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Multimodal RAG System
CLIP embeddings with ChromaDB for text, images, and tables. 0.030s retrieval with 60%+ cross-modal accuracy.
The problem
Documents mixing text, images, tables, and equations aren't searchable with a single retrieval pipeline.
- The answer is in a table on page 12, but your retrieval only indexes text.
- You maintain two separate search systems: one for prose, one for scanned figures.
- A user asks a question your docs answer visually, and the bot has nothing to say.
What NEO built
NEO combined CLIP embeddings with ChromaDB for unified text/image/table vector search, then grounded LLM answers in the retrieved multimodal evidence.
The result
0.030s retrieval, 60%+ accuracy
Delivers 0.030s retrieval with 60%+ cross-modal accuracy on multimodal document QA.
From the blog · 8 min
Building a Multimodal RAG System That Retrieves Text, Images, and Tables Together
NEO built a multimodal RAG with CLIP embeddings and ChromaDB: 0.030s retrieval, 60%+ cross-modal accuracy, ingestion for PDFs, images (OCR), and tables (schema extraction).
Try this in your workspace
Paste this into NEO chat to kick off the same workflow on your own data.
Build a RAG pipeline over my documents that indexes text, images, and tables together with CLIP embeddings in ChromaDB, and grounds answers in whichever modality actually has the evidence.
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