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* refactor(provider): use LiteLLM as unified LLM requester backend
- Replace 23+ individual requester implementations with unified litellmchat.py
- Add litellm_provider field to 27 YAML manifests for provider routing
- Delete redundant requester subclasses
- Add unit tests for LiteLLMRequester (29 tests)
- Fix num_retries parameter name (was max_retries)
- Fix exception handling order for subclass exceptions
LiteLLM provides unified API for 100+ providers, eliminating need for
provider-specific requesters.
* fix: ruff format provider.py
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
* refactor(provider): simplify LiteLLM requester usage handling
- Remove unused Anthropic-specific tool schema generation
- Share completion argument construction between normal and streaming calls
- Use LiteLLM/OpenAI native usage fields for monitoring
- Collect stream token usage from LiteLLM stream_options
- Update LiteLLM requester tests for unified usage fields
* restore: restore deleted provider requester files
Restore individual provider requester implementations that were
removed in de61b5d3. These files coexist with the unified
litellmchat.py backend.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
* feat: update requesters and improve provider selection UI
- Added `litellm_provider` field to various requesters' YAML configurations.
- Removed obsolete Python requester files for OpenRouter, PPIO, QHAIGC, ShengSuanYun, SiliconFlow, Space, TokenPony, VolcArk, and Xai.
- Introduced new requesters for Tencent and Together AI with corresponding YAML configurations and SVG icons.
- Enhanced the ProviderForm component to include a searchable dropdown for selecting providers, improving user experience.
- Updated localization files to include search provider text for both English and Chinese.
* fix(provider): align litellm rebase with master
* fix(provider): capture streaming token usage; add token observability
The LiteLLM streaming requester only captured usage when a chunk had an
empty `choices` list. Many OpenAI-compatible gateways (e.g. new-api) and
providers send the final usage payload in a chunk that still carries an
empty-delta choice, so streamed calls always recorded 0 tokens in the
monitoring logs/dashboard (non-streaming worked).
- Capture stream usage whenever a chunk carries it, regardless of choices
- Add robust _normalize_usage (dict/obj shapes, derive missing total_tokens)
- Register litellm in bootutils/deps.py (was in pyproject only)
- Add MonitoringService.get_token_statistics + /monitoring/token-statistics
endpoint: summary, per-model breakdown, token timeseries, and a
zero-token-success data-quality signal
- Add TokenMonitoring dashboard tab (summary tiles, stacked token chart,
per-model table) + i18n (en/zh)
- Regression tests for stream usage capture and usage normalization
Verified end-to-end against a real OpenAI-compatible endpoint with
gpt-5.5 and claude-opus-4-8: tokens now recorded non-zero for both
streaming and non-streaming paths.
* refactor(provider): simplify litellm capabilities
* style: simplify wrapped expressions
* feat(models): persist context metadata
* fix(provider): handle dict embeddings and openai-compatible rerank in LiteLLMRequester
- invoke_embedding: support both object- and dict-shaped response.data
entries (OpenAI-compatible gateways like new-api return dicts)
- invoke_rerank: litellm.arerank rejects the 'openai' provider, so for
openai-compatible (or unspecified) providers call the standard
Jina/Cohere-style POST /v1/rerank endpoint directly over HTTP
- accept both 'relevance_score' and 'score' fields in rerank results
- add unit tests for the openai-compatible HTTP rerank path
* feat(provider): enforce requester support_type when adding models
- frontend: AddModelPopover only shows model-type tabs (llm/embedding/
rerank) that the provider's requester declares in its manifest
support_type; ModelsDialog fetches requester manifests and maps
requester -> support_type, passed down through ProviderCard
- backend: add _validate_provider_supports guard in create_llm_model /
create_embedding_model / create_rerank_model so a model cannot be
attached to a provider whose requester does not support that type,
even if the frontend restriction is bypassed (manifests without
support_type are allowed for backward compatibility)
- manifests: correct support_type for providers that do not offer all
three model types:
- llm only: anthropic, deepseek, groq, moonshot, openrouter, xai
- llm + text-embedding: openai, gemini, mistral
- add rerank to new-api (verified working via /v1/rerank)
- set llm + text-embedding + rerank for aggregator/unknown gateways
* feat(provider): add searchable alias to requester manifests
- add a free-text 'alias' field to every requester manifest spec,
containing the vendor's English/Chinese names, pinyin, common
nicknames and flagship model-series names (e.g. moonshot -> kimi,
月之暗面; zhipu -> glm, 智谱清言)
- frontend: ProviderForm requester search now also matches against
alias (substring/contains), so searching 'kimi' surfaces Moonshot,
'硅基' surfaces SiliconFlow, etc.
- also fix support_type: openrouter (relay) supports embedding+rerank;
LangBot Space gains rerank (coming soon)
* fix(provider): make support_type guard defensive against incomplete model_mgr
- _validate_provider_supports now uses getattr to gracefully skip when
model_mgr / provider_dict / manifest lookup is unavailable, instead of
raising AttributeError (fixes unit tests that mock ap.model_mgr as a
bare SimpleNamespace)
- add TestValidateProviderSupports covering: allow supported type,
reject unsupported type, allow when support_type missing, allow when
provider unknown, degrade safely when model_mgr is incomplete
* fix(persistence): guard 0004 migration against missing llm_models table
The 0004_add_llm_model_context_length migration called
inspector.get_columns('llm_models') unconditionally, raising
NoSuchTableError when the table does not exist (e.g. migrating a
fresh/empty DB, as exercised by the integration tests where
create_all() registers no tables because the ORM models are not
imported). Every other migration guards with a table-existence check
first; add the same guard here for both upgrade and downgrade.
Also restore the test head assertion to 0004 (it had been lowered to
0003 to mask this failure).
* Merge branch 'master' into feat/litellm
Resolve conflicts:
- uv.lock: regenerated via 'uv lock' to reconcile litellm/fastuuid
(ours) with openai bump (master).
- Alembic migrations: master added 0004_add_mcp_readme while this
branch added 0004_add_llm_model_context_length, both as children of
0003 (would create multiple heads). Re-chain the litellm migration as
0005_add_llm_model_context_length with down_revision=0004_add_mcp_readme
for a single linear head. Update test head assertion accordingly.
* fix(persistence): shorten migration revision id to fit varchar(32)
PostgreSQL stores alembic_version.version_num as varchar(32).
'0005_add_llm_model_context_length' (33 chars) overflowed it, raising
StringDataRightTruncationError in the PG migration tests. Rename the
revision (and file) to '0005_add_llm_context_length' (27 chars) and
update the head assertions in both SQLite and PostgreSQL migration
tests.
---------
Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>
Co-authored-by: fdc310 <2213070223@qq.com>
Co-authored-by: RockChinQ <rockchinq@gmail.com>
294 lines
11 KiB
Python
294 lines
11 KiB
Python
"""Unit tests for ToolManager.
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Tests cover:
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- Tool schema generation for OpenAI/LiteLLM
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- Tool execution dispatch
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"""
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from __future__ import annotations
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import pytest
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from unittest.mock import Mock, AsyncMock
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from importlib import import_module
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import langbot_plugin.api.entities.builtin.resource.tool as resource_tool
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import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
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def get_toolmgr_module():
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"""Lazy import to avoid circular import issues."""
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return import_module('langbot.pkg.provider.tools.toolmgr')
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class TestToolManagerInit:
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"""Tests for ToolManager initialization."""
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def test_init_stores_app_reference(self):
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"""Test that __init__ stores the Application reference."""
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toolmgr = get_toolmgr_module()
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mock_app = Mock()
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manager = toolmgr.ToolManager(mock_app)
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assert manager.ap is mock_app
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def test_init_no_tool_loaders(self):
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"""Test that tool loaders are not initialized before initialize()."""
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toolmgr = get_toolmgr_module()
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mock_app = Mock()
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manager = toolmgr.ToolManager(mock_app)
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assert hasattr(manager, 'plugin_tool_loader') is False or manager.plugin_tool_loader is None
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class TestToolManagerSchemaGeneration:
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"""Tests for tool schema generation methods."""
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@pytest.fixture
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def mock_app(self):
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"""Create mock app."""
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mock_app = Mock()
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mock_app.logger = Mock()
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return mock_app
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@pytest.fixture
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def sample_tools(self):
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"""Create sample LLMTool list for testing."""
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def dummy_weather_func(**kwargs):
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return 'weather result'
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def dummy_calc_func(**kwargs):
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return 'calc result'
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tools = [
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resource_tool.LLMTool(
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name='get_weather',
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human_desc='Get current weather for a location',
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description='Get current weather for a location',
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parameters={
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'type': 'object',
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'properties': {'location': {'type': 'string', 'description': 'City name'}},
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'required': ['location'],
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},
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func=dummy_weather_func,
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),
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resource_tool.LLMTool(
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name='calculate',
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human_desc='Perform a calculation',
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description='Perform a calculation',
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parameters={
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'type': 'object',
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'properties': {'expression': {'type': 'string', 'description': 'Math expression'}},
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'required': ['expression'],
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},
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func=dummy_calc_func,
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),
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]
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return tools
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@pytest.mark.asyncio
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async def test_generate_tools_for_openai(self, mock_app, sample_tools):
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"""Test that generate_tools_for_openai produces correct schema."""
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toolmgr = get_toolmgr_module()
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manager = toolmgr.ToolManager(mock_app)
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result = await manager.generate_tools_for_openai(sample_tools)
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assert len(result) == 2
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# Verify first tool schema
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tool1 = result[0]
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assert tool1['type'] == 'function'
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assert tool1['function']['name'] == 'get_weather'
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assert tool1['function']['description'] == 'Get current weather for a location'
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assert 'parameters' in tool1['function']
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assert tool1['function']['parameters']['type'] == 'object'
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# Verify second tool schema
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tool2 = result[1]
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assert tool2['type'] == 'function'
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assert tool2['function']['name'] == 'calculate'
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@pytest.mark.asyncio
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async def test_generate_tools_empty_list(self, mock_app):
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"""Test that generating tools from empty list returns empty list."""
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toolmgr = get_toolmgr_module()
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manager = toolmgr.ToolManager(mock_app)
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openai_result = await manager.generate_tools_for_openai([])
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assert openai_result == []
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@pytest.mark.asyncio
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async def test_openai_schema_fields_complete(self, mock_app, sample_tools):
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"""Test that OpenAI schema includes all required fields."""
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toolmgr = get_toolmgr_module()
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manager = toolmgr.ToolManager(mock_app)
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result = await manager.generate_tools_for_openai(sample_tools)
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for tool_schema in result:
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assert 'type' in tool_schema
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assert tool_schema['type'] == 'function'
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assert 'function' in tool_schema
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func = tool_schema['function']
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assert 'name' in func
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assert 'description' in func
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assert 'parameters' in func
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class TestToolManagerExecuteFuncCall:
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"""Tests for execute_func_call method."""
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@pytest.fixture
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def mock_app_with_loaders(self):
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"""Create mock app with mock tool loaders.
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Returns (app, plugin_loader, mcp_loader). The native and skill loaders
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are attached directly to the app for tests that don't need to assert
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against them — they all default to ``has_tool == False`` so the
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execute_func_call probe falls through to the plugin/mcp pair.
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"""
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mock_app = Mock()
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mock_app.logger = Mock()
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def _make_inert_loader():
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loader = Mock()
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loader.has_tool = AsyncMock(return_value=False)
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loader.invoke_tool = AsyncMock(return_value=None)
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loader.initialize = AsyncMock()
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loader.shutdown = AsyncMock()
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return loader
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# Create mock plugin loader
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mock_plugin_loader = _make_inert_loader()
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mock_plugin_loader.invoke_tool = AsyncMock(return_value='plugin_result')
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# Create mock MCP loader
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mock_mcp_loader = _make_inert_loader()
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mock_mcp_loader.invoke_tool = AsyncMock(return_value='mcp_result')
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# Stash inert native/skill loaders so the ToolManager probe order
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# (native → plugin → mcp → skill) doesn't AttributeError. Tests that
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# need to override these can replace the attributes on the manager.
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mock_app._inert_native_loader = _make_inert_loader()
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mock_app._inert_skill_loader = _make_inert_loader()
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return mock_app, mock_plugin_loader, mock_mcp_loader
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@staticmethod
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def _wire_loaders(manager, mock_app, plugin_loader, mcp_loader):
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"""Attach all four loaders (native + plugin + mcp + skill) to manager."""
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manager.native_tool_loader = mock_app._inert_native_loader
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manager.plugin_tool_loader = plugin_loader
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manager.mcp_tool_loader = mcp_loader
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manager.skill_tool_loader = mock_app._inert_skill_loader
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@pytest.fixture
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def sample_query(self):
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"""Create sample query for testing."""
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query = Mock(spec=pipeline_query.Query)
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return query
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@pytest.mark.asyncio
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async def test_execute_calls_plugin_loader_when_has_tool(self, mock_app_with_loaders, sample_query):
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"""Test that execute_func_call uses plugin loader when tool exists there."""
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toolmgr = get_toolmgr_module()
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mock_app, mock_plugin_loader, mock_mcp_loader = mock_app_with_loaders
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mock_plugin_loader.has_tool = AsyncMock(return_value=True)
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manager = toolmgr.ToolManager(mock_app)
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self._wire_loaders(manager, mock_app, mock_plugin_loader, mock_mcp_loader)
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result = await manager.execute_func_call('test_tool', {'param': 'value'}, sample_query)
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assert result == 'plugin_result'
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mock_plugin_loader.invoke_tool.assert_called_once_with('test_tool', {'param': 'value'}, sample_query)
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# MCP loader should not be called
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mock_mcp_loader.invoke_tool.assert_not_called()
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@pytest.mark.asyncio
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async def test_execute_calls_mcp_loader_when_plugin_not_found(self, mock_app_with_loaders, sample_query):
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"""Test that execute_func_call uses MCP loader when plugin doesn't have tool."""
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toolmgr = get_toolmgr_module()
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mock_app, mock_plugin_loader, mock_mcp_loader = mock_app_with_loaders
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mock_plugin_loader.has_tool = AsyncMock(return_value=False)
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mock_mcp_loader.has_tool = AsyncMock(return_value=True)
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manager = toolmgr.ToolManager(mock_app)
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self._wire_loaders(manager, mock_app, mock_plugin_loader, mock_mcp_loader)
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result = await manager.execute_func_call('test_tool', {'param': 'value'}, sample_query)
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assert result == 'mcp_result'
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mock_mcp_loader.invoke_tool.assert_called_once_with('test_tool', {'param': 'value'}, sample_query)
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@pytest.mark.asyncio
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async def test_execute_raises_when_tool_not_found(self, mock_app_with_loaders, sample_query):
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"""Test that execute_func_call raises ValueError when tool not found."""
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toolmgr = get_toolmgr_module()
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mock_app, mock_plugin_loader, mock_mcp_loader = mock_app_with_loaders
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mock_plugin_loader.has_tool = AsyncMock(return_value=False)
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mock_mcp_loader.has_tool = AsyncMock(return_value=False)
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manager = toolmgr.ToolManager(mock_app)
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self._wire_loaders(manager, mock_app, mock_plugin_loader, mock_mcp_loader)
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with pytest.raises(ValueError, match='未找到工具'):
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await manager.execute_func_call('unknown_tool', {}, sample_query)
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@pytest.mark.asyncio
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async def test_plugin_loader_checked_first(self, mock_app_with_loaders, sample_query):
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"""Test that plugin loader is checked before MCP loader."""
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toolmgr = get_toolmgr_module()
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mock_app, mock_plugin_loader, mock_mcp_loader = mock_app_with_loaders
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# Both loaders have the tool, but plugin should be used
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mock_plugin_loader.has_tool = AsyncMock(return_value=True)
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mock_mcp_loader.has_tool = AsyncMock(return_value=True)
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manager = toolmgr.ToolManager(mock_app)
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self._wire_loaders(manager, mock_app, mock_plugin_loader, mock_mcp_loader)
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await manager.execute_func_call('test_tool', {}, sample_query)
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# Plugin loader should be invoked, MCP should not
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mock_plugin_loader.invoke_tool.assert_called_once()
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mock_mcp_loader.invoke_tool.assert_not_called()
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class TestToolManagerShutdown:
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"""Tests for shutdown method."""
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@pytest.mark.asyncio
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async def test_shutdown_calls_loader_shutdown(self):
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"""Test that shutdown calls shutdown on every registered loader."""
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toolmgr = get_toolmgr_module()
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mock_app = Mock()
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def _make_loader():
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loader = Mock()
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loader.shutdown = AsyncMock()
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return loader
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mock_native_loader = _make_loader()
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mock_plugin_loader = _make_loader()
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mock_mcp_loader = _make_loader()
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mock_skill_loader = _make_loader()
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manager = toolmgr.ToolManager(mock_app)
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manager.native_tool_loader = mock_native_loader
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manager.plugin_tool_loader = mock_plugin_loader
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manager.mcp_tool_loader = mock_mcp_loader
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manager.skill_tool_loader = mock_skill_loader
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await manager.shutdown()
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mock_native_loader.shutdown.assert_called_once()
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mock_plugin_loader.shutdown.assert_called_once()
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mock_mcp_loader.shutdown.assert_called_once()
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mock_skill_loader.shutdown.assert_called_once()
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