Build Mega Service of ChatQnA (with Qdrant) on Xeon

This document outlines the deployment process for a ChatQnA application utilizing the GenAIComps 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 llm. 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 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. 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

qdrant-vector-db
===============
Port 6333 - Open to 0.0.0.0/0
Port 6334 - Open to 0.0.0.0/0

dataprep-qdrant-server
======================
Port 6043 - Open to 0.0.0.0/0

tei_embedding_service
=====================
Port 6040 - Open to 0.0.0.0/0

embedding
=========
Port 6044 - Open to 0.0.0.0/0

retriever
=========
Port 6045 - Open to 0.0.0.0/0

tei_reranking_service
================
Port 6041 - Open to 0.0.0.0/0

reranking
=========
Port 6046 - Open to 0.0.0.0/0

tgi-service
===========
Port 6042 - Open to 0.0.0.0/0

llm
===
Port 6047 - Open to 0.0.0.0/0

chaqna-xeon-backend-server
==========================
Port 8912 - Open to 0.0.0.0/0

chaqna-xeon-ui-server
=====================
Port 5173 - Open to 0.0.0.0/0

🚀 Build Docker Images

First of all, you need to build Docker Images locally and install the python package of it.

git clone https://github.com/opea-project/GenAIComps.git
cd GenAIComps

1. Build Embedding Image

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

2. Build Retriever Image

docker build --no-cache -t opea/retriever-qdrant:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/retrievers/qdrant/haystack/Dockerfile .

3. Build Rerank Image

docker build --no-cache -t opea/reranking-tei:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/reranks/tei/Dockerfile .`

4. Build LLM Image

docker build --no-cache -t opea/llm-tgi:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/llms/text-generation/tgi/Dockerfile .

5. Build Dataprep Image

docker build --no-cache -t opea/dataprep-qdrant:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/dataprep/qdrant/langchain/Dockerfile .
cd ..

6. Build MegaService Docker Image

To construct the Mega Service, we utilize the GenAIComps microservice pipeline within the chatqna.py Python script. Build MegaService Docker image via below command:

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 ../../..

7. Build UI Docker Image

Build frontend Docker image via below command:

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 ../../../..

8. 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

cd GenAIExamples/ChatQnA//ui
export BACKEND_SERVICE_ENDPOINT="http://${host_ip}:8912/v1/chatqna"
export DATAPREP_SERVICE_ENDPOINT="http://${host_ip}:6043/v1/dataprep"
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 ../../../..

Then run the command docker images, you will have the following 7 Docker Images:

1. opea/dataprep-qdrant:latest

2. opea/embedding-tei:latest

3. opea/retriever-qdrant:latest

4. opea/reranking-tei:latest

5. opea/llm-tgi:latest

6. opea/chatqna:latest

7. opea/chatqna-ui: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	Intel/neural-chat-7b-v3-3

Change the xxx_MODEL_ID below for your needs.

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

export host_ip="External_Public_IP"

Export the value of your Huggingface API token to the your_hf_api_token environment variable

Change the Your_Huggingface_API_Token below with tyour actual Huggingface API Token value

export your_hf_api_token="Your_Huggingface_API_Token"

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

export your_no_proxy=${your_no_proxy},"External_Public_IP"

export no_proxy=${your_no_proxy}
export http_proxy=${your_http_proxy}
export https_proxy=${your_http_proxy}
export EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5"
export RERANK_MODEL_ID="BAAI/bge-reranker-base"
export LLM_MODEL_ID="Intel/neural-chat-7b-v3-3"
export TEI_EMBEDDING_ENDPOINT="http://${host_ip}:6040"
export TEI_RERANKING_ENDPOINT="http://${host_ip}:6041"
export TGI_LLM_ENDPOINT="http://${host_ip}:6042"
export QDRANT_HOST=${host_ip}
export QDRANT_PORT=6333
export INDEX_NAME="rag-qdrant"
export HUGGINGFACEHUB_API_TOKEN=${your_hf_api_token}
export MEGA_SERVICE_HOST_IP=${host_ip}
export EMBEDDING_SERVICE_HOST_IP=${host_ip}
export RETRIEVER_SERVICE_HOST_IP=${host_ip}
export RERANK_SERVICE_HOST_IP=${host_ip}
export LLM_SERVICE_HOST_IP=${host_ip}
export BACKEND_SERVICE_ENDPOINT="http://${host_ip}:8912/v1/chatqna"
export DATAPREP_SERVICE_ENDPOINT="http://${host_ip}:6043/v1/dataprep"

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

cd GenAIExamples/ChatQnA/docker_compose/intel/cpu/xeon/
docker compose -f compose_qdrant.yaml up -d

Validate Microservices

1. TEI Embedding Service

   curl ${host_ip}:6040/embed \
       -X POST \
       -d '{"inputs":"What is Deep Learning?"}' \
       -H 'Content-Type: application/json'
   

2. Embedding Microservice

   curl http://${host_ip}:6044/v1/embeddings\
     -X POST \
     -d '{"text":"hello"}' \
     -H 'Content-Type: application/json'
   

3. 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 vecotor dimension of your embedding model, set your_embedding dimension equals to it.

   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'
   

4. TEI Reranking Service

   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'
   

5. Reranking Microservice

   curl http://${host_ip}:6046/v1/reranking\
     -X POST \
     -d '{"initial_query":"What is Deep Learning?", "retrieved_docs": [{"text":"Deep Learning is not..."}, {"text":"Deep learning is..."}]}' \
     -H 'Content-Type: application/json'
   

6. TGI Service

   In first startup, this service will take more time to download the model files. After it’s finished, the service will be ready.

   Try the command below to check whether the TGI service is ready.

   docker logs ${CONTAINER_ID} | 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 TGI.

   curl http://${host_ip}:6042/generate \
     -X POST \
     -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}' \
     -H 'Content-Type: application/json'
   

7. LLM Microservice

   curl http://${host_ip}:6047/v1/chat/completions\
     -X POST \
     -d '{"query":"What is Deep Learning?","max_new_tokens":17,"top_k":10,"top_p":0.95,"typical_p":0.95,"temperature":0.01,"repetition_penalty":1.03,"streaming":true}' \
     -H 'Content-Type: application/json'
   

8. MegaService

   curl http://${host_ip}:8912/v1/chatqna -H "Content-Type: application/json" -d '{
        "messages": "What is the revenue of Nike in 2023?"
        }'
   

9. 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:

   curl -X POST "http://${host_ip}:6043/v1/dataprep" \
        -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:

   curl -X POST "http://${host_ip}:6043/v1/dataprep" \
        -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:

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

🚀 Launch the Conversational UI (react)

To access the Conversational UI frontend, 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:

  chaqna-xeon-conversation-ui-server:
    image: opea/chatqna-conversation-ui:latest
    ...
    ports:
      - "80:80"

Here is an example of running ChatQnA:

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