ReMe/tests/unit/test_reme_cli.py
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feat: add Auto Fin cookbook and managed outbound proxy support (#392)
* feat: add ssh proxy

* feat: add ssh proxy

* feat: add ssh proxy

* feat: add ssh proxy

* feat: add prompt

* feat: add agent wrapper

* feat: add agent wrapper

* feat: add agent wrapper

* feat: add tushare skill

* feat: add tushare skill

* feat: add tushare skill

* feat: add none stream

* chore(deps): update dependency versions in pyproject.toml

- Bump claude-agent-sdk from 0.2.123 to 0.2.126
- Upgrade pre-commit to version 4.6.1 or higher
- Upgrade pytest to version 9.1.1 or higher

* feat(agent_wrapper): add session compaction support and unify session commands

- Introduce compact_session method to BaseAgentWrapper and implement it in AsAgentWrapper, CcAgentWrapper, and CodexAgentWrapper
- Add session_command module with SessionCommandResult dataclass and handle_session_command function for /clear and /compact commands
- Update __init__.py exports to include session_command handlers
- Modify DingTalkWaitStep to handle session commands via handle_session_command function
- Remove streaming mode from DingTalkWaitStep and simplify reply handling to final Markdown replies only
- Add unit tests for session compaction methods and session command handling across wrappers and DingTalk integration
- Clean up and remove obsolete streaming and card rendering code from DingTalk wait step
- Adjust daily_cookbook.yaml to remove stream and card_update_interval config entries for DingTalk wait step

* feat(auto_fin): add Auto Fin simulated portfolio cookbook workflow

- Add comprehensive Auto Fin schema exports for multiple models and enums
- Implement base class and helpers for Auto Fin analysis steps
- Create file, state, and formatting utilities for Auto Fin with atomic file writes and locking
- Define Auto Fin pipeline with four analysis agents: backtest, event, portfolio, and US correlation
- Register Auto Fin package in cookbook workflows and schema initialization
- Add detailed documentation in markdown describing the system design, workflow, and data contracts

* feat(outbound_proxy): add application-scoped outbound HTTP proxy components

- Introduce BaseOutboundProxy and OutboundProxyEndpoint as core contracts
- Implement FixedHttpOutboundProxy for external HTTP proxy integration
- Add SshHttpOutboundProxy providing SSH-backed local HTTP proxy tunnels
- Register outbound proxy components in component registry and enumeration
- Update components package to include outbound_proxy module
- Add dependency on pproxy for SSH HTTP proxy bridging
- Include comprehensive unit tests covering proxy lifecycle, validation,
  environment merging, error handling, readiness, and monitoring mechanisms

* refactor(network): replace SSH proxy with explicit HTTP outbound proxy

- Remove SSH proxy helper implementation and references in codebase
- Add support for explicit HTTP proxy URL in arXiv and HuggingFace clients
- Modify clients to use async context manager for consistent resource handling
- Update daily paper steps to forward outbound proxy configuration explicitly
- Change tests to cover new proxy usage model and remove SSH proxy mocks
- Add outbound proxy component configuration in daily_cookbook.yaml
- Ensure proxy URL usage disables environment trust in HTTP clients
- Fix app context component enum access to be defensive against missing keys

* feat(agent_wrapper): add managed proxy support for command environments

- Introduce BaseOutboundProxy binding in BaseAgentWrapper for outbound proxy management
- Add bash_environment and command_proxy_environment properties to apply proxy settings
- Update WorkspaceBackend instantiation in AsAgentWrapper to use bash_environment
- Inject managed proxy export commands into Claude Code Bash commands via hooks
- Enhance CodexAgentWrapper to include managed proxy in shell environment policy
- Modify daily_cookbook.yaml steps to specify outbound_proxy as default where needed
- Add comprehensive unit tests verifying managed proxy injection and environment isolation
- Ensure subprocess_environment remains unchanged while proxy is applied selectively to commands

* refactor(memory): replace search job_tools with memory in daily cookbook config

- Change workspace_dir default from .reme to reme_workspace
- Replace search job_tools with memory across multiple components and jobs
- Update descriptions to reflect long-term memory retrieval instead of search
- Modify system prompts to instruct using memory for retrieving notes
- Adjust unit tests to verify memory job_tools and job presence instead of search
- Ensure consistency in configuration and tests for memory backend usage

* refactor(config): rename memory to memory_search in daily cookbook config

- Change all occurrences of "memory" to "memory_search" in job_tools and job definitions
- Update related system prompts to reflect the new memory_search terminology
- Modify unit tests to assert the presence of memory_search instead of memory
- Ensure consistency across skills, job tools, and backend configurations in multiple components

* feat(auto_fin): add deterministic quantitative research and ranking fusion

- Introduce new schema models: EtfScore, RankingMetrics, ExtremeAnalysis,
  DimensionRanking, and FusionRanking to represent deterministic research outputs
- Add ranking data to event, backtest, us_correlation, and portfolio analysis outputs
- Implement ranking_section renderer to format Top20 scores and diagnostics in Markdown
- Develop AutoFinQuantStep for deterministic ETF ranking using TuShare data, Polars,
  and a custom extremely randomized tree ensemble
- Integrate quantitative rankings into backtest and portfolio analysis steps and reports
- Extend auto_fin pipeline with new quant_enabled and quant_required config options
- Enforce ranking constraints like unique codes, contiguous ranks, and normalized fusion weights
- Update analysis YAMLs with rules limiting data freshness, universe, and ranking usage
- Incorporate ranking outputs into all major markdown report bodies in Auto Fin pipeline
- Add concurrency-limited asynchronous TuShare client to fetch required market data
- Introduce cross-sectional rank correlation and NDCG metrics for ranking quality evaluation

* feat(auto_fin): implement stage-wise notification and reporting for analysis pipeline

- Refactor notification config in daily_cookbook.yaml to support dispatch steps
- Update AutoFinNotificationStep to deduplicate notifications per run stage
- Add _notify_stage method in pipeline to send notifications for each analysis stage
- Implement persistence and notification for event, backtest, US correlation, and portfolio stages
- Modify pipeline flow to persist reports and notify after each stage completion
- Adjust metadata to track notifications and errors per stage
- Update tests to verify stage-wise notification sending and deduplication
- Remove older combined report persistence in favor of modular stage handling

* feat(auto_fin): add outbound proxy support for Tushare API usage

- Introduce BaseOutboundProxy reference in AutoFinPipelineStep and AutoFinQuantStep
- Update TushareResearchClient and trade calendar fetch to accept and use proxy URL
- Create _ProxiedTushareApi adapter to route Tushare requests via explicit HTTP proxy
- Modify create_tushare_api utility to optionally return proxied API client
- Add unit tests covering proxy forwarding and client behavior with managed proxies
- Ensure proxy usage respects explicit proxy URL over environment fallback
- Integrate outbound proxy into data fetching and quantitative research steps

* feat(auto_fin): enforce checkpoint time validation and add state models

- Introduce AnalysisState base class and specific states for event, backtest, and US correlation analyses
- Replace analysis output types with corresponding state classes in run schemas
- Add require_checkpoint_reached method to validate decision_at/data_cutoff against current time
- Enforce checkpoint time checks before analysis steps in event, backtest, portfolio, and quant analyses
- Refactor quant data loading to include adjustment factors and apply price adjustments without fallback
- Update analysis YAML docs to require real-time checkpoint validation and forbid using future data
- Improve portfolio run serialization by excluding redundant legacy fields and nested proposed actions
- Add helper to extract readable sections from persisted checkpoint documents
- Fix event analysis output validation to reject events and sources with future timestamps

* feat(auto_fin): auto-select latest reached checkpoint if none specified

- Extend checkpoint config to accept empty string for auto selection
- Add static method to compute latest checkpoint reached by current time
- Modify pipeline step to auto-select checkpoint based on trade calendar and time
- Adjust force flag default depending on whether checkpoint is explicit or auto
- Log details when checkpoint is auto-selected to improve observability
- Add comprehensive tests for auto checkpoint selection logic and edge cases
- Remove deprecated default and required constraints from force parameter in config

* refactor(auto_fin): unify datetime comparison with compare_datetimes utility

- Replace direct datetime comparisons with compare_datetimes function calls
- Use cmp_to_key with compare_datetimes for sorting datetime tuples and lists
- Update validation logic in backtest, event, analysis, and ledger modules for consistent datetime handling
- Add unit tests to verify handling of naive and aware datetime comparisons in event and backtest validations
- Ensure marked_at and interval_end timestamps are set and compared consistently using compare_datetimes
- Improve correctness of ordering and conditional checks related to timestamps throughout auto_fin steps and ledger code

* feat(auto_fin): add datetime comparison helper for mixed timezone data

- Implement compare_datetimes function to handle naive and aware datetimes
- Ensure naive datetime is interpreted in the known timezone of the counterpart
- Facilitate comparisons between legacy and timezone-aware Auto Fin data
- Add module docstring explaining purpose of the helpers

* docs(auto_fin): enforce unique ETF representative per sub-theme in analysis rules

- Update backtest.yaml to recommend or highlight only one ETF per sub-theme for ETF analyses
- Modify event.yaml to map only one representative ETF per sub-theme, avoiding duplicate recommendations
- Revise portfolio.yaml to restrict holdings/buys to a single ETF per sub-theme, preventing repeated buys of highly overlapping ETFs
- Adjust us_correlation.yaml to retain only one representative A-share ETF per sub-theme for mapping or recommendation
- Add test to verify presence of new sub-theme uniqueness guidance in step prompts

* feat(auto_fin): separate draft model and include deterministic fusion ranking

- Introduce _PortfolioProposalDraft pydantic model for agent-authored fields before ranking
- Discard any "fusion_ranking" data from draft to prevent conflicts with canonical ranking
- Modify AutoFinPortfolioStep to receive draft, enrich with fusion_ranking, and produce final output
- Update tests to use _PortfolioProposalDraft and validate deterministic fusion ranking propagation
- Add async test verifying fusion ranking is correctly set in portfolio output with no errors

* refactor(auto_fin): rewrite and simplify Auto Fin schema and steps

- Remove legacy Auto Fin analysis step modules and helpers
- Replace complex ranking and portfolio models with simplified current-news models
- Update schema to focus on news-case workflow with new domain models
- Remove A-share decision checkpoints and backtest details from schema
- Simplify recommendation and decision output structures
- Clean up deprecated state and utility functions
- Update Auto Fin steps initialization to new pipeline steps only
- Improve uniqueness validation for themes and ETFs in research plan

* feat(auto_fin): implement full local cache and analysis workflow for Auto Fin

- Add AutoFinDataStep to prepare and cache daily TuShare data with lookback
- Add AutoFinAnalysisStep to analyze cached data and generate Markdown report
- Implement detailed time window, ETF filtering, and historical case validation
- Introduce YAML prompts for planning and decision-making steps
- Update .gitignore to include reme_workspace/
- Clean up config and import structure for auto_fin steps
- Remove old pipeline.py and consolidate functionality into new modules
- Use polars for efficient CSV reading and data processing
- Ensure atomic writes and strict JSON serialization for cache files
- Enforce rules on news timing, ETF universe, and historical case usage

* fix(auto_fin): restrict news data source to '财联社' in analysis and cache

- Update analysis templates to specify current news as from '财联社' only
- Modify news fetching functions to filter by source '财联社'
- Add validation method to check cached news source correctness
- Update news caching logic to exclude non-'财联社' news
- Enhance unit tests with multiple sources to ensure filtering works
- Confirm news API calls include source filter parameter as '财联社'

* refactor(auto_fin): convert I/O methods to asynchronous implementations

- Change _news, _dataset, and _theme_data methods to async for improved concurrency
- Move JSONL and CSV reading operations to asynchronous wrappers using asyncio.to_thread
- Remove synchronous _read_jsonl and _read_csv functions, integrate them as static async class methods
- Update cache validation methods to async, awaiting I/O operations accordingly
- Adjust usage of dataset and news retrieval in analysis step to await asynchronous methods
- Add async unit test to validate JSONL reading with unicode line separators
- Preserve existing functionality while enabling non-blocking file and data access

* fix(nx_file_graph): defer networkx import and improve dependency handling

- Move networkx import inside NxFileGraph constructor for lazy loading
- Raise ImportError with original exception context if networkx is missing
- Remove module-level fallback assignment of nx to None
- Expand test to block loading of multiple optional core dependencies eagerly
- Change exception type in test from ModuleNotFoundError to AssertionError
- Update test comments to reflect broader optional dependency checks

* feat(embedding_store): add quota retry delay mechanism for embedding requests

- Introduce quota_retry_delay parameter to configure wait time before retry on quota exhaustion
- Implement detection of insufficient quota errors in LocalEmbeddingStore without external SDK
- Add retry logic with custom delay when quota is insufficient during embedding requests
- Update configuration to set max_retries and quota_retry_delay defaults for embedding store
- Add unit tests covering quota exhaustion retry behavior with delay and opt-in control
- Ensure existing retry behavior remains unchanged if quota_retry_delay is not set

* feat(auto_fin): add detailed logging to analysis and data fetching steps

- Add _preview static method for bounded diagnostic output in analysis.py
- Log prompt start, completion, errors, and validation details in _reply method
- Add info logs for major processing steps in execute method of analysis.py
- Add debug and info logs for cache validation, data fetching, and pagination in data.py
- Log conditions for skipping reports and cache plans in data.py execute method
- Log download summaries and cache writes for news and ETF data
- Improve error logging with exception details in cache validation functions
- Ensure all logs include context such as record counts, paths, and parameters

* refactor(auto_fin): overhaul Auto Fin workflow and schema contracts

- Replace old Auto Fin schema models with comprehensive new data classes
- Remove legacy Auto Fin analysis step in favor of modular agent-based steps
- Introduce AutoFinAgentStep for validating structured agent replies
- Simplify data cleaning and JSONL writing utilities for news cache
- Remove synchronous and asynchronous dataset methods from analysis step
- Redefine Auto Fin analysis configuration for 360-day news retention and multi-step pipeline
- Remove embedded analysis prompt templates and replace with agent-driven logic
- Update __init__.py exports to match new step implementations and remove deprecated classes
- Improve error handling and validation in agent step reply processing
- Clean up redundant imports and unused code in analysis and data preparation modules

* feat(auto_fin): add detailed logging for analysis and data processing steps

- Add timing logs to measure agent prompt processing duration in analysis.py
- Log news cache hits and news write paths with record counts in data.py
- Include detailed info logs for news download start and completion in data.py
- Add start, progress, and completion logs with topic and event counts in history.py
- Log start and completion of merge step including path and ETF count in merge.py
- Add start and done logs with window and news counts in topic.py

* feat(auto_fin): enhance schema and steps with detailed ETF and event modeling

- Replace and add multiple AutoFin schema classes to support detailed ETF selection,
  historical research, market analysis, forecast models, and report output with validation
- Implement Shanghai timezone normalization and strict validation in schema models
- Remove deprecated AutoFin analysis agent step and consolidate reply handling in base step
- Introduce AutoFinStep base class with shared helpers for prompt handling, data fetching,
  logging, and JSONL file operations
- Add AutoFinDataStep to manage daily news data complete with schedule validation, caching,
  and source validation logic
- Update cookbook configuration to customize auto_fin step parameters and simplify
  outbound proxy settings
- Refactor imports and clean unused code for better maintainability

* feat(auto_fin): introduce detailed historical event resolution and market similarity analysis

- Add AutoFinHistoricalEventReference and AutoFinHistoricalSimilarity models for refined event referencing and similarity judgment
- Implement validation to ensure non-empty critical fields and uniqueness of historical news IDs
- Develop method to resolve Agent-selected historical event references from workspace files with strict path and existence checks
- Enrich historical events with market entry and future returns data after resolution
- Redesign market step to calculate similarity-weighted ETF forecasts based on matched historical event similarities
- Enforce validation on matched historical events for uniqueness and proper weight summation
- Simplify merge step output to final Markdown report without YAML frontmatter and redundant fields
- Update user instructions for history search, market, and merge steps to reflect new data structures and responsibilities
- Adjust test suite to cover new schema and step behavior changes, including enhanced validation and JSON output formats

* feat(auto_fin): add new cron jobs and output analysis jsonl

- Add new cron jobs auto_fin_1145_cron and auto_fin_1800_cron with auto_fin_steps
- Change auto_fin_0930_cron schedule to run Monday to Sunday
- Extend merge step to write analysis data to auto_fin_analysis.jsonl
- Update unit tests to verify new cron jobs and their steps configuration

* fix(auto_fin): improve atomic file write and refresh daily index

- Change temporary file naming to include UUID for uniqueness and hidden prefix
- Replace atomic write method from using Path.replace to os.replace with safe unlink
- Add import and use os.replace for safer file replace operation
- Refresh daily index after writing auto finance markdown and JSONL files
- Import and call refresh_day_index in merge step to update file index asynchronously

* docs(cookbook): add optional SSH proxy configuration in README files

- Introduce optional SSH proxy setup in auto-fin and daily_paper cookbooks
- Provide instructions to enable outbound proxy via `daily_cookbook.yaml` and environment variables
- Add `REME_PROXY_IP` and `REME_PROXY_ACCOUNT` environment variables descriptions in multiple README files
- Update English and Chinese README and README_ZH documents with proxy details
- Maintain consistent formatting of environment variable tables across documents

* fix(file_io): include schema_version in hidden metadata keys

- Added "schema_version" to _INDEX_HIDDEN_METADATA_KEYS in _daily_index.py
- Updated _render_notes_block to always include additional keys regardless of schema_version

fix(deps): move pproxy dependency to later in pyproject.toml

- Removed pproxy from early dependencies list
- Added pproxy back near the end of dependency list for better ordering

fix(outbound_proxy): require pproxy package for ssh_http proxy

- Added importlib.util check for pproxy package presence
- Raise RuntimeError if pproxy is not installed when using SSH HTTP outbound proxy
- Improved error message suggests installing reme-ai with 'core' extra

* docs(readme): update News section with new Cookbook workflows

- Clarify introduction of optional Cookbooks with Daily Paper and Auto Fin workflows
- Update English README to reflect both paper discovery and file-native ETF event research
- Revise Chinese README to include financial news and historical market data research capability
- Maintain announcement of paper acceptance at Findings of ACL 2026

* feat(auto_fin): add calculation results to final Markdown output

- Implement _calculation_results to summarize forecast for each ETF analyzed
- Include program-calculated results in the JSON input for the Markdown report
- Update YAML template to incorporate calculation results and adjust recommendation rules
- Refine recommendation logic to rely on event impact judgments combined with calculation outputs
- Modify tests to verify presence of calculation results and updated report content and format

* up prompt

* fix(keyword_index): ignore non-indexable chunks during keyword sync

- Add is_indexable method to base and BM25 keyword index classes to check text tokenizability
- Update local file store to exclude non-indexable chunks from expected document IDs to prevent rebuild
- Fix JSONL chunker to correctly handle Unicode line separator U+2028 inside JSON strings without splitting
- Add test to ensure non-empty but non-indexable chunk does not trigger keyword index rebuild
- Add test to verify U+2028 character does not cause incorrect JSONL record splitting
2026-07-25 18:09:39 +08:00

347 lines
11 KiB
Python

"""Tests for the ReMe CLI entry helpers."""
import asyncio
from pathlib import Path
import subprocess
import sys
from types import SimpleNamespace
import pytest
from reme.components.service import cli_service
from reme.components.service.cli_service import CliService
from reme import reme as reme_module
def test_package_import_does_not_load_optional_core_dependencies():
"""The base package leaves optional core dependencies unloaded."""
script = """
import importlib.abc
class BlockOptionalCoreDependencies(importlib.abc.MetaPathFinder):
def find_spec(self, fullname, path, target=None):
blocked = (
"claude_agent_sdk",
"dingtalk_stream",
"openai_codex",
"faiss",
"jieba",
"rjieba",
"neo4j",
"networkx",
"pypdf",
"polars",
"tushare",
)
if any(fullname == name or fullname.startswith(f"{name}.") for name in blocked):
raise AssertionError(f"eagerly imported optional core dependency: {fullname}")
return None
import sys
sys.meta_path.insert(0, BlockOptionalCoreDependencies())
import reme
"""
result = subprocess.run(
[sys.executable, "-c", script],
cwd=Path(__file__).resolve().parents[2],
capture_output=True,
text=True,
check=False,
)
assert result.returncode == 0, result.stderr
def test_main_loads_env_before_calling_server(monkeypatch):
"""Client actions can resolve connection settings from the local .env."""
events = []
main_globals = reme_module.main.__globals__
monkeypatch.setitem(main_globals, "load_env", lambda: events.append("load_env"))
monkeypatch.setitem(main_globals, "parse_args", lambda *_args: ("shell", {"cmd": "pwd"}))
async def fake_call_server(action, **kwargs):
events.append(("call_server", action, kwargs))
monkeypatch.setitem(main_globals, "call_server", fake_call_server)
reme_module.main()
assert events == ["load_env", ("call_server", "shell", {"cmd": "pwd"})]
def test_main_saves_loaded_environment_in_start_config(monkeypatch):
"""The startup config keeps the environment captured by the single global load."""
observed = {}
class FakeReMe:
"""Capture the fully resolved application configuration."""
def __init__(self, **kwargs):
"""Record the application startup configuration."""
observed["config"] = kwargs
def run_app(self):
"""Record that application startup continued."""
observed["ran"] = True
main_globals = reme_module.main.__globals__
monkeypatch.setitem(main_globals, "load_env", lambda: {"TOOL_ENV": "configured"})
monkeypatch.setitem(main_globals, "parse_args", lambda *_args: ("start", {}))
monkeypatch.setitem(main_globals, "prepare_start_config", lambda _kwargs: {"service": {"backend": "cli"}})
monkeypatch.setitem(main_globals, "ReMe", FakeReMe)
reme_module.main()
assert observed == {
"config": {
"service": {"backend": "cli"},
"environment": {"TOOL_ENV": "configured"},
},
"ran": True,
}
def test_prepare_start_config_moves_unknown_start_args_to_job_args(monkeypatch):
"""``reme start job=...`` is translated into a one-shot cli service config."""
monkeypatch.setattr(
cli_service,
"resolve_app_config",
lambda **kwargs: {
**kwargs,
"service": {"backend": "http", "host": "127.0.0.1"},
},
)
cfg = cli_service.prepare_start_config(
{
"config": "jinli_lme",
"workspace_dir": "/tmp/reme",
"job": "search",
"query": "hello",
"limit": 3,
},
)
assert cfg["config"] == "jinli_lme"
assert cfg["workspace_dir"] == "/tmp/reme"
assert cfg["enable_logo"] is False
assert cfg["log_to_console"] is False
assert cfg["service"] == {
"backend": "cli",
"host": "127.0.0.1",
"job": "search",
"job_args": {"query": "hello", "limit": 3},
}
def test_should_precheck_start_skips_cli_service():
"""CLI service is local execution and should not run port prechecks."""
assert cli_service.should_precheck_start({"service": {"backend": "cli"}}) is False
assert cli_service.should_precheck_start({"service": {"backend": "http"}}) is True
def test_cli_service_runs_configured_job_and_closes_app(capsys):
"""CLI service runs one local job through app lifecycle and prints its answer."""
events = []
class FakeApp:
"""Minimal app stub for exercising CliService lifecycle."""
async def start(self):
"""Record app startup."""
events.append("start")
async def close(self):
"""Record app shutdown."""
events.append("close")
async def run_job(self, name, **kwargs):
"""Record job execution and return a successful response."""
events.append(("run_job", name, kwargs))
return SimpleNamespace(answer="found it", success=True, metadata={"hits": 1})
service = CliService(job="search", job_args={"query": "hello"})
service.start_service(FakeApp())
assert events == [
"start",
("run_job", "search", {"query": "hello"}),
"close",
]
assert capsys.readouterr().out == "found it\n"
def test_cli_service_can_print_metadata_from_service_config(capsys):
"""service.show_metadata controls optional CLI metadata output."""
class FakeApp:
"""Minimal app stub for exercising metadata output."""
async def start(self):
"""No-op app startup."""
async def close(self):
"""No-op app shutdown."""
async def run_job(self, _name, **_kwargs):
"""Return a successful response with metadata."""
return SimpleNamespace(answer="found it", success=True, metadata={"hits": 1})
service = CliService(job="search", show_metadata=True)
service.start_service(FakeApp())
assert capsys.readouterr().out == 'found it\n{"hits": 1}\n'
def test_cli_service_exits_nonzero_on_failed_response(capsys):
"""Failed local CLI jobs write to stderr and produce a failing process status."""
events = []
class FakeApp:
"""Minimal app stub for exercising failure handling."""
async def start(self):
"""Record app startup."""
events.append("start")
async def close(self):
"""Record app shutdown."""
events.append("close")
async def run_job(self, name, **kwargs):
"""Record job execution and return a failed response."""
events.append(("run_job", name, kwargs))
return SimpleNamespace(answer="boom", success=False, metadata={})
service = CliService(job="search", job_args={"query": "hello"})
with pytest.raises(SystemExit) as exc_info:
service.start_service(FakeApp())
assert exc_info.value.code == 1
assert events == [
"start",
("run_job", "search", {"query": "hello"}),
"close",
]
captured = capsys.readouterr()
assert captured.out == ""
assert captured.err == "boom\n"
def test_call_server_passes_client_kwargs_to_client(monkeypatch, capsys):
"""CLI helper forwards connection options to the selected client."""
seen = {}
class FakeClient:
"""Async client stub that records call arguments."""
def __init__(self, **kwargs):
seen["client_kwargs"] = kwargs
async def __aenter__(self):
return self
async def __aexit__(self, exc_type, exc_val, exc_tb):
return None
async def __call__(self, action: str, **kwargs):
seen["action"] = action
seen["payload"] = kwargs
yield "ok"
monkeypatch.setattr(reme_module.R, "get", lambda component_type, backend: FakeClient)
monkeypatch.setattr(reme_module, "running_service_config", lambda: None)
async def run():
await reme_module.call_server(
"search",
backend="http",
host="127.0.0.2",
port=2444,
timeout=1.5,
query="hello",
)
asyncio.run(run())
assert seen["client_kwargs"] == {"host": "127.0.0.2", "port": 2444, "timeout": 1.5}
assert seen["action"] == "search"
assert seen["payload"] == {"query": "hello"}
assert capsys.readouterr().out == "ok\n"
def test_call_server_treats_show_metadata_as_client_kwarg(monkeypatch, capsys):
"""show_metadata controls client display and is not sent as a tool argument."""
seen = {}
class FakeClient:
"""Async client stub that records call arguments."""
def __init__(self, **kwargs):
seen["client_kwargs"] = kwargs
async def __aenter__(self):
return self
async def __aexit__(self, exc_type, exc_val, exc_tb):
return None
async def __call__(self, action: str, **kwargs):
seen["action"] = action
seen["payload"] = kwargs
yield "ok"
monkeypatch.setattr(reme_module.R, "get", lambda component_type, backend: FakeClient)
monkeypatch.setattr(reme_module, "running_service_config", lambda: None)
async def run():
await reme_module.call_server("version", backend="http", show_metadata=True)
asyncio.run(run())
assert seen["client_kwargs"] == {"show_metadata": True}
assert seen["action"] == "version"
assert seen["payload"] == {}
assert capsys.readouterr().out == "ok\n"
def test_call_server_passes_shell_parameters_as_payload(monkeypatch, capsys):
"""Shell-specific parameter names do not collide with client options."""
seen = {}
class FakeClient:
"""Async client stub that records shell request arguments."""
def __init__(self, **kwargs):
seen["client_kwargs"] = kwargs
async def __aenter__(self):
return self
async def __aexit__(self, exc_type, exc_val, exc_tb):
return None
async def __call__(self, action: str, **kwargs):
seen["action"] = action
seen["payload"] = kwargs
yield "ok"
monkeypatch.setattr(reme_module.R, "get", lambda component_type, backend: FakeClient)
monkeypatch.setattr(reme_module, "running_service_config", lambda: None)
async def run():
await reme_module.call_server("shell", backend="http", cmd="ls", shell_timeout=5)
asyncio.run(run())
assert seen["action"] == "shell"
assert seen["payload"] == {"cmd": "ls", "shell_timeout": 5}
assert capsys.readouterr().out == "ok\n"