Merge pull request #77 from intel/update-branch

feat: add initial commit for llm-pipeline-verification use case (#240)

Author: gooishin

README.md

@@ -20,14 +20,13 @@ Based on the platform you are using, select the following to get started:
 2. [Intel® Data Center GPU Flex Series](gpu/flex/ats#intel-data-center-gpu-flex-series-products-formerly-arctic-sound)
 
 ## Use Cases
-1. [Intel® Distribution of OpenVINO™ Toolkit](usecases/openvino/README.md)
+1. [Intel® Distribution of OpenVINO™ Toolkit](usecases/ai/openvino/README.md)
 2. [Vector Packet Processing](usecases/netsec/vpp/README.md)
 3. [OpenVINO™ Bert Model Benchmarking](usecases/netsec/openvino-bert/README.md)
-4. [Ollama with Open WebUI on Intel® Discrete GPU](usecases/llm/ollama/README.md)
-5. [LLM Finetuning Benchmark](usecases/llm/finetuning-benchmark/README.md)
-6. [LLM RAG Toolkit](usecases/llm/rag-toolkit/README.md)
-7. [Gen AI validated systems](usecases/genai/README.md)
-9. [AI Video Analytics](usecases/llm/ai-video-analytics/README.md)
+4. [Open WebUI with Ollama](usecases/ai/openwebui-ollama/README.md)
+5. [LLM Finetuning Benchmark](usecases/ai/finetuning-benchmark/README.md)
+6. [LLM RAG Toolkit](usecases/ai/rag-toolkit/README.md)
+7. [AI Video Analytics](usecases/ai/ai-video-analytics/README.md)
 
 ## Disclaimer
 GStreamer* is an open source framework licensed under LGPL. See https://gstreamer.freedesktop.org/documentation/frequently-asked-questions/licensing.html. You are solely responsible for determining if your use of GStreamer requires any additional licenses.  Intel is not responsible for obtaining any such licenses, nor liable for any licensing fees due, in connection with your use of GStreamer.

…ses/llm/ai-video-analytics/.dockerignore …ases/ai/ai-video-analytics/.dockerignoreusecases/llm/ai-video-analytics/.dockerignore renamed to usecases/ai/ai-video-analytics/.dockerignore usecases/llm/ai-video-analytics/.dockerignore renamed to usecases/ai/ai-video-analytics/.dockerignore

@@ -4,12 +4,16 @@ A sample application to perform semantic search and face identification with emb
 ## Example
 ![Alt Text](./assets/example.gif)
 
-## Requirements
-### Validated hardware
+## Validated hardware
 * CPU: Intel® Core™ Ultra 7 processors
-* RAM: 32GB
+* RAM: 16GB
 * DISK: 128GB
 
+## Prerequisite
+### 1. Install operating system
+Install the latest [Ubuntu* 22.04 LTS Desktop](https://releases.ubuntu.com/jammy/). Refer to [Ubuntu Desktop installation tutorial](https://ubuntu.com/tutorials/install-ubuntu-desktop#1-overview) if needed.
+
+
 ### Application ports
 Please ensure that you have these ports available before running the applications.
 
@@ -18,22 +22,7 @@ Please ensure that you have these ports available before running the application
 | Server   | 5980 |
 
 ## Quick Start
-### 1. Install operating system
-Install the latest [Ubuntu* 22.04 LTS Desktop](https://releases.ubuntu.com/jammy/). Refer to [Ubuntu Desktop installation tutorial](https://ubuntu.com/tutorials/install-ubuntu-desktop#1-overview) if needed.
-
-### 2. Clone repository
-This step will clone the repository
-```
-sudo apt install git
-git clone https://github.com/intel/edge-developer-kit-reference-scripts
-```
-
-### 3. Go to the AI Video Analytics use case directory
-This step will redirect user to the current platform setup directory.
-```bash
-cd edge-developer-kit-reference-scripts/usecases/llm/ai-video-analytics
-```
-### 4. Model Preparation
+### 1. Model Preparation
 Download face detection and face regression models
 ```bash
 sudo apt-get update
@@ -46,7 +35,7 @@ wget -O ./data/model/facial_recognition/face-detection-retail-0004.bin https://s
 wget -O ./data/model/facial_recognition/landmarks-regression-retail-0009.xml https://storage.openvinotoolkit.org/repositories/open_model_zoo/2023.0/models_bin/1/landmarks-regression-retail-0009/FP32/landmarks-regression-retail-0009.xml
 wget -O ./data/model/facial_recognition/landmarks-regression-retail-0009.bin https://storage.openvinotoolkit.org/repositories/open_model_zoo/2023.0/models_bin/1/landmarks-regression-retail-0009/FP32/landmarks-regression-retail-0009.bin
 ```
-### 5. Setup environment
+### 2. Setup environment
 Setup the application dependencies
 ```bash
 sudo apt-get update
@@ -57,20 +46,19 @@ python3 -m pip install -U pip
 python3 -m pip install -r requirements.txt
 ```
 
-### 6. Run the application
+### 3. Run the application
 Run the following command to start the app
 ```bash
 source .venv/bin/activate
 python3 app.py
 ```
 
-### 7. Access the App
-1. Open browser Google Chrome
-2. Access http://localhost:5980
+### 4. Access the App
+Navigate to http://localhost:5980
 
 ## Docker Setup
 ### Prerequisite
-1. Docker and docker compose should be setup before running the commands below. Refer to [here](https://docs.docker.com/engine/install/) to setup docker.
+Docker and docker compose should be setup before running the commands below. Refer to [here](https://docs.docker.com/engine/install/) to setup docker.
 
 ### 1. Build docker container
 ```
@@ -82,5 +70,4 @@ docker compose build
 docker compose up -d
 ```
 ### 3. Access the App
-1. Open browser Google Chrome
-2. Access http://localhost:5980
+Navigate to http://localhost:5980