AFFAANhandClaude Opus 5
19e1e84fb7
Independent FastAPI backend for the MaskanX agentic growth platform. Includes the agent runtime, MCP client integrations (Meta Ads, LinkedIn, HubSpot, Tavily, Exa, xAI, Citedy, image generation), PostgreSQL storage for chats and cron jobs, provider and secret management, and the CLI. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
75 lines
2.4 KiB
Python
75 lines
2.4 KiB
Python
# -*- coding: utf-8 -*-
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"""Shared fixtures for AOM tests."""
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import pytest
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from adclaw.memory_agent.embeddings import FakeEmbeddingPipeline
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from adclaw.memory_agent.models import AOMConfig
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from adclaw.memory_agent.store import MemoryStore
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from adclaw.config.config import PersonaConfig, Config, AgentsConfig
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@pytest.fixture
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async def aom_store():
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"""In-memory SQLite store — zero I/O."""
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store = MemoryStore(":memory:", dimensions=32)
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await store.initialize()
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yield store
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await store.close()
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@pytest.fixture
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def fake_embedder():
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"""Deterministic embedding pipeline for tests."""
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return FakeEmbeddingPipeline(dimensions=32)
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@pytest.fixture
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def fake_llm_caller():
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"""Canned LLM: extraction → JSON, consolidation → insight text."""
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async def caller(prompt: str) -> str:
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if "extract" in prompt.lower():
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return '{"entities": ["test_entity"], "topics": ["test_topic"], "importance": 0.7}'
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if "consolidat" in prompt.lower() or "synthesiz" in prompt.lower() or "cluster" in prompt.lower():
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return "INSIGHT: Test insight about the data.\nIMPORTANCE: 0.8"
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if "memory" in prompt.lower() or "question" in prompt.lower():
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return "Based on the memories, the answer is test. [Memory #abc123]"
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return "Test response"
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return caller
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@pytest.fixture
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def aom_config():
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"""Default AOM configuration for tests."""
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return AOMConfig(
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enabled=True,
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embedding_backend="local",
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embedding_dimensions=32,
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importance_threshold=0.3,
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)
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@pytest.fixture
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def sample_personas():
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"""Three personas: coordinator + researcher + writer."""
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return [
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PersonaConfig(id="coordinator", name="Coordinator", is_coordinator=True, soul_md="## Role\nOrchestrate the team."),
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PersonaConfig(id="researcher", name="Mike", soul_md="## Role\nResearch and analyze."),
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PersonaConfig(id="content-writer", name="Mira", soul_md="## Role\nWrite content."),
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]
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@pytest.fixture
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def persona_manager(sample_personas, tmp_path):
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"""PersonaManager with 3 personas and temp working dir."""
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from adclaw.agents.persona_manager import PersonaManager
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mgr = PersonaManager(working_dir=str(tmp_path), personas=sample_personas)
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mgr.ensure_dirs()
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return mgr
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@pytest.fixture
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def config_with_personas(sample_personas):
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"""Config object with 3 personas."""
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return Config(agents=AgentsConfig(personas=sample_personas))
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