MultimodalQnA Application

Multimodal question answering is the process of extracting insights from documents that contain a mix of text, images, videos, audio, and PDFs. It involves reasoning over both textual and non-textual content to answer user queries.

The MultimodalQnA framework enables this by leveraging the BridgeTower model, which encodes visual and textual data into a shared semantic space. During ingestion, it processes content and stores embeddings in a vector database. At query time, relevant multimodal segments are retrieved and passed to a vision-language model to generate responses in text or audio form.

Table of Contents

1. Architecture

2. Deployment Options

3. Monitoring and Tracing

Architecture

The MultimodalQnA application is an end-to-end workflow designed for multimodal question answering across video, image, audio, and PDF inputs. The architecture is illustrated below:

The MultimodalQnA example is implemented using the component-level microservices defined in GenAIComps, the MultimodalQnA Flow Chart shows below:

flowchart LR %% Colors %% classDef blue fill:#ADD8E6,stroke:#ADD8E6,stroke-width:2px,fill-opacity:0.5 classDef orange fill:#FBAA60,stroke:#ADD8E6,stroke-width:2px,fill-opacity:0.5 classDef orchid fill:#C26DBC,stroke:#ADD8E6,stroke-width:2px,fill-opacity:0.5 classDef invisible fill:transparent,stroke:transparent; style MultimodalQnA-MegaService stroke:#000000 %% Subgraphs %% subgraph MultimodalQnA-MegaService["MultimodalQnA-MegaService"] direction LR EM([Embedding <br>]):::blue RET([Retrieval <br>]):::blue LVM([LVM <br>]):::blue end subgraph UserInterface[" User Interface "] direction LR a([User Input Query]):::orchid Ingest([Ingest data]):::orchid UI([UI server<br>]):::orchid end ASR{{Whisper service <br>}} TEI_EM{{Embedding service <br>}} VDB{{Vector DB<br><br>}} R_RET{{Retriever service <br>}} DP([Data Preparation<br>]):::blue LVM_gen{{LVM Service <br>}} GW([MultimodalQnA GateWay<br>]):::orange TTS{{SpeechT5 service <br>}} %% Data Preparation flow %% Ingest data flow direction LR Ingest[Ingest data] --> UI UI -->DP DP <-.-> TEI_EM %% Questions interaction direction LR a[User Input Query] --> UI UI --> GW GW <==> MultimodalQnA-MegaService EM ==> RET RET ==> LVM %% Embedding service flow direction LR EM <-.-> TEI_EM RET <-.-> R_RET LVM <-.-> LVM_gen direction TB %% Vector DB interaction R_RET <-.->VDB DP <-.->VDB %% Audio speech recognition used for translating audio queries to text GW <-.-> ASR %% Generate spoken responses with text-to-speech using the SpeechT5 model GW <-.-> TTS

This MultimodalQnA use case performs Multimodal-RAG using LangChain, Redis VectorDB and Text Generation Inference on Intel Gaudi2, Intel Xeon Scalable Processors and AMD EPYC™ Processors, and we invite contributions from other hardware vendors to expand the example.

Deployment Options

The table below lists currently available deployment options. They outline in detail the implementation of this example on selected hardware.

Validated Configurations

Deploy Method	LLM Engine	LLM Model	Database	Hardware
Docker Compose	LLAVA	llava-hf/llava-1.5-7b-hf	Milvus, Redis	Intel Xeon
Docker Compose	LLAVA	llava-hf/llava-v1.6-vicuna-13b-hf	Redis	Intel Gaudi
Docker Compose	LLAVA	llava-hf/llava-1.5-7b-hf	Milvus, Redis	AMD EPYC
Docker Compose	TGI, vLLM	Xkev/Llama-3.2V-11B-cot	Redis	AMD ROCm

Validated Configurations

Deploy Method	LLM Engine	LLM Model	Database	Hardware
Docker Compose	LLAVA	llava-hf/llava-1.5-7b-hf	Milvus, Redis	Intel Xeon
Docker Compose	LLAVA	llava-hf/llava-v1.6-vicuna-13b-hf	Redis	Intel Gaudi
Docker Compose	LLAVA	llava-hf/llava-1.5-7b-hf	Milvus, Redis	AMD EPYC
Docker Compose	TGI, vLLM	Xkev/Llama-3.2V-11B-cot	Redis	AMD ROCm