Getting Started with Multimodal RAG

What is Multimodal RAG

Traditional RAG systems only handle text—splitting documents into text chunks, vectorizing them with text embedding models, and retrieving based on text similarity. But real-world knowledge carriers are far more than pure text: charts in PPTs, scanned tables in PDFs, architecture diagrams in technical documents, and even whiteboard content in video conferences—these multimodal information are completely ignored in traditional RAG.

Multimodal RAG is designed to solve this problem. It incorporates non-text information such as images, tables, and audio into the retrieval system, so that when a user queries, it can retrieve not only relevant text passages but also relevant visual information like charts and screenshots. For example, if a user asks "What is the sales trend this quarter?", Multimodal RAG can retrieve not only relevant text analysis but also directly return a line chart of the sales trend.

Architecture Design of Multimodal RAG

The core architecture of Multimodal RAG adds a multimodal processing layer on top of traditional RAG. It mainly includes three key components: a multimodal parser (extracting non-text content from PDFs, PPTs, and images into searchable descriptions), a multimodal embedding model (such as CLIP, which can map images and text into the same vector space), and a multimodal retriever (supporting cross-modal retrieval from text to image and image to text).

A typical processing pipeline: document upload → multimodal parsing (extracting text and images) → image description generation (using a vision model to generate text descriptions for each image) → vectorizing text and images separately → storing in a vector database. At query time, the user's question is matched against both text vectors and image vectors for similarity, returning the most relevant mixed results.

from openai import OpenAI

client = OpenAI(api_key="your-deepseek-api-key", base_url="https://api.deepseek.com")

class MultimodalRAG:
    def __init__(self):
        self.text_chunks = []
        self.image_chunks = []

    def process_document(self, filepath):
        """Process multimodal documents"""
        import fitz  # PyMuPDF for PDF parsing
        doc = fitz.open(filepath)
        for page_num, page in enumerate(doc):
            # Extract text
            text = page.get_text()
            if text.strip():
                self.text_chunks.append({
                    "content": text,
                    "page": page_num,
                    "type": "text"
                })
            # Extract images
            for img_idx, img in enumerate(page.get_images(full=True)):
                xref = img[0]
                base_image = doc.extract_image(xref)
                image_bytes = base_image["image"]
                # Generate description using DeepSeek (via vision-capable model)
                description = self.describe_image(image_bytes)
                self.image_chunks.append({
                    "content": description,
                    "image_data": image_bytes,
                    "page": page_num,
                    "type": "image"
                })
        print(f"Processing complete: {len(self.text_chunks)} text chunks, {len(self.image_chunks)} image chunks")

    def describe_image(self, image_bytes):
        """Generate text description for an image using AI"""
        # In actual projects, use vision models like deepseek-vl or gpt-4o
        return f"[Image description: chart {len(self.image_chunks)+1}]"

    def search(self, query, k=5):
        """Cross-modal search"""
        all_chunks = self.text_chunks + [
            {"content": c["content"], "type": "image"} for c in self.image_chunks
        ]
        # Use embedding search (in practice, vectorize and do similarity matching)
        # Simplified example: keyword matching
        results = [c for c in all_chunks if any(w in c["content"] for w in query.split())]
        return results[:k]

rag = MultimodalRAG()
rag.process_document("quarterly_report.pdf")
results = rag.search("Q3 sales data chart")
for r in results:
    print(f"Type: {r['type']}, Content: {r['content'][:100]}...")

Multimodal Embedding Models

The key technology of Multimodal RAG is the multimodal embedding model—which can map data from different modalities into the same vector space. OpenAI's CLIP is the most classic multimodal model, trained on 400 million image-text pairs via contrastive learning, enabling mutual retrieval between images and text in the vector space. For Chinese scenarios, models like Chinese-CLIP provide better Chinese multimodal understanding capabilities. Choosing the right embedding model is the first step to success in Multimodal RAG.

Practical Application Scenarios

Intelligent Customer Service: Users upload product screenshots with questions, and the RAG system retrieves relevant product manual text and interface screenshots. Educational Tutoring: Students photograph math problems, and the system retrieves similar problems and solution steps (including formula images). Medical Assistance: Doctors upload X-ray images, and the system retrieves relevant case descriptions and imaging data. Technical Documentation: Developers ask "How to use this API?", and the system returns a mixed result of text explanations, architecture diagrams, and code examples. The application scenarios of Multimodal RAG cover almost all AI applications that require "looking at pictures and talking".

From Beginner to Advanced

Multimodal RAG is still in a stage of rapid development. Current main challenges include: high-quality parsing of multimodal content (especially complex layouts in PDFs and PPTs), accuracy of cross-modal retrieval (text descriptions cannot fully capture all information in images), and ranking and fusion of multimodal results (the combination of text and images requires careful design). With the advancement of multimodal models like DeepSeek, these challenges are being gradually overcome. It is recommended to start with the simplest "text + image" scenario, first use existing tools to run an end-to-end pipeline, and then gradually expand to more complex modalities such as audio and video.

Challenges and Countermeasures in Multimodal Parsing

High-quality parsing of multimodal content is the primary challenge faced by Multimodal RAG. Taking PDF as an example, a typical quarterly report PDF may contain: two-column text layout, embedded Excel charts, scanned copies with handwritten signatures, and even watermarks and headers/footers. Simple text extraction tools (like PyPDF2) often struggle with these complex layouts. Recommended multimodal parsing solutions: for well-structured PDFs, use PyMuPDF to extract text and image position information; for scanned documents, use OCR engines (like PaddleOCR, which works well for Chinese) to extract text; for PPTs, use python-pptx to parse text and images page by page. Parsing quality directly determines the upper limit of subsequent retrieval quality—if text extraction is wrong, even the best embedding model cannot help. Image description generation is another key step in Multimodal RAG. Currently, the mainstream approach is to use vision-language models (like GPT-4o, Qwen-VL) to generate a text description for each image, then vectorize the description and store it in the database. The quality of the description depends on the capability of the vision model and the design of the description prompt. A good description prompt should guide the model to focus on: chart type (line chart/bar chart/pie chart), key data points, trend direction, outliers, and chart title and axis labels.

Ranking Multimodal Results

When retrieval returns mixed results (text + images), how to rank them is a challenge—you cannot simply compare text similarity and image similarity directly. Recommended ranking strategy: first do an initial ranking in a unified embedding space, then use a reranker model for fine ranking. In terms of presentation, it is recommended to display text results and image results in separate areas—text results at the top in a list form, and related images in a sidebar or at the bottom as thumbnails. Users can click on thumbnails to view larger images and corresponding context text.

The technology stack for Multimodal RAG is maturing rapidly, and leading teams have begun exploring video RAG and 3D model RAG. Video RAG can extract and index key frames from meeting recordings and product demo videos, allowing users to search via natural language for "the video where we discussed the budget at the last meeting". 3D model RAG has great potential in industrial design and architecture—users upload a part model, and the system retrieves similar parts and related documentation. These cutting-edge directions, although technically complex, have enormous application value and are worth continuous attention.