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Updated the Pinecone readme to reflect the new structure (#1222)

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Signed-off-by: Pallavi Jaini <pallavi.jaini@intel.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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2024-12-04 18:04:09 -08:00
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ChatQnA/docker_compose/intel/cpu/xeon/README_pinecone.md
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@@ -1,62 +1,100 @@
# Build Mega Service of ChatQnA (with Pinecone) on Xeon
# Build Mega Service of ChatQnA on Xeon
This document outlines the deployment process for a ChatQnA 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 `embedding`, `retriever`, `rerank`, and `llm`. We will publish the Docker images to Docker Hub soon, it will simplify the deployment process for this service.
Quick Start:
1. Set up the environment variables.
2. Run Docker Compose.
3. Consume the ChatQnA Service.
## 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"
export PINECONE_API_KEY="Pinecone_API_Key"
export PINECONE_INDEX_NAME="Pinecone_Index_Name"
export INDEX_NAME="Pinecone_Index_Name"
```
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-xeon-ui-server,chatqna-xeon-backend-server,dataprep-pinecone-service,tei-embedding-service,retriever,tei-reranking-service,tgi-service,vllm-service
```
3. Set up other environment variables, make sure to update the INDEX_NAME variable to Pinecone index value:
```bash
source ./set_env.sh
```
## Quick Start: 2.Run Docker Compose
```bash
docker compose -f compose_pinecone.yaml up -d
```
It will automatically download the docker image on `docker hub`:
```bash
docker pull opea/chatqna:latest
docker pull opea/chatqna-ui:latest
```
NB: You should build docker image from source by yourself if:
- You are developing off the git main branch (as the container's ports in the repo may be different from the published docker image).
- You can't download the docker image.
- You want to use a specific version of Docker image.
Please refer to ['Build Docker Images'](#🚀-build-docker-images) in below.
## QuickStart: 3.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?"
}'
```
## 🚀 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.
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 4th Generation Intel Xeon Scalable processors that are optimized for 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**
### Network Port & Security
> See one example below. Please open up these ports in the EC2 instance based on the IP addresses you want to allow
- Access the ChatQnA UI by web browser
```
It supports to access by `80` port. Please confirm the `80` port is opened in the firewall of EC2 instance.
data_prep_service
=====================
Port 6007 - Open to 0.0.0.0/0
Port 6008 - Open to 0.0.0.0/0
- Access the microservice by tool or API
tei_embedding_service
=====================
Port 6006 - Open to 0.0.0.0/0
1. Login to the EC2 instance and access by **local IP address** and port.
embedding
=========
Port 6000 - Open to 0.0.0.0/0
It's recommended and do nothing of the network port setting.
retriever
=========
Port 7000 - Open to 0.0.0.0/0
2. Login to a remote client and access by **public IP address** and port.
tei_xeon_service
================
Port 8808 - Open to 0.0.0.0/0
You need to open the port of the microservice in the security group setting of firewall of EC2 instance setting.
reranking
=========
Port 8000 - Open to 0.0.0.0/0
For detailed guide, please refer to [Validate Microservices](#validate-microservices).
tgi-service
===========
Port 9009 - Open to 0.0.0.0/0
llm
===
Port 9000 - Open to 0.0.0.0/0
chaqna-xeon-backend-server
==========================
Port 8888 - Open to 0.0.0.0/0
chaqna-xeon-ui-server
=====================
Port 5173 - Open to 0.0.0.0/0
```
Note, it will increase the risk of security, so please confirm before do it.
## 🚀 Build Docker Images
@@ -67,241 +105,258 @@ git clone https://github.com/opea-project/GenAIComps.git
cd GenAIComps
```
### 1. Build Embedding Image
```bash
docker build --no-cache -t opea/embedding-tei:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/embeddings/tei/langchain/Dockerfile .
```
### 2. Build Retriever Image
### 1. Build Retriever Image
```bash
docker build --no-cache -t opea/retriever-pinecone:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/retrievers/pinecone/langchain/Dockerfile .
```
### 3. Build Rerank Image
```bash
docker build --no-cache -t opea/reranking-tei:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/reranks/tei/Dockerfile .
```
### 4. Build LLM Image
```bash
docker build --no-cache -t opea/llm-tgi:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/llms/text-generation/tgi/Dockerfile .
```
### 5. Build Dataprep Image
### 2. Build Dataprep Image
```bash
docker build --no-cache -t opea/dataprep-pinecone:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/dataprep/pinecone/langchain/Dockerfile .
cd ..
```
### 6. Build MegaService Docker Image
### 3. Build MegaService Docker Image
To construct the Mega Service, we utilize the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice pipeline within the `chatqna.py` Python script. Build MegaService Docker image via below command:
1. MegaService with Rerank
```bash
git clone https://github.com/opea-project/GenAIExamples.git
cd GenAIExamples/ChatQnA/docker
docker build --no-cache -t opea/chatqna:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile .
cd ../../..
```
To construct the Mega Service with Rerank, we utilize the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice pipeline within the `chatqna.py` Python script. Build MegaService Docker image via below command:
### 7. Build UI Docker Image
```bash
git clone https://github.com/opea-project/GenAIExamples.git
cd GenAIExamples/ChatQnA
docker build --no-cache -t opea/chatqna:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile .
```
2. MegaService without Rerank
To construct the Mega Service without Rerank, we utilize the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice pipeline within the `chatqna_without_rerank.py` Python script. Build MegaService Docker image via below command:
```bash
git clone https://github.com/opea-project/GenAIExamples.git
cd GenAIExamples/ChatQnA
docker build --no-cache -t opea/chatqna-without-rerank:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile.without_rerank .
```
### 4. Build UI Docker Image
Build frontend Docker image via below command:
```bash
cd GenAIExamples/ChatQnA/docker/ui/
cd GenAIExamples/ChatQnA/ui
docker build --no-cache -t opea/chatqna-ui:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f ./docker/Dockerfile .
cd ../../../..
```
### 8. Build Conversational React UI Docker Image (Optional)
### 5. Build Conversational React UI Docker Image (Optional)
Build frontend Docker image that enables Conversational experience with ChatQnA megaservice via below command:
**Export the value of the public IP address of your Xeon server to the `host_ip` environment variable**
```bash
cd GenAIExamples/ChatQnA/docker/ui/
export BACKEND_SERVICE_ENDPOINT="http://${host_ip}:8888/v1/chatqna"
export DATAPREP_SERVICE_ENDPOINT="http://${host_ip}:6007/v1/dataprep"
export DATAPREP_GET_FILE_ENDPOINT="http://${host_ip}:6008/v1/dataprep/get_file"
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 --build-arg DATAPREP_GET_FILE_ENDPOINT=$DATAPREP_GET_FILE_ENDPOINT -f ./docker/Dockerfile.react .
cd ../../../..
cd GenAIExamples/ChatQnA/ui
docker build --no-cache -t opea/chatqna-conversation-ui:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f ./docker/Dockerfile.react .
```
Then run the command `docker images`, you will have the following 7 Docker Images:
### 6. Build Nginx Docker Image
```bash
cd GenAIComps
docker build -t opea/nginx:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/nginx/Dockerfile .
```
Then run the command `docker images`, you will have the following 5 Docker Images:
1. `opea/dataprep-pinecone:latest`
2. `opea/embedding-tei:latest`
3. `opea/retriever-pinecone:latest`
4. `opea/reranking-tei:latest`
5. `opea/llm-tgi:latest`
6. `opea/chatqna:latest`
7. `opea/chatqna-ui:latest`
2. `opea/retriever-pinecone:latest`
3. `opea/chatqna:latest` or `opea/chatqna-without-rerank:latest`
4. `opea/chatqna-ui:latest`
5. `opea/nginx:latest`
## 🚀 Start Microservices
### Required Models
By default, the embedding, reranking and LLM models are set to a default value as listed below:
| Service | Model |
| --------- | ------------------------- |
| Embedding | BAAI/bge-base-en-v1.5 |
| Reranking | BAAI/bge-reranker-base |
| LLM | Intel/neural-chat-7b-v3-3 |
Change the `xxx_MODEL_ID` below for your needs.
For users in China who are unable to download models directly from Huggingface, you can use [ModelScope](https://www.modelscope.cn/models) or a Huggingface mirror to download models. TGI can load the models either online or offline as described below:
1. Online
```bash
export HF_TOKEN=${your_hf_token}
export HF_ENDPOINT="https://hf-mirror.com"
model_name="Intel/neural-chat-7b-v3-3"
docker run -p 8008:80 -v ./data:/data --name tgi-service -e HF_ENDPOINT=$HF_ENDPOINT -e http_proxy=$http_proxy -e https_proxy=$https_proxy --shm-size 1g ghcr.io/huggingface/text-generation-inference:2.4.0-intel-cpu --model-id $model_name
```
2. Offline
- Search your model name in ModelScope. For example, check [this page](https://www.modelscope.cn/models/ai-modelscope/neural-chat-7b-v3-1/files) for model `neural-chat-7b-v3-1`.
- Click on `Download this model` button, and choose one way to download the model to your local path `/path/to/model`.
- Run the following command to start TGI service.
```bash
export HF_TOKEN=${your_hf_token}
export model_path="/path/to/model"
docker run -p 8008:80 -v $model_path:/data --name tgi_service --shm-size 1g ghcr.io/huggingface/text-generation-inference:2.4.0-intel-cpu --model-id /data
```
### Setup Environment Variables
Since the `compose_pinecone.yaml` will consume some environment variables, you need to setup them in advance as below.
1. Set the required environment variables:
**Export the value of the public IP address of your Xeon server to the `host_ip` environment variable**
```bash
# Example: host_ip="192.168.1.1"
export host_ip="External_Public_IP"
export HUGGINGFACEHUB_API_TOKEN="Your_Huggingface_API_Token"
# Example: NGINX_PORT=80
export NGINX_PORT=${your_nginx_port}
export PINECONE_API_KEY="Pinecone_API_Key"
export PINECONE_INDEX_NAME="Pinecone_Index_Name"
export INDEX_NAME="Pinecone_Index_Name"
```
> Change the External_Public_IP below with the actual IPV4 value
2. If you are in a proxy environment, also set the proxy-related environment variables:
```
export host_ip="External_Public_IP"
```
```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-xeon-ui-server,chatqna-xeon-backend-server,dataprep-pinecone-service,tei-embedding-service,retriever,tei-reranking-service,tgi-service,vllm-service
```
**Export the value of your Huggingface API token to the `your_hf_api_token` environment variable**
3. Set up other environment variables, make sure to update the INDEX_NAME variable to use Pinecone Index name:
> 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=${your_no_proxy},"External_Public_IP"
```
\*\*Get the PINECONE_API_KEY and the INDEX_NAME
```
export pinecone_api_key=${api_key}
export pinecone_index_name=${pinecone_index}
```
```bash
export no_proxy=${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://${host_ip}:6006"
export TEI_RERANKING_ENDPOINT="http://${host_ip}:8808"
export TGI_LLM_ENDPOINT="http://${host_ip}:9009"
export PINECONE_API_KEY=${pinecone_api_key}
export PINECONE_INDEX_NAME=${pinecone_index_name}
export INDEX_NAME=${pinecone_index_name}
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"
export DATAPREP_GET_FILE_ENDPOINT="http://${host_ip}:6008/v1/dataprep/get_file"
export DATAPREP_DELETE_FILE_ENDPOINT="http://${host_ip}:6009/v1/dataprep/delete_file"
```
Note: Please replace with `host_ip` with you external IP address, do not use localhost.
```bash
source ./set_env.sh
```
### 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/ChatQnA/docker/xeon/
cd GenAIExamples/ChatQnA/docker_compose/intel/cpu/xeon/
```
If use TGI backend.
```bash
# Start ChatQnA with Rerank Pipeline
docker compose -f compose_pinecone.yaml up -d
```
### Validate Microservices
Note, when verify the microservices by curl or API from remote client, please make sure the **ports** of the microservices are opened in the firewall of the cloud node.
Follow the instructions to validate MicroServices.
For details on how to verify the correctness of the response, refer to [how-to-validate_service](../../hpu/gaudi/how_to_validate_service.md).
1. TEI Embedding Service
```bash
curl ${host_ip}:6006/embed \
-X POST \
-d '{"inputs":"What is Deep Learning?"}' \
-H 'Content-Type: application/json'
```
```bash
curl ${host_ip}:6006/embed \
-X POST \
-d '{"inputs":"What is Deep Learning?"}' \
-H 'Content-Type: application/json'
```
2. Embedding Microservice
2. Retriever Microservice
```bash
curl http://${host_ip}:6000/v1/embeddings\
-X POST \
-d '{"text":"hello"}' \
-H 'Content-Type: application/json'
```
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.
3. Retriever Microservice
To validate the retriever microservice, you need to generate a mock embedding vector of length 768 in Python script:
Check the vector dimension of your embedding model, set `your_embedding` dimension equals to it.
```Python
import random
embedding = [random.uniform(-1, 1) for _ in range(768)]
print(embedding)
```
```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\":${your_embedding}}" \
-H 'Content-Type: application/json'
```
Then substitute your mock embedding vector for the `${your_embedding}` in the following cURL command:
3. TEI Reranking Service
```bash
curl http://${host_ip}:7000/v1/retrieval \
-X POST \
-d '{"text":"What is the revenue of Nike in 2023?","embedding":"'"${your_embedding}"'"}' \
-H 'Content-Type: application/json'
```
> Skip for ChatQnA without Rerank pipeline
4. TEI Reranking Service
```bash
curl http://${host_ip}:8808/rerank \
-X POST \
-d '{"query":"What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}' \
-H 'Content-Type: application/json'
```
```bash
curl http://${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. LLM backend Service
5. Reranking Microservice
In first startup, this service will take more time to download the model files. After it's finished, the service will be ready.
```bash
curl http://${host_ip}:8000/v1/reranking\
-X POST \
-d '{"initial_query":"What is Deep Learning?", "retrieved_docs": [{"text":"Deep Learning is not..."}, {"text":"Deep learning is..."}]}' \
-H 'Content-Type: application/json'
```
Try the command below to check whether the LLM serving is ready.
6. TGI Service
```bash
docker logs tgi-service | grep Connected
```
```bash
curl http://${host_ip}:9009/generate \
-X POST \
-d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}' \
-H 'Content-Type: application/json'
```
If the service is ready, you will get the response like below.
7. LLM Microservice
```text
2024-09-03T02:47:53.402023Z INFO text_generation_router::server: router/src/server.rs:2311: Connected
```
```bash
curl http://${host_ip}:9000/v1/chat/completions\
-X POST \
-d '{"query":"What is Deep Learning?","max_new_tokens":17,"top_k":10,"top_p":0.95,"typical_p":0.95,"temperature":0.01,"repetition_penalty":1.03,"streaming":true}' \
-H 'Content-Type: application/json'
```
Then try the `cURL` command below to validate services.
8. MegaService
```bash
# TGI service
curl http://${host_ip}:9009/v1/chat/completions \
-X POST \
-d '{"model": "Intel/neural-chat-7b-v3-3", "messages": [{"role": "user", "content": "What is Deep Learning?"}], "max_tokens":17}' \
-H 'Content-Type: application/json'
```
```bash
curl http://${host_ip}:8888/v1/chatqna -H "Content-Type: application/json" -d '{
"messages": "What is the revenue of Nike in 2023?"
}'
```
5. MegaService
9. Dataprep Microservice（Optional）
```bash
curl http://${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:
Update Knowledge Base via Local File [nke-10k-2023.pdf](https://github.com/opea-project/GenAIComps/blob/main/comps/retrievers/redis/data/nke-10k-2023.pdf). Or
click [here](https://raw.githubusercontent.com/opea-project/GenAIComps/main/comps/retrievers/redis/data/nke-10k-2023.pdf) to download the file via any web browser.
Or run this command to get the file on a terminal.
```bash
wget https://raw.githubusercontent.com/opea-project/GenAIComps/main/comps/retrievers/redis/data/nke-10k-2023.pdf
```
Upload:
```bash
curl -X POST "http://${host_ip}:6007/v1/dataprep" \
@@ -321,34 +376,19 @@ curl -X POST "http://${host_ip}:6007/v1/dataprep" \
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:
To delete the files/link you uploaded:
```bash
curl -X POST "http://${host_ip}:6008/v1/dataprep/get_file" \
# delete all uploaded files and links
curl -X POST "http://${host_ip}:6009/v1/dataprep/delete_file" \
-d '{"file_path": "all"}' \
-H "Content-Type: application/json"
```
## 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 compose_pinecone.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:
```bash
pip install -U langsmith
```
2. Set the necessary environment variables:
```bash
export LANGCHAIN_TRACING_V2=true
export LANGCHAIN_API_KEY=ls_...
```
## 🚀 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
@@ -359,12 +399,33 @@ To access the frontend, open the following URL in your browser: http://{host_ip}
- "80:5173"
```
## 🚀 Launch the Conversational UI (react)
### Launch with Nginx
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:
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-xeon-ui-server` service with the `chatqna-xeon-conversation-ui-server` service as per the config below:
```yaml
chaqna-xeon-conversation-ui-server:
chaqna-xeon-conversation-ui-server:
image: opea/chatqna-conversation-ui:latest
container_name: chatqna-xeon-conversation-ui-server
environment:
- APP_BACKEND_SERVICE_ENDPOINT=${BACKEND_SERVICE_ENDPOINT}
- APP_DATA_PREP_SERVICE_URL=${DATAPREP_SERVICE_ENDPOINT}
ports:
- "5174:80"
depends_on:
- chaqna-xeon-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-gaudi-conversation-ui-server:
image: opea/chatqna-conversation-ui:latest
...
ports: