Hi folks, my name is B Ranadeer, a working professional who strives to work on AI models and is curious about Model Architecture and the math behind. I am beginner in some topics, and used AI in some areas here to explain things, however I am aware about what I am doing and writing here, AI is a buzzword these days, in the beginning AI was capable of working on text based inputs and outputs, however in recent developments, everything changed, AI can now work on images, videos and you can name it. Here I am explaining the RAG model which is capable of understanding the text, images, and videos, also known as Multimodel RAG, which is an essential part of AI models these days, and I am going to explain it with ElasticSearch.
The Evolution of Multimodel Era:
For years, artificial intelligence was confined behind a "Text Wall," requiring users to translate multifaceted human experiences into rigid strings of characters. While early RAG systems successfully bridged the gap between LLMs and private text documents, the modern business landscape, a rich gallery of images, audio, and video demanded more than just a library. The shift into the real world was ignited by a massive technological convergence, starting with the 2021 release of OpenAI’s CLIP, which created a mathematical "shared bridge" between text and vision. This breakthrough was followed by the rise of "eyes and ears" for AI through multimodal models like GPT-4o and Gemini; however, these models lacked a specific memory of private data until Elasticsearch industrialized vector databases. By treating various data types as high-dimensional vectors, Elasticsearch provided the scalable "External Memory" necessary to search through millions of visual and auditory assets in milliseconds. This culminated in the "Gotham Moment," a metaphor for high-stakes, messy data environments where detectives and now AI must synthesize crime scene photos, wiretaps, and reports simultaneously. Multimodal RAG is the ultimate synthesis of these technologies, finally allowing AI to interpret the world with the same sensory depth as a human expert.
1.Introduction: The Evolution of Search
Traditional Search was a world of keywords. RAG (Retrieval-Augmented Generation) evolved this into a world of meaning but primarily textual meaning. However, humans experience the world multimodally: we see, hear, and read simultaneously.
Multimodal RAG shatters the text-only barrier. It allows an AI to act like a detective in Gotham City, connecting a surveillance photo of a purple suit to a police report and a 911 audio recording of a sinister laugh. By using Elasticsearch as the central nervous system, we can store these diverse data types in a single "Shared Vector Space" to build truly omniscient AI applications.
2.Prerequisites & Environment Setup
To build a production-grade multimodal system, you need a stack that supports high dimensional vector math and industrial scaling.
Hardware: 16GB RAM (recommended) and an NVIDIA GPU (optional but faster for inference).
Elasticsearch: Version 8.16+ is required to leverage Better Binary Quantization (BBQ).
Python Stack:
pip install torch torchvision torchaudio For ImageBind
pip install git+https://github.com/hkchengrex/ImageBind.git
pip install elasticsearch openai python-dotenv
3.The Core Concept; Shared Vector Space
The secret sauce is ImageBind. Unlike models that only link text and images (like CLIP), ImageBind binds six modalities text, image, audio, depth, thermal, and IMU into one shared mathematical coordinate system.
In the "Gotham City" example, if we embed a photo of a bat and the sound of flapping wings, their vectors will be numerically close in Elasticsearch. This allows "Cross-Modal Retrieval": searching for a sound and finding a picture.
The following is the step-by-step process involved in this example:
1. Data Ingestion
2. Indexing with Better Binary Quantization
3. Cross-Model Retrieval
4. The Generation of the Output
Flowchart of Multimodal RAG
Shared vector space with ImageBind
We chose shared vector space, a strategy that aligns perfectly with the need for efficient multimodal searches. Our implementation is based on ImageBind, a model capable of representing multiple modalities (text, image, audio, and video) in a common vector space. This allows us to:
Perform cross-modal searches between different media formats without needing to convert everything to text.
Use highly expressive embeddings to capture relationships between different modalities.
Ensure scalability and efficiency, storing optimized embeddings for fast retrieval in Elasticsearch.
By adopting this approach, we built a robust multimodal search pipeline, where a text query can directly retrieve images or audio without additional pre-processing. This method expands practical applications from intelligent search in large repositories to advanced multimodal recommendation systems.
The following figure illustrates the data flow within the Multimodal RAG pipeline, highlighting the indexing, retrieval, and response generation process based on multimodal data:
Phase 1: Ingestion & Multimodal Embedding
We must convert raw files into vectors. Using the ImageBind model, we "encode" our data:
from imagebind import data
from imagebind.models import imagebind_model
#Load model
model = imagebind_model.imagebind_huge(pretrained=True)
model.eval()
#Example: Generating an embedding for an audio file
inputs = {
"audio": data.load_and_transform_audio_data(["surveillance_audio.wav"], device='cpu')
}
with torch.no_grad():
embeddings = model(inputs)
audio_vector = embeddings['audio'].numpy()
Phase 2: Indexing with BBQ Optimization
Storing 1024-dimensional vectors is memory-intensive. Elasticsearch 8.16 introduced Better Binary Quantization (BBQ), which compresses vectors by up to 32x with almost zero loss in accuracy.
Mapping with BBQ:(json)
PUT /gotham-evidence
{
"mappings": {
"properties": {
"evidence_type": { "type": "keyword" },
"vector": {
"type": "dense_vector",
"dims": 1024,
"index": true,
"index_options": {
"type": "bbq_hnsw" // The "Better" way to quantize
}
}
}
}
}
Phase 3: Cross-Modal Retrieval
We use Hybrid Search to combine the power of semantic vectors with the precision of keyword filters (e.g., date ranges or locations).
The Search Query:
GET /gotham-evidence/_search
{
"retriever": {
"rrf": {
"retrievers": [
{ "knn": { "field": "vector", "query_vector": [...], "k": 10 } },
{ "standard": { "query": { "match": { "report_text": "Joker" } } } }
]
}
}
}
Note: We use Reciprocal Rank Fusion (RRF) to merge the vector and keyword results into a single, optimized list.
Phase 4: The Generative Loop
Once Elasticsearch returns the evidence (e.g., a photo of a green hair strand and an audio clip), we pass these "citations" to a Multimodal LLM like GPT-4o.
Prompt Logic:
"I have retrieved the following evidence from the database: , [Audio Transcription: 'Why so serious?']. As a Gotham Detective, synthesize this into a suspect profile."
Performance & Scaling: Why BBQ Wins
Why use BBQ instead of standard Float32 vectors?
| Metrics | Float32 | BBQ(Better Binary Quantization |
|---|---|---|
| Memory Usage | 100% (High) | ~5%(32x Reduction |
| Search Speed | Standard | 2-5x Faster |
| Accuracy | 100% | >99% |
BBQ allows you to run massive multimodal datasets on a fraction of the hardware, making "Search for Everything" affordable for any enterprise.
Conclusion & Resources
Multimodal RAG with Elasticsearch is more than a technical feat; it's a bridge between human perception and machine logic. By leveraging ImageBind and BBQ, we can build systems that understand context across every sense.
GitHub: https://github.com/elastic/elasticsearch-labs
Docs: https://www.elastic.co/guide/en/elasticsearch/reference/current/knn-search.html
Model: https://github.com/facebookresearch/ImageBind
Core Example link: https://www.elastic.co/search-labs/blog/building-multimodal-rag-system
Disclaimer: This is part of Elasticsearch and hackerearth Blogathon
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