flashqda 1.2.2

A package for using Large Language Models for qualitative data analysis.

FlashQDA

FlashQDA is a Python package for LLM-powered qualitative data analysis (QDA). It provides a structured, resumable pipeline of functions that take a corpus of text documents and progressively classify, label, extract, group, and link the concepts within them. It is designed for academic researchers, policy analysts, and social scientists working with interview transcripts, policy documents, or published literature.

Installation

pip install flashqda

Optional dependency groups:

pip install "flashqda[grouping]"        # semantic grouping (group_items)
pip install "flashqda[preprocessing]"   # PDF and DOCX support
pip install "flashqda[anthropic]"       # Anthropic Claude provider

What it does

Step	Function	Purpose
1	preprocess_documents	Segment .txt/.pdf/.docx files into sentences or paragraphs
2	classify_items	LLM binary/multiclass classification (e.g. causal / non-causal)
3	label_items	Apply user-defined filter tags at any stage
4	extract_from_classified	Structured extraction of relationships (e.g. cause/effect pairs)
5	refine_extracted	LLM validation and correction of extracted pairs
6	explode_extractions	Normalize from one row per source to one row per extracted item
7	embed_items	Generate and cache vector embeddings
8	group_items	AHC semantic clustering with LLM-generated category labels
9	link_items	Cosine-similarity linking of semantically related items

Built-in pipeline presets: "causal", "thematic", "tradeoff", "synergy".

Quick example

import flashqda

# Set up project
flashqda.initialize_project("/path/to/my_project")
project = flashqda.ProjectContext("/path/to/my_project")
config = flashqda.PipelineConfig.from_type("causal")

# Run the pipeline
flashqda.preprocess_documents(project=project, granularity="sentence")

classified = flashqda.classify_items(
    project=project, config=config,
    input_file=project.data / "sentence.csv"
)

extracted = flashqda.extract_from_classified(
    project=project, config=config, input_file=classified
)

exploded = flashqda.explode_extractions(project=project, config=config, input_file=extracted)

embedding_file = flashqda.embed_items(project=project, config=config, input_file=exploded)

flashqda.link_items(project=project, config=config,
                    input_file=exploded, embedding_file=embedding_file)

Supported providers

LLM: openai (default), anthropic, ollama (local), openai_compatible

Embeddings: openai, sentence_transformers, openai_compatible

Sensitive-data workflows are fully supported via local Ollama models — no API key required.

Documentation

Full walkthrough with all functions and examples: docs/QUICK_START.md

API reference: help(flashqda.classify_items) (all public functions have docstrings)

Citation

If you use FlashQDA in your research, please cite:

Kearney, N. (2026). flashQDA (v1.2.2). https://github.com/nmkearney/flashqda

Links

• GitHub: https://github.com/nmkearney/flashqda
• PyPI: https://pypi.org/project/flashqda
• Issues: https://github.com/nmkearney/flashqda/issues