Add ChatQnA docker-compose example on Intel Xeon using MariaDB Vector

ChatQnA/docker_compose/intel/cpu/xeon/README.md

@@ -156,6 +156,7 @@ In the context of deploying a ChatQnA pipeline on an Intel® Xeon® platform, we
 | [compose_faqgen_tgi.yaml](./compose_faqgen_tgi.yaml)         | Enables FAQ generation using TGI as the LLM serving framework. For more details, refer to [README_faqgen.md](./README_faqgen.md).                                     |
 | [compose.telemetry.yaml](./compose.telemetry.yaml)           | Helper file for telemetry features for vllm. Can be used along with any compose files that serves vllm                                                                |
 | [compose_tgi.telemetry.yaml](./compose_tgi.telemetry.yaml)   | Helper file for telemetry features for tgi. Can be used along with any compose files that serves tgi                                                                  |
+| [compose_mariadb.yaml](./compose_mariadb.yaml)               | Uses MariaDB Server as the vector database. All other configurations remain the same as the default                                                                   |
 
 ## ChatQnA with Conversational UI (Optional)
 

ChatQnA/docker_compose/intel/cpu/xeon/README_mariadb.md

@@ -0,0 +1,259 @@
+# Deploying ChatQnA with MariaDB Vector on Intel® Xeon® Processors
+
+This document outlines the deployment process for a ChatQnA application utilizing the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice pipeline on Intel® Xeon® servers. The pipeline integrates **MariaDB Vector** as the vector database and includes microservices such as `embedding`, `retriever`, `rerank`, and `llm`.
+
+---
+
+## Table of Contents
+
+1. [Build Docker Images](#build-docker-images)
+2. [Validate Microservices](#validate-microservices)
+3. [Launch the UI](#launch-the-ui)
+4. [Launch the Conversational UI (Optional)](#launch-the-conversational-ui-optional)
+
+---
+
+## Build Docker Images
+
+First of all, you need to build Docker Images locally and install the python package of it.
+
+```bash
+git clone https://github.com/opea-project/GenAIComps.git
+cd GenAIComps
+```
+
+### 1. Build Retriever Image
+
+```bash
+docker build --no-cache -t opea/retriever:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/retrievers/src/Dockerfile .
+```
+
+### 2. Build Dataprep Image
+
+```bash
+docker build --no-cache -t opea/dataprep:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/dataprep/src/Dockerfile .
+cd ..
+```
+
+### 3. Build MegaService Docker Image
+
+To construct the Mega Service, we utilize the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice pipeline within the `chatqna.py` Python script. Build MegaService Docker image via below command:
+
+```bash
+git clone https://github.com/opea-project/GenAIExamples.git
+cd GenAIExamples/ChatQnA/
+docker build --no-cache -t opea/chatqna:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile .
+cd ../..
+```
+
+### 4. Build UI Docker Image
+
+Build frontend Docker image via below command:
+
+```bash
+cd GenAIExamples/ChatQnA/ui
+docker build --no-cache -t opea/chatqna-ui:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f ./docker/Dockerfile .
+cd ../../..
+```
+
+### 5. Build Conversational React UI Docker Image (Optional)
+
+Build frontend Docker image that enables Conversational experience with ChatQnA megaservice via below command:
+
+**Export the value of the public IP address of your Xeon server to the `host_ip` environment variable**
+
+```bash
+cd GenAIExamples/ChatQnA/ui
+export BACKEND_SERVICE_ENDPOINT="http://${host_ip}:8912/v1/chatqna"
+export DATAPREP_SERVICE_ENDPOINT="http://${host_ip}:6043/v1/dataprep/ingest"
+docker build --no-cache -t opea/chatqna-conversation-ui:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy --build-arg BACKEND_SERVICE_ENDPOINT=$BACKEND_SERVICE_ENDPOINT --build-arg DATAPREP_SERVICE_ENDPOINT=$DATAPREP_SERVICE_ENDPOINT -f ./docker/Dockerfile.react .
+cd ../../..
+```
+
+### 6. Build Nginx Docker Image
+
+```bash
+cd GenAIComps
+docker build -t opea/nginx:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/third_parties/nginx/src/Dockerfile .
+```
+
+Then run the command `docker images`, you will have the following 5 Docker Images:
+
+1. `opea/dataprep:latest`
+2. `opea/retriever:latest`
+3. `opea/chatqna:latest`
+4. `opea/chatqna-ui:latest`
+5. `opea/nginx:latest`
+
+## Start Microservices
+
+### Required Models
+
+By default, the embedding, reranking and LLM models are set to a default value as listed below:
+
+| Service   | Model                               |
+| --------- | ----------------------------------- |
+| Embedding | BAAI/bge-base-en-v1.5               |
+| Reranking | BAAI/bge-reranker-base              |
+| LLM       | meta-llama/Meta-Llama-3-8B-Instruct |
+
+Change the `xxx_MODEL_ID` below for your needs.
+
+### Setup Environment Variables
+
+Since the `compose.yaml` will consume some environment variables, you need to set them up in advance as below.
+
+**Export the value of the public IP address of your Xeon server to the `host_ip` environment variable**
+
+> Change the External_Public_IP below with the actual IPV4 value
+
+```bash
+export host_ip="External_Public_IP"
+```
+
+> Change to your actual Huggingface API Token value
+
+```bash
+export HUGGINGFACEHUB_API_TOKEN="Your_Huggingface_API_Token"
+```
+
+**Append the value of the public IP address to the no_proxy list if you are in a proxy environment**
+
+```bash
+export no_proxy=${your_no_proxy},chatqna-xeon-ui-server,chatqna-xeon-backend-server,dataprep-mariadb-vector,tei-embedding-service,retriever,tei-reranking-service,tgi-service,vllm-service
+```
+
+```bash
+export no_proxy=${your_no_proxy}
+export http_proxy=${your_http_proxy}
+export https_proxy=${your_http_proxy}
+export EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5"
+export RERANK_MODEL_ID="BAAI/bge-reranker-base"
+export LLM_MODEL_ID="meta-llama/Meta-Llama-3-8B-Instruct"
+export MARIADB_DATABASE="vectordb"
+export MARIADB_USER="chatqna"
+export MARIADB_PASSWORD="password"
+```
+
+Note: Please replace with `host_ip` with you external IP address, do not use localhost.
+
+### Start all the services Docker Containers
+
+> Before running the docker compose command, you need to be in the folder that has the docker compose yaml file
+
+```bash
+cd GenAIExamples/ChatQnA/docker_compose/intel/cpu/xeon/
+docker compose -f compose_mariadb.yaml up -d
+```
+
+### Validate Microservices
+
+Follow the instructions to validate MicroServices.
+For details on how to verify the correctness of the response, refer to [how-to-validate_service](../../hpu/gaudi/how_to_validate_service.md).
+
+1. TEI Embedding Service
+
+   ```bash
+   curl ${host_ip}:6040/embed \
+       -X POST \
+       -d '{"inputs":"What is Deep Learning?"}' \
+       -H 'Content-Type: application/json'
+   ```
+
+2. Retriever Microservice
+
+   To consume the retriever microservice, you need to generate a mock embedding vector by Python script. The length of embedding vector
+   is determined by the embedding model.
+   Here we use the model `EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5"`, which vector size is 768.
+
+   Check the vector dimension of your embedding model, set `your_embedding` dimension equals to it.
+
+   ```bash
+   export your_embedding=$(python3 -c "import random; embedding = [random.uniform(-1, 1) for _ in range(768)]; print(embedding)")
+   curl http://${host_ip}:6045/v1/retrieval \
+     -X POST \
+     -d '{"text":"What is the revenue of Nike in 2023?","embedding":"'"${your_embedding}"'"}' \
+     -H 'Content-Type: application/json'
+   ```
+
+3. TEI Reranking Service
+
+   ```bash
+   curl http://${host_ip}:6041/rerank \
+       -X POST \
+       -d '{"query":"What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}' \
+       -H 'Content-Type: application/json'
+   ```
+
+4. LLM Backend Service
+
+   In the first startup, this service will take more time to download, load and warm up the model. After it's finished, the service will be ready.
+
+   Try the command below to check whether the LLM service is ready.
+
+   ```bash
+   docker logs vllm-service 2>&1 | grep complete
+   ```
+
+   If the service is ready, you will get the response like below.
+
+   ```text
+   INFO: Application startup complete.
+   ```
+
+   Then try the `cURL` command below to validate vLLM service.
+
+   ```bash
+   curl http://${host_ip}:6042/v1/chat/completions \
+     -X POST \
+     -d '{"model": "meta-llama/Meta-Llama-3-8B-Instruct", "messages": [{"role": "user", "content": "What is Deep Learning?"}], "max_tokens":17}' \
+     -H 'Content-Type: application/json'
+   ```
+
+5. MegaService
+
+   ```bash
+   curl http://${host_ip}:8912/v1/chatqna -H "Content-Type: application/json" -d '{
+        "messages": "What is the revenue of Nike in 2023?"
+        }'
+   ```
+
+6. Dataprep Microservice（Optional）
+
+   If you want to update the default knowledge base, you can use the following commands:
+
+   Update Knowledge Base via Local File Upload:
+
+   ```bash
+   curl -X POST "http://${host_ip}:6043/v1/dataprep/ingest" \
+        -H "Content-Type: multipart/form-data" \
+        -F "files=@./your_file.pdf"
+   ```
+
+   This command updates a knowledge base by uploading a local file for processing. Update the file path according to your environment.
+
+   Add Knowledge Base via HTTP Links:
+
+   ```bash
+   curl -X POST "http://${host_ip}:6043/v1/dataprep/ingest" \
+        -H "Content-Type: multipart/form-data" \
+        -F 'link_list=["https://opea.dev"]'
+   ```
+
+## Launch the UI
+
+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
+  chatqna-xeon-ui-server:
+    image: opea/chatqna-ui:latest
+    ...
+    ports:
+      - "80:5173"
+```
+
+![project-screenshot](../../../../assets/img/chat_ui_init.png)
+
+Here is an example of running ChatQnA:
+
+![project-screenshot](../../../../assets/img/chat_ui_response.png)