Search Images with Text: Build a Multimodal AI Engine (Python Tutorial)

Social media data is messy—it’s a mix of text, images, and captions. In this LBSocial tutorial, we move beyond simple keyword search. We built a Multimodal Search Engine that can understand both images and text in the same mathematical space.

You will learn how to:

Generate Embeddings: Use OpenAI’s CLIP model to turn text and images into vectors.
Split Strategy: Store visual and linguistic data in MongoDB.
Double-Tap Search: Search for photos using text queries (and vice versa).

📂 Get the Code & Data: https://github.com/lbsocial/data-anal...

📖 Read the Blog Post: [will be updated soon]

⏱️ Timecodes:
0:00 - Introduction to Multimodal Search
01:05 - Setup: Python, MongoDB & CLIP
01:54 - Connecting to MongoDB
02:30 - Loading the OpenAI CLIP Model
03:53 - Step 2: Generating Synthetic Social Data
05:23 - Step 3: The Split Strategy (Image vs Text Embeddings)
06:41 - Step 4: Building the Vector Search Index
08:08 - Step 5: Defining the "Double-Tap" Search Logic
09:25 - Testing the Engine (Pizza & Dog Examples)
10:55 - Why this matters for Data Science

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▶️ Watch the Full Series:    • Introduction to Database and Data Collection  

#️⃣ Tags: #multimodalai #openai #python #mongodb #datascience #vectordatabases #imagesearch #lbsocial #machinelearning #socialmediaanalysis