Phase 5: Full AI engine + caselaw seed data (23 FL cases)
fl_ai_engine.py: Complete Ollama integration with 7-step pipeline: rule-based issue tagging, caselaw matching (3rd DCA-prioritized), case context serialization, prompt construction, Ollama HTTP call (llama3.1 at 192.168.2.10:11434), JSON parsing with fence-strip, and fl.analysis persistence with attorney referral chatter alert. fl_caselaw_data.xml: 23 seeded Florida cases covering modification threshold (Daly, Regan, Pimm, El Kohen, Rolfe), income imputation (Barner, Sitterson), self-employment (Smith, Young), timesharing (Freid, Kennedy, Boykin), domestic violence (Conlin, Kahle), default judgment (Lindsey, North), residency (Fults), parenting class (Maddox), fee waiver (Abdool, Kielbania), disclosure, withholding, above-schedule, and discovery sanctions. fl_case.py: trigger_ai_analysis() wired to engine.analyze_case(); returns form popup of fl.analysis result. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
Carlos Garcia
@@ -1,51 +1,389 @@
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import json
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import logging
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from odoo import models
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_logger = logging.getLogger(__name__)
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OLLAMA_URL = 'http://192.168.2.10:11434/api/generate'
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OLLAMA_MODEL = 'llama3.1'
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# ─────────────────────────────────────────────────────────────────────────────
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# Rule-based issue tag weights
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# Maps (field_name, value_or_True) → list of issue tag XML ids
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# ─────────────────────────────────────────────────────────────────────────────
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ISSUE_RULES = [
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# Modification threshold
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('threshold_met', True, ['modification_threshold']),
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# Income imputation triggers
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('respondent_employment_status', 'unemployed', ['income_imputation']),
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('respondent_employment_status', 'self_employed', ['self_employment_income', 'income_imputation']),
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('petitioner_employment_status', 'self_employed', ['self_employment_income']),
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# Timesharing deviation
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('substantial_timesharing', True, ['timesharing_deviation']),
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# Domestic violence
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('domestic_violence_flag', True, ['domestic_violence']),
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# Fee waiver
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('fee_waiver_eligible', True, ['fee_waiver']),
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# Default judgment track
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('respondent_answered', False, ['default_judgment']),
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# Residency
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('residency_requirement_met', False, ['residency']),
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# Parenting class
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('parenting_class_required', True, ['parenting_class']),
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# Post-order
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('case_type', 'modification', ['post_order']),
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]
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# ─────────────────────────────────────────────────────────────────────────────
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# Caselaw topic → issue tag matching
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# Used by _match_caselaw to find relevant cases
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# ─────────────────────────────────────────────────────────────────────────────
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TAG_TO_CASELAW_DOMAINS = {
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'modification_threshold': [('issue_tag_ids.name', '=', 'modification_threshold')],
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'income_imputation': [('issue_tag_ids.name', '=', 'income_imputation')],
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'self_employment_income': [('issue_tag_ids.name', '=', 'self_employment_income')],
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'timesharing_deviation': [('issue_tag_ids.name', '=', 'timesharing_deviation')],
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'domestic_violence': [('issue_tag_ids.name', '=', 'domestic_violence')],
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'fee_waiver': [('issue_tag_ids.name', '=', 'fee_waiver')],
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'default_judgment': [('issue_tag_ids.name', '=', 'default_judgment')],
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'residency': [('issue_tag_ids.name', '=', 'residency')],
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'parenting_class': [('issue_tag_ids.name', '=', 'parenting_class')],
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'post_order': [('issue_tag_ids.name', '=', 'post_order')],
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}
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# Maximum caselaw records to pass to Ollama (keep prompt size manageable)
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MAX_CASELAW_IN_PROMPT = 8
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class FlAiEngine(models.AbstractModel):
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"""
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Phase 5 — Full Ollama integration.
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Phase 1: Stub service model.
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Phase 5 — Full Ollama integration with rule-based pre-processing.
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This is an AbstractModel — not stored in the database.
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Used as a service class for AI analysis calls.
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Workflow:
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1. _rule_based_tagging — tag issue tags from case field values (fast, deterministic)
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2. _match_caselaw — find relevant FL cases from the caselaw library
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3. _build_case_context — serialize case data to a JSON dict
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4. _build_prompt — compose the Ollama prompt
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5. _call_ollama — HTTP call to Ollama with error handling
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6. _store_analysis — persist fl.analysis record with results
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AbstractModel — not stored in the database.
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"""
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_name = 'fl.ai.engine'
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_description = 'Family Law AI Analysis Engine (Ollama)'
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# ──────────────────────────────────────────────────────────────────────────
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# Public entry point
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# ──────────────────────────────────────────────────────────────────────────
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def analyze_case(self, case_id):
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"""
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Phase 5 entry point.
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Full workflow:
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1. Rule-based issue tagging
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2. Build case context JSON
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3. Call Ollama (llama3.1)
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4. Parse JSON response
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5. Store fl.analysis record
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Full Phase 5 analysis entry point.
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Returns fl.analysis record.
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"""
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case = self.env['fl.case'].browse(case_id)
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analysis = self.env['fl.analysis'].create({
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if not case.exists():
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raise ValueError(f"Case {case_id} not found")
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analysis_vals = {
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'case_id': case.id,
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'state': 'pending',
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'model_used': OLLAMA_MODEL,
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'plain_english_summary': (
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'AI analysis not yet implemented. '
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'Full analysis will be available in Phase 5.'
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),
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'plain_english_summary_es': (
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'El análisis de IA aún no está implementado. '
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'El análisis completo estará disponible en la Fase 5.'
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),
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'state': 'complete',
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})
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}
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analysis = self.env['fl.analysis'].create(analysis_vals)
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try:
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# Step 1: Rule-based tagging
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triggered_tags = self._rule_based_tagging(case)
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if triggered_tags:
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existing_tags = case.issue_tag_ids.mapped('name')
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new_tag_recs = self.env['fl.issue.tag'].search([
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('name', 'in', triggered_tags),
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('name', 'not in', existing_tags),
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])
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if new_tag_recs:
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case.write({'issue_tag_ids': [(4, t.id) for t in new_tag_recs]})
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# Step 2: Match caselaw
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matched_cases = self._match_caselaw(triggered_tags)
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# Step 3: Build case context
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context = self._build_case_context(case, matched_cases)
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# Step 4: Determine complexity (used in prompt and result)
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complexity = self._assess_complexity(case, triggered_tags)
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# Step 5: Build prompt
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prompt = self._build_prompt(context, complexity)
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# Step 6: Call Ollama
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result = self._call_ollama(prompt)
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# Step 7: Store results
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self._store_analysis(analysis, result, matched_cases, complexity)
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except Exception as exc:
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_logger.error("AI analysis failed for case %s: %s", case_id, exc, exc_info=True)
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analysis.write({
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'state': 'failed',
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'error_message': str(exc),
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'plain_english_summary': (
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"AI analysis could not be completed at this time. "
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"Please try again later or contact support."
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),
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'plain_english_summary_es': (
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"El análisis de IA no pudo completarse en este momento. "
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"Por favor intente más tarde o contacte soporte."
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),
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})
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return analysis
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# ──────────────────────────────────────────────────────────────────────────
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# Step 1: Rule-based tagging
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# ──────────────────────────────────────────────────────────────────────────
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def _rule_based_tagging(self, case):
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"""
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Apply deterministic rules to identify legal issues present in the case.
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Returns a set of issue tag name strings that were triggered.
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"""
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triggered = set()
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for rule in ISSUE_RULES:
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field_name, expected, tags = rule
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if not hasattr(case, field_name):
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continue
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val = getattr(case, field_name)
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# Handle relational fields (Many2one returns recordset)
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if hasattr(val, '_name'):
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continue
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if val == expected:
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triggered.update(tags)
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# Additional logic: income imputation if large discrepancy
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if (case.petitioner_net_income and case.respondent_net_income
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and case.respondent_employment_status not in ('unemployed', 'self_employed')):
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pet = case.petitioner_net_income
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resp = case.respondent_net_income
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if resp > 0 and pet > 0:
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ratio = max(pet, resp) / min(pet, resp)
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if ratio > 3.0:
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triggered.add('income_imputation')
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# Emancipation approaching
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if case.child_ids:
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for child in case.child_ids:
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if child.approaching_emancipation:
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triggered.add('post_order')
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break
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return triggered
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# ──────────────────────────────────────────────────────────────────────────
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# Step 2: Match caselaw
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# ──────────────────────────────────────────────────────────────────────────
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def _match_caselaw(self, triggered_tags):
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"""
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Search the fl.caselaw library for cases matching the triggered issue tags.
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Prioritizes 3rd DCA cases (Miami-Dade's primary appellate court),
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then FL Supreme Court, then other DCAs.
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Returns up to MAX_CASELAW_IN_PROMPT records.
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"""
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if not triggered_tags:
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# Return a small set of foundational cases
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return self.env['fl.caselaw'].search(
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[('active', '=', True)],
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order='year desc',
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limit=4,
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)
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# Collect matching case IDs per tag, with deduplication
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matched_ids = set()
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for tag in triggered_tags:
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domain = TAG_TO_CASELAW_DOMAINS.get(tag, [])
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if domain:
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recs = self.env['fl.caselaw'].search(
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[('active', '=', True)] + domain,
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limit=6,
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)
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matched_ids.update(recs.ids)
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if not matched_ids:
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return self.env['fl.caselaw'].browse()
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# Fetch and sort: 3rd DCA first, then FL Supreme, then by year desc
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cases = self.env['fl.caselaw'].browse(list(matched_ids))
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court_priority = {
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'3rd_dca': 0,
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'fl_supreme': 1,
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'4th_dca': 2,
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'2nd_dca': 3,
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'1st_dca': 4,
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'5th_dca': 5,
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'11th_circuit': 6,
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'other': 7,
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}
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sorted_cases = sorted(
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cases,
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key=lambda c: (court_priority.get(c.court, 9), -c.year)
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)
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return self.env['fl.caselaw'].browse([c.id for c in sorted_cases[:MAX_CASELAW_IN_PROMPT]])
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# ──────────────────────────────────────────────────────────────────────────
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# Step 3: Build case context
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# ──────────────────────────────────────────────────────────────────────────
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def _build_case_context(self, case, matched_cases):
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"""
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Serialize the case into a JSON-serializable dict for the prompt.
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Keeps PII minimal — uses role names, not actual SSNs etc.
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"""
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context = {
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'case_type': case.case_type,
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'stage': case.stage_id.name if case.stage_id else 'unknown',
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'filing_date': str(case.filing_date) if case.filing_date else None,
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'service_date': str(case.service_date) if case.service_date else None,
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'petitioner': {
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'employment': case.petitioner_id.employment_status if case.petitioner_id else 'unknown',
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'monthly_net_income': case.petitioner_net_income or 0,
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},
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'respondent': {
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'employment': case.respondent_id.employment_status if case.respondent_id else 'unknown',
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'monthly_net_income': case.respondent_net_income or 0,
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'answered': case.respondent_answered,
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'has_counsel': case.respondent_has_counsel,
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},
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'support': {
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'current_order_amount': case.current_order_amount or 0,
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'calculated_support': case.calculated_support or 0,
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'support_difference': case.support_difference or 0,
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'support_difference_pct': round(case.support_difference_pct or 0, 1),
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'threshold_met': case.threshold_met,
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'substantial_change_basis': case.substantial_change_basis or None,
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},
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'children': [
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{
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'age': c.age,
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'approaching_emancipation': c.approaching_emancipation,
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'days_until_emancipation': c.days_until_emancipation if c.approaching_emancipation else None,
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}
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for c in (case.child_ids or [])
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if not c.emancipated
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],
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'timesharing': {
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'petitioner_overnights': case.petitioner_overnights_year or 0,
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'respondent_overnights': case.respondent_overnights_year or 0,
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'substantial_timesharing': case.substantial_timesharing,
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},
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'residency_met': case.residency_requirement_met,
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'domestic_violence': case.domestic_violence_flag,
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'fee_waiver_eligible': case.fee_waiver_eligible,
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'parenting_class_required': case.parenting_class_required,
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'parenting_class_completed': case.parenting_class_completed,
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'issue_tags': list(case.issue_tag_ids.mapped('name')),
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'caselaw': [
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{
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'citation': cl.citation,
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'holding': (cl.holding or '')[:300],
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'favorable_to': cl.favorable_to,
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}
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for cl in matched_cases
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],
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}
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return context
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# ──────────────────────────────────────────────────────────────────────────
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# Step 4: Complexity assessment
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# ──────────────────────────────────────────────────────────────────────────
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def _assess_complexity(self, case, triggered_tags):
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"""
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Simple heuristic complexity scorer.
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Returns 'simple', 'moderate', or 'complex'.
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"""
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score = 0
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if case.domestic_violence_flag:
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score += 3
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if case.respondent_has_counsel:
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score += 2
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if 'income_imputation' in triggered_tags:
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score += 2
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if 'self_employment_income' in triggered_tags:
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score += 2
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if case.child_ids and len(case.child_ids) > 2:
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score += 1
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if triggered_tags:
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score += len(triggered_tags)
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if score <= 3:
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return 'simple'
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elif score <= 7:
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return 'moderate'
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else:
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return 'complex'
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# ──────────────────────────────────────────────────────────────────────────
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# Step 5: Build Ollama prompt
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# ──────────────────────────────────────────────────────────────────────────
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def _build_prompt(self, context, complexity):
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"""
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Construct the system + user prompt for Ollama.
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Returns a string.
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The LLM is instructed to respond with a JSON object only.
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"""
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context_json = json.dumps(context, indent=2)
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attorney_trigger = (
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context.get('domestic_violence') or
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context.get('respondent', {}).get('has_counsel') or
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complexity == 'complex'
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)
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prompt = f"""You are an AI legal assistant for a Florida family law case management system. \
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Your role is to help pro se (self-represented) litigants in Miami-Dade County understand their case. \
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You are NOT providing legal advice — you are explaining the legal framework and procedure.
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IMPORTANT RULES:
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- Always recommend an attorney when the case involves domestic violence, opposing counsel, or high complexity.
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- Florida uses the FL 61.30 income-shares model for child support.
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- Modification threshold: 15% AND $50 difference required (FL 61.30(1)(b)).
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- Timesharing credit applies when either parent has >73 overnights/year (FL 61.30(11)(b)).
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- All output must be JSON only — no markdown, no prose outside the JSON object.
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CASE DATA:
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{context_json}
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TASK: Analyze this case and return a JSON object with exactly these fields:
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{{
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"plain_english_summary": "3-5 sentence plain English explanation of the case situation and key legal issues — no jargon",
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"plain_english_summary_es": "Same 3-5 sentences in Spanish",
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"petitioner_arguments": ["argument 1", "argument 2", "argument 3"],
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"respondent_counterarguments": ["counterargument 1", "counterargument 2"],
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"procedural_risks": ["risk 1", "risk 2"],
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"attorney_referral_flag": {"attorney_trigger" if attorney_trigger else "false"},
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"attorney_referral_reason": "reason if flag is true, else null",
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"confidence_level": "high|medium|low",
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"case_complexity": "{complexity}",
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"next_steps": ["step 1", "step 2", "step 3"]
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}}
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Respond with the JSON object only. No other text."""
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return prompt
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# ──────────────────────────────────────────────────────────────────────────
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# Step 6: Call Ollama
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# ──────────────────────────────────────────────────────────────────────────
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def _call_ollama(self, prompt):
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"""Call Ollama API and return parsed JSON response."""
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"""
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Call Ollama API (llama3.1) and return parsed JSON dict.
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Raises on network error or JSON parse failure.
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"""
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try:
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import requests
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except ImportError:
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@@ -53,6 +391,9 @@ class FlAiEngine(models.AbstractModel):
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'requests library not available. '
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'Install with: pip install requests'
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)
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_logger.info("FL AI Engine: calling Ollama at %s (model=%s)", OLLAMA_URL, OLLAMA_MODEL)
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response = requests.post(
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OLLAMA_URL,
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json={
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@@ -68,11 +409,92 @@ class FlAiEngine(models.AbstractModel):
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timeout=180,
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)
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response.raise_for_status()
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raw = response.json().get('response', '{}').strip()
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_logger.debug("FL AI Engine raw response (first 200 chars): %s", raw[:200])
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# Strip markdown code fences if present
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if raw.startswith('```'):
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parts = raw.split('```')
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raw = parts[1] if len(parts) > 1 else raw
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if raw.startswith('json'):
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if len(parts) >= 3:
|
||||
raw = parts[1]
|
||||
elif len(parts) == 2:
|
||||
raw = parts[1]
|
||||
if raw.lower().startswith('json'):
|
||||
raw = raw[4:]
|
||||
return json.loads(raw.strip())
|
||||
raw = raw.strip()
|
||||
|
||||
# Extract first JSON object if extra text leaked through
|
||||
if raw and raw[0] == '{':
|
||||
brace_depth = 0
|
||||
end_idx = 0
|
||||
for i, ch in enumerate(raw):
|
||||
if ch == '{':
|
||||
brace_depth += 1
|
||||
elif ch == '}':
|
||||
brace_depth -= 1
|
||||
if brace_depth == 0:
|
||||
end_idx = i + 1
|
||||
break
|
||||
raw = raw[:end_idx]
|
||||
|
||||
try:
|
||||
return json.loads(raw)
|
||||
except json.JSONDecodeError as exc:
|
||||
_logger.error("FL AI Engine: JSON parse error: %s\nRaw: %s", exc, raw[:500])
|
||||
raise RuntimeError(f"Ollama returned invalid JSON: {exc}") from exc
|
||||
|
||||
# ──────────────────────────────────────────────────────────────────────────
|
||||
# Step 7: Store analysis
|
||||
# ──────────────────────────────────────────────────────────────────────────
|
||||
|
||||
def _store_analysis(self, analysis, result, matched_cases, complexity):
|
||||
"""
|
||||
Write parsed Ollama result into the fl.analysis record.
|
||||
"""
|
||||
# Normalize boolean field from Ollama (may come as string "true")
|
||||
attorney_flag = result.get('attorney_referral_flag', False)
|
||||
if isinstance(attorney_flag, str):
|
||||
attorney_flag = attorney_flag.lower() in ('true', '1', 'yes')
|
||||
|
||||
# Serialize list fields to JSON strings for Text fields
|
||||
petitioner_args = result.get('petitioner_arguments', [])
|
||||
respondent_counter = result.get('respondent_counterarguments', [])
|
||||
procedural_risks = result.get('procedural_risks', [])
|
||||
|
||||
vals = {
|
||||
'state': 'complete',
|
||||
'plain_english_summary': result.get('plain_english_summary', ''),
|
||||
'plain_english_summary_es': result.get('plain_english_summary_es', ''),
|
||||
'attorney_referral_flag': bool(attorney_flag),
|
||||
'attorney_referral_reason': result.get('attorney_referral_reason') or '',
|
||||
'confidence_level': result.get('confidence_level', 'medium'),
|
||||
'case_complexity': result.get('case_complexity', complexity),
|
||||
'petitioner_arguments': json.dumps(petitioner_args, ensure_ascii=False, indent=2),
|
||||
'respondent_counterarguments': json.dumps(respondent_counter, ensure_ascii=False, indent=2),
|
||||
'procedural_risks': json.dumps(procedural_risks, ensure_ascii=False, indent=2),
|
||||
'raw_response': json.dumps(result, ensure_ascii=False, indent=2),
|
||||
}
|
||||
|
||||
if matched_cases:
|
||||
vals['matched_caselaw_ids'] = [(6, 0, matched_cases.ids)]
|
||||
|
||||
analysis.write(vals)
|
||||
|
||||
# If attorney referral flagged, post urgent chatter message on the case
|
||||
if attorney_flag and analysis.case_id:
|
||||
analysis.case_id.message_post(
|
||||
body=(
|
||||
"<strong>⚠ AI ANALYSIS — ATTORNEY REFERRAL RECOMMENDED</strong><br/>"
|
||||
f"{result.get('attorney_referral_reason', 'Case complexity warrants legal counsel.')}<br/>"
|
||||
"FL Volunteer Lawyers Project: <a href='https://www.flvlp.org'>flvlp.org</a> | "
|
||||
"Three-Day Rule: 3-1-1 Legal Info: <a href='https://www.flcourts.gov'>flcourts.gov</a>"
|
||||
),
|
||||
message_type='comment',
|
||||
subtype_xmlid='mail.mt_comment',
|
||||
)
|
||||
|
||||
_logger.info(
|
||||
"FL AI Engine: analysis %s complete (complexity=%s, attorney_referral=%s)",
|
||||
analysis.id, complexity, attorney_flag
|
||||
)
|
||||
|
||||
@@ -888,13 +888,24 @@ class FlCase(models.Model):
|
||||
|
||||
def trigger_ai_analysis(self):
|
||||
"""
|
||||
Trigger AI case analysis via Ollama.
|
||||
Stub for Phase 1 — full implementation in Phase 5.
|
||||
Trigger AI case analysis via Ollama (fl.ai.engine).
|
||||
Phase 5 — full implementation.
|
||||
"""
|
||||
self.ensure_one()
|
||||
engine = self.env['fl.ai.engine']
|
||||
self.message_post(
|
||||
body='🤖 AI analysis queued. Full AI analysis will be available in Phase 5.',
|
||||
body='🤖 AI analysis started. This may take up to 3 minutes...',
|
||||
subtype_xmlid='mail.mt_note',
|
||||
)
|
||||
analysis = engine.analyze_case(self.id)
|
||||
return {
|
||||
'type': 'ir.actions.act_window',
|
||||
'name': 'AI Analysis Result',
|
||||
'res_model': 'fl.analysis',
|
||||
'res_id': analysis.id,
|
||||
'view_mode': 'form',
|
||||
'target': 'new',
|
||||
}
|
||||
|
||||
# ══════════════════════════════════════════════════════════════════════
|
||||
# ACTIONS
|
||||
|
||||
Reference in New Issue
Block a user