{"repo":"build-on-aws/rag-postgresql-agent-bedrock","free":true,"listed":false,"github":"https://github.com/build-on-aws/rag-postgresql-agent-bedrock","clone":"git clone https://github.com/build-on-aws/rag-postgresql-agent-bedrock.git","description":"This application is built in four stages using infrastructure as code with CDK with Python to deploy. In the first stage, an Amazon Aurora PostgreSQL vector database is set up. In the second stage, the Knowledge Base for Amazon Bedrock is created using the established database. The third stage involves creating an Amazon","language":"Python","stars":36,"topics":["aurora","bedrock","dynamodb","lambda","postgresql","python3","whatsapp","embeddings","llm","cdk"],"license":"MIT-0","category":"databases-storage","readme_excerpt":"WhatsApp-Powered RAG Travel Support Agent: Elevating Customer Experience with PostgreSQL Knowledge Retrieval In our previous blog post, \"Elevating Customer Support With a Whatsapp Assistant,\" we explored how advanced technologies like Generative AI and Retrieval Augmented Generation (RAG) can revolutionize traditional customer support models in the travel industry. Today, we'd like to present an alternative approach that leverages the power of Agents for Amazon Bedrock, a vectorized Amazon Aurora a PostgreSQL knowledge base for Amazon Bedrock. This architecture eliminates the need for complex conversation management logic, as Bedrock agents handle session tracking, while the Knowledge Base for Amazon Bedrock using Aurora PostgreSQL ensures highly accurate and contextual responses, and Amazon DynamoDB serves a dual purpose: storing both passenger information and support tickets. Key features of our solution include: 1. Intelligent query handling using RAG technique. 2. Personalized support based on individual traveler data. 3. Automatic creation of support tickets for unresolved issues. 4. Ability to query and manage the support ticket database. This application is built in four stages using infrastructure as code with AWS Cloud Development Kit (CDK) for python. In the first stage, an Amazon Aurora PostgreSQL vector database is set up. In the second stage, the Knowledge Base for Amazon Bedrock is created using the established database. The third stage involves creating an Amaz","default_branch":null,"files":null,"tree":[],"storefront":"/r/build-on-aws","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/build-on-aws/rag-postgresql-agent-bedrock/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}