RAGvidence
Diagnose and compare RAG retrieval techniques on your own data.
RAGvidence helps you decide which retrieval technique to use in your RAG project. Run experiments on local files, inspect what each technique retrieves, and compare the evidence without building every experiment yourself. You can also import results from your own retriever as a baseline.
The CLI is fully free and open source under Apache-2.0.
Install
Install uv, then run:
uv tool install --python 3.12 "ragvidence==0.2.0"
ragvi --version
ragvi verify
Python 3.12 is required; uv can install it for you. The command is available from any folder. See the installation guide for upgrades, Windows/macOS/Linux setup, optional PDF/model support and source builds.
Run on your own data
Ask a question about a file:
ragvi bm25 ./manual.txt --query "How do I reset the device?"
Run several questions against a folder and select your reports:
ragvi bm25 ./docs --questions ./questions.txt --format html --format json --format markdown
The questions file contains one question per nonblank line. Without question
flags, the terminal offers question entry; use --no-input for source-only
diagnostics without prompts. Your source files stay unchanged.
The CLI prints a summary and report paths. Open the HTML report in your browser;
it is self-contained and works offline. Reports go under ragvi-reports/ by
default. Use --output DIRECTORY to choose another location.
Supported inputs: UTF-8 TXT and Markdown, visible-text HTML, document JSON/JSONL,
CSV and text-bearing PDF with the pdf extra. Folders may mix these formats.
See input schemas and limits.
Compare techniques
ragvi techniques
ragvi compare-data ./docs --questions ./questions.txt --techniques bm25,parent-child
Comparisons use the same frozen inputs and questions. Inspect the returned passages, context size, timings and stage traces. Add reviewed references to measure whether the retrieved context contains the evidence each question needs:
ragvi compare-data ./docs --reviewed ./reviewed.json --techniques bm25,parent-child
| What you provide | What you learn |
|---|---|
| Sources | Extraction, structure and chunk diagnostics |
| Sources and questions | Which passages each technique retrieves |
| Sources and reviewed evidence spans | Evidence sufficiency, coverage, rank metrics, gains and regressions |
Questions alone do not establish relevance. Reviewed references are optional, but required for scored quality comparisons. Results apply to the data and questions you tested. RAGvidence helps you choose; it does not change your RAG system or judge generated answers and citations.
Choose your next experiment
BM25 runs offline without a model or API key. The catalog also includes dense search, hybrid RRF, cross-encoder reranking, parent-child retrieval, heading/context enrichment, multi-query retrieval, metadata filters, MMR, query rewriting and exact extractive compression. Splitting, overlap, embedding settings and context budgets can be compared through shared configuration.
- Getting started: a complete first experiment and how to read it.
- Techniques and reviewed evidence: requirements, references and your retriever's results.
- Comparisons and suites: configuration grids, heldout questions, intervals and resume.
- Advanced techniques: filters, diversity, rewriting and compression.
- Coverage and limits: supported experiments and missing techniques.
- Command reference: available commands; use
ragvi COMMAND --helpfor options.
Want to explore without preparing data? Run ragvi tutorial for a small fictional
reviewed comparison and HTML report.
Models and sharing
Optional FastEmbed embeddings/rerankers run locally after a first model download. User-owned Ollama models and OpenAI-compatible endpoints are supported for selected operations. You supply the model/server or API access. See provider setup.
Remote endpoints receive the text needed for the operation. Reports can contain full passages, questions and transformation prompts/outputs. Review them before sharing. See privacy and providers.
Contribute and get help
Report an issue, contribute, improve the docs or star the repository. For problems, start with troubleshooting and support.
See what's new in 0.2.0, security and third-party notices.
Copyright © 2026 Medo-ID. Third-party components and models retain their own licenses.
Metadata
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Source distribution (sdist)
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|---|---|---|---|---|
| ragvidence-0.2.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 672.1 kB
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