daveadmin
7e6463ed22
Restores the dbn-legal-agent-v3 fine-tune on ocelot (was silently aliased to plain qwen2.5:14b in LiteLLM since the viper retirement) and ships a new tool that uses it via a two-pass flow: Pass 1 (Azure 4o-mini) → extract up to 5 distinct legal issues Pass 2 (ocelot v3 only) → answer each issue, ≤350 tokens, with corpus Pass 3 (Azure 4o-mini) → synthesise overall assessment + next steps The 12GB-VRAM constraint motivates the split: dbn-legal-agent-v3 stays hot in VRAM through the 5 sequential per-issue calls because issue extraction and synthesis run on Azure, not on ocelot. New surface: - includes/LegalAnalysisAgent.php - api/legal-analysis.php (NDJSON streaming endpoint) - legal-analysis.php (dedicated tool page) - assets/js/legal-analysis.js (streamed UI with per-issue cards) - Save-result + case-result.php rendering for legal-analysis output - Nav registration in all four UI languages Add-on integration: a "⚖️🇳🇴 Run deep legal analysis on this text" button now appears on Summarize, Ask, and Redact result pages and streams the same pipeline inline below the existing result. Existing tools relabelled: the misleading "🇳🇴 Norwegian specialist v3 ⭐" option on advocate/deep-research/discrepancy/barnevernet is now honestly "DBN Legal Agent" — now that the real fine-tune is actually deployed, the label finally matches reality. The advocate.php v2 option was removed since the v2 GGUF is retired. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
310 lines
12 KiB
PHP
310 lines
12 KiB
PHP
<?php
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declare(strict_types=1);
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require_once __DIR__ . '/bootstrap.php';
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require_once __DIR__ . '/AzureOpenAiGateway.php';
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require_once __DIR__ . '/LegalTools.php';
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/**
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* Two-pass legal analysis:
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* 1. Extract distinct legal issues from a document (Azure GPT-4o-mini)
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* 2. For each issue: retrieve corpus passages, ask dbn-legal-agent-v3 a single
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* targeted question (cap 350 tokens to avoid the documented loop bug)
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* 3. Synthesise overall assessment + next steps (Azure GPT-4o-mini)
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*
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* Only step 2 touches the GPU. Steps 1 and 3 use Azure so dbn-legal-agent-v3
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* stays hot in the 12GB RTX 3060 VRAM across all per-issue calls.
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*/
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final class DbnLegalAnalysisAgent
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{
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private const MAX_ISSUES = 5;
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private const LEGAL_MAX_TOKENS = 350;
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private const LEGAL_TIMEOUT = 60;
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private const LEGAL_MODEL = 'dbn-legal-agent-v3';
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private DbnAzureOpenAiGateway $azureMini;
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private DbnLegalToolsService $legalSvc;
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public function __construct()
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{
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$this->azureMini = (new DbnAzureOpenAiGateway())->withDeployment('gpt-4o-mini');
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$this->legalSvc = new DbnLegalToolsService();
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}
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/**
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* Pass 1 — extract distinct legal issues. Azure-only.
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*
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* @return array<int,array{id:int,question:string,brief_context:string,doc_type:string,severity_hint:string}>
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*/
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public function extractIssues(string $text, string $language, string $docType): array
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{
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$locale = dbnToolsLanguageName($language);
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$text = mb_substr($text, 0, 24000, 'UTF-8'); // keep prompt within 4o-mini context
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$prompt = <<<PROMPT
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You analyse the document below and extract up to 5 DISTINCT legal issues that warrant
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expert Norwegian-law review (barnevernsloven, EMK/ECHR, Hague Convention, family law,
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process law). Each issue must be answerable as a SINGLE focused legal question
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(≤ 25 words), not a multi-part essay.
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Document type hint: {$docType}
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Document language: {$locale}
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Return JSON only:
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{
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"issues": [
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{
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"id": 1,
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"question": "<short Norwegian legal question, single issue>",
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"brief_context": "<≤2 sentences from the document that triggered this question>",
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"doc_type": "<barnevernet|adopsjon|emergency|samvær|other>",
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"severity_hint": "<high|medium|low>"
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}
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]
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}
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Rules:
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- Skip non-legal observations (logistics, social commentary, opinions).
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- Each question should be answerable with citations to barnevernsloven, EMK Art. X,
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named Høyesterett/EMD cases — NOT general advice.
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- If the document has fewer than 5 real legal issues, return fewer entries.
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- If NO real legal issue exists, return {"issues": []}.
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DOCUMENT:
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---
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{$text}
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---
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PROMPT;
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$raw = $this->azureMini->chatText(
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[
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['role' => 'system', 'content' => 'You return valid JSON only. No prose, no fences.'],
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['role' => 'user', 'content' => $prompt],
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],
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['json' => true, 'temperature' => 0.1, 'max_tokens' => 1500, 'timeout' => 90]
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);
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$decoded = $this->azureMini->decodeJsonObject($raw);
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$issues = is_array($decoded['issues'] ?? null) ? $decoded['issues'] : [];
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$clean = [];
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$id = 1;
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foreach ($issues as $issue) {
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$question = trim((string)($issue['question'] ?? ''));
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if ($question === '' || mb_strlen($question, 'UTF-8') < 10) {
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continue;
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}
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$clean[] = [
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'id' => $id++,
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'question' => mb_substr($question, 0, 280, 'UTF-8'),
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'brief_context' => mb_substr(trim((string)($issue['brief_context'] ?? '')), 0, 400, 'UTF-8'),
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'doc_type' => (string)($issue['doc_type'] ?? $docType),
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'severity_hint' => in_array($issue['severity_hint'] ?? '', ['high','medium','low'], true)
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? $issue['severity_hint']
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: 'medium',
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];
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if (count($clean) >= self::MAX_ISSUES) {
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break;
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}
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}
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return $clean;
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}
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/**
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* Pass 2 — single targeted question to dbn-legal-agent-v3 with corpus context.
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* Ocelot-only. Capped at 350 tokens / 60s to avoid the documented loop bug.
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*
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* @param array{id:int,question:string,brief_context:string,doc_type:string,severity_hint:string} $issue
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* @return array{id:int,question:string,answer:string,severity:string,legal_basis:string,citations_from_corpus:array,what_to_check:string,brief_context:string}
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*/
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public function answerIssue(array $issue, string $corpusContext, string $language): array
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{
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$sysMsg = 'Du er en ekspert på norsk barnevernsloven og EMD-praksis. '
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. 'Svar alltid på norsk med korrekt juridisk terminologi. '
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. 'Bruk terskler fra barnevernsloven 2021: § 4-25 krever «klar nødvendighet». '
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. 'Strand Lobben mot Norge (37283/13) setter krav om rehabiliteringsplan før adopsjon. '
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. 'Aldri oppfinn paragrafnumre, saksnumre eller dommernavn. '
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. 'Avslutt med en «Kilder:»-seksjon som lister lovparagrafer og dommer du har sitert.';
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$userMsg = $issue['question'];
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if ($issue['brief_context'] !== '') {
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$userMsg .= "\n\nKontekst fra saken: " . $issue['brief_context'];
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}
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if ($corpusContext !== '') {
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$userMsg .= "\n\nRelevante kilder fra Do Better Norge-korpuset:\n" . $corpusContext;
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}
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$answer = '';
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$error = null;
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try {
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$response = dbnToolsCallGpuLlm(
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[
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['role' => 'system', 'content' => $sysMsg],
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['role' => 'user', 'content' => $userMsg],
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],
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[
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'model' => self::LEGAL_MODEL,
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'temperature' => 0.1,
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'max_tokens' => self::LEGAL_MAX_TOKENS,
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'timeout' => self::LEGAL_TIMEOUT,
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]
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);
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$answer = trim((string)($response['choices'][0]['message']['content'] ?? ''));
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} catch (Throwable $e) {
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$error = $e->getMessage();
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}
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$clean = dbnToolsExtractCleanAnswer($answer);
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if (mb_strlen($clean, 'UTF-8') < 30) {
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$clean = $answer !== ''
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? $answer
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: ($error !== null ? "[Modellfeil: $error]" : '[Modellen returnerte ingen brukbar tekst.]');
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}
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$severity = $clean !== '' ? dbnToolsInferCheckSeverity($clean) : $issue['severity_hint'];
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$legalBasis = dbnToolsExtractCheckLegalBasis($clean);
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return [
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'id' => $issue['id'],
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'question' => $issue['question'],
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'brief_context' => $issue['brief_context'],
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'answer' => $clean,
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'severity' => $severity,
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'legal_basis' => $legalBasis,
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'citations_from_corpus' => [], // populated by orchestrator if it kept the chunks
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'what_to_check' => 'Verifiser med norsk familieretsadvokat før handling.',
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];
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}
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/**
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* Pass 3 — synthesise overall assessment. Azure-only.
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*/
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public function synthesise(array $issues, string $language, string $docType): array
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{
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$locale = dbnToolsLanguageName($language);
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$bullets = [];
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foreach ($issues as $i) {
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$bullets[] = sprintf(
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"- [%s] %s\n Svar: %s",
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strtoupper((string)$i['severity']),
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$i['question'],
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mb_substr((string)$i['answer'], 0, 600, 'UTF-8')
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);
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}
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$issuesBlock = implode("\n", $bullets);
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$prompt = <<<PROMPT
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Below are 1-5 legal questions raised about a {$docType} document, each with an answer
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from a Norwegian-law specialist model. Write a concise overall assessment in {$locale}.
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ISSUES + ANSWERS:
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{$issuesBlock}
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Return JSON only:
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{
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"overall_assessment": "<3-5 sentences summarising the legal picture across all issues>",
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"next_steps": ["<concrete action 1>", "<concrete action 2>", "<concrete action 3>"],
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"disclaimer": "This is automated legal analysis, not legal advice. Verify with a qualified Norwegian lawyer before acting."
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}
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PROMPT;
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try {
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$raw = $this->azureMini->chatText(
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[
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['role' => 'system', 'content' => 'You return valid JSON only. No prose, no fences.'],
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['role' => 'user', 'content' => $prompt],
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],
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['json' => true, 'temperature' => 0.2, 'max_tokens' => 700, 'timeout' => 60]
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);
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$decoded = $this->azureMini->decodeJsonObject($raw);
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if (is_array($decoded) && !empty($decoded['overall_assessment'])) {
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return [
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'overall_assessment' => (string)$decoded['overall_assessment'],
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'next_steps' => is_array($decoded['next_steps'] ?? null) ? array_slice($decoded['next_steps'], 0, 5) : [],
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'disclaimer' => (string)($decoded['disclaimer'] ?? 'Automated analysis — not legal advice.'),
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];
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}
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} catch (Throwable $e) {
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error_log('legal-analysis synthesis failed: ' . $e->getMessage());
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}
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return [
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'overall_assessment' => 'Synthesis step did not return structured output. See individual issue answers below.',
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'next_steps' => [],
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'disclaimer' => 'Automated analysis — not legal advice. Verify with a qualified Norwegian lawyer.',
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];
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}
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/**
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* Full orchestrated run. Emits progress events via the $emit callable.
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*
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* @param callable $emit (string $event, array $payload): void
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*/
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public function runFullAnalysis(string $text, string $language, string $docType, callable $emit): array
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{
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$startMs = (int)round(microtime(true) * 1000);
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// Pass 1
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$emit('progress', ['step' => 'extracting_issues', 'detail' => 'Identifying distinct legal issues…']);
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$issues = $this->extractIssues($text, $language, $docType);
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if (empty($issues)) {
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return [
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'ok' => true,
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'issues' => [],
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'overall_assessment' => 'No discrete legal issues identified in this document.',
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'next_steps' => [],
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'disclaimer' => 'Automated analysis — not legal advice.',
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'model' => self::LEGAL_MODEL,
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'latency_ms' => (int)round(microtime(true) * 1000) - $startMs,
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];
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}
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$emit('progress', [
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'step' => 'issues_extracted',
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'detail' => sprintf('Found %d legal issue(s); asking specialist…', count($issues)),
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'issues' => array_map(fn($i) => ['id' => $i['id'], 'question' => $i['question'], 'severity_hint' => $i['severity_hint']], $issues),
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]);
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// Pass 2 — one issue at a time
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$answered = [];
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foreach ($issues as $issue) {
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$emit('progress', [
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'step' => 'issue_searching_corpus',
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'detail' => sprintf('Issue %d: searching legal corpus…', $issue['id']),
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'issue_id' => $issue['id'],
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]);
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$corpusQuery = $issue['question'] . "\n" . $issue['brief_context'];
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$corpusContext = $this->legalSvc->corpusContextForSummarize($corpusQuery, 3);
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$emit('progress', [
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'step' => 'issue_answering',
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'detail' => sprintf('Issue %d: asking dbn-legal-agent-v3…', $issue['id']),
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'issue_id' => $issue['id'],
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]);
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$answer = $this->answerIssue($issue, $corpusContext, $language);
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$answered[] = $answer;
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$emit('issue_answered', ['issue' => $answer]);
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}
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// Pass 3
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$emit('progress', ['step' => 'synthesising', 'detail' => 'Synthesising overall assessment…']);
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$synth = $this->synthesise($answered, $language, $docType);
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return [
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'ok' => true,
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'issues' => $answered,
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'overall_assessment' => $synth['overall_assessment'],
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'next_steps' => $synth['next_steps'],
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'disclaimer' => $synth['disclaimer'],
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'doc_type' => $docType,
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'model' => self::LEGAL_MODEL,
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'latency_ms' => (int)round(microtime(true) * 1000) - $startMs,
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];
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}
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}
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