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from unittest.mock import MagicMock, patch
import json
from src.agent.plan_and_execute import (
PlanAndExecuteRouter,
PlanExecState,
PlanStep,
_extract_last_ai_text,
_parse_plan,
)
from src.models import (
DocumentChunk,
GenerationResponse,
IntentType,
QueryResult,
)
from src.retrieval.hybrid import HybridSearchResult
# ---------------------------------------------------------------------------
# Helpers
# ---------------------------------------------------------------------------
def _chunk(chunk_id: str = "c1", text: str = "text") -> DocumentChunk:
return DocumentChunk(
chunk_id=chunk_id, document_id="doc.pdf", text=text,
metadata={"page_number": 1, "chunk_index": 0},
)
def _qr(chunk_id: str = "c1", score: float = 0.8, text: str = "text") -> QueryResult:
return QueryResult(chunk=_chunk(chunk_id=chunk_id, text=text), score=score, source="hybrid")
def _hybrid_result(results: list[QueryResult]) -> HybridSearchResult:
return HybridSearchResult(
dense_results=results, sparse_results=results, fused_results=results,
)
# ---------------------------------------------------------------------------
# _parse_plan
# ---------------------------------------------------------------------------
class TestParsePlan:
def test_valid_json(self) -> None:
raw = '[{"action": "search", "detail": "exam rules"}]'
steps = _parse_plan(raw)
assert len(steps) == 1
assert steps[0]["action"] == "search"
assert steps[0]["detail"] == "exam rules"
def test_multiple_steps(self) -> None:
raw = json.dumps([
{"action": "search", "detail": "policy A"},
{"action": "search", "detail": "policy B"},
{"action": "summarize", "detail": "doc.pdf"},
])
steps = _parse_plan(raw)
assert len(steps) == 3
def test_markdown_fenced(self) -> None:
raw = '```json\n[{"action": "search", "detail": "test"}]\n```'
steps = _parse_plan(raw)
assert len(steps) == 1
assert steps[0]["action"] == "search"
def test_json_with_surrounding_text(self) -> None:
raw = 'Here is the plan:\n[{"action": "search", "detail": "x"}]\nDone.'
steps = _parse_plan(raw)
assert len(steps) == 1
def test_invalid_json_falls_back(self) -> None:
raw = "this is not json at all"
steps = _parse_plan(raw)
assert len(steps) == 1
assert steps[0]["action"] == "search"
def test_empty_array_falls_back(self) -> None:
raw = "[]"
steps = _parse_plan(raw)
assert len(steps) == 1 # fallback to single search
def test_malformed_items_skipped(self) -> None:
raw = json.dumps([
{"action": "search", "detail": "good"},
{"bad": "step"},
{"action": "search", "detail": "also good"},
])
steps = _parse_plan(raw)
assert len(steps) == 2
def test_non_list_wrapped(self) -> None:
raw = '{"action": "search", "detail": "test"}'
steps = _parse_plan(raw)
assert len(steps) == 1
# ---------------------------------------------------------------------------
# _extract_last_ai_text
# ---------------------------------------------------------------------------
class TestExtractLastAIText:
def test_returns_last_ai_message(self) -> None:
from langchain_core.messages import AIMessage, HumanMessage
messages = [
HumanMessage(content="question"),
AIMessage(content="first"),
AIMessage(content="second"),
]
assert _extract_last_ai_text(messages) == "second"
def test_skips_tool_calls(self) -> None:
from langchain_core.messages import AIMessage
msg_with_tools = AIMessage(content="calling tool", tool_calls=[{"name": "t", "args": {}, "id": "1"}])
msg_final = AIMessage(content="the answer")
assert _extract_last_ai_text([msg_with_tools, msg_final]) == "the answer"
def test_empty_messages(self) -> None:
assert _extract_last_ai_text([]) == ""
# ---------------------------------------------------------------------------
# PlanAndExecuteRouter — plan node
# ---------------------------------------------------------------------------
class TestPlanNode:
def test_plan_node_generates_steps(self) -> None:
llm = MagicMock()
llm.invoke.return_value = '[{"action": "search", "detail": "KU regler"}]'
router = PlanAndExecuteRouter(
llm=llm,
hybrid_retriever=MagicMock(),
reranker=MagicMock(),
vector_store=MagicMock(),
)
state = PlanExecState(
query="What are the rules?",
top_k=5, plan=[], step_index=0, step_results=[], answer="",
)
result = router._plan_node(state)
assert len(result["plan"]) == 1
assert result["plan"][0]["action"] == "search"
assert result["step_index"] == 0
def test_plan_node_handles_bad_llm_output(self) -> None:
llm = MagicMock()
llm.invoke.return_value = "I cannot produce JSON"
router = PlanAndExecuteRouter(
llm=llm,
hybrid_retriever=MagicMock(),
reranker=MagicMock(),
vector_store=MagicMock(),
)
state = PlanExecState(
query="test", top_k=5, plan=[], step_index=0, step_results=[], answer="",
)
result = router._plan_node(state)
assert len(result["plan"]) >= 1 # fallback plan
# ---------------------------------------------------------------------------
# PlanAndExecuteRouter — should_execute
# ---------------------------------------------------------------------------
class TestShouldExecute:
def test_more_steps_returns_execute(self) -> None:
state = PlanExecState(
query="q", top_k=5,
plan=[PlanStep(action="search", detail="x")],
step_index=0, step_results=[], answer="",
)
assert PlanAndExecuteRouter._should_execute(state) == "execute"
def test_all_steps_done_returns_synthesize(self) -> None:
state = PlanExecState(
query="q", top_k=5,
plan=[PlanStep(action="search", detail="x")],
step_index=1, step_results=[], answer="",
)
assert PlanAndExecuteRouter._should_execute(state) == "synthesize"
def test_empty_plan_returns_synthesize(self) -> None:
state = PlanExecState(
query="q", top_k=5, plan=[], step_index=0, step_results=[], answer="",
)
assert PlanAndExecuteRouter._should_execute(state) == "synthesize"
def test_max_steps_cap(self) -> None:
"""Step index at _MAX_STEPS should stop execution."""
state = PlanExecState(
query="q", top_k=5,
plan=[PlanStep(action="search", detail=f"q{i}") for i in range(10)],
step_index=6, # == _MAX_STEPS
step_results=[], answer="",
)
assert PlanAndExecuteRouter._should_execute(state) == "synthesize"
# ---------------------------------------------------------------------------
# PlanAndExecuteRouter — synthesize node
# ---------------------------------------------------------------------------
class TestSynthesizeNode:
def test_synthesize_combines_results(self) -> None:
llm = MagicMock()
llm.invoke.return_value = "Combined answer about exams."
router = PlanAndExecuteRouter(
llm=llm,
hybrid_retriever=MagicMock(),
reranker=MagicMock(),
vector_store=MagicMock(),
)
state = PlanExecState(
query="exam rules",
top_k=5, plan=[],
step_index=2,
step_results=[
("search: exam bachelor", "Found bachelor exam rules..."),
("search: exam master", "Found master exam rules..."),
],
answer="",
)
result = router._synthesize_node(state)
assert result["answer"] == "Combined answer about exams."
# Verify prompt includes both step results
prompt = llm.invoke.call_args[0][0]
assert "bachelor exam rules" in prompt
assert "master exam rules" in prompt
# ---------------------------------------------------------------------------
# PlanAndExecuteRouter — full route (integration with mocks)
# ---------------------------------------------------------------------------
class TestFullRoute:
def test_route_produces_response(self) -> None:
"""Full route with mocked LLM and retrieval components."""
llm = MagicMock()
retriever = MagicMock()
reranker = MagicMock()
vector_store = MagicMock()
# Plan: single search step
plan_json = '[{"action": "search", "detail": "test query"}]'
# Sub-agent answer after executing step
from langchain_core.messages import AIMessage
sub_agent_result = {"messages": [AIMessage(content="Found relevant info about test.")]}
# Final synthesis
final_answer = "The test policy states..."
# LLM calls: plan, executor system/tools, synthesis
# We mock the LLM and also mock the sub-agent creation
llm.invoke.side_effect = [plan_json, final_answer]
results = [_qr(chunk_id="c1", score=0.9, text="test policy")]
retriever.search_detailed.return_value = _hybrid_result(results)
reranker.rerank.return_value = results
vector_store.list_document_ids.return_value = ["doc.pdf"]
router = PlanAndExecuteRouter(llm, retriever, reranker, vector_store)
# Patch create_react_agent to return a mock that returns our sub_agent_result
mock_agent = MagicMock()
mock_agent.invoke.return_value = sub_agent_result
with patch("src.agent.plan_and_execute.create_react_agent", return_value=mock_agent):
response = router.route("test question", top_k=5)
assert isinstance(response, GenerationResponse)
assert response.answer == "The test policy states..."
def test_route_with_no_results(self) -> None:
"""Route when retrieval finds nothing."""
llm = MagicMock()
retriever = MagicMock()
reranker = MagicMock()
vector_store = MagicMock()
plan_json = '[{"action": "search", "detail": "nonexistent"}]'
from langchain_core.messages import AIMessage
sub_agent_result = {"messages": [AIMessage(content="No relevant documents found.")]}
llm.invoke.side_effect = [plan_json, "I could not find information."]
mock_agent = MagicMock()
mock_agent.invoke.return_value = sub_agent_result
router = PlanAndExecuteRouter(llm, retriever, reranker, vector_store)
with patch("src.agent.plan_and_execute.create_react_agent", return_value=mock_agent):
response = router.route("nonexistent topic", top_k=5)
assert response.intent == IntentType.FACTUAL
assert response.confidence == 0.0
def test_route_multi_step(self) -> None:
"""Route with a multi-step plan."""
llm = MagicMock()
retriever = MagicMock()
reranker = MagicMock()
vector_store = MagicMock()
plan_json = json.dumps([
{"action": "search", "detail": "policy A"},
{"action": "search", "detail": "policy B"},
])
from langchain_core.messages import AIMessage
sub_result_1 = {"messages": [AIMessage(content="Policy A info")]}
sub_result_2 = {"messages": [AIMessage(content="Policy B info")]}
llm.invoke.side_effect = [plan_json, "Comparison of A and B."]
mock_agent = MagicMock()
mock_agent.invoke.side_effect = [sub_result_1, sub_result_2]
router = PlanAndExecuteRouter(llm, retriever, reranker, vector_store)
with patch("src.agent.plan_and_execute.create_react_agent", return_value=mock_agent):
response = router.route("Compare A and B", top_k=5)
assert response.answer == "Comparison of A and B."
# Sub-agent should have been called twice
assert mock_agent.invoke.call_count == 2
# ---------------------------------------------------------------------------
# PlanAndExecuteRouter — route_stream
# ---------------------------------------------------------------------------
class TestRouteStream:
def test_stream_yields_plan_execute_synthesize_done(self) -> None:
"""Streaming should yield events in order: plan, execute, synthesize, done."""
llm = MagicMock()
retriever = MagicMock()
reranker = MagicMock()
vector_store = MagicMock()
plan_json = '[{"action": "search", "detail": "test"}]'
from langchain_core.messages import AIMessage
sub_agent_result = {"messages": [AIMessage(content="Found info.")]}
llm.invoke.side_effect = [plan_json, "Final answer."]
mock_agent = MagicMock()
mock_agent.invoke.return_value = sub_agent_result
router = PlanAndExecuteRouter(llm, retriever, reranker, vector_store)
with patch("src.agent.plan_and_execute.create_react_agent", return_value=mock_agent):
events = list(router.route_stream("test", top_k=5))
step_names = [e["step"] for e in events]
assert "plan" in step_names
assert "done" in step_names
# done event has the result
done_event = [e for e in events if e["step"] == "done"][0]
assert "result" in done_event
assert done_event["result"]["answer"] == "Final answer."
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