rag-postgres-openai-python

Introduction to RAG on PostgreSQL

RAG on PostgreSQL is an innovative project designed to integrate advanced AI capabilities with a PostgreSQL database through a web-based chat application. This project utilizes OpenAI's sophisticated chat models to provide users with intelligent responses to inquiries about database contents. Leveraging a blend of technologies, the project aims to facilitate seamless deployment and operation on the Azure platform.

Project Overview

The application consists of a React and FluentUI-based frontend coupled with a Python and FastAPI backend. It's not just an ordinary chat app; it allows users to interact with and query a PostgreSQL database in natural language, which is made possible by employing OpenAI models for understanding and generating responses.

Key Features

1. Hybrid Search Capabilities: The project introduces a hybrid search feature that uses the pgvector extension for vector searches and full-text search capabilities within the PostgreSQL database. It combines these results through Reciprocal Rank Fusion (RRF) for more accurate and comprehensive search results.

2. OpenAI Integration: OpenAI's functions are used to translate user queries into filter conditions necessary for database interrogation. For instance, a simple user query like "Climbing gear cheaper than $30?" is converted into a precise database search condition, such as "WHERE price < 30".

3. Vector Conversion: User queries are transformed into vectors using the OpenAI Embedding API, enhancing the application's ability to understand and respond to complex queries.

Deployment Architecture

The application architecture is optimized for Azure, utilizing Azure services extensively. Key components include:

• Azure Container Apps for hosting the web application.
• Azure PostgreSQL Flexible Server for database management.
• Azure OpenAI for deploying chat models.
• A user-assigned managed identity for authentication across Azure services, ensuring robust security connections.

Getting Started

For users eager to get started, RAG on PostgreSQL provides various environments and tools:

• GitHub Codespaces: Provides a quick start by setting up the development environment with a single click.
• VS Code Dev Containers: Allows users to work locally by setting up the necessary tooling in a Docker environment.
• Local Development: Supports full setup on a local machine with the required software and dependencies.

Deployment Process

Deployment to Azure involves:

1. Using the Azure Developer CLI (azd) for environment setup and authentication.
2. Provisioning resources and deploying code with the command azd up.
3. Selecting appropriate Azure regions for the resources and models to ensure service availability.

Cost Considerations

Costs are dependent on the specific Azure region and resource usage. Users can leverage the Azure pricing calculator for precise estimates based on services like Azure Container Apps, Azure OpenAI, and Azure PostgreSQL Flexible Server.

Security Measures

RAG on PostgreSQL employs Managed Identity for secure authentication within Azure services. Additionally, a GitHub Action is present to scan the codebase for security issues, ensuring best practices are maintained continuously.

Additional Guidance and Resources

The project documentation provides a comprehensive guide to customizing data, monitoring with Azure, and more. For further exploration, video resources and related GitHub repositories are available for deeper insights.

Overall, RAG on PostgreSQL offers a cutting-edge solution for integrating AI with database management, making it a valuable tool for businesses seeking intelligent data interaction capabilities.