{"slug": "visualizing-how-multimodal-vector-search-works-under-the-hood", "title": "Visualizing how multimodal vector search works under the hood", "summary": "A new open-source project demonstrates how multimodal vector search works by combining OpenAI's CLIP model with Qdrant's vector database to enable text-to-image and image-to-image search. The system embeds both text and images into a shared 512-dimensional vector space and includes a real-time UI animation that visualizes the AI processing pipeline from tokenization to ranked results.", "body_md": "Search images using natural language or image upload — powered by OpenAI CLIP and Qdrant vector search.\n\nThis project was built from scratch to explore how modern image search works under the hood. Instead of relying on manual tags or filenames, this app \"understands\" visual content by embedding both text and images into a shared 512-dimensional vector space.\n\n**Text-to-Image Search:** Describe what you are looking for (e.g., \"sunset over mountains\").**Image-to-Image Search:** Upload an image to find visually similar ones.**Pipeline Visualization:** A real-time UI animation that shows the AI processing your query step-by-step: Tokenization → CLIP Encoder → Vector Embedding → Qdrant Search → Ranked Results.\n\n| Component | Technology |\n|---|---|\nEmbedding Model |\nOpenAI CLIP (ViT-B/32) |\nVector Database |\nQdrant (HNSW indexing) |\nFrontend/UI |\nHTML / CSS / Vanilla JS |\nBackend |\nFastAPI, Python (PyTorch, Transformers) |\n\nFollow these steps to run the project locally on your machine.\n\n**Python 3.10+****Docker**(to run the local Qdrant database)\n\nOpen a terminal and start a local Qdrant instance using Docker:\n\n```\ndocker run -p 6333:6333 -p 6334:6334 \\\n    -v $(pwd)/qdrant_storage:/qdrant/storage:z \\\n    qdrant/qdrant\n```\n\nOpen a new terminal window in the project directory, create a virtual environment, and install the required packages:\n\n```\n# Create and activate virtual environment\npython3 -m venv .venv\nsource .venv/bin/activate\n\n# Install dependencies\npip install -r requirements.txt\n```\n\nThe repository includes a pre-exported file of vectors (`data/vectors.json`\n\n) containing 541 sample images.\n\nStart the FastAPI server:\n\n```\nuvicorn app.api:app --host 0.0.0.0 --port 8000\n```\n\n*Note: On the very first run, the app will automatically read data/vectors.json and import the vectors into your local Qdrant database. It will also download the CLIP model weights.*\n\nOpen your browser and navigate to:\n[http://localhost:8000](http://localhost:8000)\n\nType a query or upload an image, and watch the pipeline animate as it searches!\n\n`app/api.py`\n\n: FastAPI server routes and static file serving.`ui/app.html`\n\n: Custom HTML/JS frontend with the animated pipeline.`app/embeddings.py`\n\n: CLIP model initialization and inference code.`app/vector_store.py`\n\n: Qdrant client connection and search logic.`app/config.py`\n\n: Configuration settings and environment variables.`scripts/`\n\n: Utilities for downloading datasets and importing/exporting vectors.`data/`\n\n: Contains the image dataset and the exported vectors JSON.", "url": "https://wpnews.pro/news/visualizing-how-multimodal-vector-search-works-under-the-hood", "canonical_source": "https://github.com/Faizan711/multimodal-search", "published_at": "2026-07-19 06:56:41+00:00", "updated_at": "2026-07-19 07:21:19.225985+00:00", "lang": "en", "topics": ["artificial-intelligence", "computer-vision", "natural-language-processing"], "entities": ["OpenAI", "Qdrant", "FastAPI", "Python", "Docker", "CLIP", "HNSW"], "alternates": {"html": "https://wpnews.pro/news/visualizing-how-multimodal-vector-search-works-under-the-hood", "markdown": "https://wpnews.pro/news/visualizing-how-multimodal-vector-search-works-under-the-hood.md", "text": "https://wpnews.pro/news/visualizing-how-multimodal-vector-search-works-under-the-hood.txt", "jsonld": "https://wpnews.pro/news/visualizing-how-multimodal-vector-search-works-under-the-hood.jsonld"}}