Input

from aitradestore import AiTradeStore  client = AiTradeStore()
 # API key via api_key param or AITRADESTORE_API_KEY env var  # Query image with Llama 4 Maverick model response = client.chat.completions.create( 
  model="meta-llama/Llama-4-Scout-17B-16E-Instruct", 
  messages=[{ 
      "role": "user", 
      "content": [ 
          {"type": "text", "text": "What can you see in this image?"}, 
          {"type": "image_url", "image_url": {"url": "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/yosemite.png"}} 
      ]
   }] )
 print(response.choices[0].message.content)

Output

The image depicts a serene landscape of Yosemite National Park, featuring a river flowing through a valley surrounded by towering cliffs and lush greenery.  *
  **River:**
    *
  The river is calm and peaceful, with clear water that reflects the surrounding scenery.
    *
  It flows gently from the bottom-left corner to the center-right of the image.
    *
  The riverbank is lined with rocks and grasses, adding to the natural beauty of the scene. *
  **Cliffs:**
    *
  The cliffs are massive and imposing, rising steeply from the valley floor.
    *
  They are composed of light-colored rock, possibly granite, and feature vertical striations.
    *
  The cliffs are covered in trees and shrubs, which adds to their rugged charm. *
  **Trees and Vegetation:**
    *
  The valley is densely forested, with tall trees growing along the riverbanks and on the cliffsides.
    *
  The trees are a mix of evergreen and deciduous species, with some displaying vibrant green foliage.
    *
  Grasses and shrubs grow in the foreground, adding texture and color to the scene. *
  **Sky:**
    *
  The sky is a brilliant blue, with only a few white clouds scattered across it.
    *
  The sun appears to be shining from the right side of the image, casting a warm glow over the scene.
 In summary, the image presents a breathtaking view of Yosemite National Park, showcasing the natural beauty of the valley and its surroundings. The calm river, towering cliffs, and lush vegetation all contribute to a sense of serenity and wonder. 

Function Calling

Input

import os import json import openai  client = openai.OpenAI( 
   base_url = "https://aitradestore.com/api/v1", 
   api_key = os.environ['AITRADESTORE_API_KEY'], )
 tools = [
  { 
   "type": "function", 
   "function": { 
     "name": "get_current_weather", 
     "description": "Get the current weather in a given location", 
     "parameters": { 
       "type": "object", 
       "properties": { 
         "location": { 
           "type": "string", 
           "description": "The city and state, e.g. San Francisco, CA" 
         }, 
         "unit": { 
           "type": "string", 
           "enum": [ 
             "celsius", 
             "fahrenheit" 
           ]
          }
        }
      }
    }
  } ]
 messages = [ 
   {"role": "system", "content": "You are a helpful assistant that can access external functions. The responses from these function calls will be appended to this dialogue. Please provide responses based on the information from these function calls."}, 
   {"role": "user", "content": "What is the current temperature of New York, San Francisco and Chicago?"} ]
     response = client.chat.completions.create( 
   model="meta-llama/Llama-4-Scout-17B-16E-Instruct", 
   messages=messages,
    tools=tools, 
   tool_choice="auto", )
 print(json.dumps(response.choices[0].message.model_dump()['tool_calls'], indent=2)) 

Output

[ 
 { 
   "id": "call_1p75qwks0etzfy1g6noxvsgs", 
   "function": { 
     "arguments": "{"location":"New York, NY","unit":"fahrenheit"}", 
     "name": "get_current_weather" 
   }, 
   "type": "function" 
 },
  { 
   "id": "call_aqjfgn65d0c280fjd3pbzpc6", 
   "function": { 
     "arguments": "{"location":"San Francisco, CA","unit":"fahrenheit"}", 
     "name": "get_current_weather" 
   }, 
   "type": "function" 
 },
  { 
   "id": "call_rsg8muko8hymb4brkycu3dm5", 
   "function": { 
     "arguments": "{"location":"Chicago, IL","unit":"fahrenheit"}", 
     "name": "get_current_weather" 
   }, 
   "type": "function" 
 } ] 

Query models with multiple images

Currently this model supports 5 images as input.

Input

# Multi-modal message with multiple images response = client.chat.completions.create( 
   model="meta-llama/Llama-4-Scout-17B-16E-Instruct", 
   messages=[{ 
       "role": "user", 
       "content": [ 
           { 
               "type": "text", 
               "text": "Compare these two images." 
           },
            { 
               "type": "image_url", 
               "image_url": { 
                   "url": "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/yosemite.png" 
               }
            },
            { 
               "type": "image_url", 
               "image_url": { 
                   "url": "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/slack.png" 
               }
            }
        ]
    }] ) print(response.choices[0].message.content) 

Output

The first image is a collage of multiple identical landscape photos showing a natural scene with rocks, trees, and a stream under a blue sky. The second image is a screenshot of a mobile app interface, specifically the navigation menu of the Canva app, which includes icons for Home, DMs (Direct Messages), Activity, Later, Canvases, and More.  ### Comparison: 1. **Content**:
   - The first image focuses on a natural landscape.
   - The second image shows a digital interface from an app.
 2. **Purpose**:
   - The first image could be used for showcasing nature, design elements in graphic work, or as a background.
   - The second image represents the functionality and layout of the Canva app's navigation system.
 3. **Visual Style**:
   - The first image has vibrant colors and realistic textures typical of outdoor photography.
   - The second image uses flat design icons with a simple color palette suited for user interface design.
 4. **Context**:
   - The first image is likely intended for artistic or environmental contexts.
   - The second image is relevant to digital design and app usability discussions. 

• Model String: meta-llama/Llama-4-Scout-17B-16E-Instruct
• Specs:
  • 17B active parameters (109B total)
  • 16-expert MoE architecture
  • 327,680 context length (will be increased to 10M)
  • Support for 12 languages: Arabic, English, French, German, Hindi, Indonesian, Italian, Portuguese, Spanish, Tagalog, Thai, and Vietnamese
  • Multimodal capabilities (text + images)
  • Support Function Calling
• Best for: Multi-document analysis, codebase reasoning, and personalized tasks
• Knowledge Cutoff: August 2024

• Multi-document summarization for legal/financial analysis: Analyze multiple legal contracts or financial statements simultaneously, identifying key terms, inconsistencies, and patterns across documents to generate comprehensive summaries and risk assessments.
• Personalized task automation using years of user data: Create tailored automation workflows by analyzing an individual's historical data patterns, communication style, and preferences, enabling highly personalized digital assistants that adapt to specific user needs.
• Efficient image parsing for multimodal applications: Process and understand image content in conjunction with text to power applications like visual search, content moderation, and accessibility features that require understanding the relationship between visual and textual elements.