Listening to the World: Multimodal Search with Qdrant

​What's Happening

​You'll build a multimodal search system that answers questions like "What did the CEO say about margins in last quarter's earnings call?" and returns relevant audio clips, transcripts, and related news articles from a single query.

​What You'll Build

​Earnings call recordings from Benzinga's Earnings Gold are already being chunked, transcribed, and queued for you. You'll experiment with:

• ​Multimodal embeddings: One Gemini E2 call site that puts audio chunks and article text into the same vector space

• ​Upsert + live feedback: Write points to your own Qdrant Cloud collection

• ​Query layer: Cross-modal and multi-modal retrieval with filtering and hybrid search

• ​Search engineering: Qdrant's APIs enabling recommendations, diversity sampling, time-decay boosting, and relevance retrieval loops

​PLEASE BRING: Laptop, charger, headphones. We'll work with audio data, so headphones are a must!

​Who Should Come

​Search engineers. ML engineers. Backend developers building retrieval systems. Anyone who's touched embeddings and wants to see what multimodal search in production looks like, and learn from the AskNews team how you can turn multimodal retrieval projects into a viable business.

​What You Get

• ​A working repo running against your own Qdrant Cloud collection

• ​Hands-on experience with production-grade retrieval APIs

​What You Need

​A laptop with a browser. Headphones.

​Built With

​Qdrant · Gemini 2 Embeddings · AskNews · Benzinga Earnings Call Data · HackerSquad