# Build Mega Service of ChatQnA on Xeon

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 server. The steps include Docker image creation, container deployment via Docker Compose, and service execution to integrate microservices such as `embedding`, `retriever`, `rerank`,`llm` and `faqgen`.

The default pipeline deploys with vLLM as the LLM serving component and leverages rerank component. It also provides options of not using rerank in the pipeline and using TGI backend for LLM microservice, please refer to [start-all-the-services-docker-containers](#start-all-the-services-docker-containers) section in this page. Besides, refer to [Build with Pinecone VectorDB](./README_pinecone.md) and [Build with Qdrant VectorDB](./README_qdrant.md) for other deployment variants.

Quick Start:

1. Set up the environment variables.
2. Run Docker Compose.
3. Consume the ChatQnA Service.

Note: The default LLM is `meta-llama/Meta-Llama-3-8B-Instruct`. Before deploying the application, please make sure either you've requested and been granted the access to it on [Huggingface](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) or you've downloaded the model locally from [ModelScope](https://www.modelscope.cn/models).

## Quick Start: 1.Setup Environment Variable

To set up environment variables for deploying ChatQnA services, follow these steps:

1. Set the required environment variables:

   ```bash
   # Example: host_ip="192.168.1.1"
   export host_ip="External_Public_IP"
   export HUGGINGFACEHUB_API_TOKEN="Your_Huggingface_API_Token"
   ```

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"
   # Example: no_proxy="localhost, 127.0.0.1, 192.168.1.1"
   export no_proxy="Your_No_Proxy",chatqna-xeon-ui-server,chatqna-xeon-backend-server,dataprep-redis-service,tei-embedding-service,retriever,tei-reranking-service,tgi-service,vllm-service,llm-faqgen
   ```

3. Set up other environment variables:

   ```bash
   source ./set_env.sh
   ```

4. Change Model for LLM serving

   By default, Meta-Llama-3-8B-Instruct is used for LLM serving, the default model can be changed to other validated LLM models.  
   Please pick a [validated llm models](https://github.com/opea-project/GenAIComps/tree/main/comps/llms/src/text-generation#validated-llm-models) from the table.  
   To change the default model defined in set_env.sh, overwrite it by exporting LLM_MODEL_ID to the new model or by modifying set_env.sh, and then repeat step 3.  
   For example, change to Llama-2-7b-chat-hf using the following command.

   ```bash
   export LLM_MODEL_ID="meta-llama/Llama-2-7b-chat-hf"
   ```

## Quick Start: 2.Run Docker Compose

```bash
docker compose up -d
```

To enable Open Telemetry Tracing, compose.telemetry.yaml file need to be merged along with default compose.yaml file.  
CPU example with Open Telemetry feature:

> NOTE : To get supported Grafana Dashboard, please run download_opea_dashboard.sh following below commands.

```bash
./grafana/dashboards/download_opea_dashboard.sh
docker compose -f compose.yaml -f compose.telemetry.yaml up -d
```

It will automatically download the docker image on `docker hub`:

```bash
docker pull opea/chatqna:latest
docker pull opea/chatqna-ui:latest
```

NB: You should build docker image from source by yourself if:

- You are developing off the git main branch (as the container's ports in the repo may be different from the published docker image).
- You can't download the docker image.
- You want to use a specific version of Docker image.

Please refer to ['Build Docker Images'](#🚀-build-docker-images) in below.

## QuickStart: 3.Consume the ChatQnA Service

```bash
curl http://${host_ip}:8888/v1/chatqna \
    -H "Content-Type: application/json" \
    -d '{
        "messages": "What is the revenue of Nike in 2023?"
    }'
```

## 🚀 Apply Xeon Server on AWS

To apply a Xeon server on AWS, start by creating an AWS account if you don't have one already. Then, head to the [EC2 Console](https://console.aws.amazon.com/ec2/v2/home) to begin the process. Within the EC2 service, select the Amazon EC2 M7i or M7i-flex instance type to leverage 4th Generation Intel Xeon Scalable processors that are optimized for demanding workloads.

For detailed information about these instance types, you can refer to this [link](https://aws.amazon.com/ec2/instance-types/m7i/). Once you've chosen the appropriate instance type, proceed with configuring your instance settings, including network configurations, security groups, and storage options.

After launching your instance, you can connect to it using SSH (for Linux instances) or Remote Desktop Protocol (RDP) (for Windows instances). From there, you'll have full access to your Xeon server, allowing you to install, configure, and manage your applications as needed.

### Network Port & Security

- Access the ChatQnA UI by web browser

  It supports to access by `80` port. Please confirm the `80` port is opened in the firewall of EC2 instance.

- Access the microservice by tool or API

  1. Login to the EC2 instance and access by **local IP address** and port.

     It's recommended and do nothing of the network port setting.

  2. Login to a remote client and access by **public IP address** and port.

     You need to open the port of the microservice in the security group setting of firewall of EC2 instance setting.

     For detailed guide, please refer to [Validate Microservices](#validate-microservices).

     Note, it will increase the risk of security, so please confirm before do it.

## 🚀 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 FaqGen LLM Image (Optional)

If you want to enable FAQ generation LLM in the pipeline, please use the below command:

```bash
git clone https://github.com/opea-project/GenAIComps.git
cd GenAIComps
docker build -t opea/llm-faqgen:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/llms/src/faq-generation/Dockerfile .
```

### 4. Build MegaService Docker Image

To construct the Mega Service with Rerank, 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 .
```

### 5. 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 .
```

### 6. 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
docker build --no-cache -t opea/chatqna-conversation-ui:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f ./docker/Dockerfile.react .
```

### 7. 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`

If FaqGen related docker image is built, you will find one more image:

- `opea/llm-faqgen: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.

For users in China who are unable to download models directly from Huggingface, you can use [ModelScope](https://www.modelscope.cn/models) or a Huggingface mirror to download models. The vLLM/TGI can load the models either online or offline as described below:

1. Online

   ```bash
   export HF_TOKEN=${your_hf_token}
   export HF_ENDPOINT="https://hf-mirror.com"
   model_name="meta-llama/Meta-Llama-3-8B-Instruct"
   # Start vLLM LLM Service
   docker run -p 8008:80 -v ./data:/root/.cache/huggingface/hub --name vllm-service -e HF_ENDPOINT=$HF_ENDPOINT -e http_proxy=$http_proxy -e https_proxy=$https_proxy --shm-size 128g opea/vllm:latest --model $model_name --host 0.0.0.0 --port 80
   # Start TGI LLM Service
   docker run -p 8008:80 -v ./data:/data --name tgi-service -e HF_ENDPOINT=$HF_ENDPOINT -e http_proxy=$http_proxy -e https_proxy=$https_proxy --shm-size 1g ghcr.io/huggingface/text-generation-inference:2.4.0-intel-cpu --model-id $model_name
   ```

2. Offline

   - Search your model name in ModelScope. For example, check [this page](https://modelscope.cn/models/LLM-Research/Meta-Llama-3-8B-Instruct/files) for model `Meta-Llama-3-8B-Instruct`.

   - Click on `Download this model` button, and choose one way to download the model to your local path `/path/to/model`.

   - Run the following command to start the LLM service.

     ```bash
     export HF_TOKEN=${your_hf_token}
     export model_path="/path/to/model"
     # Start vLLM LLM Service
     docker run -p 8008:80 -v $model_path:/root/.cache/huggingface/hub --name vllm-service --shm-size 128g opea/vllm:latest --model /root/.cache/huggingface/hub --host 0.0.0.0 --port 80
     # Start TGI LLM Service
     docker run -p 8008:80 -v $model_path:/data --name tgi-service --shm-size 1g ghcr.io/huggingface/text-generation-inference:2.4.0-intel-cpu --model-id /data
     ```

### Setup Environment Variables

1. Set the required environment variables:

   ```bash
   # Example: host_ip="192.168.1.1"
   export host_ip="External_Public_IP"
   export HUGGINGFACEHUB_API_TOKEN="Your_Huggingface_API_Token"
   # Example: NGINX_PORT=80
   export NGINX_PORT=${your_nginx_port}
   ```

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"
   # Example: no_proxy="localhost, 127.0.0.1, 192.168.1.1"
   export no_proxy="Your_No_Proxy",chatqna-xeon-ui-server,chatqna-xeon-backend-server,dataprep-redis-service,tei-embedding-service,retriever,tei-reranking-service,tgi-service,vllm-service
   ```

3. Set up other environment variables:

   ```bash
   source ./set_env.sh
   ```

### 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/
```

If use vLLM as the LLM serving backend.

```bash
# Start ChatQnA with Rerank Pipeline
docker compose -f compose.yaml up -d
# Start ChatQnA without Rerank Pipeline
docker compose -f compose_without_rerank.yaml up -d
# Start ChatQnA with Rerank Pipeline and Open Telemetry Tracing
docker compose -f compose.yaml -f compose.telemetry.yaml up -d
# Start ChatQnA with FaqGen Pipeline
docker compose -f compose_faqgen.yaml up -d
```

If use TGI as the LLM serving backend.

```bash
docker compose -f compose_tgi.yaml up -d
# Start ChatQnA with Open Telemetry Tracing
docker compose -f compose_tgi.yaml -f compose_tgi.telemetry.yaml up -d
# Start ChatQnA with FaqGen Pipeline
docker compose -f compose_faqgen_tgi.yaml up -d
```

### Validate Microservices

Note, when verify the microservices by curl or API from remote client, please make sure the **ports** of the microservices are opened in the firewall of the cloud node.  
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 http://${host_ip}:6006/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}:7000/v1/retrieval \
     -X POST \
     -d "{\"text\":\"test\",\"embedding\":${your_embedding}}" \
     -H 'Content-Type: application/json'
   ```

3. TEI Reranking Service

   > Skip for ChatQnA without Rerank pipeline

   ```bash
   curl http://${host_ip}:8808/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 serving is ready.

   ```bash
   # vLLM service
   docker logs vllm-service 2>&1 | grep complete
   # If the service is ready, you will get the response like below.
   INFO:     Application startup complete.
   ```

   ```bash
   # TGI service
   docker logs tgi-service | grep Connected
   # If the service is ready, you will get the response like below.
   2024-09-03T02:47:53.402023Z  INFO text_generation_router::server: router/src/server.rs:2311: Connected
   ```

   Then try the `cURL` command below to validate services.

   ```bash
   # either vLLM or TGI service
   curl http://${host_ip}:9009/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. FaqGen LLM Microservice (if enabled)

```bash
curl http://${host_ip}:${LLM_SERVICE_PORT}/v1/faqgen \
  -X POST \
  -d '{"query":"Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5."}' \
  -H 'Content-Type: application/json'
```

6. MegaService

   ```bash
    curl http://${host_ip}:8888/v1/chatqna -H "Content-Type: application/json" -d '{
          "messages": "What is the revenue of Nike in 2023?"
          }'
   ```

7. Nginx Service

   ```bash
   curl http://${host_ip}:${NGINX_PORT}/v1/chatqna \
       -H "Content-Type: application/json" \
       -d '{"messages": "What is the revenue of Nike in 2023?"}'
   ```

8. Dataprep Microservice（Optional）

If you want to update the default knowledge base, you can use the following commands:

Update Knowledge Base via Local File [nke-10k-2023.pdf](https://github.com/opea-project/GenAIComps/blob/v1.1/comps/retrievers/redis/data/nke-10k-2023.pdf). Or
click [here](https://raw.githubusercontent.com/opea-project/GenAIComps/v1.1/comps/retrievers/redis/data/nke-10k-2023.pdf) to download the file via any web browser.
Or run this command to get the file on a terminal.

```bash
wget https://raw.githubusercontent.com/opea-project/GenAIComps/v1.1/comps/retrievers/redis/data/nke-10k-2023.pdf
```

Upload:

```bash
curl -X POST "http://${host_ip}:6007/v1/dataprep/ingest" \
     -H "Content-Type: multipart/form-data" \
     -F "files=@./nke-10k-2023.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}:6007/v1/dataprep/ingest" \
     -H "Content-Type: multipart/form-data" \
     -F 'link_list=["https://opea.dev"]'
```

This command updates a knowledge base by submitting a list of HTTP links for processing.

Also, you are able to get the file list that you uploaded:

```bash
curl -X POST "http://${host_ip}:6007/v1/dataprep/get" \
     -H "Content-Type: application/json"
```

Then you will get the response JSON like this. Notice that the returned `name`/`id` of the uploaded link is `https://xxx.txt`.

```json
[
  {
    "name": "nke-10k-2023.pdf",
    "id": "nke-10k-2023.pdf",
    "type": "File",
    "parent": ""
  },
  {
    "name": "https://opea.dev.txt",
    "id": "https://opea.dev.txt",
    "type": "File",
    "parent": ""
  }
]
```

To delete the file/link you uploaded:

The `file_path` here should be the `id` get from `/v1/dataprep/get` API.

```bash
# delete link
curl -X POST "http://${host_ip}:6007/v1/dataprep/delete" \
     -d '{"file_path": "https://opea.dev.txt"}' \
     -H "Content-Type: application/json"

# delete file
curl -X POST "http://${host_ip}:6007/v1/dataprep/delete" \
     -d '{"file_path": "nke-10k-2023.pdf"}' \
     -H "Content-Type: application/json"

# delete all uploaded files and links
curl -X POST "http://${host_ip}:6007/v1/dataprep/delete" \
     -d '{"file_path": "all"}' \
     -H "Content-Type: application/json"
```

### Profile Microservices

To further analyze MicroService Performance, users could follow the instructions to profile MicroServices.

#### 1. vLLM backend Service

Users could follow previous section to testing vLLM microservice or ChatQnA MegaService.  
 By default, vLLM profiling is not enabled. Users could start and stop profiling by following commands.

##### Start vLLM profiling

```bash
curl http://${host_ip}:9009/start_profile \
  -H "Content-Type: application/json" \
  -d '{"model": "meta-llama/Meta-Llama-3-8B-Instruct"}'
```

Users would see below docker logs from vllm-service if profiling is started correctly.

```bash
INFO api_server.py:361] Starting profiler...
INFO api_server.py:363] Profiler started.
INFO:     x.x.x.x:35940 - "POST /start_profile HTTP/1.1" 200 OK
```

After vLLM profiling is started, users could start asking questions and get responses from vLLM MicroService  
 or ChatQnA MicroService.

##### Stop vLLM profiling

By following command, users could stop vLLM profliing and generate a \*.pt.trace.json.gz file as profiling result  
 under /mnt folder in vllm-service docker instance.

```bash
# vLLM Service
curl http://${host_ip}:9009/stop_profile \
  -H "Content-Type: application/json" \
  -d '{"model": "meta-llama/Meta-Llama-3-8B-Instruct"}'
```

Users would see below docker logs from vllm-service if profiling is stopped correctly.

```bash
INFO api_server.py:368] Stopping profiler...
INFO api_server.py:370] Profiler stopped.
INFO:     x.x.x.x:41614 - "POST /stop_profile HTTP/1.1" 200 OK
```

After vllm profiling is stopped, users could use below command to get the \*.pt.trace.json.gz file under /mnt folder.

```bash
docker cp  vllm-service:/mnt/ .
```

##### Check profiling result

Open a web browser and type "chrome://tracing" or "ui.perfetto.dev", and then load the json.gz file, you should be able  
 to see the vLLM profiling result as below diagram.
![image](https://github.com/user-attachments/assets/55c7097e-5574-41dc-97a7-5e87c31bc286)

## 🚀 Launch the UI

### Launch with origin port

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
  chaqna-gaudi-ui-server:
    image: opea/chatqna-ui:latest
    ...
    ports:
      - "80:5173"
```

### Launch with Nginx

If you want to launch the UI using Nginx, open this URL: `http://${host_ip}:${NGINX_PORT}` in your browser to access the frontend.

## 🚀 Launch the Conversational UI (Optional)

To access the Conversational UI (react based) frontend, modify the UI service in the `compose.yaml` file. Replace `chaqna-xeon-ui-server` service with the `chatqna-xeon-conversation-ui-server` service as per the config below:

```yaml
chaqna-xeon-conversation-ui-server:
  image: opea/chatqna-conversation-ui:latest
  container_name: chatqna-xeon-conversation-ui-server
  environment:
    - APP_BACKEND_SERVICE_ENDPOINT=${BACKEND_SERVICE_ENDPOINT}
    - APP_DATA_PREP_SERVICE_URL=${DATAPREP_SERVICE_ENDPOINT}
  ports:
    - "5174:80"
  depends_on:
    - chaqna-xeon-backend-server
  ipc: host
  restart: always
```

Once the services are up, open the following URL in your browser: http://{host_ip}:5174. By default, the UI runs on port 80 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
  chaqna-gaudi-conversation-ui-server:
    image: opea/chatqna-conversation-ui:latest
    ...
    ports:
      - "80:80"
```

![project-screenshot](../../../../assets/img/chat_ui_init.png)

Here is an example of running ChatQnA:

![project-screenshot](../../../../assets/img/chat_ui_response.png)

Here is an example of running ChatQnA with Conversational UI (React):

![project-screenshot](../../../../assets/img/conversation_ui_response.png)