RAG-Anything Tutorial: Build a Multimodal Retrieval Pipeline for Text, Tables, Equations, and Images in Colab

AI Agents 📅 2026-07-03 👁 15 views ⭐ 8/10

rag pipeline retrieval-augmented generation

Introduction

In this tutorial, we build a complete RAG-Anything workflow and explore how multimodal retrieval functions across text, tables, equations, and images. By the end, you'll have a fully functional retrieval pipeline running in Google Colab, capable of searching and reasoning over diverse document types.

Prerequisites and Environment Setup

We start by preparing the Colab environment. Install the required packages and securely enter your OpenAI API key at runtime—this keeps the notebook practical and safe to share. As of 2026, RAG-Anything supports the latest OpenAI models (GPT-4o, GPT-4.1) and works seamlessly with Python 3.10+ in Colab.

!pip install rag-anything openai pypdf2 pillow matplotlib pandas
import openai
from getpass import getpass

api_key = getpass("Enter your OpenAI API key: ")
openai.api_key = api_key

Creating a Synthetic Multimodal Report

To demonstrate the pipeline, we create a synthetic multimodal report containing:

Text paragraphs discussing AI trends
Tables with structured data (e.g., model performance metrics)
Equations rendered as LaTeX expressions
Images (a generated chart, saved as PNG)

We then convert this content into RAG-Anything's native content_list format:

from rag_anything import ContentItem

content_list = [
    ContentItem(type="text", data="Retrieval-Augmented Generation (RAG) combines..."),
    ContentItem(type="table", data={"columns": ["Model", "Accuracy"], "rows": [["GPT-4", 0.97], ["Claude-3", 0.95]]}),
    ContentItem(type="equation", data="E = mc^2"),
    ContentItem(type="image", data="chart.png"),
]

Building the Retrieval System

We initialize RAG-Anything with OpenAI-based chat, vision, and embedding functions. The 2026 version uses text-embedding-3-large for dense embeddings and supports hybrid search (dense + sparse) for better recall.

from rag_anything import RAGAnything

rag = RAGAnything(
    embedding_model="text-embedding-3-large",
    chat_model="gpt-4o",
    vision_model="gpt-4o",
    api_key=api_key
)

# Index the content
rag.index(content_list)

Querying the Pipeline

We test retrieval with multimodal queries:

"Find tables with model accuracy > 0.95"
"Identify images related to neural networks"
"Retrieve equations involving energy"

RAG-Anything returns ranked results with relevance scores and source metadata.

results = rag.query("Which models have over 95% accuracy?")
for item in results:
    print(f"Type: {item.type}, Score: {item.score:.2f}")
    print(item.data[:200] if isinstance(item.data, str) else item.data)

Advanced Configuration (2026 Updates)

Multi-modal chunking: Automatically splits large images into sub-regions for finer retrieval
Cross-modal reranking: Uses vision-language models to rerank image-text matches
Streaming support: Real-time retrieval for chat applications

Conclusion

You've built a complete multimodal RAG pipeline using RAG-Anything in Colab. This workflow can be extended to production systems supporting PDFs, slides, and web content. The code is available on GitHub.

Published July 2, 2026 — Editors Pick: Artificial Intelligence, Applications, Language Model, RAG.

via MarkTechPost