Multi-Modal Vision RAG Blueprint with Gemini 2.5 Pro & Qdrant

By Deepak Bagada, CEO at SaaSNext & Principal AI Architect

Introduction to Multi-Modal RAG

Retrieval-Augmented Generation (RAG) has historically been limited to text. However, a vast majority of enterprise data exists in multi-modal formats: scanned PDFs, architectural diagrams, medical imaging, and instructional videos. In August 2026, the frontier of enterprise AI is Multi-Modal Vision RAG. This blueprint demonstrates how to construct a state-of-the-art visual RAG pipeline using Gemini 2.5 Pro, Google's flagship multi-modal reasoning engine, paired with Qdrant, an ultra-fast vector database optimized for complex, high-dimensional multi-modal embeddings.

This workflow enables your applications to search across text and images simultaneously, answer questions based on visual context (like charts or schematics), and synthesize information across disparate media types.

System Architecture

The Multi-Modal RAG pipeline consists of an Indexing Phase and a Retrieval/Generation Phase.

Indexing Phase:

1. Data Ingestion: Documents (PDFs) are parsed. Text is extracted, and pages/diagrams are rendered as images.
2. Multi-Modal Embedding: A model (like CLIP or Gemini's native embedding API) converts both text chunks and images into a shared vector space.
3. Vector Storage: Qdrant stores the dense vectors along with rich payload metadata (e.g., source file, page number, media type).

Retrieval Phase:

1. Query Embedding: The user's natural language query is vectorized.
2. Similarity Search: Qdrant retrieves the top-k most relevant chunks (which could be text snippets OR images).
3. Visual Reasoning: Gemini 2.5 Pro receives the user's prompt alongside the retrieved text AND images in its context window to generate a comprehensive, accurate response.

ASCII Architecture Diagram

[ Documents / Images ]
          |
          v
+-----------------------+      +-----------------------+
| Data Parsing & Chunking| ---> | Multi-Modal Embeddings|
+-----------------------+      +-----------+-----------+
                                           |
                                           v
                               +-----------------------+
                               |    Qdrant Vector DB   |
                               +-----------+-----------+
                                           |
[ User Query ] ----------------------------+ (Vector Search)
                                           |
                                           v
                               +-----------------------+
                               |   Gemini 2.5 Pro      |
                               |  (Context: Text + Img)|
                               +-----------+-----------+
                                           |
                                           v
                                 [ Final Generation ]

Implementation: Multi-File Code Blueprint

1. indexer.py - Ingestion and Qdrant Setup

import qdrant_client
from qdrant_client.http import models
import google.generativeai as genai
from PIL import Image
import os

# Initialize Qdrant and Gemini
client = qdrant_client.QdrantClient(url="http://localhost:6333")
genai.configure(api_key=os.environ["GEMINI_API_KEY"])

# Create Collection
COLLECTION_NAME = "vision_rag"
client.recreate_collection(
    collection_name=COLLECTION_NAME,
    vectors_config=models.VectorParams(size=768, distance=models.Distance.COSINE),
)

def get_embedding(text=None, image=None):
    # Simplified: Using a hypothetical multi-modal embedding function
    # In practice, use Google's multimodal embedding model or CLIP
    model = genai.GenerativeModel('models/multimodal-embedding-001')
    if image:
        return model.embed_content(image).embedding
    return model.embed_content(text).embedding

def index_document(image_path: str, description: str):
    img = Image.open(image_path)
    vector = get_embedding(image=img)
    
    client.upsert(
        collection_name=COLLECTION_NAME,
        points=[
            models.PointStruct(
                id=1, # Generate unique UUIDs in production
                vector=vector,
                payload={"description": description, "image_path": image_path, "type": "image"}
            )
        ]
    )
    print(f"Indexed: {image_path}")

2. retriever.py - Querying and Gemini 2.5 Pro Generation

import qdrant_client
import google.generativeai as genai
from PIL import Image
from indexer import get_embedding, COLLECTION_NAME

client = qdrant_client.QdrantClient(url="http://localhost:6333")
model = genai.GenerativeModel('gemini-2.5-pro')

def ask_visual_question(query: str):
    # 1. Embed the query
    query_vector = get_embedding(text=query)
    
    # 2. Retrieve from Qdrant
    search_results = client.search(
        collection_name=COLLECTION_NAME,
        query_vector=query_vector,
        limit=3
    )
    
    context_items = []
    for hit in search_results:
        if hit.payload["type"] == "image":
            img = Image.open(hit.payload["image_path"])
            context_items.append(img)
        context_items.append(f"Metadata: {hit.payload['description']}")
        
    # 3. Generate with Gemini 2.5 Pro
    prompt = f"Answer the following query based on the provided images and text context.
Query: {query}"
    
    # Gemini accepts a list of mixed text and PIL Images natively
    response = model.generate_content([prompt] + context_items)
    
    return response.text

The Edge of Gemini 2.5 Pro

Gemini 2.5 Pro is heavily leveraged in this workflow because of its native, interleaved multi-modal capabilities. Unlike older models that required complex workarounds to stitch OCR text with image captions, Gemini can natively process high-resolution images alongside massive text contexts. This allows the model to perform spatial reasoning—understanding the relationship between nodes in a flowchart, or identifying anomalies in a medical scan—directly from the pixels retrieved by Qdrant.

Qdrant acts as the perfect pairing due to its high-performance Rust backend and advanced filtering capabilities. In enterprise environments, you often need to filter vector searches by metadata (e.g., "Only search images from Q3 Financial Reports"). Qdrant handles these complex payload filters seamlessly while maintaining millisecond query latency on high-dimensional multi-modal embeddings.

Internal Linking Strategy

To optimize your vector search performance, review our guide on Advanced Qdrant Indexing Strategies and explore other RAG architectures in The Evolution of Retrieval-Augmented Generation.

Conclusion

Multi-Modal Vision RAG is transforming how organizations interact with their unstructured data. By combining the rapid, metadata-rich retrieval of Qdrant with the profound visual reasoning of Gemini 2.5 Pro, you can unlock insights from diagrams, presentations, and visual documents that were previously opaque to traditional AI systems. As you scale this architecture, consider implementing advanced chunking strategies for PDFs that preserve the spatial relationship between text and embedded images, further enhancing the context provided to the LLM.