verifiable-rag 0.5.1

Document-grounded Q&A with sentence-level citations and faithfulness verification

verifiable-rag

Document-grounded Q&A with sentence-level citations, NLI verification, and calibrated refusal.

Status: pre-alpha · v0.5 launch sprint · interfaces are still subject to change

📚 Full documentation at firish.github.io/rag-rack — quickstart, concept guides, how-to recipes, API reference, benchmark reports.

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What this is

A Python library for building RAG pipelines that:

1. Produce sentence-level citations — every generated sentence traces back to exact source spans (doc_id, page, char_start, char_end).
2. Verify every claim via NLI against its cited span before returning it.
3. Refuse when uncertain — calibrated abstention with a user-tunable strictness slider, not a "say I don't know" prompt.
4. Are fully auditable — inspect retrieval scores, reranker decisions, per-claim NLI results, and a self-contained HTML report per query.

One benchmark result that drives the design: on RAGTruth (the canonical 2,700-example RAG hallucination benchmark), a dual NLI ensemble of two small open-source models (HHEM-2.1-open + MiniCheck-Flan-T5-Large) matches Claude Sonnet 4.6 as a judge — AUROC 0.844 vs 0.846 — at ~250× lower per-call cost. Full result in benchmarks/PUBLISHED_ragtruth.md.

Quickstart

The bundled demo document ships with the package. No setup required beyond an LLM API key:

import verifiable_rag
from verifiable_rag.demo import sample_paper_path

answer = verifiable_rag.ask(
    "What is the mechanism of action of penicillin?",
    docs=sample_paper_path(),
)
print(answer.text)

export ANTHROPIC_API_KEY=...
python -c "import verifiable_rag; from verifiable_rag.demo import sample_paper_path; \
           print(verifiable_rag.ask('Who discovered penicillin?', docs=sample_paper_path()).text)"

For an actual production setup, point docs= at your own PDFs and pick a preset:

import verifiable_rag

answer = verifiable_rag.ask(
    "What did the authors find?",
    docs=["paper1.pdf", "paper2.pdf"],
    preset="hybrid_balanced",                  # RECOMMENDED — Cohere + Dual NLI + Haiku
    output_html="audit.html",                   # optional — write the HTML audit report
)
print(answer.text)

# Programmatic access to the audit trail:
for sentence in answer.unsupported_sentences:  # sentences the verifier flagged
    print(f"⚠ unsupported: {sentence.text}")

# Or emit a structured audit dump for logging / metrics:
metrics_client.emit(answer.audit_trail())

See examples/ for runnable demos covering the headline UX patterns. The full quickstart walks through each step in detail.

Presets

Five named presets cover most use cases. Switch via preset="..." or call the factories directly:

Preset	Components	Required keys	When to use
local_minimal	BGE + PyMuPDF + Haiku, no verifier	ANTHROPIC_API_KEY	Hobbyist / quickest start
local_verified	+ BGE rerank + HHEM NLI	ANTHROPIC_API_KEY	Local with verification
hybrid_balanced	Docling + Cohere + Dual NLI + constrained Haiku	ANTHROPIC_API_KEY + COHERE_API_KEY	Default — the published baseline
hybrid_strict	Same as balanced, refuse below faithfulness 0.7	same	Higher-trust use cases
hybrid_paranoid	Sonnet generator, refuse below faithfulness 0.9	same	Compliance / high-trust

For mix-and-match outside the presets, use verifiable_rag.build_pipeline(...) or load a YAML config (see examples/pipeline.yaml, Pipeline.from_yaml(), and the YAML config guide).

Architecture

PDF/DOCX → Parser → Document model → Chunker → Indexer
                                                  ↓
Answer ← Abstention ← Verifier ← Generator ← Retriever + Reranker

Every step preserves character-level spans. Every generated sentence carries (supporting_sentence_ids, confidence) linked to exact source locations. Citation granularity is decoupled from chunk granularity by design.

Audit trail

Every Answer exposes its full audit trail:

answer = verifiable_rag.ask(question, docs=...)

answer.text                      # final answer string
answer.sentences                 # list of CitedSentence with supporting_sentence_ids
answer.verification_results      # per-sentence NLI checks
answer.retrieved_chunks          # the reranked passages the generator saw

# Convenience accessors:
answer.supported_sentences       # list[CitedSentence] (passed verification)
answer.unsupported_sentences     # list[CitedSentence] (verifier flagged)
answer.verification_for(idx)     # VerificationResult | None for a sentence index
answer.cited_sentence_ids        # frozenset of all source IDs cited
answer.min_nli_score             # worst-case sentence — the bottleneck
answer.audit_trail()             # JSON-serializable dict for logging / metrics

# Or render the full audit as a self-contained HTML page:
answer.to_html()                  # returns HTML string
# or pass output_html="report.html" to verifiable_rag.ask()

The HTML report includes the query, the answer with per-sentence verification color coding, the faithfulness components, per-sentence NLI scores, and every reranked passage with its retrieval score — citations are anchored links into the passage list.

Installation

pip install verifiable-rag                          # core, no heavy deps
pip install "verifiable-rag[docling,bge,lancedb]"   # parser + embedder + index
pip install "verifiable-rag[hhem,minicheck]"        # NLI verifiers (adds torch + transformers)
pip install "verifiable-rag[litellm]"               # LLM-judge verifier
pip install "verifiable-rag[yaml]"                  # YAML config loader
pip install "verifiable-rag[all]"                   # everything

First-run model downloads

Verifier model weights are not bundled in the wheel — they're downloaded lazily from HuggingFace Hub on first use and cached forever in ~/.cache/huggingface/hub/.

Verifier	Model	Size
HHEMVerifier	vectara/hallucination_evaluation_model	~600 MB
MiniCheckVerifier	lytang/MiniCheck-Flan-T5-Large	~770 MB
LLMJudgeVerifier	(hosted API, no local model)	0

Published benchmark results

Benchmark	Headline	Report	Blog post
ALCE (Princeton citation quality)	Constrained decoding beats prompted by +4–7 F1 under dual-LLM-judge cross-validation	report	post
RAGTruth (hallucination detection)	Dual NLI ensemble = Sonnet judge at 1/250× the cost (AUROC 0.844 vs 0.846)	report	post
LitQA2 (biomedical scientific Q&A)	Constrained decoding lifts MC; contextual retrieval is a null result on saturated retrieval	report	post

Roadmap

Phase	Milestone	Status
0–1	Repo skeleton, data model, baseline pipeline	✅ done
2	Eval harness + BENCHMARKS.md	✅ done
3	Sentence-level citations (prompted / constrained / SAFE)	✅ done
4	Faithfulness verification + calibrated refusal (v0.4)	✅ done
5	Hardening, mkdocs docs, Gradio demo on HF Spaces (v0.5)	in progress
6	Launch — PyPI release + Show HN	pending

Contributing

See CLAUDE.md for architecture decisions, hard rules, and contribution conventions. Methodology critiques on the published benchmarks are especially welcome — eval rigor is the whole moat, and the only way to find the holes is to invite people to look for them.

License

MIT