async def complete():
    response_astream_complete = await mm_llm.astream_complete(
        prompt="Describe the images as an alternative text",
        image_documents=image_documents,
    )
    async for delta in response_astream_complete:
        print(delta.delta, end="")
asyncio.run(complete())

The image depicts the Colosseum in Rome, illuminated at night. The iconic structure is lit up with colorful lights, displaying the Italian flag's colors: green, white, and red. The lighting highlights the architectural details of the Colosseum, showcasing its arches and columns. The sky above is dark, with a few clouds, and the surrounding area is dimly lit, emphasizing the vibrant colors of the Colosseum.