Build a Fully Local RAG App With PostgreSQL, Mistral, and Ollama

In this tutorial, we built a fully local Retrieval-augmented Generation (RAG) application using PostgreSQL, Mistral, and Ollama to ensure data privacy and security. RAG combines information retrieval with text generation to mitigate hallucination in large language models (LLMs). We used PostgreSQL as a vector storage house and Ollama to host a local model like Mistral. The architecture includes document collection, data indexing, query processing, embedding model execution, vector database search, top result retrieval, generation model execution, and final response generation. This approach ensures that all data is processed locally, leveraging both embedding and generation models to provide accurate and relevant responses to user queries while maintaining privacy and security for confidential information.