> For the complete documentation index, see [llms.txt](https://docs.apolo.us/index/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.apolo.us/index/examples-use-cases/visual-rag-on-complex-pdfs-enterprise-ready-multimodal-ai.md).

# Visual RAG on Complex PDFs: Enterprise-Ready Multimodal AI

{% embed url="<https://www.loom.com/share/29bea291db4c48f9ae74ef1377a2dafd>" %}

{% embed url="<https://github.com/neuro-inc/apolo-reference-architectures>" %}

Building enterprise-ready generative AI applications is more than just deploying an LLM; it’s about ensuring **security**, **performance**, and the ability to handle complex, real-world data. Today, we dive into **Visual Retrieval-Augmented Generation (RAG)** for complex PDF documents using the **Apolo platform**, showcasing how to extract actionable insights from visually rich documents.

This guide demonstrates how to build a **Visual RAG** application on Apolo.

Here’s **what makes it enterprise-ready**:

1. **Security**: All data stays within your controlled environment with full monitoring and auditability.
2. **Performance**: Comparable to top-tier LLM services like OpenAI but running entirely on-premises for complete autonomy.

For this project, we leverage:

* **Llama 3.2 Vision**: A generative LLM with multimodal capabilities.
* **ColPali**: A cutting-edge visual embedder for processing complex documents.
* **LanceDB**: A lightweight, Rust-based vector database seamlessly integrated with Apolo’s object storage.

By combining these tools, we build a system that processes **complex PDFs with images**, **plots**, and **tables**, enabling natural language queries with visual understanding.

### Why Visual RAG?

Traditional RAG systems rely on text-based embeddings, but many enterprise documents, such as **financial reports, technical manuals, and research papers**, contain critical information embedded in visuals. Traditional OCR-based methods struggle with:

* Parsing complex layouts and relationships between text and visuals.
* Processing rich, interdependent plots and tables.

<figure><img src="https://lh7-rt.googleusercontent.com/docsz/AD_4nXdHJBbkxo_Yap-no-_2Px9QajaHRfaXmy27FWFxaQx5X_DJICVloJqCRgApU5wONyvW9SRYxXBIstE-NCR9nhbyRhBhgDJyrHPSsAVLcBOjDMRgfRyTP6HHBf5_IQk62CdXTtS3xg?key=otIZGGH9IV7FtNlKEVwhAb5T" alt=""><figcaption></figcaption></figure>

The **ColPali** model eliminates this bottleneck by embedding entire document pages - text and visuals combined - into vectors. Paired with **Llama 3.2 Vision**, a multimodal large language model (LLM), it enables precise question answering across complex, visually rich documents.

<figure><img src="https://lh7-rt.googleusercontent.com/docsz/AD_4nXfWCmCbDOGPUTlusiNtEU7AzXMAo0aE_SoxVo7KarSYFsFGMDgyF3wRsOZ-b3d0XbRPrTt6LfqLxofqHw-o276EJQ07lWo5E7zbG3tnIEGSKxG-fxaZkCKPsClrAtEoaRf1OXIQng?key=otIZGGH9IV7FtNlKEVwhAb5T" alt=""><figcaption></figcaption></figure>

### Advantages:

1. **Security & Performance**: The entire pipeline runs on-premise, ensuring data security while maintaining high throughput.
2. **Scalability**: Apolo’s platform supports seamless scaling for processing large datasets.
3. **State-of-the-Art Technology**: By integrating ColPali, LanceDB, and Llama 3.2 Vision-Instruct, the system achieves best-in-class document understanding.

**Future developments may include:**

* Fine-tuning the LLM for domain-specific use cases.
* Expanding support for additional document types.
* Optimizing query performance with more advanced retrieval methods.

Visual RAG on Apolo demonstrates how modern AI can transform document processing for enterprises. By leveraging multimodal LLMs, vector databases, and scalable infrastructure, this system sets a new standard for handling unstructured data.

If you’re interested in exploring this further, feel free to contact us (<start@apolo.us>) for a demo or check out [the code on GitHub](https://github.com/neuro-inc/apolo-reference-architectures).


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