Vision RAG with Cohere Embed-4
Vision RAG with Cohere Embed-4 is an open-source visual Retrieval-Augmented Generation system. Its task is to find the most relevant image or PDF page for a text question and then answer questions about that retrieved visual content. It combines Cohere's Embed-4 model for multimodal embedding with Google's Gemini 2.5 Flash model for producing answers about images and PDF pages. This describes the app's documented design; it is not a report of hands-on use.
Why it caught our eye
As a concrete possibility, because the app avoids a separate OCR step and processes complex images and visual elements in PDF pages directly, it could be pointed at documents where meaning lives in layout, charts, or diagrams rather than extractable text. A user might ask a natural-language question and receive the most relevant page or image together with an answer. This is an interpretation of the documented capabilities, not a tested claim.
What it does
The system finds the image or PDF page most relevant to a text question, then answers questions about it. Cohere Embed-4 generates dense vector embeddings for each image or PDF page image, and a text query is embedded with the same model and matched by cosine similarity. The retrieved image plus the original question are then passed to Gemini 2.5 Flash, which generates a textual answer. Uploaded PDFs are rendered page by page as images, each embedded and retrievable individually.
Intended users and use cases
Based on the documented purpose, the app is intended for people who want to search and question collections of images or PDF pages using natural language. It processes complex images and visual elements in PDF pages directly without a separate OCR text-extraction step, which supports questions about visually rich material. This is an interpretation of the stated capabilities rather than a record of use.
Access
The app runs locally as a Streamlit web interface, started with `streamlit run vision_rag.py`. Running it requires both a Cohere API key and a Google Gemini API key.
Known limitations
Image and PDF processing, including page rendering and embedding, can take time for many items or large files. PDF pages are reprocessed on each upload within a session.
Sources & freshness
- raw.githubusercontent.com Checked 2026-10-23
AI-assisted introduction based on public sources. We have not conducted a hands-on review. Check the official site for current access and pricing.