# Deploying ChatQnA on Intel® Xeon® Processors This document outlines the single node deployment process for a ChatQnA application utilizing the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservices on Intel Xeon server. The steps include pulling Docker images, container deployment via Docker Compose, and service execution to integrate microservices such as `embedding`, `retriever`, `rerank` and `llm`. ## Table of contents 1. [ChatQnA Quick Start Deployment](#chatqna-quick-start-Deployment) 2. [ChatQnA Docker Compose file Options](#chatqna-docker-compose-files) 3. [ChatQnA with Conversational UI](#chatqna-with-conversational-ui-optional) ## ChatQnA Quick Start Deployment This section describes how to quickly deploy and test the ChatQnA service manually on an Intel® Xeon® processor. The basic steps are: 1. [Access the Code](#access-the-code) 2. [Generate a HuggingFace Access Token](#generate-a-huggingface-access-token) 3. [Configure the Deployment Environment](#configure-the-deployment-environment) 4. [Deploy the Services Using Docker Compose](#deploy-the-services-using-docker-compose) 5. [Check the Deployment Status](#check-the-deployment-status) 6. [Test the Pipeline](#test-the-pipeline) 7. [Cleanup the Deployment](#cleanup-the-deployment) ### Access the Code Clone the GenAIExample repository and access the ChatQnA Intel® Gaudi® platform Docker Compose files and supporting scripts: ``` git clone https://github.com/opea-project/GenAIExamples.git cd GenAIExamples/ChatQnA/docker_compose/intel/cpu/xeon/ ``` Checkout a released version, such as v1.2: ``` git checkout v1.2 ``` ### Generate a HuggingFace Access Token Some HuggingFace resources, such as some models, are only accessible if the developer have an access token. In the absence of a HuggingFace access token, the developer can create one by first creating an account by following the steps provided at [HuggingFace](https://huggingface.co/) and then generating a [user access token](https://huggingface.co/docs/transformers.js/en/guides/private#step-1-generating-a-user-access-token). ### Configure the Deployment Environment To set up environment variables for deploying ChatQnA services, set up some parameters specific to the deployment environment and source the _setup_env.sh_ script in this directory: ``` export host_ip="External_Public_IP" #ip address of the node export HUGGINGFACEHUB_API_TOKEN="Your_Huggingface_API_Token" export http_proxy="Your_HTTP_Proxy" #http proxy if any export https_proxy="Your_HTTPs_Proxy" #https proxy if any export no_proxy=localhost,127.0.0.1,$host_ip #additional no proxies if needed export no_proxy=$no_proxy,chatqna-xeon-ui-server,chatqna-xeon-backend-server,dataprep-redis-service,tei-embedding-service,retriever,tei-reranking-service,tgi-service,vllm-service,llm-faqgen source ./set_env.sh ``` Consult the section on [ChatQnA Service configuration](#chatqna-configuration) for information on how service specific configuration parameters affect deployments. ### Deploy the Services Using Docker Compose To deploy the ChatQnA services, execute the `docker compose up` command with the appropriate arguments. For a default deployment, execute the command below. It uses the 'compose.yaml' file. ```bash docker compose up -d ``` To enable Open Telemetry Tracing, compose.telemetry.yaml file need to be merged along with default compose.yaml file. CPU example with Open Telemetry feature: > NOTE : To get supported Grafana Dashboard, please run download_opea_dashboard.sh following below commands. ```bash ./grafana/dashboards/download_opea_dashboard.sh docker compose -f compose.yaml -f compose.telemetry.yaml up -d ``` **Note**: developers should build docker image from source when: - Developing off the git main branch (as the container's ports in the repo may be different from the published docker image). - Unable to download the docker image. - Use a specific version of Docker image. Please refer to the table below to build different microservices from source: | Microservice | Deployment Guide | | ------------ | --------------------------------------------------------------------------------------------- | | Dataprep | https://github.com/opea-project/GenAIComps/tree/main/comps/dataprep | | Embedding | https://github.com/opea-project/GenAIComps/tree/main/comps/embeddings | | Retriever | https://github.com/opea-project/GenAIComps/tree/main/comps/retrievers | | Reranker | https://github.com/opea-project/GenAIComps/tree/main/comps/rerankings | | LLM | https://github.com/opea-project/GenAIComps/tree/main/comps/llms | | Megaservice | [Megaservice build guide](../../../../README_miscellaneous.md#build-megaservice-docker-image) | | UI | [Basic UI build guide](../../../../README_miscellaneous.md#build-ui-docker-image) | ### Check the Deployment Status After running docker compose, check if all the containers launched via docker compose have started: ``` docker ps -a ``` For the default deployment, the following 10 containers should have started: ``` CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES 3b5fa9a722da opea/chatqna-ui:${RELEASE_VERSION} "docker-entrypoint.s…" 32 hours ago Up 2 hours 0.0.0.0:5173->5173/tcp, :::5173->5173/tcp chatqna-xeon-ui-server d3b37f3d1faa opea/chatqna:${RELEASE_VERSION} "python chatqna.py" 32 hours ago Up 2 hours 0.0.0.0:8888->8888/tcp, :::8888->8888/tcp chatqna-xeon-backend-server b3e1388fa2ca opea/reranking-tei:${RELEASE_VERSION} "python reranking_te…" 32 hours ago Up 2 hours 0.0.0.0:8000->8000/tcp, :::8000->8000/tcp reranking-tei-xeon-server 24a240f8ad1c opea/retriever-redis:${RELEASE_VERSION} "python retriever_re…" 32 hours ago Up 2 hours 0.0.0.0:7000->7000/tcp, :::7000->7000/tcp retriever-redis-server 9c0d2a2553e8 opea/embedding-tei:${RELEASE_VERSION} "python embedding_te…" 32 hours ago Up 2 hours 0.0.0.0:6000->6000/tcp, :::6000->6000/tcp embedding-tei-server 24cae0db1a70 opea/llm-vllm:${RELEASE_VERSION} "bash entrypoint.sh" 32 hours ago Up 2 hours 0.0.0.0:9000->9000/tcp, :::9000->9000/tcp llm-vllm-server ea3986c3cf82 opea/dataprep-redis:${RELEASE_VERSION} "python prepare_doc_…" 32 hours ago Up 2 hours 0.0.0.0:6007->6007/tcp, :::6007->6007/tcp dataprep-redis-server e10dd14497a8 redis/redis-stack:7.2.0-v9 "/entrypoint.sh" 32 hours ago Up 2 hours 0.0.0.0:6379->6379/tcp, :::6379->6379/tcp, 0.0.0.0:8001->8001/tcp, :::8001->8001/tcp redis-vector-db b98fa07a4f5c opea/vllm:${RELEASE_VERSION} "python3 -m vllm.ent…" 32 hours ago Up 2 hours 0.0.0.0:9009->80/tcp, :::9009->80/tcp vllm-service 79276cf45a47 ghcr.io/huggingface/text-embeddings-inference:cpu-1.2 "text-embeddings-rou…" 32 hours ago Up 2 hours 0.0.0.0:6006->80/tcp, :::6006->80/tcp tei-embedding-server 4943e5f6cd80 ghcr.io/huggingface/text-embeddings-inference:cpu-1.2 "text-embeddings-rou…" 32 hours ago Up 2 hours 0.0.0.0:8808->80/tcp, :::8808->80/tcp ``` If any issues are encountered during deployment, refer to the [troubleshooting](../../../../README_miscellaneous.md##troubleshooting) section. ### Test the Pipeline Once the ChatQnA services are running, test the pipeline using the following command. This will send a sample query to the ChatQnA service and return a response. ```bash curl http://${host_ip}:8888/v1/chatqna \ -H "Content-Type: application/json" \ -d '{ "messages": "What is the revenue of Nike in 2023?" }' ``` **Note** : Access the ChatQnA UI by web browser through this URL: `http://${host_ip}:80`. Please confirm the `80` port is opened in the firewall. To validate each microservice used in the pipeline refer to the [Validate microservices](#validate-microservices) section. ### Cleanup the Deployment To stop the containers associated with the deployment, execute the following command: ``` docker compose -f compose.yaml down ``` ## ChatQnA Docker Compose Files In the context of deploying a ChatQnA pipeline on an Intel® Xeon® platform, we can pick and choose different vector databases, large language model serving frameworks, and remove pieces of the pipeline such as the reranker. The table below outlines the various configurations that are available as part of the application. These configurations can be used as templates and can be extended to different components available in [GenAIComps](https://github.com/opea-project/GenAIComps.git). | File | Description | | ------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | [compose.yaml](./compose.yaml) | Default compose file using vllm as serving framework and redis as vector database | | [compose_milvus.yaml](./compose_milvus.yaml) | Uses Milvus as the vector database. All other configurations remain the same as the default | | [compose_pinecone.yaml](./compose_pinecone.yaml) | Uses Pinecone as the vector database. All other configurations remain the same as the default. For more details, refer to [README_pinecone.md](./README_pinecone.md). | | [compose_qdrant.yaml](./compose_qdrant.yaml) | Uses Qdrant as the vector database. All other configurations remain the same as the default. For more details, refer to [README_qdrant.md](./README_qdrant.md). | | [compose_tgi.yaml](./compose_tgi.yaml) | Uses TGI as the LLM serving framework. All other configurations remain the same as the default | | [compose_without_rerank.yaml](./compose_without_rerank.yaml) | Default configuration without the reranker | | [compose_faqgen.yaml](./compose_faqgen.yaml) | Enables FAQ generation using vLLM as the LLM serving framework. For more details, refer to [README_faqgen.md](./README_faqgen.md). | | [compose_faqgen_tgi.yaml](./compose_faqgen_tgi.yaml) | Enables FAQ generation using TGI as the LLM serving framework. For more details, refer to [README_faqgen.md](./README_faqgen.md). | | [compose.telemetry.yaml](./compose.telemetry.yaml) | Helper file for telemetry features for vllm. Can be used along with any compose files that serves vllm | | [compose_tgi.telemetry.yaml](./compose_tgi.telemetry.yaml) | Helper file for telemetry features for tgi. Can be used along with any compose files that serves tgi | ## ChatQnA with Conversational UI (Optional) To access the Conversational UI (react based) frontend, modify the UI service in the `compose` file used to deploy. Replace `chaqna-xeon-ui-server` service with the `chatqna-xeon-conversation-ui-server` service as per the config below: ```yaml chatqna-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 the browser: http://{host_ip}:5174. By default, the UI runs on port 80 internally. If the developer prefers to use a different host port to access the frontend, it can be modified by port mapping in the `compose.yaml` file as shown below: ```yaml chaqna-gaudi-conversation-ui-server: image: opea/chatqna-conversation-ui:latest ... ports: - "80:80" ``` Here is an example of running ChatQnA (default UI): ![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) ### Validate Microservices Note, when verifying 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** Send a test request to the TEI Embedding Service to ensure it is running correctly: ```bash curl http://${host_ip}:6006/embed \ -X POST \ -d '{"inputs":"What is Deep Learning?"}' \ -H 'Content-Type: application/json' ``` If you receive a connection error, ensure that the service is running and the port 6006 is open in the firewall. 2. **Retriever Microservice** To consume the retriever microservice, you need to generate a mock embedding vector by Python script. The length of embedding vector is determined by the embedding model. Here we use the model `EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5"`, which vector size is 768. Check the vector dimension of your embedding model, set `your_embedding` dimension equal to it. ```bash export your_embedding=$(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' ``` If the response indicates an invalid embedding vector, verify that the vector size matches the model's expected dimension. 3. **TEI Reranking Service** To test the TEI Reranking Service, use the following `curl` command: > Skip for ChatQnA without Rerank pipeline ```bash curl http://${host_ip}:8808/rerank \ -X POST \ -d '{"query":"What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}' \ -H 'Content-Type: application/json' ``` 4. **LLM Backend Service** In the first startup, this service will take more time to download, load and warm up the model. After it's finished, the service will be ready. Try the command below to check whether the LLM serving is ready. ```bash # vLLM service docker logs vllm-service 2>&1 | grep complete # If the service is ready, you will get the response like below. INFO: Application startup complete. ``` ```bash # TGI service docker logs tgi-service | grep Connected # If the service is ready, you will get the response like below. 2024-09-03T02:47:53.402023Z INFO text_generation_router::server: router/src/server.rs:2311: Connected ``` Then try the `cURL` command below to validate services. ```bash # either vLLM or TGI service curl http://${host_ip}:9009/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** Use the following `curl` command to test the MegaService: ```bash curl http://${host_ip}:8888/v1/chatqna -H "Content-Type: application/json" -d '{ "messages": "What is the revenue of Nike in 2023?" }' ``` 6. **Nginx Service** Use the following curl command to test the Nginx Service: ```bash curl http://${host_ip}:${NGINX_PORT}/v1/chatqna \ -H "Content-Type: application/json" \ -d '{"messages": "What is the revenue of Nike in 2023?"}' ``` 7. **Dataprep Microservice(Optional) ** If you want to update the default knowledge base, you can use the following commands: Update Knowledge Base via Local File [nke-10k-2023.pdf](https://github.com/opea-project/GenAIComps/blob/v1.1/comps/retrievers/redis/data/nke-10k-2023.pdf). Or click [here](https://raw.githubusercontent.com/opea-project/GenAIComps/v1.1/comps/retrievers/redis/data/nke-10k-2023.pdf) to download the file via any web browser. Or run this command to get the file on a terminal. ```bash wget https://raw.githubusercontent.com/opea-project/GenAIComps/v1.1/comps/retrievers/redis/data/nke-10k-2023.pdf ``` Upload: ```bash curl -X POST "http://${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" ``` 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: The `file_path` here should be the `id` get from `/v1/dataprep/get` API. ```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://${host_ip}:6007/v1/dataprep/delete" \ -d '{"file_path": "all"}' \ -H "Content-Type: application/json" ``` ### Profile Microservices To further analyze MicroService Performance, users could follow the instructions to profile MicroServices. #### 1. vLLM backend Service Users could follow previous section to testing vLLM microservice or ChatQnA MegaService. By default, vLLM profiling is not enabled. Users could start and stop profiling by following commands. ##### Start vLLM profiling ```bash curl http://${host_ip}:9009/start_profile \ -H "Content-Type: application/json" \ -d '{"model": "meta-llama/Meta-Llama-3-8B-Instruct"}' ``` 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 profiling 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": "meta-llama/Meta-Llama-3-8B-Instruct"}' ``` 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. ![image](https://github.com/user-attachments/assets/55c7097e-5574-41dc-97a7-5e87c31bc286) ## Conclusion This guide should enable developer to deploy the default configuration or any of the other compose yaml files for different configurations. It also highlights the configurable parameters that can be set before deployment.