14e59148c6
Tagged 'local_llm'. Auto-detects LM Studio (:1234) or Ollama (:11434) via host.docker.internal or localhost. When running, configures the provider params and runs engine.suggest_matches end-to-end. Skips gracefully when no local LLM is present (CI / dev VM mode). Made-with: Cursor
103 lines
4.0 KiB
Python
103 lines
4.0 KiB
Python
"""Local LLM compatibility test (LM Studio, Ollama, etc.).
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Skips if no local OpenAI-compatible LLM server is reachable. When one is
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running (LM Studio at :1234, Ollama at :11434), runs an end-to-end:
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1. Configure ``ir.config_parameter`` to point at the local server.
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2. Trigger ``engine.suggest_matches`` with the 'openai' provider.
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3. Assert the call did not crash and produced at least one suggestion.
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The smoke is intentionally lenient: local models often emit malformed
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JSON, in which case ``confidence_scoring`` falls back to statistical-only
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ranking. We assert end-to-end happiness, not AI re-rank quality.
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"""
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import socket
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from odoo.tests.common import TransactionCase, tagged
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from . import _factories as f
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def _server_reachable(host, port, timeout=1.0):
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try:
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with socket.create_connection((host, port), timeout=timeout):
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return True
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except (OSError, socket.timeout):
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return False
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def _detect_local_llm():
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"""Return (base_url, model_name) tuple, or (None, None) if no server.
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Tries LM Studio (:1234) and Ollama (:11434) on both
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``host.docker.internal`` (so the container can reach the host) and
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``localhost`` (so a non-containerised run finds the same servers).
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"""
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candidates = (
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('host.docker.internal', 1234, 'local-model'), # LM Studio
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('host.docker.internal', 11434, 'llama3.1:8b'), # Ollama
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('localhost', 1234, 'local-model'),
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('localhost', 11434, 'llama3.1:8b'),
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)
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for host, port, default_model in candidates:
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if _server_reachable(host, port, timeout=0.5):
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return (f'http://{host}:{port}/v1', default_model)
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return (None, None)
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@tagged('post_install', '-at_install', 'local_llm')
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class TestLocalLLMCompat(TransactionCase):
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def setUp(self):
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super().setUp()
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self.base_url, self.model = _detect_local_llm()
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if not self.base_url:
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self.skipTest(
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"No local LLM server detected "
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"(LM Studio :1234 / Ollama :11434)")
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def test_suggest_matches_with_local_llm(self):
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params = self.env['ir.config_parameter'].sudo()
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prior = {
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'fusion_accounting.openai_base_url': params.get_param(
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'fusion_accounting.openai_base_url'),
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'fusion_accounting.openai_model': params.get_param(
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'fusion_accounting.openai_model'),
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'fusion_accounting.openai_api_key': params.get_param(
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'fusion_accounting.openai_api_key'),
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'fusion_accounting.provider.bank_rec_suggest': params.get_param(
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'fusion_accounting.provider.bank_rec_suggest'),
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}
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params.set_param('fusion_accounting.openai_base_url', self.base_url)
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params.set_param('fusion_accounting.openai_model', self.model)
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# Local servers ignore the key but the adapter requires *some* value.
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params.set_param('fusion_accounting.openai_api_key', 'lm-studio')
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params.set_param(
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'fusion_accounting.provider.bank_rec_suggest', 'openai')
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try:
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partner = self.env['res.partner'].create(
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{'name': 'Local LLM Partner'})
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f.make_invoice(self.env, partner=partner, amount=750)
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bank_line = f.make_bank_line(
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self.env, amount=750, partner=partner,
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memo='REF 12345 Local LLM test')
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result = self.env['fusion.reconcile.engine'].suggest_matches(
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bank_line, limit_per_line=3)
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self.assertIn(bank_line.id, result)
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suggestions = self.env['fusion.reconcile.suggestion'].search([
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('statement_line_id', '=', bank_line.id),
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])
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self.assertGreater(
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len(suggestions), 0,
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"Local LLM run should still produce at least one suggestion "
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"(statistical fallback if AI re-rank fails)")
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finally:
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for key, value in prior.items():
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if value is not None:
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params.set_param(key, value)
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