Overview of Multimodal AI

Multimodal AI can simultaneously understand and process multiple types of data, including text, images, audio, and video. This opens up entirely new possibilities for AI applications: from image captioning and visual question answering to video understanding and cross-modal retrieval, multimodal AI is becoming the foundational capability for next-generation AI applications.

Getting Started with Vision-Language Models (VLM)

Using vision-language models like DeepSeek to process images:

from openai import OpenAI
import base64

client = OpenAI(
    api_key="your-api-key",
    base_url="https://api.deepseek.com"
)

# Encode image to base64
def encode_image(image_path):
    with open(image_path, "rb") as f:
        return base64.b64encode(f.read()).decode("utf-8")

# Send image-text message
response = client.chat.completions.create(
    model="deepseek-chat",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "What is in this image? Please describe in detail"},
            {"type": "image_url", "image_url": {
                "url": f"data:image/jpeg;base64,{encode_image('photo.jpg')}"
            }}
        ]
    }]
)

print(response.choices[0].message.content)

Multimodal RAG System

Traditional RAG can only handle text, but multimodal RAG can retrieve and reference visual information such as images and tables:

from langchain.document_loaders import UnstructuredImageLoader
from langchain.vectorstores import Chroma

# 1. Image understanding and indexing
# First generate image descriptions with VLM, then index along with images
image_descriptions = []
for img_path in image_paths:
    desc = vlm_describe(img_path)
    image_descriptions.append({
        "image_path": img_path,
        "description": desc
    })

# 2. Multimodal retrieval
# When user queries, retrieve both text and images
results = vectorstore.similarity_search(query, k=5)

# 3. Multimodal generation
# Pass retrieved images and text together to LLM to generate answer
context = build_multimodal_context(results)
answer = llm.generate(query, context)

Document Understanding and OCR

Multimodal models can understand and extract text, tables, and chart information from documents:

def extract_document_info(image_path):
    """Extract structured information from document image"""
    prompt = """Please analyze this document image and extract the following information:
1. Document type (invoice/contract/report, etc.)
2. Key fields and values
3. Table data (if any)
4. Document summary

Please output in JSON format."""

    response = vlm_analyze(image_path, prompt)
    return json.loads(response)

# Usage example
invoice_data = extract_document_info("invoice.jpg")
print(f"Invoice amount: {invoice_data['amount']}")
print(f"Invoice date: {invoice_data['date']}")

Developing Multimodal Agents

Building agents that can understand visual information and take actions:

class MultimodalAgent:
    def __init__(self):
        self.vlm = VLMClient()
        self.tools = {
            "screenshot": self.take_screenshot,
            "analyze_chart": self.analyze_chart,
            "compare_images": self.compare_images
        }

    def take_screenshot(self):
        """Take a screenshot"""
        import pyautogui
        screenshot = pyautogui.screenshot()
        return screenshot

    def analyze_chart(self, image):
        """Analyze chart data"""
        prompt = """Analyze this chart and extract:
1. Chart type
2. Meaning of X and Y axes
3. Data trends
4. Key data points"""
        return self.vlm.analyze(image, prompt)

    def run(self, task):
        """Execute multimodal task"""
        if "screenshot" in task:
            img = self.take_screenshot()
            return self.analyze_chart(img)
        elif "compare" in task:
            return self.compare_images(task["images"])

Performance Optimization Tips

  • Image Compression: Compress images to a reasonable resolution (e.g., 1024x1024) before uploading to reduce transmission time
  • Cache Results: Cache analysis results for identical images
  • Batch Processing: Combine analysis requests for multiple images
  • Asynchronous Processing: Use asynchronous API calls to avoid blocking

Summary

Multimodal AI is moving from "usable" to "user-friendly". It is recommended to start with simple image understanding and gradually build complex multimodal applications. In the future, multimodal capabilities will become a standard feature of AI applications.