AI & Agents · Advanced
Multimodal Semantic Search Engine
Index images and text into one embedding space with CLIP, build a low-latency vector index with metadata filtering, and serve cross-modal search where text queries find images.
You build a search engine that accepts plain English queries and returns semantically matching images by projecting both modalities into a shared CLIP embedding space. The indexing pipeline encodes images and optional text captions into 512-dimensional vectors, stores them in Qdrant with structured metadata, and exposes sub-10ms filtered nearest-neighbor lookups. A FastAPI backend handles ingest and query endpoints, and a Next.js front end provides a query box plus a results grid. This project is worth building because cross-modal retrieval is the core primitive behind image search, product discovery, and visual RAG systems.
What you build
- Text-to-image search: type a natural language description and retrieve visually matching images ranked by cosine similarity
- Image-to-image search: upload an image as the query to find visually similar items in the index
- Metadata filtering: narrow results by structured fields such as category, date range, or source before the vector search runs
- Batch ingestion pipeline: process a folder of images through CLIP via ONNX Runtime and upsert embeddings with metadata into Qdrant
- FastAPI REST API with /ingest and /search endpoints, including pagination and score thresholding
- Next.js search UI with a debounced query input, image grid, and per-result similarity score display
What it teaches
- How CLIP aligns image and text representations in a shared latent space using contrastive pretraining
- Approximate nearest-neighbor search with Qdrant: collections, vectors, payloads, and filtered HNSW queries
- Serving ONNX models with ONNX Runtime for portable, dependency-light inference in a FastAPI service
- Designing a dual-encoder retrieval system and the trade-offs between recall and latency at different index sizes
- Structuring a multimodal ingest pipeline that normalizes heterogeneous inputs into a uniform vector representation
- Connecting a Next.js front end to a Python API with typed fetch calls and handling streaming or paginated search results
How it works
- 1
Ingest
- Image folder
- CLIP ONNX encoder
↓ 512-d vectors
- 2
Index
- Qdrant collection
- Payload metadata
↓ stored
- 3
Query
- Text or image input
- CLIP text encoder
↓ query vector
- 4
ANN Search
- HNSW index
- Metadata filter
↓ top-K hits
- 5
Results
- Ranked images
- Similarity scores
↓ JSON response
- 6
UI
- Next.js grid
- Score display
Sign in to open the build guide
Free account. Get the step-by-step build and every resource link.
Take it further
- Add re-ranking with a cross-encoder or BLIP-2 caption model to refine the top-20 ANN candidates before returning the top-5 to the client.
- Build an incremental sync worker that watches a cloud storage bucket, encodes new uploads automatically, and removes deleted items from the Qdrant collection using payload-based soft deletes.
- Expose an image-query path in the UI so users can drag and drop an image and get visually similar results, using the CLIP image encoder on the server to produce the query vector.
