Developer builds LLM-powered QA assistant using RAG and PostgreSQL

A developer has created a system to answer quality assurance questions by integrating a large language model with a wiki, test management system, and database. The solution uses PostgreSQL with the pgvector extension to store and retrieve information, employing a retrieval-augmented generation (RAG) approach. This setup allows the LLM to answer complex QA queries by synthesizing information from these disparate sources, with the initial setup taking approximately 90 minutes. AI

IMPACT Enables more efficient QA by allowing LLMs to query structured and unstructured data sources.

RANK_REASON Developer-created tool integrating LLM with existing data sources.

Read on dev.to — LLM tag →

• Docker
• Docker Compose
• FastAPI
• Linux
• macOS
• Microsoft Windows
• pgvector
• PostgreSQL
• psycopg2-binary
• Python 3
• Requests
• retrieval-augmented generation
• uvicorn
• Windows Subsystem for Linux

• product
• infra

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