Build a Private Mistral Codebook Generator

This guide provides a framework for creating a private qualitative research codebook workflow using Mistral Small 3.1, Ollama, and FastAPI.

The workflow begins by receiving a clearly defined research corpus, such as interview excerpts or survey comments. The text data is then normalized and quality-checked, while maintaining an auditable connection to the original source rows.

Next, the excerpts are grouped together using a method selected by the research team. A local AI model is then employed to generate a proposal for a codebook entry based on the text within each group. This proposed code is evidence-bounded, meaning it should only reference information contained within the group of excerpts.

However, the generated code must still undergo human review. The researcher has the ability to approve, edit, merge, split, reject, or document the final codes. This human validation step ensures that the codebook remains an analytical tool rather than a definitive finding.

The guide emphasizes that running models locally on a GPU-powered machine can provide greater control over sensitive text data. However, it also stresses that privacy depends on the entire system, including endpoint access, system controls, encrypted storage, backups, user permissions, logging, retention, incident response, and data governance.

Before implementing this workflow, organizations should verify several key points. These include confirming the exact model name and distribution terms, installation procedures, operational interfaces, capacities, and any associated license requirements. They should also review the operational interface and any default settings, such as context window limits, embedding models, vector databases, clustering algorithms, and quality benchmarks for qualitative coding.