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KaifAhmad1andClaude Sonnet 4.6
f8ec5ac010
- test_add_decision_kwargs_form: verifies add_decision() accepts kwargs directly (category, scenario, reasoning, outcome, confidence) without requiring a Decision object - test_add_decision_kwargs_and_object_both_return_id: verifies both call forms return a non-empty string ID - test_agent_context_inmemory_store_and_retrieve: verifies AgentContext with VectorStore(backend="inmemory") stores memories without faiss-cpu Closes #433 Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
85 lines
2.5 KiB
Python
85 lines
2.5 KiB
Python
import pytest
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from semantica.context import AgentContext, ContextGraph
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from semantica.context.decision_models import Policy
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from semantica.vector_store import VectorStore
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from datetime import datetime
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def test_agent_context_minimal_decisions_and_chain():
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vs = VectorStore(backend="inmemory", dimension=64)
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graph = ContextGraph()
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ctx = AgentContext(
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vector_store=vs,
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knowledge_graph=graph,
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decision_tracking=True,
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kg_algorithms=False,
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vector_store_features=False,
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)
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d1 = ctx.record_decision(
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category="credit_approval",
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scenario="s1",
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reasoning="r1",
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outcome="rejected",
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confidence=0.8,
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entities=["e1"],
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decision_maker="tester",
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)
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d2 = ctx.record_decision(
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category="credit_approval",
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scenario="s2",
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reasoning="r2",
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outcome="rejected",
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confidence=0.85,
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entities=["e1"],
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decision_maker="tester",
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)
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graph.add_causal_relationship(d1, d2, "INFLUENCED")
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chain = ctx.get_causal_chain(d2, direction="upstream", max_depth=5)
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assert isinstance(chain, list)
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assert len(chain) >= 1
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def test_agent_context_inmemory_store_and_retrieve():
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"""VectorStore(backend="inmemory") stores memories without faiss-cpu."""
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vs = VectorStore(backend="inmemory")
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ctx = AgentContext(
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vector_store=vs,
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knowledge_graph=ContextGraph(),
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decision_tracking=True,
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kg_algorithms=False,
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vector_store_features=False,
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)
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memory_id = ctx.store(
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"GPT-4 outperforms GPT-3.5 on reasoning benchmarks by 40%",
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conversation_id="test_session",
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)
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assert isinstance(memory_id, str)
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assert len(memory_id) > 0
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def test_agent_context_policy_engine_with_graph_backend():
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vs = VectorStore(backend="inmemory", dimension=64)
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graph = ContextGraph()
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ctx = AgentContext(
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vector_store=vs,
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knowledge_graph=graph,
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decision_tracking=True,
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kg_algorithms=False,
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vector_store_features=False,
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)
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pe = ctx.get_policy_engine()
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pol = Policy(
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policy_id="cp",
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name="Credit Policy",
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description="d",
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rules={"min_confidence": 0.8, "allowed_outcomes": ["approved", "rejected"]},
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category="credit_approval",
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version="1.0.0",
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created_at=datetime.now(),
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updated_at=datetime.now(),
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metadata={},
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)
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pe.add_policy(pol)
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found = pe.get_policy("cp")
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assert found is not None
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