From 729fcdf7da8602e9e5a9eb83f5b0050688010f61 Mon Sep 17 00:00:00 2001 From: "Wang, Xigui" Date: Fri, 29 Nov 2024 09:43:14 +0800 Subject: [PATCH] Add build_chatbot_blog --- getting-started/assets/UI.png | Bin 0 -> 30957 bytes getting-started/assets/chatqna-flow.png | Bin 0 -> 199552 bytes getting-started/assets/cpu-amx.png | Bin 0 -> 31739 bytes getting-started/assets/cpu-model.png | Bin 0 -> 57880 bytes getting-started/assets/framework.png | Bin 0 -> 64719 bytes getting-started/assets/kube-pod.png | Bin 0 -> 34799 bytes getting-started/assets/kube_service.png | Bin 0 -> 44681 bytes getting-started/build_chatbot_blog.md | 261 ++++++++++++++++++++++++ 8 files changed, 261 insertions(+) create mode 100644 getting-started/assets/UI.png create mode 100644 getting-started/assets/chatqna-flow.png create mode 100644 getting-started/assets/cpu-amx.png create mode 100644 getting-started/assets/cpu-model.png create mode 100644 getting-started/assets/framework.png create mode 100644 getting-started/assets/kube-pod.png create mode 100644 getting-started/assets/kube_service.png create mode 100644 getting-started/build_chatbot_blog.md diff --git a/getting-started/assets/UI.png b/getting-started/assets/UI.png new file mode 100644 index 0000000000000000000000000000000000000000..470b21c3c7f58116ed23cb19fc4dda152365b9cd GIT binary patch literal 30957 zcmeHwcT|(v*DfN8f`W=zXu*nf6$NPl2NjX3NDoD+(n7BZfnk6_lvwBp0TGbiq(eX% zL}^iwP6z^_gc3+-0g`+#{$@1J=UVsvao1h8T_geEk``7S)9`wg&fOh=+{C~dC zKBxzl4eLo`oavvr&w;4BfBB||!K4z={M~2%+Zu#bRVmYQzZecy)n|)AYTvtj?pKRw zRd4v!Fk8~eG?+6--qpRo*sdOUWB)IPg+CqMfw$H--hb=g_5pSkO!^G{#V|`q)}C{q z)+24 zVt7aoT+#K}Le)uGS+=yS$k#=EvvgH+Ft_tTo!YhC#g#P5MS_&C& zSPYD4OR=U#sZ`+~FI9HF=MvWqZfpnkkqCz=^s(~b_@^maFQF1^X5P|<%b=9hk(1{@ zA~)Cr)s6%$kveO`-%EdK*e?>%`$aH(jNe6$3$p@krZIZ#0SPy3ul0(HHC2(GL;m0d z4LYEdE4+iXjPo4GUhY}KsgOuDs-=UjazWu$+~tP0mXDEYf$ST-J`(kIy|ubJ`*3wi zc3-Q9ZBwYymch@3MSOfNT{%(>F+;ApN7FP)_!%7LNM%fPZM+Mrv2|vM*$~xX*F%ZJh)N3F50l zXt&@EM4+8p)5+bZ>QR4+3u*Fc_7PJP+SFn&y89sdgZ`~JmswOb3w(N*qo1FFIyiMl zKf;NIzENf%)N90t_BNFOdLMeR$zx8gaP#v!v+UzM2}OW`DFHU7juz@0MGEmW7jPWX z{b(>MkZdeLk6WEfX+jen5LlUJ9zmO-nsoUW+L~0J`y~wL_oBA33sZ0C%u8?1R!0r5 zxn_h!Ys1#XQ79b%ThnFI)Hk=gHre)E+j|a_QT0c%u$z}v)?~^2v(?y?S?J8XByHeR z^-I#}xG)Q_4i~iQ(&rHa7l~@;>r>%lxqZHc`2p)%23d=x^SL2ikEN5{oDZo*V;hvJ z$ffv< z6@gN;;_eZ#_}FcvtN2rA<&^!#`u~bJfr~w%Dr}%iNoXDGkU!+0*H~T-J@!zn|b|jEA?%rMDJaL|AELQ8&5!Y^FDW*mN-> zKuBs?6{?+XaIREpRxlFfm_c$UHBR#G7kXJgk%U(B*2YbQpgYCmzN|j*9?~qFK7Q-2 za|wQY;?rQwRnL36QDE?=r=*@+^$F0sV4X+K76Vofj`}XwD|zA+yEd05EL1F%w0J`E zo)M=!1Qlo72G&mz_!-b};0iRv$P&>2Ikj+1;W&6?wgm&0g>AfNkCwJv%OLGJYAzqp zjPmF3sd{-jOo$H#%vkz-i zHXow9sK-yWwbX2i91${udrlumV^WWy8!Ff0-l0~E`pj?9mhyUa;dl4#i=ON*eAH>aa&wXLyXeY&*XHQ1 zzHYFj^xCtwJmP(mfZBCmv!3#y9+kL{>4Tg4XP=2S&yqGZ3!GQ3Snx1}ZYv!&pM5>D z$Q0lD0SAnTPLSWj}SEh2+R-y+h|`mJdMR9&ybB_fZoo^%>4Uv8=M#hyppkk~mi zhgoKnzqP%d*nX!QkS=$6I%)r&7L+l{gV%?2e`+a3Sy?f>g~wjaaPs2PUr9+aES^pg zj-{e9IZk)=FIGeFkaw+I{B;U<5T*Bc%>yX?z36+zTULpY#Fb2C+1h&OC0pbQ@=f6b z=avOai}BRF7d%N9(<&KAR``=rNZ-g-+Qy5P@-?BruRTe0*Sxu{g`zt1eTb#cmEuHM zKW`w`;-BLD*D`jCLV-Pq1AE{tQC4Rajh{8V?C|vY%>r`Vh+&}M(g#WJ>@$-J>}@L( zbBcmF^+w~Rq}aZ0OPxfGN&I-b*8{vY6)P#?+sB(zJ)DGHq2JP}Znklez>ATVg*<(% z_A>C@o);)p@9XR9BgYq9Pz|0_US#9^!*U4kINDMq5p5;~is0H>-pZO8Vl%x7u)=Tb zfkBeqle;B73Pocr3$2BhR0%l;XN6uoYuh!P=H_w#{{5{_BkAcW^+r8P%koBnIUcQA zldoWPZ-(Dw#UKrk8KO-(Tm?DGmL-J2c@GKv*TI!FyuhrPp`xKJ7@uCpaN@zL&v%W$ zHl3?MTWvHRnYsDSkZa^)T`Fkuf;#?f<$QpB_*?k3VAXOUpD=Bjl|Ot_5>zB~4qJWR z^{i+tPGnPmK{27U`wa;QLpJu7#KGuQTg}-8vT=850V;^#`2Y=^usBFWXg2@&nfNw? 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ChatQnA Dataflow + + +### Prerequisites + +**Hardware** + +* CPU: the 4th (and later) Gen Intel Xeon with Intel (Advacned Matrix Extension) AMX +* Minimum Memeor Size: 64G +* Storage: 100GB disk space + +The recommended configuration are Amazon EC2 c7i.8xlarge, c7i.16xlarg instance type. These instance are Intel Xeon with AMX, to leverage 4th Generation (and later) Intel Xeon Scalable processors that are optimized for demanding workloads. + +**Software** + +* OS Ubuntu 22.04 LTS + +**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 | + +### Deploy by Docker on AWS EC2 Instance + +Here is the steps to deployment chatQnA by docker + +1. Download code and set up the environment variables. +2. Run docker compose. +3. Consume the ChatQnA service. + +#### 1. Download Code and Setup Environment Variable + +Follow these steps to download code set up environment variables: + +``` +git clone https://github.com/opea-project/GenAIExamples.git +``` + +Set the required environment variables: +``` +cd GenAIExamples/ChatQnA/docker_compose/intel/cpu/xeon +export HUGGINGFACEHUB_API_TOKEN="Your_Huggingface_API_Token" +source set_env.sh +``` + +#### 2. Start Docker Container + +``` +docker compose up -d +``` +It auto matically download following docker images from docker hub and start up docker container. +|Image name | tag | +|-----------|---------------------------| +| redis/redis-stack |7.2.0-v9 | +| opea/dataprep-redis | latest| +| ghcr.io/huggingface/text-embeddings-inference |cpu-1.5| +| opea/retriever-redis | latest | +| ghcr.io/huggingface/text-embeddings-inference |cpu-1.5| +| ghcr.io/huggingface/text-generation-inference |sha-e4201f4-intel-cpu| +| opea/chatqna | latest | +| opea/chatqna-ui | latest | +| opea/nginx | latest | + +#### Check Docker Container Status + +run this command to check docker container status +`docker ps -a` + +Make sure all the docker container status are `UP` as following: + +``` +CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES +ef155b97ef13 opea/nginx:latest "/docker-entrypoint.…" 3 minutes ago Up 3 minutes 0.0.0.0:80->80/tcp, :::80->80/tcp chatqna-xeon-nginx-server +79173ee7a359 opea/chatqna-ui:latest "docker-entrypoint.s…" 3 minutes ago Up 3 minutes 0.0.0.0:5173->5173/tcp, :::5173->5173/tcp chatqna-xeon-ui-server +bdb99b1263cd opea/chatqna:latest "python chatqna.py" 3 minutes ago Up 3 minutes 0.0.0.0:8888->8888/tcp, :::8888->8888/tcp chatqna-xeon-backend-server +7e5c3f8c2bba opea/retriever-redis:latest "python retriever_re…" 3 minutes ago Up 3 minutes 0.0.0.0:7000->7000/tcp, :::7000->7000/tcp retriever-redis-server +7e8254869ee4 opea/dataprep-redis:latest "python prepare_doc_…" 3 minutes ago Up 3 minutes 0.0.0.0:6007->6007/tcp, :::6007->6007/tcp dataprep-redis-server +135e0e180ce5 ghcr.io/huggingface/text-generation-inference:2.4.0-intel-cpu "text-generation-lau…" 3 minutes ago Up 41 seconds 0.0.0.0:9009->80/tcp, [::]:9009->80/tcp tgi-service +ffefc6d4ada2 ghcr.io/huggingface/text-embeddings-inference:cpu-1.5 "text-embeddings-rou…" 3 minutes ago Up 3 minutes 0.0.0.0:6006->80/tcp, [::]:6006->80/tcp tei-embedding-server +17b22a057002 ghcr.io/huggingface/text-embeddings-inference:cpu-1.5 "text-embeddings-rou…" 3 minutes ago Up 3 minutes 0.0.0.0:8808->80/tcp, [::]:8808->80/tcp tei-reranking-server +cf91b1a4f5d2 redis/redis-stack:7.2.0-v9 "/entrypoint.sh" 3 minutes ago Up 3 minutes 0.0.0.0:6379->6379/tcp, :::6379->6379/tcp, 0.0.0.0:8001->8001/tcp, :::8001->8001/tcp redis-vector-db + +``` + +#### Check TGI Service Is Ready + +It takes minutes for TGI service to download LLM models and do warm up inference. + +Check the TGI service log to make sure it is ready. + +Run this command to check the log: +`docker logs tgi-service | grep Connected` + +The following log indicates tgi-sercie is ready. +``` +2024-09-03T02:47:53.402023Z INFO text_generation_router::server: router/src/server.rs:2311: Connected +``` + +#### Consume the ChatQnA Service + + Please consume ChatQnA service until **tgi-service is ready** + +Open the following URL in your browser: + +``` +http://{Public-IPv4-address}:80 +``` +Make sure to acces AWS throught Public-IPv4-address. + +video/or gif + +### Deploy by Kubernets on AWS EC2 Instance + +Assumed you set up the kubernets on EC2 instance. Please refer to [k8s_install_kubespray](https://github.com/opea-project/docs/blob/main/guide/installation/k8s_install/k8s_install_kubespray.md) to set up Kubernetes. + +Here is the steps to Deployment ChatQnA by Kubernetes: + +1. Download code and set up the environment variables. +2. Start kubernetes Services +3. Consume the ChatQnA service. + +#### 1. Download Code and Setup Environment Variable + +Follow these steps to download code set up environment variables: + +(Skip this if have downloaded the code) +``` +git clone https://github.com/opea-project/GenAIExamples.git +``` + +Set the required environment variables: +``` +cd GenAIExamples/ChatQnA/kubernetes/intel/cpu/xeon/manifest +export HUGGINGFACEHUB_API_TOKEN="YourOwnToken" +sed -i "s|insert-your-huggingface-token-here|${HUGGINGFACEHUB_API_TOKEN}|g" chatqna.yaml +``` +#### 2. Start Kubernetes Services + +``` +kubectl apply -f chatqna.yaml +``` +##### Check Kubernetes Status + +1. Check services status to get the port number to access the ChatQnA: +``` +kubectl get services +``` + +![kubernetes sercies](assets/kube_service.png) +Figure 2. Kubernets Service +Here the nginx nodeport is **31146**. + +2. Check pod status +``` +kubectl get pods +``` +Make sure all pods are ready in state +![kubernetes pods](assets/kube_pod.png) +Figure 3. Kubernets Pod Status + +#### Consume the ChatQnA Service + +Open the following URL in your browser. +``` +http://{Public-IPv4-address}:31146 +``` + +Here the port number 31146 is from Kubernets service in Figure 2. +``` +chatqna-nginx NodePort 10.233.7.44 80:31146/TCP 24m +``` +Refer to [consume service section](#Consume-the-ChatQnA-Service) for details. + +![chatbot_UI](assets/UI.png) +

+Chatbot UI Examples +
+ + +In Figure 1, [GenAIExampls](https://github.com/opea-project/GenAIExamples), the end to end applications, are implemented as MegaService instance. And a Gateway serves as the interface for users to access the Megaservice. + +MegaService is a higher-level architectural construct composed of one or more Microservices. Microservice is designed to perform a specific function or task within the application architecture. Microservices are akin to building blocks, offering the fundamental services for constructing AI applications. + +[GenAIComps](https://github.com/opea-project/GenAIComps) provides a suite of microservices, leveraging a service composer to assemble a mega-service tailored for real-world Enterprise AI applications. All the microservices are containerized, allowing cloud native deployment. + +GenAICompos micro-service covered Embedding, Retriver, Reranking, Large Language Modle (LLM) Data-prepration,Text2Image, Image2Video, Agent, intent detection, texttosql, Text2Speech(TTS), Automatic Speech Recognition (ASR) etc. +You can find all the [comps](https://github.com/opea-project/GenAIComps/tree/main/comps) here. +~~supports Intel Xeon Data Center CPU, Gauid Accelatator, Intel Arc GPU, AIPC, Nivdia GPU and AMD RoCm GPU.~~