This article demonstrates how to build AI-powered applications using pgvector and Amazon Aurora PostgreSQL for natural language processing, chatbots, and sentiment analysis.

• Build a Retrieval Augmented Generation (RAG) chatbot using Amazon Bedrock and pgvector for accurate question answering
• Upload PDF documents and ask natural language questions to retrieve context-specific answers
• Use Streamlit framework to create an interactive web interface for the chatbot application
• Integrate Amazon Comprehend with Aurora PostgreSQL for sentiment analysis on text data
• Store vector embeddings in Aurora PostgreSQL using pgvector extension for efficient similarity searches
• Leverage LangChain for simplified LLM integration and conversational memory management
• Use Anthropic Claude and Amazon Titan models via Amazon Bedrock for text generation and embeddings
• Implement conversation history tracking to enable follow-up questions and context awareness

The solution combines pgvector, Aurora PostgreSQL, and generative AI models to enable enterprises to build intelligent applications for knowledge retrieval, customer service chatbots, and sentiment analysis without extensive machine learning expertise.