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Build MegaService of ChatQnA on Gaudi

This document outlines the deployment process for a ChatQnA application utilizing the GenAIComps microservice pipeline on Intel Gaudi 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, it will simplify the deployment process for this service.

🚀 Build Docker Images

First of all, you need to build Docker Images locally. This step can be ignored after the Docker images published to Docker hub.

1. Source Code install GenAIComps

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

2. 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/langchain/docker/Dockerfile .

3. Build Retriever Image

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

4. 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/langchain/docker/Dockerfile .

5. 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 .

6. Build Dataprep Image

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

7. Build TEI Gaudi Image

Since a TEI Gaudi Docker image hasn't been published, we'll need to build it from the tei-guadi repository.

git clone https://github.com/huggingface/tei-gaudi
cd tei-gaudi/
docker build --no-cache -f Dockerfile-hpu -t opea/tei-gaudi:latest .

8. Build MegaService Docker Image

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

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

9. Build UI Docker Image

Construct the frontend Docker image using the command below:

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 .

Then run the command docker images, you will have the following 8 Docker Images:

  1. opea/embedding-tei:latest
  2. opea/retriever-redis:latest
  3. opea/reranking-tei:latest
  4. opea/llm-tgi:latest
  5. opea/tei-gaudi:latest
  6. opea/dataprep-redis:latest
  7. opea/chatqna:latest
  8. opea/chatqna-ui:latest

🚀 Start MicroServices and MegaService

Setup Environment Variables

Since the docker_compose.yaml will consume some environment variables, you need to setup them in advance as below.

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://${host_ip}:8090"
export TEI_RERANKING_ENDPOINT="http://${host_ip}:8808"
export TGI_LLM_ENDPOINT="http://${host_ip}:8008"
export REDIS_URL="redis://${host_ip}:6379"
export INDEX_NAME="rag-redis"
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://${host_ip}:8888/v1/chatqna"
export DATAPREP_SERVICE_ENDPOINT="http://${host_ip}:6007/v1/dataprep"

Note: Please replace with host_ip with you external IP address, do NOT use localhost.

Start all the services Docker Containers

cd GenAIExamples/ChatQnA/docker-composer/gaudi/
docker compose -f docker_compose.yaml up -d

Validate MicroServices and MegaService

  1. TEI Embedding Service
curl ${host_ip}:8090/embed \
    -X POST \
    -d '{"inputs":"What is Deep Learning?"}' \
    -H 'Content-Type: application/json'
  1. Embedding Microservice
curl http://${host_ip}:6000/v1/embeddings \
  -X POST \
  -d '{"text":"hello"}' \
  -H 'Content-Type: application/json'
  1. Retriever Microservice

To consume the retriever microservice, you need to generate a mock embedding vector of length 768 in Python script:

import random

embedding = [random.uniform(-1, 1) for _ in range(768)]
print(embedding)

Then substitute your mock embedding vector for the ${your_embedding} in the following curl command:

curl http://${host_ip}:7000/v1/retrieval \
  -X POST \
  -d '{"text":"test", "embedding":${your_embedding}}' \
  -H 'Content-Type: application/json'
  1. TEI Reranking Service
curl http://${host_ip}:8808/rerank \
    -X POST \
    -d '{"query":"What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}' \
    -H 'Content-Type: application/json'
  1. Reranking Microservice
curl http://${host_ip}:8000/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'
  1. TGI Service
curl http://${host_ip}:8008/generate \
  -X POST \
  -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":64, "do_sample": true}}' \
  -H 'Content-Type: application/json'
  1. LLM Microservice
curl http://${host_ip}:9000/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'
  1. MegaService
curl http://${host_ip}:8888/v1/chatqna -H "Content-Type: application/json" -d '{
     "messages": "What is the revenue of Nike in 2023?"
     }'
  1. 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}:6007/v1/dataprep" \
     -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:

curl -X POST "http://${host_ip}:6007/v1/dataprep" \
     -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.

Enable LangSmith for Monotoring Application (Optional)

LangSmith offers tools to debug, evaluate, and monitor language models and intelligent agents. It can be used to assess benchmark data for each microservice. Before launching your services with docker compose -f docker_compose.yaml up -d, you need to enable LangSmith tracing by setting the LANGCHAIN_TRACING_V2 environment variable to true and configuring your LangChain API key.

Here's how you can do it:

  1. Install the latest version of LangSmith:
pip install -U langsmith
  1. Set the necessary environment variables:
export LANGCHAIN_TRACING_V2=true
export LANGCHAIN_API_KEY=ls_...

🚀 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 docker_compose.yaml file as shown below:

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

project-screenshot