8b84378fe1
Elevate MiroFish/CrowdSight from single-container dev to a SaaS foundation: - Local memory backend (Zep-compatible): memory services/models, local graph builder + updater, AgentActivity seam, import-boundary isolation; Zep stays default, local is opt-in behind MEMORY_BACKEND. Semantic parity not yet proven. - Durable product persistence: projects/simulations/reports schema (migration 0007) + tenant/owner-scoped ProductRepository + dual-write + scoped_project read-first + ArtifactStore abstraction; durable JobQueue + worker.py. - SaaS hardening: durable RateLimiter (wired to login), UsageService (LLM accounting), redacted AuditService, idempotency, CORS allowlist, safe API errors, single-use PasswordResetService + endpoints (covers invite-pending). - Exactly 3 roles (super_admin/admin/user) with tenant authz policy. - Admin UI: GET/POST/PATCH /api/admin/users + GET/PUT /api/admin/settings (super-admin only, encrypted/masked); AdminView.vue + SettingsView.vue with admin/super-admin route guards, th/en i18n. - Production deploy topology: multi-stage Dockerfile (frontend build + gunicorn wsgi + nginx SPA-proxy + supervisord worker), backend/wsgi.py, gunicorn dep. Backend 197 passed; frontend 10 tests + build green. ruff unavailable (gap). No commit of credentials; secrets handled via env/.env.example. Deferred: Zep semantic A/B parity, object storage cutover, mobile QA, EasyPanel container build of deploy topology.
89 lines
2.9 KiB
Python
89 lines
2.9 KiB
Python
from app.services.memory_extraction import MemoryExtractionResult
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from app.services.memory_repository import SqlAlchemyMemoryRepository
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from app.services.memory_service import MemoryExtractionService
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from app.db import Base, create_session_factory
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from sqlalchemy import create_engine
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class FakeLLM:
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def __init__(self, payload):
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self.payload = payload
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self.messages = None
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def chat_json(self, messages, temperature=0.3, max_tokens=4096):
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self.messages = messages
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assert temperature <= 0.3
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assert max_tokens >= 4096
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return self.payload
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def test_memory_extraction_service_calls_json_llm_and_persists_validated_result():
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payload = {
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"entities": [
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{
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"mention": "Alice",
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"canonical_name": "Alice",
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"labels": ["Person"],
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"aliases": [],
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"attributes": {},
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"summary": "Founder.",
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"confidence": 0.9,
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},
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{
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"mention": "Bob",
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"canonical_name": "Bob",
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"labels": ["Person"],
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"aliases": [],
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"attributes": {},
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"summary": "Partner.",
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"confidence": 0.8,
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},
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],
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"edges": [
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{
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"source_entity_ref": "Alice",
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"target_entity_ref": "Bob",
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"relation": "KNOWS",
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"fact": "Alice knows Bob.",
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"attributes": {},
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"valid_at": None,
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"invalid_at": None,
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"expired_at": None,
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"confidence": 0.8,
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"evidence": ["episode-1:0-15"],
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}
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],
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"episode_summary": "Relationship.",
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"unresolved_mentions": [],
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}
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client = FakeLLM(payload)
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service = MemoryExtractionService(client)
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result = service.extract(
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language="en",
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ontology={"entity_types": ["Person"], "edge_types": ["KNOWS"]},
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episode_text="Alice knows Bob.",
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)
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assert isinstance(result, MemoryExtractionResult)
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assert client.messages[0]["content"].startswith("IMPORTANT:")
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engine = create_engine("sqlite+pysqlite:///:memory:")
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Base.metadata.create_all(engine)
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try:
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with create_session_factory(engine)() as session:
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repository = SqlAlchemyMemoryRepository(session, organization_id="org-a", graph_id="graph-a")
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repository.create_graph(project_id="project-a")
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ingest = service.persist(
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repository,
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result,
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source_type="document",
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source_ref="episode-1",
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episode_text="Alice knows Bob.",
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)
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session.commit()
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assert ingest.entity_count == 2
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assert ingest.edge_count == 1
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assert repository.search("Alice").total_count == 1
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finally:
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engine.dispose()
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