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Add Legal Analysis tool — two-pass DBN-legal pipeline

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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>
This commit is contained in:
davegilligan
2026-05-24 04:21:01 +02:00
parent 2013648ee0
commit 7e6463ed22
14 changed files with 1361 additions and 25 deletions
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includes/LegalAnalysisAgent.php
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<?php
declare(strict_types=1);
require_once __DIR__ . '/bootstrap.php';
require_once __DIR__ . '/AzureOpenAiGateway.php';
require_once __DIR__ . '/LegalTools.php';
/**
* Two-pass legal analysis:
* 1. Extract distinct legal issues from a document (Azure GPT-4o-mini)
* 2. For each issue: retrieve corpus passages, ask dbn-legal-agent-v3 a single
* targeted question (cap 350 tokens to avoid the documented loop bug)
* 3. Synthesise overall assessment + next steps (Azure GPT-4o-mini)
*
* Only step 2 touches the GPU. Steps 1 and 3 use Azure so dbn-legal-agent-v3
* stays hot in the 12GB RTX 3060 VRAM across all per-issue calls.
*/
final class DbnLegalAnalysisAgent
{
private const MAX_ISSUES = 5;
private const LEGAL_MAX_TOKENS = 350;
private const LEGAL_TIMEOUT = 60;
private const LEGAL_MODEL = 'dbn-legal-agent-v3';
private DbnAzureOpenAiGateway $azureMini;
private DbnLegalToolsService $legalSvc;
public function __construct()
{
$this->azureMini = (new DbnAzureOpenAiGateway())->withDeployment('gpt-4o-mini');
$this->legalSvc = new DbnLegalToolsService();
}
/**
* Pass 1 — extract distinct legal issues. Azure-only.
*
* @return array<int,array{id:int,question:string,brief_context:string,doc_type:string,severity_hint:string}>
*/
public function extractIssues(string $text, string $language, string $docType): array
{
$locale = dbnToolsLanguageName($language);
$text = mb_substr($text, 0, 24000, 'UTF-8'); // keep prompt within 4o-mini context
$prompt = <<<PROMPT
You analyse the document below and extract up to 5 DISTINCT legal issues that warrant
expert Norwegian-law review (barnevernsloven, EMK/ECHR, Hague Convention, family law,
process law). Each issue must be answerable as a SINGLE focused legal question
(≤ 25 words), not a multi-part essay.
Document type hint: {$docType}
Document language: {$locale}
Return JSON only:
{
"issues": [
{
"id": 1,
"question": "<short Norwegian legal question, single issue>",
"brief_context": "<≤2 sentences from the document that triggered this question>",
"doc_type": "<barnevernet|adopsjon|emergency|samvær|other>",
"severity_hint": "<high|medium|low>"
}
]
}
Rules:
- Skip non-legal observations (logistics, social commentary, opinions).
- Each question should be answerable with citations to barnevernsloven, EMK Art. X,
named Høyesterett/EMD cases — NOT general advice.
- If the document has fewer than 5 real legal issues, return fewer entries.
- If NO real legal issue exists, return {"issues": []}.
DOCUMENT:
---
{$text}
---
PROMPT;
$raw = $this->azureMini->chatText(
[
['role' => 'system', 'content' => 'You return valid JSON only. No prose, no fences.'],
['role' => 'user', 'content' => $prompt],
],
['json' => true, 'temperature' => 0.1, 'max_tokens' => 1500, 'timeout' => 90]
);
$decoded = $this->azureMini->decodeJsonObject($raw);
$issues = is_array($decoded['issues'] ?? null) ? $decoded['issues'] : [];
$clean = [];
$id = 1;
foreach ($issues as $issue) {
$question = trim((string)($issue['question'] ?? ''));
if ($question === '' || mb_strlen($question, 'UTF-8') < 10) {
continue;
}
$clean[] = [
'id' => $id++,
'question' => mb_substr($question, 0, 280, 'UTF-8'),
'brief_context' => mb_substr(trim((string)($issue['brief_context'] ?? '')), 0, 400, 'UTF-8'),
'doc_type' => (string)($issue['doc_type'] ?? $docType),
'severity_hint' => in_array($issue['severity_hint'] ?? '', ['high','medium','low'], true)
? $issue['severity_hint']
: 'medium',
];
if (count($clean) >= self::MAX_ISSUES) {
break;
}
}
return $clean;
}
/**
* Pass 2 — single targeted question to dbn-legal-agent-v3 with corpus context.
* Ocelot-only. Capped at 350 tokens / 60s to avoid the documented loop bug.
*
* @param array{id:int,question:string,brief_context:string,doc_type:string,severity_hint:string} $issue
* @return array{id:int,question:string,answer:string,severity:string,legal_basis:string,citations_from_corpus:array,what_to_check:string,brief_context:string}
*/
public function answerIssue(array $issue, string $corpusContext, string $language): array
{
$sysMsg = 'Du er en ekspert på norsk barnevernsloven og EMD-praksis. '
. 'Svar alltid på norsk med korrekt juridisk terminologi. '
. 'Bruk terskler fra barnevernsloven 2021: § 4-25 krever «klar nødvendighet». '
. 'Strand Lobben mot Norge (37283/13) setter krav om rehabiliteringsplan før adopsjon. '
. 'Aldri oppfinn paragrafnumre, saksnumre eller dommernavn. '
. 'Avslutt med en «Kilder:»-seksjon som lister lovparagrafer og dommer du har sitert.';
$userMsg = $issue['question'];
if ($issue['brief_context'] !== '') {
$userMsg .= "\n\nKontekst fra saken: " . $issue['brief_context'];
}
if ($corpusContext !== '') {
$userMsg .= "\n\nRelevante kilder fra Do Better Norge-korpuset:\n" . $corpusContext;
}
$answer = '';
$error = null;
try {
$response = dbnToolsCallGpuLlm(
[
['role' => 'system', 'content' => $sysMsg],
['role' => 'user', 'content' => $userMsg],
],
[
'model' => self::LEGAL_MODEL,
'temperature' => 0.1,
'max_tokens' => self::LEGAL_MAX_TOKENS,
'timeout' => self::LEGAL_TIMEOUT,
]
);
$answer = trim((string)($response['choices'][0]['message']['content'] ?? ''));
} catch (Throwable $e) {
$error = $e->getMessage();
}
$clean = dbnToolsExtractCleanAnswer($answer);
if (mb_strlen($clean, 'UTF-8') < 30) {
$clean = $answer !== ''
? $answer
: ($error !== null ? "[Modellfeil: $error]" : '[Modellen returnerte ingen brukbar tekst.]');
}
$severity = $clean !== '' ? dbnToolsInferCheckSeverity($clean) : $issue['severity_hint'];
$legalBasis = dbnToolsExtractCheckLegalBasis($clean);
return [
'id' => $issue['id'],
'question' => $issue['question'],
'brief_context' => $issue['brief_context'],
'answer' => $clean,
'severity' => $severity,
'legal_basis' => $legalBasis,
'citations_from_corpus' => [], // populated by orchestrator if it kept the chunks
'what_to_check' => 'Verifiser med norsk familieretsadvokat før handling.',
];
}
/**
* Pass 3 — synthesise overall assessment. Azure-only.
*/
public function synthesise(array $issues, string $language, string $docType): array
{
$locale = dbnToolsLanguageName($language);
$bullets = [];
foreach ($issues as $i) {
$bullets[] = sprintf(
"- [%s] %s\n Svar: %s",
strtoupper((string)$i['severity']),
$i['question'],
mb_substr((string)$i['answer'], 0, 600, 'UTF-8')
);
}
$issuesBlock = implode("\n", $bullets);
$prompt = <<<PROMPT
Below are 1-5 legal questions raised about a {$docType} document, each with an answer
from a Norwegian-law specialist model. Write a concise overall assessment in {$locale}.
ISSUES + ANSWERS:
{$issuesBlock}
Return JSON only:
{
"overall_assessment": "<3-5 sentences summarising the legal picture across all issues>",
"next_steps": ["<concrete action 1>", "<concrete action 2>", "<concrete action 3>"],
"disclaimer": "This is automated legal analysis, not legal advice. Verify with a qualified Norwegian lawyer before acting."
}
PROMPT;
try {
$raw = $this->azureMini->chatText(
[
['role' => 'system', 'content' => 'You return valid JSON only. No prose, no fences.'],
['role' => 'user', 'content' => $prompt],
],
['json' => true, 'temperature' => 0.2, 'max_tokens' => 700, 'timeout' => 60]
);
$decoded = $this->azureMini->decodeJsonObject($raw);
if (is_array($decoded) && !empty($decoded['overall_assessment'])) {
return [
'overall_assessment' => (string)$decoded['overall_assessment'],
'next_steps' => is_array($decoded['next_steps'] ?? null) ? array_slice($decoded['next_steps'], 0, 5) : [],
'disclaimer' => (string)($decoded['disclaimer'] ?? 'Automated analysis — not legal advice.'),
];
}
} catch (Throwable $e) {
error_log('legal-analysis synthesis failed: ' . $e->getMessage());
}
return [
'overall_assessment' => 'Synthesis step did not return structured output. See individual issue answers below.',
'next_steps' => [],
'disclaimer' => 'Automated analysis — not legal advice. Verify with a qualified Norwegian lawyer.',
];
}
/**
* Full orchestrated run. Emits progress events via the $emit callable.
*
* @param callable $emit (string $event, array $payload): void
*/
public function runFullAnalysis(string $text, string $language, string $docType, callable $emit): array
{
$startMs = (int)round(microtime(true) * 1000);
// Pass 1
$emit('progress', ['step' => 'extracting_issues', 'detail' => 'Identifying distinct legal issues…']);
$issues = $this->extractIssues($text, $language, $docType);
if (empty($issues)) {
return [
'ok' => true,
'issues' => [],
'overall_assessment' => 'No discrete legal issues identified in this document.',
'next_steps' => [],
'disclaimer' => 'Automated analysis — not legal advice.',
'model' => self::LEGAL_MODEL,
'latency_ms' => (int)round(microtime(true) * 1000) - $startMs,
];
}
$emit('progress', [
'step' => 'issues_extracted',
'detail' => sprintf('Found %d legal issue(s); asking specialist…', count($issues)),
'issues' => array_map(fn($i) => ['id' => $i['id'], 'question' => $i['question'], 'severity_hint' => $i['severity_hint']], $issues),
]);
// Pass 2 — one issue at a time
$answered = [];
foreach ($issues as $issue) {
$emit('progress', [
'step' => 'issue_searching_corpus',
'detail' => sprintf('Issue %d: searching legal corpus…', $issue['id']),
'issue_id' => $issue['id'],
]);
$corpusQuery = $issue['question'] . "\n" . $issue['brief_context'];
$corpusContext = $this->legalSvc->corpusContextForSummarize($corpusQuery, 3);
$emit('progress', [
'step' => 'issue_answering',
'detail' => sprintf('Issue %d: asking dbn-legal-agent-v3…', $issue['id']),
'issue_id' => $issue['id'],
]);
$answer = $this->answerIssue($issue, $corpusContext, $language);
$answered[] = $answer;
$emit('issue_answered', ['issue' => $answer]);
}
// Pass 3
$emit('progress', ['step' => 'synthesising', 'detail' => 'Synthesising overall assessment…']);
$synth = $this->synthesise($answered, $language, $docType);
return [
'ok' => true,
'issues' => $answered,
'overall_assessment' => $synth['overall_assessment'],
'next_steps' => $synth['next_steps'],
'disclaimer' => $synth['disclaimer'],
'doc_type' => $docType,
'model' => self::LEGAL_MODEL,
'latency_ms' => (int)round(microtime(true) * 1000) - $startMs,
];
}
}