BUGFIX: rename videoragqna to videoqna to align with other examples (#…

…798)

Signed-off-by: BaoHuiling <[email protected]>
Signed-off-by: chen, suyue <[email protected]>

VideoRAGQnA/Dockerfile → VideoQnA/Dockerfile

@@ -22,12 +22,12 @@ 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
+COPY ./videoqna.py /home/user/videoqna.py
 
 ENV PYTHONPATH=$PYTHONPATH:/home/user/GenAIComps
 
 USER user
 
 WORKDIR /home/user
 
-ENTRYPOINT ["python", "videoragqna.py"]
+ENTRYPOINT ["python", "videoqna.py"]

VideoRAGQnA/README.md → VideoQnA/README.md

@@ -1,8 +1,8 @@
-# VideoRAGQnA Application
+# VideoQnA 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.
+VideoQnA 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:
+VideoQnA is implemented on top of [GenAIComps](https://github.com/opea-project/GenAIComps), with the architecture flow chart shows below:
 
 ```mermaid
 ---
@@ -21,9 +21,9 @@ flowchart LR
     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
+    style VideoQnA-MegaService stroke:#000000
     %% Subgraphs %%
-    subgraph VideoRAGQnA-MegaService["VideoRAGQnA-MegaService"]
+    subgraph VideoQnA-MegaService["VideoQnA-MegaService"]
         direction LR
         EM([Embedding <br>]):::blue
         RET([Retrieval <br>]):::blue
@@ -36,10 +36,10 @@ flowchart LR
         UI([UI server<br>]):::orchid
         Ingest([Ingest<br>]):::orchid
     end
-    subgraph VideoRAGQnA GateWay
+    subgraph VideoQnA GateWay
         direction LR
         invisible1[ ]:::invisible
-        GW([VideoRAGQnA GateWay<br>]):::orange
+        GW([VideoQnA GateWay<br>]):::orange
     end
     subgraph .
         X([OPEA Micsrservice]):::blue
@@ -66,7 +66,7 @@ flowchart LR
     direction LR
     a[User Input Query] -->|1| UI
     UI -->|2| GW
-    GW <==>|3| VideoRAGQnA-MegaService
+    GW <==>|3| VideoQnA-MegaService
     EM ==>|4| RET
     RET ==>|5| RER
     RER ==>|6| LLM
@@ -85,11 +85,14 @@ flowchart LR
     DP <-.->|d|VDB
 ```
 
-This VideoRAGQnA use case performs RAG using LangChain, Intel VDMS VectorDB and Text Generation Inference on Intel XEON Scalable Processors.
+- This project implements a Retrieval-Augmented Generation (RAG) workflow using LangChain, Intel VDMS VectorDB, and Text Generation Inference, optimized for Intel XEON Scalable Processors.
+- Video Processing: Videos are converted into feature vectors using mean aggregation and stored in the VDMS vector store.
+- Query Handling: When a user submits a query, the system performs a similarity search in the vector store to retrieve the best-matching videos.
+- Contextual Inference: The retrieved videos are then sent to the Large Vision Model (LVM) for inference, providing supplemental context for the query.
 
-## Deploy VideoRAGQnA Service
+## Deploy VideoQnA Service
 
-The VideoRAGQnA service can be effortlessly deployed on Intel XEON Scalable Processors.
+The VideoQnA service can be effortlessly deployed on Intel XEON Scalable Processors.
 
 ### Required Models
 
@@ -100,8 +103,8 @@ By default, the embedding and LVM models are set to a default value as listed be
 | Embedding | openai/clip-vit-base-patch32 |
 | LVM       | DAMO-NLP-SG/Video-LLaMA      |
 
-### Deploy VideoRAGQnA on Xeon
+### Deploy VideoQnA on Xeon
 
 For full instruction of deployment, please check [Guide](docker_compose/intel/cpu/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_compose/intel/cpu/xeon/compose.yaml).
+Currently we support deploying VideoQnA services with docker compose, using the docker images `built from source`. Find the corresponding [compose.yaml](docker_compose/intel/cpu/xeon/compose.yaml).

VideoRAGQnA/docker_compose/intel/cpu/xeon/README.md → VideoQnA/docker_compose/intel/cpu/xeon/README.md

@@ -1,8 +1,8 @@
-# Build Mega Service of videoragqna on Xeon
+# Build Mega Service of videoqna on Xeon
 
-This document outlines the deployment process for a videoragqna application utilizing the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice pipeline on Intel Xeon server. The steps include Docker image creation, container deployment via Docker Compose, and service execution to integrate microservices such as `embedding`, `retriever`, `rerank`, and `lvm`. We will publish the Docker images to Docker Hub soon, it will simplify the deployment process for this service.
+This document outlines the deployment process for a videoqna application utilizing the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice pipeline on Intel Xeon server. The steps include Docker image creation, container deployment via Docker Compose, and service execution to integrate microservices such as `embedding`, `retriever`, `rerank`, and `lvm`. We will publish the Docker images to Docker Hub soon, it will simplify the deployment process for this service.
 
-VideoRAGQnA is a pipeline 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.
+VideoQnA is a pipeline 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.
 
 ## 🚀 Port used for the microservices
 
@@ -68,7 +68,7 @@ docker build -t opea/retriever-vdms:latest --build-arg https_proxy=$https_proxy
 ### 3. Build Rerank Image
 
 ```bash
-docker build -t opea/reranking-videoragqna:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy  -f comps/reranks/video-rag-qna/Dockerfile .
+docker build -t opea/reranking-videoqna:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy  -f comps/reranks/videoqna/Dockerfile .
 ```
 
 ### 4. Build LVM Image (Xeon)
@@ -89,14 +89,14 @@ cd ..
 
 ### 6. Build MegaService Docker Image
 
-To construct the Mega Service, we utilize the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice pipeline within the `videoragqna.py` Python script.
+To construct the Mega Service, we utilize the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice pipeline within the `videoqna.py` Python script.
 
 Build MegaService Docker image via below command:
 
 ```bash
 git clone https://github.com/opea-project/GenAIExamples.git
-cd GenAIExamples/VideoRAGQnA/
-docker build -t opea/videoragqna:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile .
+cd GenAIExamples/VideoQnA/
+docker build -t opea/videoqna:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile .
 ```
 
 ### 7. Build UI Docker Image
@@ -105,19 +105,19 @@ Build frontend Docker image via below command:
 
 ```bash
 cd ui
-docker build -t opea/videoragqna-ui:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f ./docker/Dockerfile .
+docker build -t opea/videoqna-ui:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f ./docker/Dockerfile .
 ```
 
 Then run the command `docker images`, you will have the following 8 Docker Images:
 
 1. `opea/dataprep-multimodal-vdms:latest`
 2. `opea/embedding-multimodal-clip:latest`
 3. `opea/retriever-vdms:latest`
-4. `opea/reranking-videoragqna:latest`
+4. `opea/reranking-videoqna:latest`
 5. `opea/video-llama-lvm-server:latest`
 6. `opea/lvm-video-llama:latest`
-7. `opea/videoragqna:latest`
-8. `opea/videoragqna-ui:latest`
+7. `opea/videoqna:latest`
+8. `opea/videoqna-ui:latest`
 
 ## 🚀 Start Microservices
 
@@ -168,7 +168,7 @@ export DATAPREP_GET_VIDEO_LIST_ENDPOINT="http://${host_ip}:6007/v1/dataprep/get_
 
 export VDMS_HOST=${host_ip}
 export VDMS_PORT=8001
-export INDEX_NAME="mega-videoragqna"
+export INDEX_NAME="mega-videoqna"
 export LLM_DOWNLOAD="True"
 export USECLIP=1
 
@@ -189,7 +189,7 @@ There are 2 parts of the pipeline:
 In the deploy steps, you need to start the VDMS DB and dataprep firstly, then insert some sample data into it. After that you could get the megaservice up.
 
 ```bash
-cd GenAIExamples/VideoRAGQnA/docker_compose/intel/cpu/xeon/
+cd GenAIExamples/VideoQnA/docker_compose/intel/cpu/xeon/
 
 docker volume create video-llama-model
 docker compose up vdms-vector-db dataprep -d
@@ -306,7 +306,7 @@ docker compose up -d
 7. MegaService
 
    ```bash
-   curl http://${host_ip}:8888/v1/videoragqna -H "Content-Type: application/json" -d '{
+   curl http://${host_ip}:8888/v1/videoqna -H "Content-Type: application/json" -d '{
          "messages": "What is the man doing?",
          "stream": "True"
          }'
@@ -319,16 +319,16 @@ docker compose up -d
 To access the frontend, open the following URL in your browser: http://{host_ip}:5173. By default, the UI runs on port 5173 internally. If you prefer to use a different host port to access the frontend, you can modify the port mapping in the `compose.yaml` file as shown below:
 
 ```yaml
-  videoragqna-xeon-ui-server:
-    image: opea/videoragqna-ui:latest
+  videoqna-xeon-ui-server:
+    image: opea/videoqna-ui:latest
     ...
     ports:
       - "80:5173" # port map to host port 80
 ```
 
-Here is an example of running videoragqna:
+Here is an example of running videoqna:
 
-![project-screenshot](../../../../assets/img/video-rag-qna.gif)
+![project-screenshot](../../../../assets/img/videoqna.gif)
 
 ## Clean Microservices
 

VideoRAGQnA/docker_compose/intel/cpu/xeon/compose.yaml → VideoQnA/docker_compose/intel/cpu/xeon/compose.yaml

@@ -61,8 +61,8 @@ services:
     volumes:
       - /home/$USER/.cache/huggingface/hub:/home/user/.cache/huggingface/hub
   reranking:
-    image: ${REGISTRY:-opea}/reranking-videoragqna:${TAG:-latest}
-    container_name: reranking-videoragqna-server
+    image: ${REGISTRY:-opea}/reranking-videoqna:${TAG:-latest}
+    container_name: reranking-videoqna-server
     ports:
       - "8000:8000"
     ipc: host
@@ -103,9 +103,9 @@ services:
     restart: unless-stopped
     depends_on:
       - lvm-video-llama
-  videoragqna-xeon-backend-server:
-    image: ${REGISTRY:-opea}/videoragqna:${TAG:-latest}
-    container_name: videoragqna-xeon-backend-server
+  videoqna-xeon-backend-server:
+    image: ${REGISTRY:-opea}/videoqna:${TAG:-latest}
+    container_name: videoqna-xeon-backend-server
     depends_on:
       - vdms-vector-db
       - dataprep
@@ -116,7 +116,7 @@ services:
       - lvm
     ports:
       - "8888:8888"
-    entrypoint: sh -c 'sleep 45 && python videoragqna.py'
+    entrypoint: sh -c 'sleep 45 && python videoqna.py'
     environment:
       http_proxy: ${http_proxy}
       https_proxy: ${https_proxy}
@@ -128,11 +128,11 @@ services:
       LVM_SERVICE_HOST_IP: ${LVM_SERVICE_HOST_IP}
     ipc: host
     restart: always
-  videoragqna-xeon-ui-server:
-    image: ${REGISTRY:-opea}/videoragqna-ui:${TAG:-latest}
-    container_name: videoragqna-xeon-ui-server
+  videoqna-xeon-ui-server:
+    image: ${REGISTRY:-opea}/videoqna-ui:${TAG:-latest}
+    container_name: videoqna-xeon-ui-server
     depends_on:
-      - videoragqna-xeon-backend-server
+      - videoqna-xeon-backend-server
     ports:
       - "5173:5173"
     environment:

VideoRAGQnA/docker_compose/intel/cpu/xeon/set_env.sh → VideoQnA/docker_compose/intel/cpu/xeon/set_env.sh

@@ -12,14 +12,14 @@ export RERANK_SERVICE_HOST_IP=${host_ip}
 export LVM_SERVICE_HOST_IP=${host_ip}
 
 export LVM_ENDPOINT="http://${host_ip}:9009"
-export BACKEND_SERVICE_ENDPOINT="http://${host_ip}:8888/v1/videoragqna"
+export BACKEND_SERVICE_ENDPOINT="http://${host_ip}:8888/v1/videoqna"
 export BACKEND_HEALTH_CHECK_ENDPOINT="http://${host_ip}:8888/v1/health_check"
 export DATAPREP_SERVICE_ENDPOINT="http://${host_ip}:6007/v1/dataprep"
 export DATAPREP_GET_FILE_ENDPOINT="http://${host_ip}:6007/v1/dataprep/get_file"
 export DATAPREP_GET_VIDEO_LIST_ENDPOINT="http://${host_ip}:6007/v1/dataprep/get_videos"
 
 export VDMS_HOST=${host_ip}
 export VDMS_PORT=8001
-export INDEX_NAME="mega-videoragqna"
+export INDEX_NAME="mega-videoqna"
 export USECLIP=1
 export LLM_DOWNLOAD="True" # Set to "False" before redeploy LVM server to avoid model download

VideoRAGQnA/docker_image_build/build.yaml → VideoQnA/docker_image_build/build.yaml

@@ -2,54 +2,54 @@
 # SPDX-License-Identifier: Apache-2.0
 
 services:
-  videoragqna:
+  videoqna:
     build:
       args:
         http_proxy: ${http_proxy}
         https_proxy: ${https_proxy}
         no_proxy: ${no_proxy}
       context: ../
       dockerfile: ./Dockerfile
-    image: ${REGISTRY:-opea}/videoragqna:${TAG:-latest}
-  videoragqna-ui:
+    image: ${REGISTRY:-opea}/videoqna:${TAG:-latest}
+  videoqna-ui:
     build:
       context: ../ui
       dockerfile: ./docker/Dockerfile
-    extends: videoragqna
-    image: ${REGISTRY:-opea}/videoragqna-ui:${TAG:-latest}
+    extends: videoqna
+    image: ${REGISTRY:-opea}/videoqna-ui:${TAG:-latest}
   dataprep-multimodal-vdms:
     build:
       context: GenAIComps
       dockerfile: comps/dataprep/vdms/multimodal_langchain/Dockerfile
-    extends: videoragqna
+    extends: videoqna
     image: ${REGISTRY:-opea}/dataprep-multimodal-vdms:${TAG:-latest}
   embedding-multimodal-clip:
     build:
       context: GenAIComps
       dockerfile: comps/embeddings/multimodal_clip/Dockerfile
-    extends: videoragqna
+    extends: videoqna
     image: ${REGISTRY:-opea}/embedding-multimodal-clip:${TAG:-latest}
   retriever-vdms:
     build:
       context: GenAIComps
       dockerfile: comps/retrievers/vdms/langchain/Dockerfile
-    extends: videoragqna
+    extends: videoqna
     image: ${REGISTRY:-opea}/retriever-vdms:${TAG:-latest}
-  reranking-videoragqna:
+  reranking-videoqna:
     build:
       context: GenAIComps
-      dockerfile: comps/reranks/video-rag-qna/Dockerfile
-    extends: videoragqna
-    image: ${REGISTRY:-opea}/reranking-videoragqna:${TAG:-latest}
+      dockerfile: comps/reranks/videoqna/Dockerfile
+    extends: videoqna
+    image: ${REGISTRY:-opea}/reranking-videoqna:${TAG:-latest}
   video-llama-lvm-server:
     build:
       context: GenAIComps
       dockerfile: comps/lvms/video-llama/dependency/Dockerfile
-    extends: videoragqna
+    extends: videoqna
     image: ${REGISTRY:-opea}/video-llama-lvm-server:${TAG:-latest}
   lvm-video-llama:
     build:
       context: GenAIComps
       dockerfile: comps/lvms/video-llama/Dockerfile
-    extends: videoragqna
+    extends: videoqna
     image: ${REGISTRY:-opea}/lvm-video-llama:${TAG:-latest}

VideoRAGQnA/tests/test_compose_on_xeon.sh → VideoQnA/tests/test_compose_on_xeon.sh

@@ -51,7 +51,7 @@ function start_services() {
     sleep 1m
 
     # List of containers running uvicorn
-    list=("dataprep-vdms-server" "embedding-multimodal-server" "retriever-vdms-server" "reranking-videoragqna-server" "video-llama-lvm-server" "lvm-video-llama" "videoragqna-xeon-backend-server")
+    list=("dataprep-vdms-server" "embedding-multimodal-server" "retriever-vdms-server" "reranking-videoqna-server" "video-llama-lvm-server" "lvm-video-llama" "videoqna-xeon-backend-server")
 
     # Define the maximum time limit in seconds
     TIME_LIMIT=5400
@@ -101,7 +101,7 @@ function start_services() {
         sleep 5m
     done
 
-    if docker logs videoragqna-xeon-ui-server 2>&1 | grep -q "Streamlit app"; then
+    if docker logs videoqna-xeon-ui-server 2>&1 | grep -q "Streamlit app"; then
         return 0
     else
         return 1
@@ -176,7 +176,7 @@ function validate_microservices() {
         "${ip_address}:8000/v1/reranking" \
         "video_url" \
         "reranking" \
-        "reranking-videoragqna-server" \
+        "reranking-videoqna-server" \
         '{
             "retrieved_docs": [{"doc": [{"text": "retrieved text"}]}],
             "initial_query": "query",
@@ -199,10 +199,10 @@ function validate_microservices() {
 
 function validate_megaservice() {
     validate_services \
-    "${ip_address}:8888/v1/videoragqna" \
+    "${ip_address}:8888/v1/videoqna" \
     "man" \
-    "videoragqna-xeon-backend-server" \
-    "videoragqna-xeon-backend-server" \
+    "videoqna-xeon-backend-server" \
+    "videoqna-xeon-backend-server" \
     '{"messages":"What is the man doing?","stream":"True"}'
 }
 
@@ -216,12 +216,12 @@ function validate_frontend() {
             echo "Frontend Content is as expected."
         else
             echo "Frontend Content does not match the expected result: $CONTENT"
-            docker logs videoragqna-xeon-ui-server >> ${LOG_PATH}/ui.log
+            docker logs videoqna-xeon-ui-server >> ${LOG_PATH}/ui.log
             exit 1
         fi
     else
         echo "Frontend is not running correctly. Received status was $HTTP_STATUS"
-        docker logs videoragqna-xeon-ui-server >> ${LOG_PATH}/ui.log
+        docker logs videoqna-xeon-ui-server >> ${LOG_PATH}/ui.log
         exit 1
     fi
 }