GenAIExamples/MultimodalQnA/docker_compose/intel/cpu/xeon/README.md

# Build Mega Service of MultimodalQnA on Xeon

This document outlines the deployment process for a MultimodalQnA 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 `multimodal_embedding` that employs [BridgeTower](https://huggingface.co/BridgeTower/bridgetower-large-itm-mlm-gaudi) model as embedding model, `multimodal_retriever`, `lvm`, and `multimodal-data-prep`. We will publish the Docker images to Docker Hub soon, it will simplify the deployment process for this service.

## 🚀 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 the power of 4th Generation Intel Xeon Scalable processors. These instances are optimized for high-performance computing and 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.

**Certain ports in the EC2 instance need to opened up in the security group, for the microservices to work with the curl commands**

> See one example below. Please open up these ports in the EC2 instance based on the IP addresses you want to allow

```
redis-vector-db
===============
Port 6379 - Open to 0.0.0.0/0
Port 8001 - Open to 0.0.0.0/0

embedding-multimodal-bridgetower
=====================
Port 6006 - Open to 0.0.0.0/0

embedding
=========
Port 6000 - Open to 0.0.0.0/0

retriever-multimodal-redis
=========
Port 7000 - Open to 0.0.0.0/0

lvm-llava
================
Port 8399 - Open to 0.0.0.0/0

lvm
===
Port 9399 - Open to 0.0.0.0/0

whisper
===
port 7066 - Open to 0.0.0.0/0

speecht5-service
===
port 7055 - Open to 0.0.0.0/0

dataprep-multimodal-redis
===
Port 6007 - Open to 0.0.0.0/0

multimodalqna
==========================
Port 8888 - Open to 0.0.0.0/0

multimodalqna-ui
=====================
Port 5173 - Open to 0.0.0.0/0
```

## Setup Environment Variables

Since the `compose.yaml` will consume some environment variables, you need to setup them 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 when setting the `host_ip` value (do not use localhost).

```
export host_ip="External_Public_IP"
```

**Append the value of the public IP address to the no_proxy list**

```bash
export no_proxy=${no_proxy},${host_ip}
```

```bash
export MM_EMBEDDING_SERVICE_HOST_IP=${host_ip}
export MM_RETRIEVER_SERVICE_HOST_IP=${host_ip}
export LVM_SERVICE_HOST_IP=${host_ip}
export MEGA_SERVICE_HOST_IP=${host_ip}
export WHISPER_PORT=7066
export WHISPER_SERVER_ENDPOINT="http://${host_ip}:${WHISPER_PORT}/v1/asr"
export WHISPER_MODEL="base"
export TTS_PORT=7055
export TTS_ENDPOINT="http://${host_ip}:${TTS_PORT}/v1/tts"
export MAX_IMAGES=1
export REDIS_DB_PORT=6379
export REDIS_INSIGHTS_PORT=8001
export REDIS_URL="redis://${host_ip}:${REDIS_DB_PORT}"
export REDIS_HOST=${host_ip}
export INDEX_NAME="mm-rag-redis"
export DATAPREP_MMR_PORT=6007
export DATAPREP_INGEST_SERVICE_ENDPOINT="http://${host_ip}:${DATAPREP_MMR_PORT}/v1/dataprep/ingest"
export DATAPREP_GEN_TRANSCRIPT_SERVICE_ENDPOINT="http://${host_ip}:${DATAPREP_MMR_PORT}/v1/dataprep/generate_transcripts"
export DATAPREP_GEN_CAPTION_SERVICE_ENDPOINT="http://${host_ip}:${DATAPREP_MMR_PORT}/v1/dataprep/generate_captions"
export DATAPREP_GET_FILE_ENDPOINT="http://${host_ip}:${DATAPREP_MMR_PORT}/v1/dataprep/get"
export DATAPREP_DELETE_FILE_ENDPOINT="http://${host_ip}:${DATAPREP_MMR_PORT}/v1/dataprep/delete"
export EMM_BRIDGETOWER_PORT=6006
export EMBEDDING_MODEL_ID="BridgeTower/bridgetower-large-itm-mlm-itc"
export BRIDGE_TOWER_EMBEDDING=true
export MMEI_EMBEDDING_ENDPOINT="http://${host_ip}:$EMM_BRIDGETOWER_PORT"
export MM_EMBEDDING_PORT_MICROSERVICE=6000
export REDIS_RETRIEVER_PORT=7000
export LVM_PORT=9399
export LLAVA_SERVER_PORT=8399
export LVM_MODEL_ID="llava-hf/llava-1.5-7b-hf"
export LVM_ENDPOINT="http://${host_ip}:$LLAVA_SERVER_PORT"
export MEGA_SERVICE_PORT=8888
export BACKEND_SERVICE_ENDPOINT="http://${host_ip}:$MEGA_SERVICE_PORT/v1/multimodalqna"
export UI_PORT=5173
export UI_TIMEOUT=240
```

> Note: The `MAX_IMAGES` environment variable is used to specify the maximum number of images that will be sent from the LVM service to the LLaVA server.
> If an image list longer than `MAX_IMAGES` is sent to the LVM server, a shortened image list will be sent to the LLaVA service. If the image list
> needs to be shortened, the most recent images (the ones at the end of the list) are prioritized to send to the LLaVA service. Some LLaVA models have not
> been trained with multiple images and may lead to inaccurate results. If `MAX_IMAGES` is not set, it will default to `1`.

## 🚀 Build Docker Images

### 1. Build embedding-multimodal-bridgetower Image

Build embedding-multimodal-bridgetower docker image

```bash
git clone https://github.com/opea-project/GenAIComps.git
cd GenAIComps
docker build --no-cache -t opea/embedding-multimodal-bridgetower:latest --build-arg EMBEDDER_PORT=$EMM_BRIDGETOWER_PORT --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/third_parties/bridgetower/src/Dockerfile .
```

Build embedding microservice image

```bash
docker build --no-cache -t opea/embedding:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/embeddings/src/Dockerfile .
```

### 2. Build retriever-multimodal-redis 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 .
```

### 3. Build LVM Images

Build lvm-llava image

```bash
docker build --no-cache -t opea/lvm-llava:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/third_parties/llava/src/Dockerfile .
```

Build lvm microservice image

```bash
docker build --no-cache -t opea/lvm:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/lvms/src/Dockerfile .
```

### 4. Build dataprep-multimodal-redis 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 .
```

### 5. Build Whisper Server Image

Build whisper server image

```bash
docker build --no-cache -t opea/whisper:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/third_parties/whisper/src/Dockerfile .
```

### 6. Build TTS Image

```bash
docker build --no-cache -t opea/speecht5:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/third_parties/speecht5/src/Dockerfile .
```

### 7. Build MegaService Docker Image

To construct the Mega Service, we utilize the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice pipeline within the [multimodalqna.py](../../../../multimodalqna.py) Python script. Build MegaService Docker image via below command:

```bash
git clone https://github.com/opea-project/GenAIExamples.git
cd GenAIExamples/MultimodalQnA
docker build --no-cache -t opea/multimodalqna:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile .
cd ../..
```

### 8. Build UI Docker Image

Build frontend Docker image via below command:

```bash
cd GenAIExamples/MultimodalQnA/ui/
docker build --no-cache -t opea/multimodalqna-ui:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f ./docker/Dockerfile .
cd ../../../
```

Then run the command `docker images`, you will have the following 11 Docker Images:

1. `opea/dataprep:latest`
2. `opea/lvm:latest`
3. `opea/lvm-llava:latest`
4. `opea/retriever:latest`
5. `opea/whisper:latest`
6. `opea/speech5:latest`
7. `opea/redis-vector-db`
8. `opea/embedding:latest`
9. `opea/embedding-multimodal-bridgetower:latest`
10. `opea/multimodalqna:latest`
11. `opea/multimodalqna-ui:latest`

## 🚀 Start Microservices

### Required Models

By default, the multimodal-embedding and LVM models are set to a default value as listed below:

| Service   | Model                                       |
| --------- | ------------------------------------------- |
| embedding | BridgeTower/bridgetower-large-itm-mlm-gaudi |
| LVM       | llava-hf/llava-1.5-7b-hf                    |

### 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/MultimodalQnA/docker_compose/intel/cpu/xeon/
docker compose -f compose.yaml up -d
```

> Alternatively, you can run docker compose with `compose_milvus.yaml` to use the Milvus vector database:

```bash
export MILVUS_HOST=${host_ip}
export MILVUS_PORT=19530
export MILVUS_RETRIEVER_PORT=7000
export COLLECTION_NAME=LangChainCollection
cd GenAIExamples/MultimodalQnA/docker_compose/intel/cpu/xeon/
docker compose -f compose_milvus.yaml up -d
```

### Validate Microservices

1. embedding-multimodal-bridgetower

```bash
curl http://${host_ip}:${EMM_BRIDGETOWER_PORT}/v1/encode \
     -X POST \
     -H "Content-Type:application/json" \
     -d '{"text":"This is example"}'
```

```bash
curl http://${host_ip}:${EMM_BRIDGETOWER_PORT}/v1/encode \
     -X POST \
     -H "Content-Type:application/json" \
     -d '{"text":"This is example", "img_b64_str": "iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAYAAACNMs+9AAAAFUlEQVR42mP8/5+hnoEIwDiqkL4KAcT9GO0U4BxoAAAAAElFTkSuQmCC"}'
```

2. embedding

```bash
curl http://${host_ip}:$MM_EMBEDDING_PORT_MICROSERVICE/v1/embeddings \
    -X POST \
    -H "Content-Type: application/json" \
    -d '{"text" : "This is some sample text."}'
```

```bash
curl http://${host_ip}:$MM_EMBEDDING_PORT_MICROSERVICE/v1/embeddings \
    -X POST \
    -H "Content-Type: application/json" \
    -d '{"text": {"text" : "This is some sample text."}, "image" : {"url": "https://github.com/docarray/docarray/blob/main/tests/toydata/image-data/apple.png?raw=true"}}'
```

3. retriever-multimodal-redis

```bash
export your_embedding=$(python3 -c "import random; embedding = [random.uniform(-1, 1) for _ in range(512)]; print(embedding)")
curl http://${host_ip}:${REDIS_RETRIEVER_PORT}/v1/retrieval \
    -X POST \
    -H "Content-Type: application/json" \
    -d "{\"text\":\"test\",\"embedding\":${your_embedding}}"
```

4. whisper

```bash
curl ${WHISPER_SERVER_ENDPOINT} \
    -X POST \
    -H "Content-Type: application/json" \
    -d '{"audio" : "UklGRigAAABXQVZFZm10IBIAAAABAAEARKwAAIhYAQACABAAAABkYXRhAgAAAAEA"}'
```

5. tts

```bash
curl ${TTS_ENDPOINT} \
  -X POST \
  -d '{"text": "Who are you?"}' \
  -H 'Content-Type: application/json'
```

6. lvm-llava

```bash
curl http://${host_ip}:${LLAVA_SERVER_PORT}/generate \
     -X POST \
     -H "Content-Type:application/json" \
     -d '{"prompt":"Describe the image please.", "img_b64_str": "iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAYAAACNMs+9AAAAFUlEQVR42mP8/5+hnoEIwDiqkL4KAcT9GO0U4BxoAAAAAElFTkSuQmCC"}'
```

7. lvm

```bash
curl http://${host_ip}:${LVM_PORT}/v1/lvm \
    -X POST \
    -H 'Content-Type: application/json' \
    -d '{"retrieved_docs": [], "initial_query": "What is this?", "top_n": 1, "metadata": [{"b64_img_str": "iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAYAAACNMs+9AAAAFUlEQVR42mP8/5+hnoEIwDiqkL4KAcT9GO0U4BxoAAAAAElFTkSuQmCC", "transcript_for_inference": "yellow image", "video_id": "8c7461df-b373-4a00-8696-9a2234359fe0", "time_of_frame_ms":"37000000", "source_video":"WeAreGoingOnBullrun_8c7461df-b373-4a00-8696-9a2234359fe0.mp4"}], "chat_template":"The caption of the image is: '\''{context}'\''. {question}"}'
```

```bash
curl http://${host_ip}:${LVM_PORT}/v1/lvm  \
    -X POST \
    -H 'Content-Type: application/json' \
    -d '{"image": "iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAYAAACNMs+9AAAAFUlEQVR42mP8/5+hnoEIwDiqkL4KAcT9GO0U4BxoAAAAAElFTkSuQmCC", "prompt":"What is this?"}'
```

Also, validate LVM Microservice with empty retrieval results

```bash
curl http://${host_ip}:${LVM_PORT}/v1/lvm \
    -X POST \
    -H 'Content-Type: application/json' \
    -d '{"retrieved_docs": [], "initial_query": "What is this?", "top_n": 1, "metadata": [], "chat_template":"The caption of the image is: '\''{context}'\''. {question}"}'
```

8. dataprep-multimodal-redis

Download a sample video (.mp4), image (.png, .gif, .jpg), pdf, and audio file (.wav, .mp3) and create a caption

```bash
export video_fn="WeAreGoingOnBullrun.mp4"
wget http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/WeAreGoingOnBullrun.mp4 -O ${video_fn}

export image_fn="apple.png"
wget https://github.com/docarray/docarray/blob/main/tests/toydata/image-data/apple.png?raw=true -O ${image_fn}

export pdf_fn="nke-10k-2023.pdf"
wget https://raw.githubusercontent.com/opea-project/GenAIComps/v1.1/comps/retrievers/redis/data/nke-10k-2023.pdf -O ${pdf_fn}

export caption_fn="apple.txt"
echo "This is an apple."  > ${caption_fn}

export audio_fn="AudioSample.wav"
wget https://github.com/intel/intel-extension-for-transformers/raw/main/intel_extension_for_transformers/neural_chat/assets/audio/sample.wav -O ${audio_fn}
```

Test dataprep microservice with generating transcript. This command updates a knowledge base by uploading a local video .mp4 and an audio .wav or .mp3 file.

```bash
curl --silent --write-out "HTTPSTATUS:%{http_code}" \
    ${DATAPREP_GEN_TRANSCRIPT_SERVICE_ENDPOINT} \
    -H 'Content-Type: multipart/form-data' \
    -X POST \
    -F "files=@./${video_fn}" \
    -F "files=@./${audio_fn}"
```

Also, test dataprep microservice with generating an image caption using lvm microservice.

```bash
curl --silent --write-out "HTTPSTATUS:%{http_code}" \
    ${DATAPREP_GEN_CAPTION_SERVICE_ENDPOINT} \
    -H 'Content-Type: multipart/form-data' \
    -X POST -F "files=@./${image_fn}"
```

Now, test the microservice with posting a custom caption along with an image and a PDF containing images and text. The image caption can be provided as a text (`.txt`) or as spoken audio (`.wav` or `.mp3`).

> Note: Audio captions for images are currently only supported when using the Redis data prep backend.

```bash
curl --silent --write-out "HTTPSTATUS:%{http_code}" \
    ${DATAPREP_INGEST_SERVICE_ENDPOINT} \
    -H 'Content-Type: multipart/form-data' \
    -X POST -F "files=@./${image_fn}" -F "files=@./${caption_fn}" \
    -F "files=@./${pdf_fn}"
```

Also, you are able to get the list of all files that you uploaded:

```bash
curl -X POST \
    -H "Content-Type: application/json" \
    ${DATAPREP_GET_FILE_ENDPOINT}
```

Then you will get the response python-style LIST like this. Notice the name of each uploaded file e.g., `videoname.mp4` will become `videoname_uuid.mp4` where `uuid` is a unique ID for each uploaded file. The same files that are uploaded twice will have different `uuid`.

```bash
[
    "WeAreGoingOnBullrun_7ac553a1-116c-40a2-9fc5-deccbb89b507.mp4",
    "WeAreGoingOnBullrun_6d13cf26-8ba2-4026-a3a9-ab2e5eb73a29.mp4",
    "apple_fcade6e6-11a5-44a2-833a-3e534cbe4419.png",
    "nke-10k-2023_28000757-5533-4b1b-89fe-7c0a1b7e2cd0.pdf",
    "AudioSample_976a85a6-dc3e-43ab-966c-9d81beef780c.wav"
]
```

To delete all uploaded files along with data indexed with `$INDEX_NAME` in REDIS.

```bash
curl -X POST \
    -H "Content-Type: application/json" \
    -d '{"file_path": "all"}' \
    ${DATAPREP_DELETE_FILE_ENDPOINT}
```

9. MegaService

Test the MegaService with a text query:

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

Test the MegaService with an audio query:

```bash
curl http://${host_ip}:${MEGA_SERVICE_PORT}/v1/multimodalqna  \
    -H "Content-Type: application/json"  \
    -d '{"messages": [{"role": "user", "content": [{"type": "audio", "audio": "UklGRigAAABXQVZFZm10IBIAAAABAAEARKwAAIhYAQACABAAAABkYXRhAgAAAAEA"}]}]}'
```

Test the MegaService with a text and image query:

```bash
curl http://${host_ip}:${MEGA_SERVICE_PORT}/v1/multimodalqna \
    -H "Content-Type: application/json" \
    -d  '{"messages": [{"role": "user", "content": [{"type": "text", "text": "Green bananas in a tree"}, {"type": "image_url", "image_url": {"url": "http://images.cocodataset.org/test-stuff2017/000000004248.jpg"}}]}]}'
```

Test the MegaService with a back and forth conversation between the user and assistant:

```bash
curl http://${host_ip}:${MEGA_SERVICE_PORT}/v1/multimodalqna  \
    -H "Content-Type: application/json"  \
    -d '{"messages": [{"role": "user", "content": [{"type": "audio", "audio": "UklGRigAAABXQVZFZm10IBIAAAABAAEARKwAAIhYAQACABAAAABkYXRhAgAAAAEA"}]}]}'
```

Test the MegaService with a back and forth conversation between the user and assistant including a text to speech response from the assistant using `"modalities": ["text", "audio"]'`:

```bash
curl http://${host_ip}:${MEGA_SERVICE_PORT}/v1/multimodalqna \
    -H "Content-Type: application/json" \
    -d '{"messages": [{"role": "user", "content": [{"type": "text", "text": "hello, "}, {"type": "image_url", "image_url": {"url": "https://www.ilankelman.org/stopsigns/australia.jpg"}}]}, {"role": "assistant", "content": "opea project! "}, {"role": "user", "content": "chao, "}], "max_tokens": 10, "modalities": ["text", "audio"]}'
```