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@@ -4,7 +4,26 @@ Chatbots are the most widely adopted use case for leveraging the powerful chat a
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RAG bridges the knowledge gap by dynamically fetching relevant information from external sources, ensuring that responses generated remain factual and current. The core of this architecture are vector databases, which are instrumental in enabling efficient and semantic retrieval of information. These databases store data as vectors, allowing RAG to swiftly access the most pertinent documents or data points based on semantic similarity.
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## Deploy ChatQnA Service
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## 🤖 Automated Terraform Deployment using Intel® Optimized Cloud Modules for **Terraform**
| AWS | 4th Gen Intel Xeon with Intel AMX |[AWS Module](https://github.com/intel/terraform-intel-aws-vm/tree/main/examples/gen-ai-xeon-opea-chatqna)| Uses Intel/neural-chat-7b-v3-3 by default |
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| AWS Falcon2-11B | 4th Gen Intel Xeon with Intel AMX |[AWS Module with Falcon11B](https://github.com/intel/terraform-intel-aws-vm/tree/main/examples/gen-ai-xeon-opea-chatqna-falcon11B)| Uses TII Falcon2-11B LLM Model |
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| GCP | 5th Gen Intel Xeon with Intel AMX |[GCP Module](https://github.com/intel/terraform-intel-gcp-vm/tree/main/examples/gen-ai-xeon-opea-chatqna)| Also supports Confidential AI by using Intel® TDX with 4th Gen Xeon |
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| Azure | 5th Gen Intel Xeon with Intel AMX | Work-in-progress | Work-in-progress |
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| Intel Tiber AI Cloud | 5th Gen Intel Xeon with Intel AMX | Work-in-progress | Work-in-progress |
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## Automated Deployment to Ubuntu based system(if not using Terraform) using Intel® Optimized Cloud Modules for **Ansible**
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To deploy to existing Xeon Ubuntu based system, use our Intel Optimized Cloud Modules for Ansible. This is the same Ansible playbook used by Terraform.
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Use this if you are not using Terraform and have provisioned your system with another tool or manually including bare metal.
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| Operating System | Intel Optimized Cloud Module for Ansible |
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