Retrieval-Augmented Generation (RAG) Explained
1. The Foundation: Traditional RAG
Before going multimodal, it's essential to understand the classic setup used for text-based retrieval:
Offline Phase (Indexing): 1. Documents (policies, articles) are broken into chunks.
2. An Embedding Model converts these chunks into Vectors (numerical representations of meaning).
3. These vectors are stored in a Vector Database.
Online Phase (Querying):
The user provides a Query (e.g., "What is our VPN policy?").
A Retriever converts the query into a vector and finds matching chunks in the database.
The relevant chunks are bundled into a Context Block and sent to the LLM to generate an answer.
2. Approach 1: "Text-ify Everything" RAG
The simplest way to handle images, audio, and video without changing the core RAG architecture.
Process: * Images/Screenshots $\rightarrow$ Captioning Model $\rightarrow$ Text.
Video/Audio $\rightarrow$ Speech-to-Text $\rightarrow$ Transcripts.
Pros: Easy to implement; uses the existing text-only pipeline.
Cons: Information Loss. A text caption might describe a "network diagram" but miss the nuance of a red line indicating a primary connection vs. a blue line for failover.
3. Approach 2: Hybrid Multimodal RAG
Retrieval is still based on text, but the model generating the answer is much smarter.
Process:
Store text (captions/transcripts) in the vector database as before.
Keep Pointers from the text back to the original media files.
When a caption is retrieved, the system pulls the original image/video as well.
Both the text context and the raw media are fed into a Multimodal LLM.
Pros: The model can reason over visual details (like spatial relationships) that captions miss.
Cons: Retrieval is still only as good as the text descriptions. If the caption is poor, the correct image is never found.
4. Approach 3: Full Multimodal RAG
The most advanced method, where the system searches across different types of data natively.
Process:
Uses a Multimodal Embedding Stack (encoders for text, image, and audio).
All modalities are mapped into a Shared Vector Space.
A text query can directly retrieve an image or a video frame based on mathematical similarity, not just a caption match.
Pros: Richest grounding; no longer bottlenecked by the quality of captions or transcripts.
Cons: High cost and complexity; requires significant compute power and sophisticated context management.
Summary Table: Choosing Your RAG
| Approach | Search Method | LLM Type | Complexity | Best For |
| Text-ify | Text only | Text-only | Low | Simple descriptions, transcripts. |
| Hybrid | Text only | Multimodal | Medium | Reasoning over complex diagrams. |
| Full | Cross-modal | Multimodal | High | Data where visual/audio signal is dominant. |
What is RAG?
Retrieval-Augmented Generation (RAG) is a technique that allows a Large Language Model (LLM) to:
Retrieve external information (e.g., documents, policies, search results)
Add relevant content into the prompt
Generate more accurate and grounded answers
Example Use Case:
An internal help chatbot.
User asks:
“What’s our latest VPN policy?”
Instead of guessing, the system:
Searches company documents
Pulls relevant paragraphs
Sends them along with the question to the LLM
The LLM generates an informed answer
Classic RAG Architecture
Offline Phase (Indexing)
Collect documents (e.g., VPN policies, knowledge base articles)
Split them into chunks
Convert each chunk into a vector embedding
Store vectors in a vector database
Online Phase (When User Asks a Question)
User question → converted into a vector
Retriever finds similar vectors in the database
Top matching text chunks are returned
These chunks are added as context to the prompt
The LLM generates an answer using this grounded context
The Problem: Real Data is Multimodal
Documents may include:
Images (network diagrams)
Screenshots
PDFs
Videos
Audio recordings
Standard RAG handles text only.
So how do we handle multimodal data?
Three Approaches to Multimodal RAG
Approach 1: "Text-ify Everything" RAG
How it works:
Images → converted to text via captioning
Audio/video → converted to text via transcription
Everything becomes text
Use normal RAG pipeline
Advantages:
Simple
Easy to implement
Works with existing text-based systems
Disadvantages:
Loses visual details
Misses spatial relationships
Depends heavily on caption quality
Example:
A diagram showing:
Red path = primary
Blue path = failover
Caption might just say:
“Corporate network diagram with VPN gateway”
Important nuance is lost.
Approach 2: Hybrid Multimodal RAG
How it works:
Retrieval still happens over text (captions + transcripts)
But:
We keep pointers to original images/videos
We use a multimodal LLM that can process text + images together
Flow:
Retrieve relevant text chunks
Also retrieve linked original images
Send both to multimodal LLM
LLM reasons over real image + text
Advantages:
LLM sees real visuals
Better reasoning than text-only
Moderate complexity
Limitation:
Retrieval still depends on:
Quality of captions/transcripts
If caption is weak → image may never be retrieved.
Approach 3: Full Multimodal RAG
Key Difference:
Retrieval itself is multimodal.
Instead of converting everything to text:
We use:
Text encoder
Image encoder
Audio encoder
All embeddings go into the same shared vector space.
What this enables:
User question → converted into multimodal embedding
Retriever can directly find:
Text paragraphs
Images
Video frames
Audio clips
All using similarity search in the same space.
Advantages:
True cross-modal search
Not dependent on captions
Richest grounding
Better performance on visual-heavy tasks
Tradeoffs:
Higher compute cost
More complex system
Requires aligned multimodal models
Context window management becomes harder
Final Comparison
| Approach | Retrieval Type | LLM Type | Complexity | Accuracy |
|---|---|---|---|---|
| Text-ify Everything | Text only | Text LLM | Low | Moderate |
| Hybrid Multimodal | Text retrieval | Multimodal LLM | Medium | Good |
| Full Multimodal | Multimodal retrieval | Multimodal LLM | High | Best |
Summary
Classic RAG → Text only
Text-ify RAG → Convert everything to text
Hybrid RAG → Retrieve text, reason over originals
Full Multimodal RAG → Retrieve and reason across all modalities
The more multimodal your system becomes, the:
Better the grounding
Higher the complexity
Greater the compute cost
Here’s a visual architecture-style explanation of Classic RAG and Multimodal RAG, using clean flow diagrams you can picture or draw on a whiteboard.
Classic RAG Architecture (Text Only)
Offline Phase (Indexing)
Documents (Text Files)
│
▼
Text Chunking
│
▼
Embedding Model
(Convert text → vectors)
│
▼
Vector Database
What’s happening:
Documents are split into chunks
Each chunk becomes a vector
Stored in a vector database for similarity search
Online Phase (User Query)
User Question
│
▼
Embedding Model (same model)
│
▼
Query Vector
│
▼
Vector Database (Similarity Search)
│
▼
Top Relevant Text Chunks
│
▼
Build Prompt (Context + Question)
│
▼
Large Language Model
│
▼
Final Answer
Retrieval is based purely on text similarity.
Multimodal RAG – Three Architectures
A. “Text-ify Everything” RAG
Concept:
Convert all non-text into text first.
Images ──► Captioning Model ──► Text
Audio ──► Speech-to-Text ──► Text
Video ──► Transcription ──► Text
All Text
│
▼
Standard RAG Pipeline
Visual Flow
[Images] [Audio] [Video] [Text Docs]
│ │ │ │
▼ ▼ ▼ ▼
Captioning Transcribe Transcribe (No change)
│ │ │
└────────────┴────────────┘
│
▼
Embedding Model
│
▼
Vector Database
Simple
Loses visual nuance
B. Hybrid Multimodal RAG
Retrieval = Text
Generation = Multimodal
Text Retrieval
│
▼
Captions + Transcripts Retrieved
│
▼
Fetch Original Images/Videos
│
▼
Multimodal LLM (Text + Images)
Architecture Diagram
Documents (Text + Images + Audio)
│
▼
Convert non-text → Text (for indexing)
│
▼
Embedding Model
│
▼
Vector Database
Query Time
User Question
│
▼
Retriever (Text-based)
│
▼
Relevant Text Chunks
│
├──► Get Linked Image/Video
│
▼
Multimodal LLM
(Text + Real Images)
│
▼
Answer
LLM sees real visuals
Retrieval still depends on text quality
C. Full Multimodal RAG (Advanced)
Retrieval + Generation = Multimodal
This is the most powerful architecture.
🧠Shared Multimodal Embedding Space
Text Encoder
Image Encoder
Audio Encoder
│
▼
Shared Vector Space
│
▼
Vector Database
All modalities map into the same semantic space.
Offline Indexing
Text Docs ──► Text Encoder ──►
Images ──► Image Encoder ──►
Audio ──► Audio Encoder ──►
Shared Vector Space
│
▼
Vector Database
Query Time
User Question
│
▼
Multimodal Embedding Model
│
▼
Query Vector (shared space)
│
▼
Vector Database
│
▼
Retrieve:
• Text paragraphs
• Images
• Video frames
• Audio clips
│
▼
Multimodal LLM
│
▼
Answer
True cross-modal search
Not dependent on captions
Higher cost & complexity
Architecture Comparison Overview
CLASSIC RAG
Text → Embed → Search → LLM
TEXT-IFY RAG
Everything → Text → Embed → Search → LLM
HYBRID RAG
Text Search → Retrieve Originals → Multimodal LLM
FULL MULTIMODAL RAG
Multimodal Embed → Cross-modal Search → Multimodal LLM
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