Hammad Bashir, CTO of Chroma, joins the show to break down how modern vector search systems are actually built from local, embedded databases to massively distributed, object-storage-backed architectures. We dig into Chroma’s shared local-to-cloud API, log-structured storage on object stores, hybrid search, and why retrieval-augmented generation (RAG) isn’t going anywhere.

Follow Hammad:
Twitter/X:  https://twitter.com/HammadTime
LinkedIn: https://www.linkedin.com/in/hbashir
Chroma: https://trychroma.com

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Database School: https://databaseschool.com
Database School YouTube Channel: https://www.youtube.com/@UCT3XN4RtcFhmrWl8tf_o49g  (Subscribe today)
LinkedIn: https://www.linkedin.com/in/aarondfrancis
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Chapters:
00:00 – Introduction From high-school ASICs to CTO of Chroma
01:04 – Hammad’s background and why vector search stuck
03:01 – Why Chroma has one API for local and distributed systems
05:37 – Local experimentation vs production AI workflows
08:03 – What “unprincipled data” means in machine learning
10:31 – From computer vision to retrieval for LLMs
13:00 – Exploratory data analysis and why looking at data still matters
16:38 – Promoting data from local to Chroma Cloud
19:26 – Why Chroma is built on object storage
20:27 – Write-ahead logs, batching, and durability
26:56 – Compaction, inverted indexes, and storage layout
29:26 – Strong consistency and reading from the log
34:12 – How queries are routed and executed
37:00 – Hybrid search: vectors, full-text, and metadata
41:03 – Chunking, embeddings, and retrieval boundaries
43:22 – Agentic search and letting models drive retrieval
45:01 – Is RAG dead? A grounded explanation
48:24 – Why context windows don’t replace search
56:20 – Context rot and why retrieval reduces confusion
01:00:19 – Faster models and the future of search stacks
01:02:25 – Who Chroma is for and when it’s a great fit
01:04:25 – Hiring, team culture, and where to follow Chroma