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GenAIExample Dataflow + + +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.~~ + +## Build Your ChatBot with OPEA +A Retrieval-Augmented Generation (RAG) chatbot (Figure 2) integrates the power of retrieval systems to fetch relevant, domain-specific knowledge with generative AI to produce human-like responses. + +![chatbot_dataflow](assets/chatqna-flow.png) +
+Chatbot Dataflow +
+ +RAG chatbots can address various use cases by providing highly accurate and context-aware interactions. RAG Chatbot +used in customer support and service, internal knowledge management, Finance and Accounting as well as technical support etc. + +### Prerequisites + +**Hardware** + +* 4th (and later) Gen Intel Xeon with Intel AMX + + We recommened Amazon EC2 M7i or M7i-flex instance type to leverage 4th Generation 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 | + +### Quick Start Deployment Steps: + +1. Download code and set up the environment variables. +2. Run docker compose. +3. Consume the ChatQnA service. + +#### Download Code and Setup Environment Variable + +To download code set up environment variables for deploying ChatQnA services, follow these steps: + +``` +git clone https://github.com/opea-project/GenAIExamples.git +cd GenAIExamples/ChatQnA/docker_compose/intel/cpu/xeon +``` + +Set the required environment variables: +``` +# Example: host_ip="10.1.1.1" +export host_ip="External_Public_IP" +export HUGGINGFACEHUB_API_TOKEN="Your_Huggingface_API_Token" + +source set_env.sh +``` + +#### Run Docker Compose + +``` +docker compose up -d +``` + +It will 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 TGI Service Is Ready + +It takes TGI service minutes to download LLM models and warm up inference. + +Check the TIG service log: +`docker logs tgi-service | grep Connected` + +**Consume ChatQnA service until you get the TGI response like below:** + +``` +2024-09-03T02:47:53.402023Z INFO text_generation_router::server: router/src/server.rs:2311: Connected +``` + +#### Consume the ChatQnA Service. + +Open the following URL in your browser: http://{host_ip}:80. + +![chatbot_UI](assets/UI.png) +
+Chatbot UI Examples +