Add MultimodalQnA as MMRAG usecase in Example (#751)

Signed-off-by: Tiep Le <tiep.le@intel.com>
Signed-off-by: siddhivelankar23 <siddhi.velankar@intel.com>
Signed-off-by: sjagtap1803 <siddhant.jagtap@intel.com>

Author: tileintel

MultimodalQnA/Dockerfile

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+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+FROM python:3.11-slim
+
+RUN apt-get update -y && apt-get install -y --no-install-recommends --fix-missing \
+    libgl1-mesa-glx \
+    libjemalloc-dev \
+    git
+
+RUN useradd -m -s /bin/bash user && \
+    mkdir -p /home/user && \
+    chown -R user /home/user/
+
+WORKDIR /home/user/
+RUN git clone https://github.com/opea-project/GenAIComps.git
+
+WORKDIR /home/user/GenAIComps
+RUN pip install --no-cache-dir --upgrade pip && \
+    pip install --no-cache-dir -r /home/user/GenAIComps/requirements.txt
+
+COPY ./multimodalqna.py /home/user/multimodalqna.py
+
+ENV PYTHONPATH=$PYTHONPATH:/home/user/GenAIComps
+
+USER user
+
+WORKDIR /home/user
+
+ENTRYPOINT ["python", "multimodalqna.py"]
+# ENTRYPOINT ["/usr/bin/sleep", "infinity"]

MultimodalQnA/README.md

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+# MultimodalQnA Application
+
+Suppose you possess a set of videos and wish to perform question-answering to extract insights from these videos. To respond to your questions, it typically necessitates comprehension of visual cues within the videos, knowledge derived from the audio content, or often a mix of both these visual elements and auditory facts. The MultimodalQnA framework offers an optimal solution for this purpose.
+
+`MultimodalQnA` addresses your questions by dynamically fetching the most pertinent multimodal information (frames, transcripts, and/or captions) from your collection of videos. For this purpose, MultimodalQnA utilizes [BridgeTower model](https://huggingface.co/BridgeTower/bridgetower-large-itm-mlm-gaudi), a multimodal encoding transformer model which merges visual and textual data into a unified semantic space. During the video ingestion phase, the BridgeTower model embeds both visual cues and auditory facts as texts, and those embeddings are then stored in a vector database. When it comes to answering a question, the MultimodalQnA will fetch its most relevant multimodal content from the vector store and feed it into a downstream Large Vision-Language Model (LVM) as input context to generate a response for the user.
+
+The MultimodalQnA architecture shows below:
+
+![architecture](./assets/img/MultimodalQnA.png)
+
+MultimodalQnA is implemented on top of [GenAIComps](https://github.com/opea-project/GenAIComps), the MultimodalQnA Flow Chart shows below:
+
+```mermaid
+---
+config:
+  flowchart:
+    nodeSpacing: 100
+    rankSpacing: 100
+    curve: linear
+  theme: base
+  themeVariables:
+    fontSize: 42px
+---
+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 User Interface
+        direction TB
+        a([User Input Query]):::orchid
+        Ingest([Ingest data]):::orchid
+        UI([UI server<br>]):::orchid
+    end
+    subgraph MultimodalQnA GateWay
+        direction LR
+        invisible1[ ]:::invisible
+        GW([MultimodalQnA GateWay<br>]):::orange
+    end
+    subgraph .
+        X([OPEA Microservice]):::blue
+        Y{{Open Source Service}}
+        Z([OPEA Gateway]):::orange
+        Z1([UI]):::orchid
+    end
+
+    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>}}
+
+    %% Data Preparation flow
+    %% Ingest data flow
+    direction LR
+    Ingest[Ingest data] -->|a| UI
+    UI -->|b| DP
+    DP <-.->|c| TEI_EM
+
+    %% Questions interaction
+    direction LR
+    a[User Input Query] -->|1| UI
+    UI -->|2| GW
+    GW <==>|3| MultimodalQnA-MegaService
+    EM ==>|4| RET
+    RET ==>|5| LVM
+
+
+    %% Embedding service flow
+    direction TB
+    EM <-.->|3'| TEI_EM
+    RET <-.->|4'| R_RET
+    LVM <-.->|5'| LVM_gen
+
+    direction TB
+    %% Vector DB interaction
+    R_RET <-.->|d|VDB
+    DP <-.->|e|VDB
+
+
+
+
+```
+
+This MultimodalQnA use case performs Multimodal-RAG using LangChain, Redis VectorDB and Text Generation Inference on Intel Gaudi2 or Intel Xeon Scalable Processors. The Intel Gaudi2 accelerator supports both training and inference for deep learning models in particular for LLMs. Visit [Habana AI products](https://habana.ai/products) for more details.
+
+In the below, we provide a table that describes for each microservice component in the MultimodalQnA architecture, the default configuration of the open source project, hardware, port, and endpoint.
+
+<details>
+<summary><b>Gaudi default compose.yaml</b></summary>
+
+| MicroService | Open Source Project   | HW    | Port | Endpoint                                        |
+| ------------ | --------------------- | ----- | ---- | ----------------------------------------------- |
+| Embedding    | Langchain             | Xeon  | 6000 | /v1/embeddings                                  |
+| Retriever    | Langchain, Redis      | Xeon  | 7000 | /v1/multimodal_retrieval                        |
+| LVM          | Langchain, TGI        | Gaudi | 9399 | /v1/lvm                                         |
+| Dataprep     | Redis, Langchain, TGI | Gaudi | 6007 | /v1/generate_transcripts, /v1/generate_captions |
+
+</details>
+
+## Required Models
+
+By default, the embedding and LVM models are set to a default value as listed below:
+
+| Service              | Model                                       |
+| -------------------- | ------------------------------------------- |
+| embedding-multimodal | BridgeTower/bridgetower-large-itm-mlm-gaudi |
+| LVM                  | llava-hf/llava-v1.6-vicuna-13b-hf           |
+
+You can choose other LVM models, such as `llava-hf/llava-1.5-7b-hf ` and `llava-hf/llava-1.5-13b-hf`, as needed.
+
+## Deploy MultimodalQnA Service
+
+The MultimodalQnA service can be effortlessly deployed on either Intel Gaudi2 or Intel XEON Scalable Processors.
+
+Currently we support deploying MultimodalQnA services with docker compose.
+
+### Setup Environment Variable
+
+To set up environment variables for deploying MultimodalQnA services, follow these steps:
+
+1. Set the required environment variables:
+
+   ```bash
+   # Example: export host_ip=$(hostname -I | awk '{print $1}')
+   export host_ip="External_Public_IP"
+   # Example: no_proxy="localhost, 127.0.0.1, 192.168.1.1"
+   export no_proxy="Your_No_Proxy"
+   ```
+
+2. If you are in a proxy environment, also set the proxy-related environment variables:
+
+   ```bash
+   export http_proxy="Your_HTTP_Proxy"
+   export https_proxy="Your_HTTPs_Proxy"
+   ```
+
+3. Set up other environment variables:
+
+   > Notice that you can only choose **one** command below to set up envs according to your hardware. Other that the port numbers may be set incorrectly.
+
+   ```bash
+   # on Gaudi
+   source ./docker_compose/intel/hpu/gaudi/set_env.sh
+   # on Xeon
+   source ./docker_compose/intel/cpu/xeon/set_env.sh
+   ```
+
+### Deploy MultimodalQnA on Gaudi
+
+Refer to the [Gaudi Guide](./docker_compose/intel/hpu/gaudi/README.md) to build docker images from source.
+
+Find the corresponding [compose.yaml](./docker_compose/intel/hpu/gaudi/compose.yaml).
+
+```bash
+cd GenAIExamples/MultimodalQnA/docker_compose/intel/hpu/gaudi/
+docker compose -f compose.yaml up -d
+```
+
+> Notice: Currently only the **Habana Driver 1.17.x** is supported for Gaudi.
+
+### Deploy MultimodalQnA on Xeon
+
+Refer to the [Xeon Guide](./docker_compose/intel/cpu/xeon/README.md) for more instructions on building docker images from source.
+
+Find the corresponding [compose.yaml](./docker_compose/intel/cpu/xeon/compose.yaml).
+
+```bash
+cd GenAIExamples/MultimodalQnA/docker_compose/intel/cpu/xeon/
+docker compose -f compose.yaml up -d
+```
+
+## MultimodalQnA Demo on Gaudi2
+
+![MultimodalQnA-upload-waiting-screenshot](./assets/img/upload-gen-trans.png)
+
+![MultimodalQnA-upload-done-screenshot](./assets/img/upload-gen-captions.png)
+
+![MultimodalQnA-query-example-screenshot](./assets/img/example_query.png)