Add VideoRAGQnA as MMRAG usecase in Example

VideoRAGQnA/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 ./videoragqna.py /home/user/videoragqna.py
+
+ENV PYTHONPATH=$PYTHONPATH:/home/user/GenAIComps
+
+USER user
+
+WORKDIR /home/user
+
+ENTRYPOINT ["python", "videoragqna.py"]

VideoRAGQnA/README.md

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+# VideoRAGQnA Application
+
+Video RAG QnA is a framework that retrieves video based on provided user prompt. It uses only the video embeddings to perform vector similarity search in Intel's VDMS vector database and performs all operations on Intel Xeon CPU. The pipeline supports long form videos and time-based search.
+
+VideoRAGQnA is implemented on top of [GenAIComps](https://github.com/opea-project/GenAIComps), with the architecture 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 VideoRAGQnA-MegaService stroke:#000000
+    %% Subgraphs %%
+    subgraph VideoRAGQnA-MegaService["VideoRAGQnA-MegaService"]
+        direction LR
+        EM([Embedding <br>]):::blue
+        RET([Retrieval <br>]):::blue
+        RER([Rerank <br>]):::blue
+        LLM([LLM <br>]):::blue
+    end
+    subgraph User Interface
+        direction TB
+        a([User Input Query]):::orchid
+        UI([UI server<br>]):::orchid
+        Ingest([Ingest<br>]):::orchid
+    end
+    subgraph VideoRAGQnA GateWay
+        direction LR
+        invisible1[ ]:::invisible
+        GW([VideoRAGQnA GateWay<br>]):::orange
+    end
+    subgraph .
+        X([OPEA Micsrservice]):::blue
+        Y{{Open Source Service}}
+        Z([OPEA Gateway]):::orange
+        Z1([UI]):::orchid
+    end
+
+    LOCAL_RER{{Reranking service<br>}}
+    CLIP_EM{{Embedding service <br>}}
+    VDB{{Vector DB<br><br>}}
+    V_RET{{Retriever service <br>}}
+    Ingest{{Ingest data <br>}}
+    DP([Data Preparation<br>]):::blue
+    LVM_gen{{LLM Service <br>}}
+
+    %% Data Preparation flow
+    %% Ingest data flow
+    direction LR
+    Ingest[Ingest data] -->|a| DP
+    DP <-.->|b| CLIP_EM
+
+    %% Questions interaction
+    direction LR
+    a[User Input Query] -->|1| UI
+    UI -->|2| GW
+    GW <==>|3| VideoRAGQnA-MegaService
+    EM ==>|4| RET
+    RET ==>|5| RER
+    RER ==>|6| LLM
+
+
+    %% Embedding service flow
+    direction TB
+    EM <-.->|3'| CLIP_EM
+    RET <-.->|4'| V_RET
+    RER <-.->|5'| LOCAL_RER
+    LLM <-.->|6'| LVM_gen
+
+    direction TB
+    %% Vector DB interaction
+    V_RET <-.->|d|VDB
+    DP <-.->|d|VDB
+```
+
+This VideoRAGQnA use case performs RAG using LangChain, Intel VDMS VectorDB and Text Generation Inference on Intel XEON Scalable Processors.
+
+## Deploy VideoRAGQnA Service
+
+The VideoRAGQnA service can be effortlessly deployed on Intel XEON Scalable Processors.
+
+### Required Models
+
+By default, the embedding and LVM models are set to a default value as listed below:
+
+| Service   | Model                        |
+| --------- | ---------------------------- |
+| Embedding | openai/clip-vit-base-patch32 |
+| LVM       | DAMO-NLP-SG/Video-LLaMA      |
+
+### Deploy VideoRAGQnA on Xeon
+
+For full instruction of deployment, please check [Guide](docker/xeon/README.md)
+
+Currently we support deploying VideoRAGQnA services with docker compose, using the docker images `built from source`. Find the corresponding [compose.yaml](./docker/xeon/compose.yaml).