970b869838
Signed-off-by: Tsai, Louie <louie.tsai@intel.com> Co-authored-by: Wang, Kai Lawrence <109344418+wangkl2@users.noreply.github.com> Co-authored-by: xiguiw <111278656+xiguiw@users.noreply.github.com>
599 lines
23 KiB
Markdown
599 lines
23 KiB
Markdown
# Build MegaService of ChatQnA on Gaudi
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This document outlines the deployment process for a ChatQnA application utilizing the [GenAIComps](https://github.com/opea-project/GenAIComps.git) 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`.
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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, leveraging guardrails, or 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.
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Quick Start:
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1. Set up the environment variables.
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2. Run Docker Compose.
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3. Consume the ChatQnA Service.
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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). We now support running the latest DeepSeek models, including [deepseek-ai/DeepSeek-R1-Distill-Llama-70B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-70B) and [deepseek-ai/DeepSeek-R1-Distill-Qwen-32B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B) on Gaudi accelerators. To run `deepseek-ai/DeepSeek-R1-Distill-Llama-70B`, update the `LLM_MODEL_ID` and configure `NUM_CARDS` to 8 in the [set_env.sh](./set_env.sh) script. To run `deepseek-ai/DeepSeek-R1-Distill-Qwen-32B`, update the `LLM_MODEL_ID` and configure `NUM_CARDS` to 4 in the [set_env.sh](./set_env.sh) script.
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## Quick Start: 1.Setup Environment Variable
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To set up environment variables for deploying ChatQnA services, follow these steps:
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1. Set the required environment variables:
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```bash
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# Example: host_ip="192.168.1.1"
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export host_ip="External_Public_IP"
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export HUGGINGFACEHUB_API_TOKEN="Your_Huggingface_API_Token"
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```
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2. If you are in a proxy environment, also set the proxy-related environment variables:
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```bash
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export http_proxy="Your_HTTP_Proxy"
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export https_proxy="Your_HTTPs_Proxy"
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# Example: no_proxy="localhost, 127.0.0.1, 192.168.1.1"
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export no_proxy="Your_No_Proxy",chatqna-gaudi-ui-server,chatqna-gaudi-backend-server,dataprep-redis-service,tei-embedding-service,retriever,tei-reranking-service,tgi-service,vllm-service,guardrails
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```
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3. Set up other environment variables:
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```bash
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source ./set_env.sh
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```
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4. Change Model for LLM serving
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By default, Meta-Llama-3-8B-Instruct is used for LLM serving, the default model can be changed to other validated LLM models.
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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.
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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.
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For example, change to DeepSeek-R1-Distill-Qwen-32B using the following command.
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```bash
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export LLM_MODEL_ID="deepseek-ai/DeepSeek-R1-Distill-Qwen-32B"
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```
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Please also check [required gaudi cards for different models](https://github.com/opea-project/GenAIComps/tree/main/comps/llms/src/text-generation#system-requirements-for-llm-models) for new models.
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It might be necessary to increase the number of Gaudi cards for the model by exporting NUM_CARDS to the new model or by modifying set_env.sh, and then repeating step 3. For example, increase the number of Gaudi cards for DeepSeek-R1-
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Distill-Qwen-32B using the following command:
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```bash
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export NUM_CARDS=4
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```
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## Quick Start: 2.Run Docker Compose
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```bash
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docker compose up -d
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```
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To enable Open Telemetry Tracing, compose.telemetry.yaml file need to be merged along with default compose.yaml file.
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```bash
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docker compose -f compose.yaml -f compose.telemetry.yaml up -d
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```
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It will automatically download the docker image on `docker hub`:
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```bash
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docker pull opea/chatqna:latest
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docker pull opea/chatqna-ui:latest
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```
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In following cases, you could build docker image from source by yourself.
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- Failed to download the docker image.
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- If you want to use a specific version of Docker image.
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Please refer to 'Build Docker Images' in below.
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## QuickStart: 3.Consume the ChatQnA Service
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```bash
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curl http://${host_ip}:8888/v1/chatqna \
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-H "Content-Type: application/json" \
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-d '{
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"messages": "What is the revenue of Nike in 2023?"
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}'
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```
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## 🚀 Build Docker Images
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First of all, you need to build Docker Images locally. This step can be ignored after the Docker images published to Docker hub.
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```bash
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git clone https://github.com/opea-project/GenAIComps.git
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cd GenAIComps
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```
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### 1. Build Retriever Image
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```bash
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docker build --no-cache -t opea/retriever:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/retrievers/src/Dockerfile .
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```
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### 2. Build Dataprep Image
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```bash
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docker build --no-cache -t opea/dataprep:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/dataprep/src/Dockerfile .
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```
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### 3. Build Guardrails Docker Image (Optional)
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To fortify AI initiatives in production, Guardrails microservice can secure model inputs and outputs, building Trustworthy, Safe, and Secure LLM-based Applications.
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```bash
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docker build -t opea/guardrails:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/guardrails/src/guardrails/Dockerfile .
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```
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### 4. Build MegaService Docker Image
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1. MegaService with Rerank
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To construct the Mega Service with Rerank, we utilize the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice pipeline within the `chatqna.py` Python script. Build the MegaService Docker image using the command below:
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```bash
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git clone https://github.com/opea-project/GenAIExamples.git
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cd GenAIExamples/ChatQnA
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docker build --no-cache -t opea/chatqna:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile .
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```
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2. MegaService with Guardrails
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If you want to enable guardrails microservice in the pipeline, please use the below command instead:
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```bash
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git clone https://github.com/opea-project/GenAIExamples.git
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cd GenAIExamples/ChatQnA/
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docker build --no-cache -t opea/chatqna-guardrails:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile.guardrails .
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```
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3. MegaService without Rerank
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To construct the Mega Service without Rerank, we utilize the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice pipeline within the `chatqna_without_rerank.py` Python script. Build MegaService Docker image via below command:
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```bash
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git clone https://github.com/opea-project/GenAIExamples.git
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cd GenAIExamples/ChatQnA
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docker build --no-cache -t opea/chatqna-without-rerank:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile.without_rerank .
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```
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### 5. Build UI Docker Image
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Construct the frontend Docker image using the command below:
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```bash
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cd GenAIExamples/ChatQnA/ui
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docker build --no-cache -t opea/chatqna-ui:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f ./docker/Dockerfile .
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```
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### 6. Build Conversational React UI Docker Image (Optional)
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Build frontend Docker image that enables Conversational experience with ChatQnA megaservice via below command:
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**Export the value of the public IP address of your Gaudi node to the `host_ip` environment variable**
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```bash
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cd GenAIExamples/ChatQnA/ui
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docker build --no-cache -t opea/chatqna-conversation-ui:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f ./docker/Dockerfile.react .
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```
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### 7. Build Nginx Docker Image
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```bash
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cd GenAIComps
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docker build -t opea/nginx:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/third_parties/nginx/src/Dockerfile .
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```
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Then run the command `docker images`, you will have the following 5 Docker Images:
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- `opea/retriever:latest`
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- `opea/dataprep:latest`
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- `opea/chatqna:latest`
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- `opea/chatqna-ui:latest`
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- `opea/nginx:latest`
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If Conversation React UI is built, you will find one more image:
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- `opea/chatqna-conversation-ui:latest`
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If Guardrails docker image is built, you will find one more image:
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- `opea/guardrails:latest`
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## 🚀 Start MicroServices and MegaService
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### Required Models
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By default, the embedding, reranking and LLM models are set to a default value as listed below:
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| Service | Model |
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| --------- | ----------------------------------- |
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| Embedding | BAAI/bge-base-en-v1.5 |
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| Reranking | BAAI/bge-reranker-base |
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| LLM | meta-llama/Meta-Llama-3-8B-Instruct |
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Change the `xxx_MODEL_ID` below for your needs.
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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:
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1. Online
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```bash
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export HF_TOKEN=${your_hf_token}
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export HF_ENDPOINT="https://hf-mirror.com"
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model_name="meta-llama/Meta-Llama-3-8B-Instruct"
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# Start vLLM LLM Service
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docker run -p 8007:80 -v ./data:/data --name vllm-gaudi-server -e HF_ENDPOINT=$HF_ENDPOINT -e http_proxy=$http_proxy -e https_proxy=$https_proxy --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none -e HUGGING_FACE_HUB_TOKEN=$HF_TOKEN -e VLLM_TORCH_PROFILER_DIR="/mnt" --cap-add=sys_nice --ipc=host opea/vllm-gaudi:latest --model $model_name --tensor-parallel-size 1 --host 0.0.0.0 --port 80 --block-size 128 --max-num-seqs 256 --max-seq_len-to-capture 2048
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# Start TGI LLM Service
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docker run -p 8005:80 -v ./data:/data --name tgi-gaudi-server -e HF_ENDPOINT=$HF_ENDPOINT -e http_proxy=$http_proxy -e https_proxy=$https_proxy --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none -e HUGGING_FACE_HUB_TOKEN=$HF_TOKEN -e ENABLE_HPU_GRAPH=true -e LIMIT_HPU_GRAPH=true -e USE_FLASH_ATTENTION=true -e FLASH_ATTENTION_RECOMPUTE=true --cap-add=sys_nice --ipc=host ghcr.io/huggingface/tgi-gaudi:2.0.6 --model-id $model_name --max-input-tokens 1024 --max-total-tokens 2048
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```
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2. Offline
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- Search your model name in ModelScope. For example, check [this page](https://modelscope.cn/models/LLM-Research/Meta-Llama-3-8B-Instruct/files) for model `Meta-Llama-3-8B-Instruct`.
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- Click on `Download this model` button, and choose one way to download the model to your local path `/path/to/model`.
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- Run the following command to start the LLM service.
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```bash
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export HF_TOKEN=${your_hf_token}
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export model_path="/path/to/model"
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# Start vLLM LLM Service
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docker run -p 8007:80 -v $model_path:/data --name vllm-gaudi-server --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none -e HUGGING_FACE_HUB_TOKEN=$HF_TOKEN -e VLLM_TORCH_PROFILER_DIR="/mnt" --cap-add=sys_nice --ipc=host opea/vllm-gaudi:latest --model /data --tensor-parallel-size 1 --host 0.0.0.0 --port 80 --block-size 128 --max-num-seqs 256 --max-seq_len-to-capture 2048
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# Start TGI LLM Service
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docker run -p 8005:80 -v $model_path:/data --name tgi-gaudi-server --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none -e HUGGING_FACE_HUB_TOKEN=$HF_TOKEN -e ENABLE_HPU_GRAPH=true -e LIMIT_HPU_GRAPH=true -e USE_FLASH_ATTENTION=true -e FLASH_ATTENTION_RECOMPUTE=true --cap-add=sys_nice --ipc=host ghcr.io/huggingface/tgi-gaudi:2.0.6 --model-id /data --max-input-tokens 1024 --max-total-tokens 2048
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```
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### Setup Environment Variables
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1. Set the required environment variables:
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```bash
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# Example: host_ip="192.168.1.1"
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export host_ip="External_Public_IP"
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export HUGGINGFACEHUB_API_TOKEN="Your_Huggingface_API_Token"
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# Example: NGINX_PORT=80
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export NGINX_PORT=${your_nginx_port}
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```
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2. If you are in a proxy environment, also set the proxy-related environment variables:
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```bash
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export http_proxy="Your_HTTP_Proxy"
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export https_proxy="Your_HTTPs_Proxy"
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# Example: no_proxy="localhost, 127.0.0.1, 192.168.1.1"
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export no_proxy="Your_No_Proxy",chatqna-gaudi-ui-server,chatqna-gaudi-backend-server,dataprep-redis-service,tei-embedding-service,retriever,tei-reranking-service,tgi-service,vllm-service,guardrails
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```
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3. Set up other environment variables:
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```bash
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source ./set_env.sh
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```
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### Start all the services Docker Containers
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```bash
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cd GenAIExamples/ChatQnA/docker_compose/intel/hpu/gaudi/
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```
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If use vLLM as the LLM serving backend.
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```bash
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# Start ChatQnA with Rerank Pipeline
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docker compose -f compose.yaml up -d
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# Start ChatQnA without Rerank Pipeline
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docker compose -f compose_without_rerank.yaml up -d
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# Start ChatQnA with Rerank Pipeline and Open Telemetry Tracing
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docker compose -f compose.yaml -f compose.telemetry.yaml up -d
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```
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If use TGI as the LLM serving backend.
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```bash
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docker compose -f compose_tgi.yaml up -d
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# Start ChatQnA with Open Telemetry Tracing
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docker compose -f compose_tgi.yaml -f compose_tgi.telemetry.yaml up -d
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```
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If you want to enable guardrails microservice in the pipeline, please follow the below command instead:
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```bash
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cd GenAIExamples/ChatQnA/docker_compose/intel/hpu/gaudi/
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docker compose -f compose_guardrails.yaml up -d
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```
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> **_NOTE:_** Users need at least two Gaudi cards to run the ChatQnA successfully.
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### Validate MicroServices and MegaService
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Follow the instructions to validate MicroServices.
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For validation details, please refer to [how-to-validate_service](./how_to_validate_service.md).
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1. TEI Embedding Service
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```bash
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curl ${host_ip}:8090/embed \
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-X POST \
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-d '{"inputs":"What is Deep Learning?"}' \
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-H 'Content-Type: application/json'
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```
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2. Retriever Microservice
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To consume the retriever microservice, you need to generate a mock embedding vector by Python script. The length of embedding vector
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is determined by the embedding model.
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Here we use the model `EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5"`, which vector size is 768.
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Check the vecotor dimension of your embedding model, set `your_embedding` dimension equals to it.
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```bash
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export your_embedding=$(python3 -c "import random; embedding = [random.uniform(-1, 1) for _ in range(768)]; print(embedding)")
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curl http://${host_ip}:7000/v1/retrieval \
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-X POST \
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-d "{\"text\":\"test\",\"embedding\":${your_embedding}}" \
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-H 'Content-Type: application/json'
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```
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3. TEI Reranking Service
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> Skip for ChatQnA without Rerank pipeline
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```bash
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curl http://${host_ip}:8808/rerank \
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-X POST \
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-d '{"query":"What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}' \
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-H 'Content-Type: application/json'
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```
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4. LLM backend Service
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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.
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Try the command below to check whether the LLM serving is ready.
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```bash
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# vLLM service
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docker logs vllm-gaudi-server 2>&1 | grep complete
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# If the service is ready, you will get the response like below.
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INFO: Application startup complete.
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```
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```bash
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# TGI service
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docker logs tgi-gaudi-server | grep Connected
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If the service is ready, you will get the response like below.
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2024-09-03T02:47:53.402023Z INFO text_generation_router::server: router/src/server.rs:2311: Connected
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```
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Then try the `cURL` command below to validate services.
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```bash
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# vLLM Service
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curl http://${host_ip}:8007/v1/chat/completions \
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-X POST \
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-d '{"model": ${LLM_MODEL_ID}, "messages": [{"role": "user", "content": "What is Deep Learning?"}], "max_tokens":17}' \
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-H 'Content-Type: application/json'
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```
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```bash
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# TGI service
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curl http://${host_ip}:8005/v1/chat/completions \
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-X POST \
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-d '{"model": ${LLM_MODEL_ID}, "messages": [{"role": "user", "content": "What is Deep Learning?"}], "max_tokens":17}' \
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-H 'Content-Type: application/json'
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```
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5. MegaService
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```bash
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curl http://${host_ip}:8888/v1/chatqna -H "Content-Type: application/json" -d '{
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"messages": "What is the revenue of Nike in 2023?"
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}'
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```
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6. Nginx Service
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```bash
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curl http://${host_ip}:${NGINX_PORT}/v1/chatqna \
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-H "Content-Type: application/json" \
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-d '{"messages": "What is the revenue of Nike in 2023?"}'
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```
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7. Dataprep Microservice（Optional）
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|
||
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/link list that you uploaded:
|
||
|
||
```bash
|
||
curl -X POST "http://${host_ip}:6007/v1/dataprep/get" \
|
||
-H "Content-Type: application/json"
|
||
```
|
||
|
||
Then you will get the response JSON like this. Notice that the returned `name`/`id` of the uploaded link is `https://xxx.txt`.
|
||
|
||
```json
|
||
[
|
||
{
|
||
"name": "nke-10k-2023.pdf",
|
||
"id": "nke-10k-2023.pdf",
|
||
"type": "File",
|
||
"parent": ""
|
||
},
|
||
{
|
||
"name": "https://opea.dev.txt",
|
||
"id": "https://opea.dev.txt",
|
||
"type": "File",
|
||
"parent": ""
|
||
}
|
||
]
|
||
```
|
||
|
||
To delete the file/link you uploaded:
|
||
|
||
```bash
|
||
# delete link
|
||
curl -X POST "http://${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"
|
||
```
|
||
|
||
8. Guardrails (Optional)
|
||
|
||
```bash
|
||
curl http://${host_ip}:9090/v1/guardrails\
|
||
-X POST \
|
||
-d '{"text":"How do you buy a tiger in the US?","parameters":{"max_new_tokens":32}}' \
|
||
-H 'Content-Type: application/json'
|
||
```
|
||
|
||
### Profile Microservices
|
||
|
||
To further analyze MicroService Performance, users could follow the instructions to profile MicroServices.
|
||
|
||
#### 1. vLLM backend Service
|
||
|
||
Users could follow previous section to testing vLLM microservice or ChatQnA MegaService.
|
||
By default, vLLM profiling is not enabled. Users could start and stop profiling by following commands.
|
||
|
||
##### Start vLLM profiling
|
||
|
||
```bash
|
||
curl http://${host_ip}:9009/start_profile \
|
||
-H "Content-Type: application/json" \
|
||
-d '{"model": ${LLM_MODEL_ID}}'
|
||
```
|
||
|
||
Users would see below docker logs from vllm-service if profiling is started correctly.
|
||
|
||
```bash
|
||
INFO api_server.py:361] Starting profiler...
|
||
INFO api_server.py:363] Profiler started.
|
||
INFO: x.x.x.x:35940 - "POST /start_profile HTTP/1.1" 200 OK
|
||
```
|
||
|
||
After vLLM profiling is started, users could start asking questions and get responses from vLLM MicroService
|
||
or ChatQnA MicroService.
|
||
|
||
##### Stop vLLM profiling
|
||
|
||
By following command, users could stop vLLM profliing and generate a \*.pt.trace.json.gz file as profiling result
|
||
under /mnt folder in vllm-service docker instance.
|
||
|
||
```bash
|
||
# vLLM Service
|
||
curl http://${host_ip}:9009/stop_profile \
|
||
-H "Content-Type: application/json" \
|
||
-d '{"model": ${LLM_MODEL_ID}}'
|
||
```
|
||
|
||
Users would see below docker logs from vllm-service if profiling is stopped correctly.
|
||
|
||
```bash
|
||
INFO api_server.py:368] Stopping profiler...
|
||
INFO api_server.py:370] Profiler stopped.
|
||
INFO: x.x.x.x:41614 - "POST /stop_profile HTTP/1.1" 200 OK
|
||
```
|
||
|
||
After vllm profiling is stopped, users could use below command to get the \*.pt.trace.json.gz file under /mnt folder.
|
||
|
||
```bash
|
||
docker cp vllm-service:/mnt/ .
|
||
```
|
||
|
||
##### Check profiling result
|
||
|
||
Open a web browser and type "chrome://tracing" or "ui.perfetto.dev", and then load the json.gz file, you should be able
|
||
to see the vLLM profiling result as below diagram.
|
||
|
||
|
||
|
||
|
||
## 🚀 Launch the UI
|
||
|
||
### Launch with origin port
|
||
|
||
To access the frontend, open the following URL in your browser: http://{host_ip}:5173. By default, the UI runs on port 5173 internally. If you prefer to use a different host port to access the frontend, you can modify the port mapping in the `compose.yaml` file as shown below:
|
||
|
||
```yaml
|
||
chatqna-gaudi-ui-server:
|
||
image: opea/chatqna-ui:latest
|
||
...
|
||
ports:
|
||
- "80:5173"
|
||
```
|
||
|
||
### Launch with Nginx
|
||
|
||
If you want to launch the UI using Nginx, open this URL: `http://${host_ip}:${NGINX_PORT}` in your browser to access the frontend.
|
||
|
||
## 🚀 Launch the Conversational UI (Optional)
|
||
|
||
To access the Conversational UI (react based) frontend, modify the UI service in the `compose.yaml` file. Replace `chatqna-gaudi-ui-server` service with the `chatqna-gaudi-conversation-ui-server` service as per the config below:
|
||
|
||
```yaml
|
||
chatqna-gaudi-conversation-ui-server:
|
||
image: opea/chatqna-conversation-ui:latest
|
||
container_name: chatqna-gaudi-conversation-ui-server
|
||
environment:
|
||
- APP_BACKEND_SERVICE_ENDPOINT=${BACKEND_SERVICE_ENDPOINT}
|
||
- APP_DATA_PREP_SERVICE_URL=${DATAPREP_SERVICE_ENDPOINT}
|
||
ports:
|
||
- "5174:80"
|
||
depends_on:
|
||
- chatqna-gaudi-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
|
||
chatqna-gaudi-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):
|
||
|
||
|