How to Build a Private AI Codebook Generator Using Mistral and FastAPI

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A new tutorial from Gate of AI outlines a privacy-focused qualitative research workflow using Mistral Small 3.1, Ollama, and FastAPI to help researchers generate thematic codebook entries from interview or survey data. The guide is designed for teams handling sensitive text who require greater control than cloud-based tools typically allow. Mistral Small 3.1 is a 24-billion-parameter model that also served as the parent for the Ministral 3 family through pruning and distillation. The tutorial is explicitly framed as a planning blueprint rather than a ready-to-run deployment, cautioning developers to verify model identifiers, API endpoints, and hardware requirements against official documentation before writing production code. It also stresses that while local AI can propose candidate codes and organize excerpts, it cannot independently validate research conclusions or establish causality.

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