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+# Build Your ChatBot with Open Platform for Enterprise AI
+
+## Generative AI: A Transformational Force for Enterprises
+
+Generative AI demonstrates immense potential in enhancing productivity and driving innovation across various industries. Its ability to address enterprise challenges by offering innovative and efficient solutions makes it a powerful tool for businesses seeking a competitive edge.
+
+Here are several ways in which generative AI can assist enterprises:
+
+* Data Analysis and Insights: By analyzing vast amounts of enterprise data, generative AI can uncover patterns, provide actionable insights, and support better decision-making processes.
+
+* Document Management: Generative AI streamlines the organization, summarization, and retrieval of documents, enhancing efficiency in knowledge management systems.
+
+* Customer Support and Chatbots: AI-driven chatbots can provide 24/7 customer service, respond to inquiries, and even handle complex issues by understanding user intents and offering personalized solutions.
+
+* Code Generation and Software Development: AI models can write code snippets, debug software, and even recommend solutions to programming challenges, accelerating the software development lifecycle.
+
+* Fraud Detection and Risk Management: By analyzing transaction patterns and detecting anomalies, generative AI helps enterprises identify and mitigate potential risks or fraudulent activities.
+
+* Employee Training and Development: AI-powered platforms can create personalized training programs, simulate real-world scenarios, and evaluate employee performance, enhancing skill-building initiatives.
+
+* Healthcare and Well-being: In enterprises with healthcare initiatives, generative AI can support mental health programs by generating therapeutic content or helping manage employee well-being through tailored recommendations.
+
+* Decision-Making Support: Beyond traditional analytics, generative AI can simulate scenarios, model potential outcomes, and offer strategic recommendations to assist leaders in making informed decisions.
+
+By leveraging generative AI in these areas, enterprises can not only solve existing problems but also unlock new opportunities for innovation and growth.
+
+In this blog, we introduce a powerful GenAI framework - Open Platform for Enterprise AI (OPEA) to help you build you GenAI Applications.
+
+First to explore the features and attributes of OPEA and then we show you how to build your ChatBot with OPEA step by step.
+
+## Open Platform for Enterprise AI
+
+Open Platform for Enterprise AI (OPEA) is an open platform project that lets you create open, multi-provider, robust, and composable GenAI solutions that harness the best innovations across the ecosystem.
+
+OPEA platform includes:
+
+* Detailed framework of composable building blocks for state-of-the-art generative AI systems including LLMs, data stores, and prompt engines
+* Architectural blueprints of retrieval-augmented generative AI component stack structure and end-to-end workflows
+* A four-step assessment for grading generative AI systems around performance, features, trustworthiness, and enterprise-grade readiness
+
+OPEA are desgined with following consideration:
+
+**Efficient**
+Infrastructure Utilization: Harnesses existing infrastructure, including AI accelerators or other hardware of your choosing.
+It supports a wide range of hardware, including Intel Xeon, Gaudi Accelerator, Intel Arc GPU, Nvidia GPU, and AMD RoCm.
+
+**Seamless**
+Enterprise Integration: Seamlessly integrates with enterprise software, providing heterogeneous support and stability across systems and networks.
+
+**Open**
+Innovation and Flexibility: Brings together best-of-breed innovations and is free from proprietary vendor lock-in, ensuring flexibility and adaptability.
+
+**Ubiquitous**
+Versatile Deployment: Runs everywhere through a flexible architecture designed for cloud, data center, edge, and PC environments.
+
+**Trusted**
+Security and Transparency: Features a secure, enterprise-ready pipeline with tools for responsibility, transparency, and traceability.
+
+**Scalable**
+Ecosystem and Growth: Access to a vibrant ecosystem of partners to help build and scale your solution.
+
+### OPEA Framework Components
+
+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.
+
+![GenAIExample Architecture](assets/framework.png)
+<div align="center">
+Figuire 1. GenAIExample Dataflow
+</div>
+
+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)
+<div align="center">
+Chatbot Dataflow
+</div>
+
+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)
+<div align="center">
+Chatbot UI Examples
+</div>