11b1bfbc94
A request can carry several of this capability's returns — a model can call search twice in one response — and the carrier was identified by message alone, so each of them received the whole capsule. It is selected by message and part now, so exactly one carries it however many share the request. The chat TUI passed its persisted state into the run, so tool synchronisation mutated it in place while the message history was promoted only on success. A cancelled or failed run therefore kept the evidence the tools had recorded and discarded the messages that justified it, and the next question derived its identity from the shorter history: behind the recorded epoch, refused as non-append-only, the conversation unusable until cleared. The run gets a copy, promoted with the messages or not at all. Five decorators had been left attached to a helper by an earlier extraction, which pytest does not collect, so the resume case they carried was silently untested. The wire test covers both resume shapes again, no prompt and deferred results.
1244 lines
42 KiB
Python
1244 lines
42 KiB
Python
from dataclasses import dataclass, field
|
|
from types import SimpleNamespace
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from typing import Any, cast
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from unittest.mock import AsyncMock, patch
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|
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import pytest
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from pydantic_ai import Agent, DeferredToolResults, ModelRetry, RunContext, ToolFailed
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from pydantic_ai.messages import (
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ModelRequest,
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ModelResponse,
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TextPart,
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ToolCallPart,
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ToolReturnPart,
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UserPromptPart,
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)
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from pydantic_ai.models.function import FunctionModel
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from pydantic_ai.models.test import TestModel
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from pydantic_ai.usage import RunUsage
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from haiku.rag.capabilities._base import (
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CITATION_GRACE_REQUESTS,
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_called_own_tool,
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)
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from haiku.rag.capabilities.analysis import AnalysisCapability, AnalysisState
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from haiku.rag.capabilities.analysis import create_capability as create_analysis
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from haiku.rag.capabilities.ledger import (
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CapabilityEvidenceRecord,
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citation_status,
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)
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from haiku.rag.capabilities.rag import AGENT_PREAMBLE, RAGCapability, RAGState
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from haiku.rag.capabilities.rag import create_capability as create_rag
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from haiku.rag.config.models import AppConfig, PromptsConfig
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from haiku.rag.sandbox import Sandbox, SandboxResult
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from haiku.rag.store.models.chunk import Chunk, SearchResult
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|
|
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@dataclass
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class Deps:
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state: dict[str, Any] = field(default_factory=dict)
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|
|
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def make_context(deps: Deps) -> RunContext[Deps]:
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return RunContext(
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deps=deps,
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model=TestModel(),
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usage=RunUsage(),
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run_id="test-run",
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)
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|
|
|
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def test_rag_capability_api(temp_db_path):
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capability = create_rag(db_path=temp_db_path, config=AppConfig())
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assert isinstance(capability, RAGCapability)
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assert capability.id == "haiku-rag"
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assert capability.defer_loading is True
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assert set(capability.get_toolset().tools) == {"rag_search", "rag_cite"}
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toolset = capability.get_toolset()
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assert toolset.max_retries == 3
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assert toolset.sequential is True
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assert capability.state_type is RAGState
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assert capability.state_namespace == "rag"
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assert capability.request_limit == 20
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|
|
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def test_analysis_capability_api(temp_db_path):
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capability = create_analysis(db_path=temp_db_path, config=AppConfig())
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assert isinstance(capability, AnalysisCapability)
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assert capability.id == "haiku-rag-analysis"
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assert capability.defer_loading is True
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assert set(capability.get_toolset().tools) == {
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"analysis_search",
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"analysis_execute_code",
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"analysis_cite",
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}
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toolset = capability.get_toolset()
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assert toolset.max_retries == 3
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assert toolset.sequential is True
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assert capability.state_type is AnalysisState
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assert capability.request_limit == 30
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|
|
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def test_capability_factories_resolve_environment_and_defaults(
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temp_db_path, monkeypatch
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):
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config = AppConfig()
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monkeypatch.setenv("HAIKU_RAG_DB", str(temp_db_path))
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assert create_rag(config=config).db_path == temp_db_path
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monkeypatch.delenv("HAIKU_RAG_DB")
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assert create_rag(config=config).db_path == (
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config.storage.data_dir / "haiku.rag.lancedb"
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)
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with patch("haiku.rag.config.get_config", return_value=config):
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assert create_rag().config is config
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assert create_analysis().config is config
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|
|
|
|
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def test_domain_preamble_is_added_to_capability_instructions(temp_db_path):
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|
config = AppConfig(
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prompts=PromptsConfig(domain_preamble="The corpus contains solar manuals.")
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)
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capability = create_rag(db_path=temp_db_path, config=config)
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assert capability.get_instructions().startswith(
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"The corpus contains solar manuals.\n\n# RAG"
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)
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|
|
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@pytest.mark.asyncio
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@pytest.mark.parametrize(
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("factory", "agent_instructions", "heading"),
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|
[
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(create_rag, AGENT_PREAMBLE, "# RAG"),
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(create_analysis, None, "# Analysis"),
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|
],
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|
)
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async def test_capability_instructions_are_injected_once(
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temp_db_path, factory, agent_instructions, heading
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|
):
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domain = "The corpus contains solar manuals."
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seen_instructions = []
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|
|
|
def model_function(_messages, info):
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|
seen_instructions.append(info.instructions or "")
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return ModelResponse(parts=[TextPart("done")])
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|
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config = AppConfig(prompts=PromptsConfig(domain_preamble=domain))
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agent = Agent(
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|
FunctionModel(model_function),
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deps_type=Deps,
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|
instructions=agent_instructions,
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capabilities=[
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|
factory(
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|
db_path=temp_db_path,
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|
config=config,
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|
defer_loading=False,
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|
)
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],
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)
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|
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await agent.run("Answer", deps=Deps())
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|
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|
assert seen_instructions[0].count(domain) == 1
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assert seen_instructions[0].count(heading) == 1
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|
|
|
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|
@pytest.mark.asyncio
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|
async def test_request_limit_removes_only_exhausted_capability_tools_per_run(
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|
temp_db_path,
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|
):
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|
calls = 0
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|
seen_tools = []
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|
seen_instructions = []
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|
|
|
def model_function(_messages, info):
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nonlocal calls
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calls += 1
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seen_tools.append({tool.name for tool in info.function_tools})
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|
seen_instructions.append(info.instructions or "")
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if calls % 2 == 1:
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return ModelResponse(parts=[ToolCallPart("host_tool", {})])
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return ModelResponse(parts=[TextPart("best available answer")])
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|
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|
def host_tool(_ctx: RunContext[Deps]) -> str:
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"""Return host-owned context."""
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|
return "host context"
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|
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|
rag = create_rag(
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db_path=temp_db_path,
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config=AppConfig(),
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defer_loading=False,
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)
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analysis = create_analysis(
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db_path=temp_db_path,
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config=AppConfig(),
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defer_loading=False,
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request_limit=1,
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)
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agent = Agent(
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FunctionModel(model_function),
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deps_type=Deps,
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tools=[host_tool],
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capabilities=[rag, analysis],
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)
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first = await agent.run("Analyze this", deps=Deps())
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second = await agent.run("Analyze another question", deps=Deps())
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assert first.output == "best available answer"
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assert second.output == "best available answer"
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analysis_tools = {
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"analysis_search",
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"analysis_execute_code",
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"analysis_cite",
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}
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for initial, exhausted in ((0, 1), (2, 3)):
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assert analysis_tools <= seen_tools[initial]
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assert {"analysis_search", "analysis_execute_code"}.isdisjoint(
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seen_tools[exhausted]
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)
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assert "analysis_cite" in seen_tools[exhausted]
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assert {"host_tool", "rag_search", "rag_cite"} <= seen_tools[exhausted]
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assert (
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"analysis capability has reached its request limit"
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in (seen_instructions[exhausted])
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)
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|
|
|
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|
@pytest.mark.asyncio
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|
async def test_deferred_request_limit_starts_after_capability_load(temp_db_path):
|
|
seen_tools = []
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|
seen_instructions = []
|
|
|
|
def model_function(_messages, info):
|
|
seen_tools.append({tool.name for tool in info.function_tools})
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|
seen_instructions.append(info.instructions or "")
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if len(seen_tools) == 1:
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return ModelResponse(
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|
parts=[
|
|
ToolCallPart(
|
|
"load_capability",
|
|
{"id": "haiku-rag-analysis"},
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|
)
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|
]
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|
)
|
|
if len(seen_tools) == 2:
|
|
return ModelResponse(parts=[ToolCallPart("host_tool", {})])
|
|
return ModelResponse(parts=[TextPart("best available answer")])
|
|
|
|
def host_tool(_ctx: RunContext[Deps]) -> str:
|
|
"""Return host-owned context."""
|
|
return "host context"
|
|
|
|
agent = Agent(
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|
FunctionModel(model_function),
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deps_type=Deps,
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|
tools=[host_tool],
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|
capabilities=[
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|
create_analysis(
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|
db_path=temp_db_path,
|
|
config=AppConfig(),
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|
request_limit=1,
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|
)
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|
],
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)
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|
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|
result = await agent.run("Analyze this", deps=Deps())
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|
|
assert result.output == "best available answer"
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assert "load_capability" in seen_tools[0]
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|
assert "analysis_search" in seen_tools[1]
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|
assert "analysis_search" not in seen_tools[2]
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|
assert "host_tool" in seen_tools[2]
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|
assert "analysis capability has reached its request limit" in seen_instructions[2]
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_capability_isolated_per_run_and_round_trips_state(temp_db_path):
|
|
capability = create_rag(db_path=temp_db_path, config=AppConfig())
|
|
deps = Deps(
|
|
state={
|
|
"rag": RAGState(
|
|
document_filter="uri = 'manual.pdf'",
|
|
citations=["old"],
|
|
searches={"old": []},
|
|
).model_dump(mode="json")
|
|
}
|
|
)
|
|
|
|
run_capability = await capability.for_run(make_context(deps))
|
|
|
|
assert run_capability is not capability
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|
assert run_capability.state is not None
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|
assert run_capability.state.document_filter == "uri = 'manual.pdf'"
|
|
assert run_capability.state.citations == []
|
|
assert run_capability.state.searches == {}
|
|
assert deps.state["rag"]["document_filter"] == "uri = 'manual.pdf'"
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_run_error_closes_resources_and_propagates(temp_db_path):
|
|
capability = create_rag(db_path=temp_db_path, config=AppConfig())
|
|
client = AsyncMock()
|
|
capability.rag = client
|
|
error = RuntimeError("model failed")
|
|
|
|
with pytest.raises(RuntimeError, match="model failed"):
|
|
await capability.on_run_error(make_context(Deps()), error=error)
|
|
|
|
client.__aexit__.assert_awaited_once_with(None, None, None)
|
|
assert capability.rag is None
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_search_and_empty_citation_limits(temp_db_path):
|
|
config = AppConfig()
|
|
config.qa.max_searches = 0
|
|
capability = create_rag(db_path=temp_db_path, config=config)
|
|
capability.state = RAGState()
|
|
|
|
with pytest.raises(ToolFailed, match="Search limit reached"):
|
|
await capability._search("anything", None)
|
|
|
|
with pytest.raises(ModelRetry, match="chunk_ids was empty"):
|
|
await capability._cite([])
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_cite_resolves_direct_chunk_ids_and_reuses_document_lookup(temp_db_path):
|
|
capability = create_rag(db_path=temp_db_path, config=AppConfig())
|
|
capability.state = RAGState(evidence=CapabilityEvidenceRecord(question=0))
|
|
client = AsyncMock()
|
|
client.get_chunk_by_id.side_effect = [
|
|
Chunk(id="chunk-1", document_id="doc-1", content="first"),
|
|
Chunk(id="chunk-2", document_id="doc-1", content="second"),
|
|
]
|
|
client.get_document_by_id.return_value = SimpleNamespace(
|
|
uri="test://document",
|
|
title="Document",
|
|
metadata={"topic": "ai"},
|
|
)
|
|
capability.rag = client
|
|
|
|
result = await capability._cite(["chunk-1", "chunk-2"])
|
|
|
|
assert result == "Registered 2 citation(s)."
|
|
assert capability.state.citations == ["chunk-1", "chunk-2"]
|
|
assert capability.state.citation_index["chunk-1"].index == 1
|
|
assert capability.state.citation_index["chunk-2"].index == 2
|
|
assert capability.state.citation_index["chunk-1"].document_meta == {"topic": "ai"}
|
|
client.get_document_by_id.assert_awaited_once_with("doc-1")
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_cite_reports_unresolved_ids_on_partial_success(temp_db_path):
|
|
capability = create_rag(db_path=temp_db_path, config=AppConfig())
|
|
capability.state = RAGState(evidence=CapabilityEvidenceRecord(question=0))
|
|
client = AsyncMock()
|
|
client.get_chunk_by_id.side_effect = [
|
|
Chunk(id="chunk-1", document_id="doc-1", content="first"),
|
|
None,
|
|
None,
|
|
]
|
|
client.get_document_by_id.return_value = SimpleNamespace(
|
|
uri="test://document",
|
|
title="Document",
|
|
metadata={},
|
|
)
|
|
capability.rag = client
|
|
|
|
result = await capability._cite(["chunk-1", "6.43", "6.51.2"])
|
|
|
|
assert "Registered 1 citation(s)" in result
|
|
assert "6.43" in result
|
|
assert "6.51.2" in result
|
|
assert "verbatim" in result
|
|
assert capability.state.citations == ["chunk-1"]
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_cite_repairs_chunk_ids_damaged_in_transcription(temp_db_path):
|
|
"""Models mistype opaque UUIDs; near misses resolve to the retrieved id."""
|
|
true_id = "b8e25ea1-0bb3-48b1-8fea-2ac1f148bf7c"
|
|
unrelated = "9c2cd07e-5a3f-45a6-968d-cbd6f06ab57b"
|
|
capability = create_rag(db_path=temp_db_path, config=AppConfig())
|
|
capability.state = RAGState(
|
|
evidence=CapabilityEvidenceRecord(question=0),
|
|
searches={
|
|
"q": [
|
|
SearchResult(
|
|
content="evidence",
|
|
score=1.0,
|
|
chunk_id=true_id,
|
|
document_id="doc-1",
|
|
document_uri="test://document",
|
|
)
|
|
]
|
|
},
|
|
)
|
|
client = AsyncMock()
|
|
client.get_chunk_by_id.return_value = None
|
|
capability.rag = client
|
|
|
|
dropped_char = "b8e25ea1-0bb3-48b1-8fea-2ac1f148bf7"
|
|
dropped_group = "0bb3-48b1-8fea-2ac1f148bf7c"
|
|
|
|
assert await capability._cite([dropped_char]) == "Registered 1 citation(s)."
|
|
assert await capability._cite([dropped_group]) == "Registered 1 citation(s)."
|
|
assert capability.state.citations == [true_id]
|
|
|
|
# An unrelated UUID is never attributed to a retrieved neighbour.
|
|
with pytest.raises(ModelRetry, match=unrelated):
|
|
await capability._cite([unrelated])
|
|
|
|
assert capability.state.citations == [true_id]
|
|
client.get_chunk_by_id.assert_awaited_once_with(unrelated)
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_analysis_records_new_sandbox_search_results(temp_db_path):
|
|
capability = create_analysis(db_path=temp_db_path, config=AppConfig())
|
|
existing = SearchResult(content="existing", score=1, chunk_id="chunk-1")
|
|
new = SearchResult(content="new", score=1, chunk_id="chunk-2")
|
|
capability.state = AnalysisState(searches={"_sandbox": [existing]})
|
|
sandbox = AsyncMock()
|
|
sandbox.execute.return_value = SandboxResult(stdout="done", stderr="", success=True)
|
|
sandbox._search_results = [existing, new]
|
|
capability.sandbox = cast(Sandbox, sandbox)
|
|
|
|
result = await capability._execute_code("print('done')")
|
|
|
|
assert result == "done"
|
|
assert [item.chunk_id for item in capability.state.searches["_sandbox"]] == [
|
|
"chunk-1",
|
|
"chunk-2",
|
|
]
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_failed_tool_reaches_the_model_and_the_run_continues(temp_db_path):
|
|
"""A `ToolFailed` tool leaves a failed result in history and answers anyway."""
|
|
config = AppConfig()
|
|
config.qa.max_searches = 0
|
|
calls = 0
|
|
|
|
def model_function(_messages, _info):
|
|
nonlocal calls
|
|
calls += 1
|
|
if calls == 1:
|
|
return ModelResponse(parts=[ToolCallPart("rag_search", {"query": "x"})])
|
|
return ModelResponse(parts=[TextPart("answered from what I had")])
|
|
|
|
agent = Agent(
|
|
FunctionModel(model_function),
|
|
deps_type=Deps,
|
|
capabilities=[
|
|
create_rag(db_path=temp_db_path, config=config, defer_loading=False)
|
|
],
|
|
)
|
|
|
|
result = await agent.run("question", deps=Deps())
|
|
|
|
assert result.output == "answered from what I had"
|
|
failed = [
|
|
part
|
|
for message in result.all_messages()
|
|
for part in message.parts
|
|
if isinstance(part, ToolReturnPart) and part.outcome == "failed"
|
|
]
|
|
assert [part.tool_name for part in failed] == ["rag_search"]
|
|
assert "Search limit reached" in str(failed[0].content)
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_analysis_execution_limit_fails_the_tool(temp_db_path):
|
|
config = AppConfig()
|
|
config.analysis.max_executions = 0
|
|
capability = create_analysis(db_path=temp_db_path, config=config)
|
|
capability.state = AnalysisState()
|
|
|
|
with pytest.raises(ToolFailed, match="Code-execution limit reached"):
|
|
await capability._execute_code("print('done')")
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_a_spent_execution_budget_is_not_evidence(temp_db_path):
|
|
"""Nothing was produced to ground an answer on, so nothing is recorded."""
|
|
config = AppConfig()
|
|
config.analysis.max_executions = 0
|
|
capability = create_analysis(db_path=temp_db_path, config=config)
|
|
capability.state = AnalysisState()
|
|
capability.epoch = 5
|
|
|
|
with pytest.raises(ToolFailed):
|
|
await capability._execute_code("print('done')")
|
|
|
|
assert capability.state.evidence.latest_evidence_epoch == 0
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
("success", "stdout", "expected_epoch"),
|
|
[
|
|
pytest.param(True, "42", 5, id="succeeded"),
|
|
pytest.param(True, "", 5, id="succeeded without output"),
|
|
pytest.param(False, "42", 5, id="failed after printing"),
|
|
pytest.param(False, "", 0, id="failed without printing"),
|
|
],
|
|
)
|
|
@pytest.mark.asyncio
|
|
async def test_only_a_code_execution_the_model_can_read_is_evidence(
|
|
temp_db_path, success, stdout, expected_epoch
|
|
):
|
|
"""A raised error with nothing printed grounds nothing, so it is not evidence.
|
|
|
|
A failure that printed first does ground an answer, and so does a successful
|
|
run whose outcome is that it printed nothing.
|
|
"""
|
|
capability = create_analysis(db_path=temp_db_path, config=AppConfig())
|
|
capability.state = AnalysisState(evidence=CapabilityEvidenceRecord(question=0))
|
|
capability.epoch = 5
|
|
sandbox = AsyncMock(spec=Sandbox)
|
|
sandbox._search_results = []
|
|
sandbox.execute.return_value = SandboxResult(
|
|
stdout=stdout, stderr="" if success else "boom", success=success
|
|
)
|
|
capability.sandbox = sandbox
|
|
|
|
if success:
|
|
await capability._execute_code("print(42)")
|
|
else:
|
|
with pytest.raises(ToolFailed):
|
|
await capability._execute_code("print(42)")
|
|
|
|
assert capability.state.evidence.latest_evidence_epoch == expected_epoch
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_spent_search_budget_is_announced_but_keeps_the_tool(rag_db):
|
|
"""A spent budget is announced; the tool stays declared to avoid a dead run.
|
|
|
|
Withdrawing it would make a model that calls it anyway hit `Unknown tool
|
|
name`, which exhausts the agent's unknown-tool retries and aborts the run.
|
|
"""
|
|
config = AppConfig()
|
|
config.qa.max_searches = 1
|
|
seen_tools = []
|
|
seen_instructions = []
|
|
calls = 0
|
|
|
|
def model_function(_messages, info):
|
|
nonlocal calls
|
|
calls += 1
|
|
seen_tools.append({tool.name for tool in info.function_tools})
|
|
seen_instructions.append(info.instructions or "")
|
|
if calls == 1:
|
|
return ModelResponse(
|
|
parts=[ToolCallPart("rag_search", {"query": "machine learning"})]
|
|
)
|
|
return ModelResponse(parts=[TextPart("answered")])
|
|
|
|
agent = Agent(
|
|
FunctionModel(model_function),
|
|
deps_type=Deps,
|
|
capabilities=[create_rag(db_path=rag_db, config=config, defer_loading=False)],
|
|
)
|
|
|
|
result = await agent.run("question", deps=Deps())
|
|
|
|
assert result.output == "answered"
|
|
assert {"rag_search", "rag_cite"} <= seen_tools[1]
|
|
assert "spent its budget for rag_search" in seen_instructions[1]
|
|
|
|
|
|
def test_grace_window_ignores_other_capabilities_turns():
|
|
"""Only this capability's own tool calls may spend its cite window.
|
|
|
|
A multi-capability agent spends turns elsewhere; those must not expire the
|
|
window that exists to give this capability a chance to cite.
|
|
"""
|
|
rag_tools = frozenset({"rag_search", "rag_cite"})
|
|
|
|
# Nothing to attribute before the model has responded at all.
|
|
assert not _called_own_tool(
|
|
[ModelRequest(parts=[UserPromptPart(content="q")])], rag_tools
|
|
)
|
|
assert not _called_own_tool(
|
|
[ModelResponse(parts=[ToolCallPart("analysis_search", {"query": "x"})])],
|
|
rag_tools,
|
|
)
|
|
assert not _called_own_tool(
|
|
[ModelResponse(parts=[TextPart("just talking")])], rag_tools
|
|
)
|
|
assert _called_own_tool(
|
|
[ModelResponse(parts=[ToolCallPart("rag_cite", {"chunk_ids": ["a"]})])],
|
|
rag_tools,
|
|
)
|
|
# Only the most recent response counts, not any earlier one.
|
|
assert not _called_own_tool(
|
|
[
|
|
ModelResponse(parts=[ToolCallPart("rag_cite", {"chunk_ids": ["a"]})]),
|
|
ModelRequest(parts=[UserPromptPart(content="next")]),
|
|
ModelResponse(parts=[ToolCallPart("analysis_search", {"query": "x"})]),
|
|
],
|
|
rag_tools,
|
|
)
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_spent_search_notice_points_at_code_while_it_has_budget(temp_db_path):
|
|
"""Analysis must be sent to the sandbox, not told to answer, while it can.
|
|
|
|
In-code `search()` bypasses `qa.max_searches`, and the instructions tell the
|
|
model to escalate to code when search results are insufficient.
|
|
"""
|
|
config = AppConfig()
|
|
config.qa.max_searches = 2
|
|
capability = create_analysis(db_path=temp_db_path, config=config)
|
|
capability.search_count = 2
|
|
|
|
notice = capability._budget_notice()
|
|
|
|
assert notice is not None
|
|
assert "analysis_search" in notice
|
|
assert "analysis_execute_code" in notice
|
|
|
|
# Once the code budget is gone too there is nowhere left to send it.
|
|
capability.execute_count = config.analysis.max_executions
|
|
notice = capability._budget_notice()
|
|
assert notice is not None
|
|
assert "analysis_execute_code" in notice
|
|
assert capability.evidence_tool_names() <= capability._spent_tool_names()
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_spent_search_notice_tells_rag_to_answer(temp_db_path):
|
|
"""Search is the RAG capability's only evidence tool, so stopping is right."""
|
|
config = AppConfig()
|
|
config.qa.max_searches = 2
|
|
capability = create_rag(db_path=temp_db_path, config=config)
|
|
capability.search_count = 2
|
|
|
|
notice = capability._budget_notice()
|
|
|
|
assert notice is not None
|
|
assert "rag_search" in notice
|
|
assert capability.evidence_tool_names() == {"rag_search"}
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_spent_execution_budget_joins_the_notice(temp_db_path):
|
|
config = AppConfig()
|
|
config.analysis.max_executions = 3
|
|
capability = create_analysis(db_path=temp_db_path, config=config)
|
|
|
|
assert capability._spent_tool_names() == set()
|
|
|
|
capability.execute_count = 3
|
|
|
|
assert capability._spent_tool_names() == {"analysis_execute_code"}
|
|
notice = capability._budget_notice()
|
|
assert notice is not None
|
|
assert "analysis_execute_code" in notice
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_exhausted_run_can_still_register_citations(rag_db):
|
|
"""The cite tool outlives the request limit so evidence is not lost.
|
|
|
|
Reproduces the measured pathology: the model burns its request budget and
|
|
reaches the limit, at which point it must still be able to cite what it
|
|
already found.
|
|
"""
|
|
config = AppConfig()
|
|
seen_tools = []
|
|
calls = 0
|
|
chunk_id: str | None = None
|
|
|
|
def model_function(_messages, info):
|
|
nonlocal calls
|
|
calls += 1
|
|
seen_tools.append({tool.name for tool in info.function_tools})
|
|
if calls == 1:
|
|
return ModelResponse(
|
|
parts=[ToolCallPart("rag_search", {"query": "machine learning"})]
|
|
)
|
|
if calls == 2:
|
|
return ModelResponse(
|
|
parts=[ToolCallPart("rag_cite", {"chunk_ids": [chunk_id]})]
|
|
)
|
|
return ModelResponse(parts=[TextPart("answered from gathered evidence")])
|
|
|
|
capability = create_rag(
|
|
db_path=rag_db,
|
|
config=config,
|
|
defer_loading=False,
|
|
request_limit=1,
|
|
)
|
|
agent = Agent(
|
|
FunctionModel(model_function),
|
|
deps_type=Deps,
|
|
capabilities=[capability],
|
|
)
|
|
deps = Deps()
|
|
|
|
async with agent.iter("question", deps=deps) as run:
|
|
async for _node in run:
|
|
if chunk_id is None:
|
|
searches = deps.state.get("rag", {}).get("searches") or {}
|
|
for results in searches.values():
|
|
if results:
|
|
chunk_id = results[0]["chunk_id"]
|
|
break
|
|
|
|
assert chunk_id is not None
|
|
# The limit lands on request 2, where cite must still be offered.
|
|
assert "rag_search" not in seen_tools[1]
|
|
assert "rag_cite" in seen_tools[1]
|
|
assert deps.state["rag"]["citations"] == [chunk_id]
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_cite_tool_is_withdrawn_after_the_grace_window(temp_db_path):
|
|
capability = create_rag(
|
|
db_path=temp_db_path,
|
|
config=AppConfig(),
|
|
defer_loading=False,
|
|
request_limit=2,
|
|
)
|
|
tool_defs = [
|
|
SimpleNamespace(name=name, capability_id=capability.id)
|
|
for name in ("rag_search", "rag_cite")
|
|
]
|
|
ctx = make_context(Deps())
|
|
|
|
capability.request_count = 2
|
|
kept = await capability.prepare_tools(ctx, cast(Any, tool_defs))
|
|
assert {tool.name for tool in kept} == {"rag_cite"}
|
|
notice = capability._budget_notice()
|
|
assert notice is not None and "rag_cite" in notice
|
|
|
|
capability.grace_requests_used = CITATION_GRACE_REQUESTS
|
|
kept = await capability.prepare_tools(ctx, cast(Any, tool_defs))
|
|
assert kept == []
|
|
# The notice must never point at a tool prepare_tools has withdrawn:
|
|
# calling a missing tool burns the agent's unknown-tool retries and can
|
|
# abort the run.
|
|
notice = capability._budget_notice()
|
|
assert notice is not None
|
|
assert "rag_cite" not in notice
|
|
assert "no longer available" in notice
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
@pytest.mark.parametrize(
|
|
("stderr", "expect_hint"),
|
|
[
|
|
("TypeError: '_io.TextIOWrapper' object is not iterable", True),
|
|
("TypeError: 'list' object is not an iterator", False),
|
|
],
|
|
)
|
|
async def test_sandbox_iteration_failure_carries_the_workaround(
|
|
temp_db_path, stderr, expect_hint
|
|
):
|
|
"""A model that iterates a file object gets told what to do instead."""
|
|
capability = create_analysis(db_path=temp_db_path, config=AppConfig())
|
|
capability.state = AnalysisState()
|
|
sandbox = AsyncMock()
|
|
sandbox.execute.return_value = SandboxResult(
|
|
stdout="", stderr=stderr, success=False
|
|
)
|
|
sandbox._search_results = []
|
|
capability.sandbox = cast(Sandbox, sandbox)
|
|
|
|
with pytest.raises(ToolFailed) as failure:
|
|
await capability._execute_code(
|
|
"for line in open('/documents/x/items.jsonl'): pass"
|
|
)
|
|
|
|
assert (".readlines()" in str(failure.value)) is expect_hint
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_analysis_sandbox_failure_records_execution_and_fails_the_tool(
|
|
temp_db_path,
|
|
):
|
|
capability = create_analysis(db_path=temp_db_path, config=AppConfig())
|
|
capability.state = AnalysisState()
|
|
capability.outer_state = {}
|
|
sandbox = AsyncMock()
|
|
sandbox.execute.return_value = SandboxResult(
|
|
stdout="partial", stderr="NameError: undefined", success=False
|
|
)
|
|
sandbox._search_results = []
|
|
capability.sandbox = cast(Sandbox, sandbox)
|
|
|
|
with pytest.raises(ToolFailed, match="NameError: undefined"):
|
|
await capability._with_state(capability._execute_code("boom"))
|
|
|
|
entry = capability.state.executions[-1]
|
|
assert entry.success is False
|
|
assert entry.stderr == "NameError: undefined"
|
|
assert capability.outer_state["analysis"]["executions"][-1]["code"] == "boom"
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_native_agent_composition_initializes_host_state(temp_db_path):
|
|
capability = create_rag(
|
|
db_path=temp_db_path,
|
|
config=AppConfig(),
|
|
defer_loading=False,
|
|
)
|
|
deps = Deps()
|
|
agent = Agent(
|
|
TestModel(call_tools=[]),
|
|
deps_type=Deps,
|
|
capabilities=[capability],
|
|
)
|
|
|
|
result = await agent.run("Hello", deps=deps)
|
|
|
|
assert result.output == "success (no tool calls)"
|
|
assert deps.state["rag"] == RAGState(
|
|
evidence=CapabilityEvidenceRecord(question=0)
|
|
).model_dump(mode="json")
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_deferred_capability_loads_native_tools(temp_db_path):
|
|
seen_instructions = []
|
|
loaded_payloads = []
|
|
|
|
def model_function(messages, info):
|
|
seen_instructions.append(info.instructions or "")
|
|
loaded_payloads.extend(
|
|
str(part.content)
|
|
for message in messages
|
|
for part in message.parts
|
|
if isinstance(part, ToolReturnPart) and part.tool_name == "load_capability"
|
|
)
|
|
loaded = any(
|
|
isinstance(part, ToolReturnPart) and part.tool_name == "load_capability"
|
|
for message in messages
|
|
for part in message.parts
|
|
)
|
|
if not loaded:
|
|
return ModelResponse(
|
|
parts=[ToolCallPart("load_capability", {"id": "haiku-rag"})]
|
|
)
|
|
return ModelResponse(parts=[TextPart("loaded")])
|
|
|
|
agent = Agent(
|
|
FunctionModel(model_function),
|
|
deps_type=Deps,
|
|
capabilities=[create_rag(db_path=temp_db_path, config=AppConfig())],
|
|
)
|
|
|
|
result = await agent.run("Use RAG", deps=Deps())
|
|
|
|
assert result.output == "loaded"
|
|
assert "# RAG" not in seen_instructions[0]
|
|
assert "# RAG" in loaded_payloads[0]
|
|
assert "rag_search" in loaded_payloads[0]
|
|
|
|
|
|
def _resuming_deps() -> Deps:
|
|
"""State as a resumption always finds it: the question already identified.
|
|
|
|
A run that resumes has been through ``for_run`` before, so the identity of the
|
|
question in progress is stored. Fabricating the history without it is a state
|
|
the design does not produce, and is rejected rather than guessed at.
|
|
"""
|
|
return Deps(
|
|
state={
|
|
"rag": RAGState(evidence=CapabilityEvidenceRecord(question=0)).model_dump(
|
|
mode="json"
|
|
)
|
|
}
|
|
)
|
|
|
|
|
|
def _in_flight_history() -> list[Any]:
|
|
"""A question already asked and searched, still awaiting its answer."""
|
|
return [
|
|
ModelRequest(parts=[UserPromptPart("what does the supervisor do?")]),
|
|
ModelResponse(parts=[ToolCallPart("rag_search", {"query": "s"}, "call-1")]),
|
|
ModelRequest(
|
|
parts=[ToolReturnPart("rag_search", "EVIDENCE FOR THE LIVE TURN", "call-1")]
|
|
),
|
|
]
|
|
|
|
|
|
def _record(deps: Deps, namespace: str) -> CapabilityEvidenceRecord:
|
|
return CapabilityEvidenceRecord.model_validate(deps.state[namespace]["evidence"])
|
|
|
|
|
|
async def _stub_search(self, query: str, _limit: int | None) -> str:
|
|
"""Record a result the way the real search does, so citing resolves."""
|
|
cast(Any, self.state).searches[query] = [
|
|
SearchResult(content="evidence", score=1.0, chunk_id="chunk-1")
|
|
]
|
|
self._note_evidence()
|
|
return "EVIDENCE"
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_a_question_takes_its_own_identity_and_both_capabilities_agree(
|
|
temp_db_path,
|
|
):
|
|
"""Identity is derived from the conversation, so no counter is shared."""
|
|
rag = create_rag(db_path=temp_db_path, config=AppConfig(), defer_loading=False)
|
|
analysis = create_analysis(
|
|
db_path=temp_db_path, config=AppConfig(), defer_loading=False
|
|
)
|
|
|
|
async def model(_messages, _info):
|
|
return ModelResponse(parts=[TextPart("answer")])
|
|
|
|
agent = Agent(FunctionModel(model), deps_type=Deps, capabilities=[rag, analysis])
|
|
deps = Deps()
|
|
|
|
first = await agent.run("first question", deps=deps)
|
|
first_identity = _record(deps, "rag").question
|
|
await agent.run("second question", deps=deps, message_history=first.all_messages())
|
|
second_identity = _record(deps, "rag").question
|
|
|
|
assert first_identity == 0
|
|
assert second_identity is not None and second_identity > 0
|
|
assert _record(deps, "analysis").question == second_identity
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_a_resumption_keeps_the_identity_of_the_question_in_progress(
|
|
temp_db_path,
|
|
):
|
|
rag = create_rag(db_path=temp_db_path, config=AppConfig(), defer_loading=False)
|
|
|
|
async def model(_messages, _info):
|
|
return ModelResponse(parts=[TextPart("answer")])
|
|
|
|
agent = Agent(FunctionModel(model), deps_type=Deps, capabilities=[rag])
|
|
deps = Deps(
|
|
state={
|
|
"rag": RAGState(evidence=CapabilityEvidenceRecord(question=7)).model_dump(
|
|
mode="json"
|
|
)
|
|
}
|
|
)
|
|
history = [
|
|
*_in_flight_history(),
|
|
ModelResponse(parts=[ToolCallPart("external_tool", {}, "call-2")]),
|
|
]
|
|
|
|
await agent.run(
|
|
"carry on",
|
|
deferred_tool_results=DeferredToolResults(calls={"call-2": "external result"}),
|
|
message_history=history,
|
|
deps=deps,
|
|
)
|
|
|
|
assert _record(deps, "rag").question == 7
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_resuming_without_a_stored_identity_fails_instead_of_guessing(
|
|
temp_db_path,
|
|
):
|
|
"""Adopting the message count would relabel a question already in progress.
|
|
|
|
Every declaration and epoch comparison in it would then be judged against the
|
|
wrong question, silently. This state is not one the design produces, so it is
|
|
reported rather than repaired.
|
|
"""
|
|
rag = create_rag(db_path=temp_db_path, config=AppConfig(), defer_loading=False)
|
|
|
|
async def model(_messages, _info): # pragma: no cover - never reached
|
|
return ModelResponse(parts=[TextPart("answer")])
|
|
|
|
agent = Agent(FunctionModel(model), deps_type=Deps, capabilities=[rag])
|
|
|
|
with pytest.raises(RuntimeError, match="no stored question identity"):
|
|
await agent.run(
|
|
"carry on",
|
|
deferred_tool_results=DeferredToolResults(
|
|
calls={"call-2": "external result"}
|
|
),
|
|
message_history=[
|
|
*_in_flight_history(),
|
|
ModelResponse(parts=[ToolCallPart("external_tool", {}, "call-2")]),
|
|
],
|
|
deps=Deps(),
|
|
)
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_citing_after_searching_grounds_the_question(temp_db_path):
|
|
"""The whole rule, end to end, with no compactor and no policy capability."""
|
|
rag = create_rag(db_path=temp_db_path, config=AppConfig(), defer_loading=False)
|
|
calls = iter(
|
|
[
|
|
[ToolCallPart("rag_search", {"query": "supervisor"}, "call-1")],
|
|
[ToolCallPart("rag_cite", {"chunk_ids": ["chunk-1"]}, "call-2")],
|
|
[TextPart("answer")],
|
|
]
|
|
)
|
|
|
|
async def model(_messages, _info):
|
|
return ModelResponse(parts=next(calls))
|
|
|
|
agent = Agent(FunctionModel(model), deps_type=Deps, capabilities=[rag])
|
|
deps = Deps()
|
|
|
|
with patch.object(RAGCapability, "_search", _stub_search):
|
|
await agent.run("what does the supervisor do?", deps=deps)
|
|
|
|
record = _record(deps, "rag")
|
|
question = record.question
|
|
assert question is not None
|
|
assert record.declaration is not None
|
|
assert [ref.chunk_id for ref in record.declaration.refs] == ["chunk-1"]
|
|
assert record.occurrences["chunk-1"].retrieved_in_questions == [question]
|
|
assert citation_status([record], question=question) == "grounded"
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_searching_after_citing_leaves_the_question_uncited(temp_db_path):
|
|
rag = create_rag(db_path=temp_db_path, config=AppConfig(), defer_loading=False)
|
|
calls = iter(
|
|
[
|
|
[ToolCallPart("rag_search", {"query": "supervisor"}, "call-1")],
|
|
[ToolCallPart("rag_cite", {"chunk_ids": ["chunk-1"]}, "call-2")],
|
|
[ToolCallPart("rag_search", {"query": "again"}, "call-3")],
|
|
[TextPart("answer")],
|
|
]
|
|
)
|
|
|
|
async def model(_messages, _info):
|
|
return ModelResponse(parts=next(calls))
|
|
|
|
agent = Agent(FunctionModel(model), deps_type=Deps, capabilities=[rag])
|
|
deps = Deps()
|
|
|
|
with patch.object(RAGCapability, "_search", _stub_search):
|
|
await agent.run("what does the supervisor do?", deps=deps)
|
|
|
|
record = _record(deps, "rag")
|
|
question = record.question
|
|
assert question is not None
|
|
assert record.declaration is not None
|
|
assert citation_status([record], question=question) == "missing"
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_a_citation_in_the_same_request_as_its_search_is_not_current(
|
|
temp_db_path,
|
|
):
|
|
"""Two calls in one response share an epoch, and citing must follow seeing."""
|
|
rag = create_rag(db_path=temp_db_path, config=AppConfig(), defer_loading=False)
|
|
calls = iter(
|
|
[
|
|
[
|
|
ToolCallPart("rag_search", {"query": "supervisor"}, "call-1"),
|
|
ToolCallPart("rag_cite", {"chunk_ids": ["chunk-1"]}, "call-2"),
|
|
],
|
|
[TextPart("answer")],
|
|
]
|
|
)
|
|
|
|
async def model(_messages, _info):
|
|
return ModelResponse(parts=next(calls))
|
|
|
|
agent = Agent(FunctionModel(model), deps_type=Deps, capabilities=[rag])
|
|
deps = Deps()
|
|
|
|
with patch.object(RAGCapability, "_search", _stub_search):
|
|
await agent.run("what does the supervisor do?", deps=deps)
|
|
|
|
record = _record(deps, "rag")
|
|
question = record.question
|
|
assert question is not None
|
|
assert record.declaration is not None
|
|
assert record.declaration.epoch == record.latest_evidence_epoch
|
|
assert citation_status([record], question=question) == "missing"
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_evidence_cited_in_two_questions_keeps_both_in_the_record(temp_db_path):
|
|
"""Occurrences outlive the question that wrote them, through the state dict."""
|
|
rag = create_rag(db_path=temp_db_path, config=AppConfig(), defer_loading=False)
|
|
calls = iter(
|
|
[
|
|
[ToolCallPart("rag_search", {"query": "supervisor"}, "call-1")],
|
|
[ToolCallPart("rag_cite", {"chunk_ids": ["chunk-1"]}, "call-2")],
|
|
[TextPart("first answer")],
|
|
[ToolCallPart("rag_search", {"query": "supervisor again"}, "call-3")],
|
|
[ToolCallPart("rag_cite", {"chunk_ids": ["chunk-1"]}, "call-4")],
|
|
[TextPart("second answer")],
|
|
]
|
|
)
|
|
|
|
async def model(_messages, _info):
|
|
return ModelResponse(parts=next(calls))
|
|
|
|
agent = Agent(FunctionModel(model), deps_type=Deps, capabilities=[rag])
|
|
deps = Deps()
|
|
|
|
with patch.object(RAGCapability, "_search", _stub_search):
|
|
first = await agent.run("who supervises?", deps=deps)
|
|
first_question = _record(deps, "rag").question
|
|
await agent.run(
|
|
"and who supervises them?", deps=deps, message_history=first.all_messages()
|
|
)
|
|
|
|
record = _record(deps, "rag")
|
|
assert record.occurrences["chunk-1"].cited_in_questions == [
|
|
first_question,
|
|
record.question,
|
|
]
|
|
assert record.question != first_question
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_a_run_with_no_prompt_and_no_history_starts_a_question(temp_db_path):
|
|
"""An instructions-only run is a first question, not a resumption.
|
|
|
|
There is no question in progress to keep an identity for, so nothing is
|
|
missing and the run proceeds with a fresh one.
|
|
"""
|
|
rag = create_rag(db_path=temp_db_path, config=AppConfig(), defer_loading=False)
|
|
|
|
async def model(_messages, _info):
|
|
return ModelResponse(parts=[TextPart("answer")])
|
|
|
|
agent = Agent(FunctionModel(model), deps_type=Deps, capabilities=[rag])
|
|
deps = Deps()
|
|
|
|
await agent.run(deps=deps)
|
|
|
|
assert _record(deps, "rag").question == 0
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_citing_without_searching_grounds_the_question(temp_db_path):
|
|
"""A direct chunk-id citation stands on its own, with no evidence outcome.
|
|
|
|
Epochs count messages and so start above zero, which is what lets a
|
|
declaration made in the first request still beat an empty evidence horizon.
|
|
"""
|
|
rag = create_rag(db_path=temp_db_path, config=AppConfig(), defer_loading=False)
|
|
calls = iter(
|
|
[
|
|
[ToolCallPart("rag_cite", {"chunk_ids": ["chunk-1"]}, "call-1")],
|
|
[TextPart("answer")],
|
|
]
|
|
)
|
|
|
|
async def model(_messages, _info):
|
|
return ModelResponse(parts=next(calls))
|
|
|
|
client = AsyncMock()
|
|
client.get_chunk_by_id.return_value = Chunk(
|
|
id="chunk-1", document_id="doc-1", content="evidence"
|
|
)
|
|
client.get_document_by_id.return_value = None
|
|
agent = Agent(FunctionModel(model), deps_type=Deps, capabilities=[rag])
|
|
deps = Deps()
|
|
|
|
with patch.object(RAGCapability, "_ensure_rag", AsyncMock(return_value=client)):
|
|
await agent.run("cite chunk-1", deps=deps)
|
|
|
|
record = _record(deps, "rag")
|
|
question = record.question
|
|
assert question is not None
|
|
assert record.latest_evidence_epoch == 0
|
|
assert record.declaration is not None
|
|
assert record.declaration.epoch > 0
|
|
assert record.occurrences["chunk-1"].retrieved_in_questions == []
|
|
assert citation_status([record], question=question) == "grounded"
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_a_host_seeded_record_does_not_pass_for_a_resumption(temp_db_path):
|
|
"""A default record is truthy, so its presence cannot stand in for identity.
|
|
|
|
Seeding one is what a host does when it has no state to send, and taking it
|
|
at face value would silently answer as question zero.
|
|
"""
|
|
rag = create_rag(db_path=temp_db_path, config=AppConfig(), defer_loading=False)
|
|
|
|
async def model(_messages, _info): # pragma: no cover - never reached
|
|
return ModelResponse(parts=[TextPart("answer")])
|
|
|
|
agent = Agent(FunctionModel(model), deps_type=Deps, capabilities=[rag])
|
|
|
|
with pytest.raises(RuntimeError, match="no stored question identity"):
|
|
await agent.run(
|
|
"carry on",
|
|
message_history=_in_flight_history(),
|
|
deps=Deps(state={"rag": RAGState().model_dump(mode="json")}),
|
|
)
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_a_resumption_keeps_the_evidence_the_question_already_gathered(
|
|
temp_db_path,
|
|
):
|
|
"""Clearing it would lose the results the model is still answering from.
|
|
|
|
A citation after the resumption then records no provenance, and cannot resolve
|
|
against the expanded search result the model actually saw.
|
|
"""
|
|
rag = create_rag(db_path=temp_db_path, config=AppConfig(), defer_loading=False)
|
|
calls = iter(
|
|
[
|
|
[ToolCallPart("rag_search", {"query": "supervisor"}, "call-1")],
|
|
[TextPart("partial answer")],
|
|
[ToolCallPart("rag_cite", {"chunk_ids": ["chunk-1"]}, "call-3")],
|
|
[TextPart("answer")],
|
|
]
|
|
)
|
|
|
|
async def model(_messages, _info):
|
|
return ModelResponse(parts=next(calls))
|
|
|
|
agent = Agent(FunctionModel(model), deps_type=Deps, capabilities=[rag])
|
|
deps = Deps()
|
|
|
|
with patch.object(RAGCapability, "_search", _stub_search):
|
|
interrupted = await agent.run("what does the supervisor do?", deps=deps)
|
|
identity = _record(deps, "rag").question
|
|
assert identity is not None
|
|
await agent.run(
|
|
deferred_tool_results=DeferredToolResults(
|
|
calls={"call-2": "external result"}
|
|
),
|
|
message_history=[
|
|
*interrupted.all_messages(),
|
|
ModelResponse(parts=[ToolCallPart("external_tool", {}, "call-2")]),
|
|
],
|
|
deps=deps,
|
|
)
|
|
|
|
record = _record(deps, "rag")
|
|
assert record.question == identity
|
|
assert record.occurrences["chunk-1"].retrieved_in_questions == [identity]
|
|
assert citation_status([record], question=identity) == "grounded"
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_a_capability_fetches_its_own_evidences_pictures(temp_db_path):
|
|
"""Compaction rehydrates through the owner, which already holds the connection."""
|
|
capability = create_rag(db_path=temp_db_path, config=AppConfig())
|
|
client = AsyncMock()
|
|
client.document_item_repository.get_picture_bytes.return_value = b"picture-bytes"
|
|
capability.rag = client
|
|
|
|
data = await capability.get_picture_bytes("doc-1", "#/pictures/0")
|
|
|
|
assert data == b"picture-bytes"
|
|
client.document_item_repository.get_picture_bytes.assert_awaited_once_with(
|
|
"doc-1", "#/pictures/0"
|
|
)
|