# Build MegaService of ChatQnA on NVIDIA GPU This document outlines the deployment process for a ChatQnA application utilizing the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice pipeline on NVIDIA GPU platform. 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. Quick Start Deployment Steps: 1. Set up the environment variables. 2. Modify the TEI Docker Image for Reranking 3. Run Docker Compose. 4. 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-ui-server,chatqna-backend-server,dataprep-redis-service,tei-embedding-service,retriever,tei-reranking-service,tgi-service ``` 3. Set up other environment variables: ```bash source ./set_env.sh ``` ## Quick Start: 2.Modify the TEI Docker Image for Reranking > **Note:** > The default Docker image for the `tei-reranking-service` in `compose.yaml` is built for A100 and A30 backend with compute capacity 8.0. If you are using A100/A30, skip this step. For other GPU architectures, please modify the `image` with specific tag for `tei-reranking-service` based on the following table with target CUDA compute capacity. | GPU Arch | GPU | Compute Capacity | Image | | ------------ | ------------------------------------------ | ---------------- | -------------------------------------------------------- | | Volta | V100 | 7.0 | NOT SUPPORTED | | Turing | T4, GeForce RTX 2000 Series | 7.5 | ghcr.io/huggingface/text-embeddings-inference:turing-1.5 | | Ampere 80 | A100, A30 | 8.0 | ghcr.io/huggingface/text-embeddings-inference:1.5 | | Ampere 86 | A40, A10, A16, A2, GeForce RTX 3000 Series | 8.6 | ghcr.io/huggingface/text-embeddings-inference:86-1.5 | | Ada Lovelace | L40S, L40, L4, GeForce RTX 4000 Series | 8.9 | ghcr.io/huggingface/text-embeddings-inference:89-1.5 | | Hopper | H100 | 9.0 | ghcr.io/huggingface/text-embeddings-inference:hopper-1.5 | For instance, if Hopper arch GPU (such as H100/H100 NVL) is the target backend: ``` # vim compose.yaml tei-reranking-service: #image: ghcr.io/huggingface/text-embeddings-inference:1.5 image: ghcr.io/huggingface/text-embeddings-inference:hopper-1.5 ``` ## Quick Start: 3.Run Docker Compose ```bash docker compose up -d ``` It will automatically download the docker image on `docker hub`: ```bash docker pull opea/chatqna:latest docker pull opea/chatqna-ui:latest ``` In following cases, you could build docker image from source by yourself. - Failed to download the docker image. - If you want to use a specific version of Docker image. Please refer to 'Build Docker Images' in below. ## QuickStart: 4.Consume the ChatQnA Service ```bash curl http://${host_ip}:8888/v1/chatqna \ -H "Content-Type: application/json" \ -d '{ "messages": "What is the revenue of Nike in 2023?" }' ``` ## 🚀 Build Docker Images First of all, you need to build Docker Images locally. This step can be ignored after the Docker images published to Docker hub. ### 1. Source Code install GenAIComps ```bash git clone https://github.com/opea-project/GenAIComps.git cd GenAIComps ``` ### 2. Build Retriever Image ```bash docker build --no-cache -t opea/retriever:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/retrievers/src/Dockerfile . ``` ### 3. Build Dataprep Image ```bash docker build --no-cache -t opea/dataprep:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/dataprep/src/Dockerfile . ``` ### 4. Build MegaService Docker Image To construct the Mega Service, we utilize the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice pipeline within the `chatqna.py` Python script. Build the MegaService Docker image using the command below: ```bash git clone https://github.com/opea-project/GenAIExamples.git cd GenAIExamples/ChatQnA docker build --no-cache -t opea/chatqna:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile . cd ../.. ``` ### 5. Build UI Docker Image Construct the frontend Docker image using the command below: ```bash cd GenAIExamples/ChatQnA/ui docker build --no-cache -t opea/chatqna-ui:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f ./docker/Dockerfile . cd ../../../ ``` ### 6. Build React UI Docker Image (Optional) Construct the frontend Docker image using the command below: ```bash cd GenAIExamples/ChatQnA/ui docker build --no-cache -t opea/chatqna-react-ui:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f ./docker/Dockerfile.react . cd ../../.. ``` ### 7. Build Nginx Docker Image ```bash cd GenAIComps docker build -t opea/nginx:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/third_parties/nginx/src/Dockerfile . ``` Then run the command `docker images`, you will have the following 5 Docker Images: 1. `opea/retriever:latest` 2. `opea/dataprep:latest` 3. `opea/chatqna:latest` 4. `opea/chatqna-ui:latest` or `opea/chatqna-react-ui:latest` 5. `opea/nginx:latest` ## 🚀 Start MicroServices and MegaService ### Required Models By default, the embedding, reranking and LLM models are set to a default value as listed below: | Service | Model | | --------- | ----------------------------------- | | Embedding | BAAI/bge-base-en-v1.5 | | Reranking | BAAI/bge-reranker-base | | LLM | meta-llama/Meta-Llama-3-8B-Instruct | Change the `xxx_MODEL_ID` below for your needs. ### Setup Environment Variables 1. Set the required environment variables: ```bash # Example: host_ip="192.168.1.1" export host_ip="External_Public_IP" export HUGGINGFACEHUB_API_TOKEN="Your_Huggingface_API_Token" # Example: NGINX_PORT=80 export NGINX_PORT=${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-ui-server,chatqna-backend-server,dataprep-redis-service,tei-embedding-service,retriever,tei-reranking-service,tgi-service ``` 3. Set up other environment variables: ```bash source ./set_env.sh ``` ### Modify the TEI Docker Image for Reranking > **Note:** > The default Docker image for the `tei-reranking-service` in `compose.yaml` is built for A100 and A30 backend with compute capacity 8.0. If you are using A100/A30, skip this step. For other GPU architectures, please modify the `image` with specific tag for `tei-reranking-service` based on the following table with target CUDA compute capacity. | GPU Arch | GPU | Compute Capacity | Image | | ------------ | ------------------------------------------ | ---------------- | -------------------------------------------------------- | | Volta | V100 | 7.0 | NOT SUPPORTED | | Turing | T4, GeForce RTX 2000 Series | 7.5 | ghcr.io/huggingface/text-embeddings-inference:turing-1.5 | | Ampere 80 | A100, A30 | 8.0 | ghcr.io/huggingface/text-embeddings-inference:1.5 | | Ampere 86 | A40, A10, A16, A2, GeForce RTX 3000 Series | 8.6 | ghcr.io/huggingface/text-embeddings-inference:86-1.5 | | Ada Lovelace | L40S, L40, L4, GeForce RTX 4000 Series | 8.9 | ghcr.io/huggingface/text-embeddings-inference:89-1.5 | | Hopper | H100 | 9.0 | ghcr.io/huggingface/text-embeddings-inference:hopper-1.5 | For instance, if Hopper arch GPU (such as H100/H100 NVL) is the target backend: ``` # vim compose.yaml tei-reranking-service: #image: ghcr.io/huggingface/text-embeddings-inference:1.5 image: ghcr.io/huggingface/text-embeddings-inference:hopper-1.5 ``` ### Start all the services Docker Containers ```bash cd GenAIExamples/ChatQnA/docker_compose/nvidia/gpu/ docker compose up -d ``` ### Validate MicroServices and MegaService 1. TEI Embedding Service ```bash curl ${host_ip}:8090/embed \ -X POST \ -d '{"inputs":"What is Deep Learning?"}' \ -H 'Content-Type: application/json' ``` 2. Retriever Microservice To consume the retriever microservice, you need to generate a mock embedding vector by Python script. The length of embedding vector is determined by the embedding model. Here we use the model `EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5"`, which vector size is 768. Check the vecotor dimension of your embedding model, set `your_embedding` dimension equals to it. ```bash export your_embedding=$(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 ```bash curl http://${host_ip}:8808/rerank \ -X POST \ -d '{"query":"What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}' \ -H 'Content-Type: application/json' ``` 4. TGI Service In first startup, this service will take more time to download the model files. After it's finished, the service will be ready. Try the command below to check whether the TGI service is ready. ```bash docker logs tgi-server | grep Connected ``` If the service is ready, you will get the response like below. ``` 2024-09-03T02:47:53.402023Z INFO text_generation_router::server: router/src/server.rs:2311: Connected ``` Then try the `cURL` command below to validate TGI. ```bash curl http://${host_ip}:8008/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://${host_ip}:8888/v1/chatqna -H "Content-Type: application/json" -d '{ "messages": "What is the revenue of Nike in 2023?" }' ``` 6. Nginx Service ```bash curl http://${host_ip}:${NGINX_PORT}/v1/chatqna \ -H "Content-Type: application/json" \ -d '{"messages": "What is the revenue of Nike in 2023?"}' ``` 7. Dataprep Microservice（Optional） If you want to update the default knowledge base, you can use the following commands: Update Knowledge Base via Local File Upload: ```bash curl -X POST "http://${host_ip}: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" ``` To delete the file/link you uploaded: ```bash # delete link curl -X POST "http://${host_ip}:6007/v1/dataprep/delete" \ -d '{"file_path": "https://opea.dev"}' \ -H "Content-Type: application/json" # delete file curl -X POST "http://${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://${host_ip}:6007/v1/dataprep/delete" \ -d '{"file_path": "all"}' \ -H "Content-Type: application/json" ``` ## 🚀 Launch the UI ### Launch with origin port To access the frontend, open the following URL in your browser: http://{host_ip}:5173. By default, the UI runs on port 5173 internally. If you prefer to use a different host port to access the frontend, you can modify the port mapping in the `compose.yaml` file as shown below: ```yaml chaqna-ui-server: image: opea/chatqna-ui:latest ... ports: - "80:5173" ``` ### Launch with Nginx If you want to launch the UI using Nginx, open this URL: `http://${host_ip}:${NGINX_PORT}` in your browser to access the frontend. ## 🚀 Launch the Conversational UI (Optional) To access the Conversational UI (react based) frontend, modify the UI service in the `compose.yaml` file. Replace `chaqna-ui-server` service with the `chatqna-react-ui-server` service as per the config below: ```yaml chatqna-react-ui-server: image: opea/chatqna-react-ui:latest container_name: chatqna-react-ui-server environment: - APP_BACKEND_SERVICE_ENDPOINT=${BACKEND_SERVICE_ENDPOINT} - APP_DATA_PREP_SERVICE_URL=${DATAPREP_SERVICE_ENDPOINT} ports: - "5174:80" depends_on: - chaqna-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-react-ui-server: image: opea/chatqna-react-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)