Praxis

A legal assistant for Russian law. It answers a question and cites the specific provisions the answer rests on — each citation checked separately by a model. When the law does not support a claim, it says so instead of producing plausible text.

docker compose up app     # → http://localhost:8077, no API keys required

Why

A lawyer working on Russian law has three options today, each awkward in its own way. General-purpose models invent: they cite articles that do not exist, confuse revisions, and are confidently wrong. Commercial reference systems search documents rather than answer, and are expensive. Search engines return forums, superseded revisions and SEO noise.

Nothing sits between them: no tool answers at the speed of a model with references you can open and check. That is the gap Praxis fills.

How it works

The question goes through an agent that plans the search. Retrieval is hybrid over the corpus of provisions — BM25 plus dense embeddings — a reranker selects the best, and provisions connected by cross-reference are pulled in through an article-to-article graph. The generator assembles an answer in which every claim is bound to a provision, and the Citation Verifier checks every binding.

Citation Verifier. A separate NLI model checks each reference for entailment: does the text of the provision actually support this specific claim? What is not confirmed is not presented as fact.

Agentic self-RAG. The agent decomposes a complex question into sub-queries, searches again and reformulates until it has enough grounding. The chain of reasoning is visible in the answer.

GraphRAG. Provisions reference each other ("in accordance with article 15"). That is a ready-made graph: a question about damages surfaces article 393, and the graph pulls in article 15 that it names.

Extractive by default. Without an LLM key Praxis does not compose text — it quotes the applicable provisions verbatim with references, and an answer like that cannot hallucinate. Synthesis through Claude is enabled by a key and passes the same per-sentence citation check.

Legal tools

On top of search, practical tasks that turn an answer into action. Everything is grounded in the law: documents quote the provisions found, and calculators and checklist items each carry a link to the article they rest on.

• Trust layer. Every provision has a "verify against the current revision" link (zakonrf.info) and the act's details (federal-law number and date). On low confidence it says "no direct answer was found" instead of a stretched answer.
• Pre-court claim and statement of claim (/v1/claim, /v1/lawsuit). The legal basis is assembled from the cited provisions; demands, jurisdiction and the court fee are statutory blocks, the facts are placeholders.
• Calculators with a link to the norm (/v1/penalty, /v1/fee, /v1/interest): consumer penalty (art. 23 / 28 ZoZPP), court fee (art. 333.19 / 333.36 of the Tax Code), interest under art. 395 of the Civil Code.
• Contract checklist (/v1/contract) — a transparent check of essential terms and risky clauses against the law: not "AI analysis" but explicit rules, each with a link.

The Consumer Protection Act (corpus/zozpp.json) was added to the corpus — the key statute for consumer disputes these tasks rely on. The calculators and the checklist run entirely in the browser: try them without installing at drobyshevdev.github.io/praxis/try.

Quality

The eval harness over the golden set (12 questions, praxis-eval):

Metric	Real models (RTX 4060)	Offline fallback
recall@5	1.00	1.00
MRR	1.00	0.90
hit-rate	1.00	1.00
mean confidence	0.88	0.63
citation precision	0.29	0.40

Real models: BGE-M3 for embeddings, bge-reranker-v2-m3 for reranking, rubert-NLI for citation checking, all on GPU. The needed provision always reaches the top of the results. Citation precision is understated because the golden set has one reference article per question while the system also returns adjacent relevant provisions.

On the full Civil Code corpus (4,717 provisions, an 18-question golden set) the real models hold recall@5 0.92, MRR 0.94, hit-rate 1.0 and confidence 0.80. The offline fallback drops to recall 0.64 at that size — on real data the real models are not optional.

Data

Codes and federal laws are published in machine-readable form at pravo.gov.ru, and the repository contains a parser for the official text (statute_parser). The full text of the Civil Code is already extracted (corpus/gk-rf.json — 1,712 articles, 4,717 provisions, all four parts) and is loaded through PRAXIS_CORPUS_DIR. The revision in force should always be checked against the official source.

There is no open structured corpus of Russian judicial practice comparable to the Caselaw Access Project; that is the next pipeline rather than something already shipped.

Next

• Try in the browser — calculators and the contract checklist, no install.
• Public API — the /v1 endpoints, the response format, the Python client.
• Development — running it locally and what CI checks.
• Comparison — where Praxis wins and where it does not.
• Architecture — layers and data flow.

Not legal advice

Praxis surfaces provisions and checks that a citation supports a claim. It does not assess your situation, does not account for procedural context, and does not replace a lawyer. Always verify the revision in force against the official source.