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* 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
329 lines
12 KiB
Python
329 lines
12 KiB
Python
"""Unit tests for the refactored dream package."""
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import asyncio
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import tempfile
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from pathlib import Path
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from unittest.mock import patch
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import yaml
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from reme.components.application_context import ApplicationContext
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from reme.components.file_catalog import BaseFileCatalog
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from reme.components.file_store import BaseFileStore
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from reme.components.runtime_context import RuntimeContext
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from reme.schema import DreamState
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from reme.steps.evolve.dream.extract import DreamExtractStep
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from reme.steps.evolve.dream.finish import DreamFinishStep
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from reme.steps.evolve.dream.proactive import ProactiveStep
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from reme.steps.evolve.dream.topics import DreamTopicsStep
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from reme.steps.evolve.dream.utils import parse_structured_reply, recent_dates, scan_day_files
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def _touch(path: Path, text: str = "x") -> Path:
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path.parent.mkdir(parents=True, exist_ok=True)
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path.write_text(text, encoding="utf-8")
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return path
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class _Catalog(BaseFileCatalog):
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def __init__(self):
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super().__init__()
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self.upserts = []
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self.dumps = 0
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async def upsert(self, nodes):
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self.upserts.extend(nodes)
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async def delete(self, path):
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return None
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async def get_nodes(self, paths=None):
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return []
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async def dump(self):
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self.dumps += 1
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class _FileStore(BaseFileStore):
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def __init__(self, workspace: Path):
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super().__init__()
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self._workspace_path = workspace
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@property
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def workspace_path(self) -> Path:
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return self._workspace_path
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async def upsert(self, files):
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return None
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async def delete(self, path):
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return None
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async def clear(self):
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return None
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async def get_nodes(self, paths=None):
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return []
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async def get_outlinks(self, path, scope=None):
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return []
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async def get_inlinks(self, path, scope=None):
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return []
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async def vector_search(self, query, limit, search_filter):
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return []
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async def keyword_search(self, query, limit, search_filter):
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return []
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def test_scan_day_files_includes_nested_md_and_excludes_interests():
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"""Scan day files."""
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with tempfile.TemporaryDirectory() as tmp:
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workspace = Path(tmp)
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_touch(workspace / "daily" / "2026-05-28.md")
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_touch(workspace / "daily" / "2026-05-28" / "session.md")
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_touch(workspace / "daily" / "2026-05-28" / "auth-refactor" / "notes.md")
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_touch(workspace / "daily" / "2026-05-28" / "interests.yaml")
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assert scan_day_files(workspace, "2026-05-28", "daily") == [
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"daily/2026-05-28.md",
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"daily/2026-05-28/auth-refactor/notes.md",
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"daily/2026-05-28/session.md",
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]
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def test_recent_dates_includes_anchor_and_previous_days():
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"""Recent date window is inclusive and chronological."""
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assert recent_dates("2026-05-28", 3) == ["2026-05-26", "2026-05-27", "2026-05-28"]
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assert recent_dates("2026-05-28", 1) == ["2026-05-28"]
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def test_parse_structured_reply_handles_fenced_yaml_and_scalar_fallback():
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"""Parse a JSON/YAML object from an agent reply, including fenced blocks."""
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data = parse_structured_reply(
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"```yaml\n"
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"action: REFINE\n"
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"target_path: digest/personal/no-trailing-summary.md\n"
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"note: Extended node. Core rule unchanged: answer directly and stop.\n"
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"```",
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)
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assert data["action"] == "REFINE"
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assert data["target_path"] == "digest/personal/no-trailing-summary.md"
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assert data["note"].startswith("Extended node")
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def test_extract_clean_output_respects_max_units():
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"""Extract cleaning caps valid units at max_units."""
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state = DreamState(changed_paths=["daily/a.md"])
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meta = {
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"units": [
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{
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"name": f"unit-{i}",
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"bucket": "wiki",
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"summary": f"summary {i}",
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"paths": ["daily/a.md"],
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}
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for i in range(7)
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],
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}
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DreamExtractStep().clean_output(state, meta, max_units=5)
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assert len(state.units) == 5
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assert [unit["name"] for unit in state.units] == [f"unit-{i}" for i in range(5)]
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def test_extract_without_llm_marks_changed_paths_failed(tmp_path):
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"""A missing LLM must not let finish checkpoint unprocessed source files."""
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async def run():
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note = _touch(tmp_path / "daily" / "2026-05-28" / "session.md")
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step = DreamExtractStep(app_context=ApplicationContext(workspace_dir=str(tmp_path)))
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response = await step(
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RuntimeContext(
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date="2026-05-28",
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file_catalog=_Catalog(),
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file_store=_FileStore(tmp_path),
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),
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)
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dream = response.metadata["dream"]
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assert response.success is False
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assert str(note.relative_to(tmp_path)) in dream["changed_paths"]
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assert dream["failed_paths"] == dream["changed_paths"]
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asyncio.run(run())
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def test_topics_step_writes_only_target_date_interests():
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"""Topics are written only to ``state.date`` even when scan dates span multiple days."""
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async def run():
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with tempfile.TemporaryDirectory() as tmp:
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workspace = Path(tmp)
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_touch(workspace / "daily" / "2026-05-26" / "old.md")
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_touch(workspace / "daily" / "2026-05-28" / "today.md")
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old_interests = workspace / "daily" / "2026-05-26" / "interests.yaml"
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_touch(old_interests, "date: 2026-05-26\ntopics: []\n")
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state = DreamState(
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date="2026-05-28",
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dates=["2026-05-26", "2026-05-27", "2026-05-28"],
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workspace=str(workspace),
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daily_dir="daily",
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topics=[
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{
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"title": "Old changed topic",
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"reason": "Old daily material changed.",
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"paths": ["daily/2026-05-26/old.md"],
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},
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{
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"title": "Today changed topic",
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"reason": "Today's daily material changed.",
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"paths": ["daily/2026-05-28/today.md"],
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},
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],
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)
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step = DreamTopicsStep()
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resp = await step(
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RuntimeContext(dream=state.model_dump(), file_store=_FileStore(workspace)),
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)
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target = workspace / "daily" / "2026-05-28" / "interests.yaml"
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dream = resp.metadata["dream"]
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assert resp.success is True
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assert target.is_file()
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assert old_interests.read_text(encoding="utf-8") == "date: 2026-05-26\ntopics: []\n"
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assert dream["interests_paths"] == ["daily/2026-05-28/interests.yaml"]
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assert yaml.safe_load(target.read_text(encoding="utf-8"))["date"] == "2026-05-28"
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asyncio.run(run())
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def test_proactive_answer_includes_topics_and_requested_content(tmp_path):
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"""Successful proactive reads expose useful data through the primary answer."""
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async def run():
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content = (
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"date: 2026-05-28\n"
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"topics:\n"
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" - title: Retrieval quality\n"
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" reason: Search behavior changed repeatedly.\n"
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" evidence: daily/2026-05-28/session.md\n"
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)
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_touch(tmp_path / "daily" / "2026-05-28" / "interests.yaml", content)
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step = ProactiveStep(app_context=ApplicationContext(workspace_dir=str(tmp_path)))
|
|
|
|
response = await step(
|
|
RuntimeContext(date="2026-05-28", include_content=True, file_store=_FileStore(tmp_path)),
|
|
)
|
|
|
|
assert response.success is True
|
|
assert response.answer == {
|
|
"summary": "Read 1 proactive topic(s) from daily/2026-05-28/interests.yaml",
|
|
"topics": [
|
|
{
|
|
"title": "Retrieval quality",
|
|
"reason": "Search behavior changed repeatedly.",
|
|
"evidence": "daily/2026-05-28/session.md",
|
|
"keywords": [],
|
|
"paths": [],
|
|
},
|
|
],
|
|
"content": content,
|
|
}
|
|
assert response.metadata["topics"] == response.answer["topics"]
|
|
assert response.metadata["content"] == content
|
|
|
|
asyncio.run(run())
|
|
|
|
|
|
def test_proactive_answer_omits_unrequested_content(tmp_path):
|
|
"""Raw YAML is absent from the primary answer when include_content is false."""
|
|
|
|
async def run():
|
|
_touch(
|
|
tmp_path / "daily" / "2026-05-28" / "interests.yaml",
|
|
"topics:\n - title: Topic\n reason: Reason\n",
|
|
)
|
|
step = ProactiveStep(app_context=ApplicationContext(workspace_dir=str(tmp_path)))
|
|
|
|
response = await step(
|
|
RuntimeContext(date="2026-05-28", include_content=False, file_store=_FileStore(tmp_path)),
|
|
)
|
|
|
|
assert response.success is True
|
|
assert "content" not in response.answer
|
|
assert response.answer["topics"][0]["title"] == "Topic"
|
|
assert response.metadata["content"] == ""
|
|
|
|
asyncio.run(run())
|
|
|
|
|
|
def test_proactive_keeps_skipped_and_error_answers_explicit(tmp_path):
|
|
"""Empty and failure outcomes remain distinguishable without reading metadata."""
|
|
|
|
async def run():
|
|
step = ProactiveStep(app_context=ApplicationContext(workspace_dir=str(tmp_path)))
|
|
skipped = await step(RuntimeContext(date="2026-05-28", file_store=_FileStore(tmp_path)))
|
|
|
|
assert skipped.success is True
|
|
assert skipped.answer == "Skipped: interests file not found at daily/2026-05-28/interests.yaml"
|
|
assert skipped.metadata["skipped"] is True
|
|
|
|
_touch(tmp_path / "daily" / "2026-05-28" / "interests.yaml", "topics: []\n")
|
|
with patch("reme.steps.evolve.dream.proactive.load_yaml_topics", side_effect=ValueError("bad topics")):
|
|
failed = await step(RuntimeContext(date="2026-05-28", file_store=_FileStore(tmp_path)))
|
|
|
|
assert failed.success is False
|
|
assert failed.answer == "Error: ValueError: bad topics"
|
|
assert failed.metadata["error"] == "ValueError: bad topics"
|
|
|
|
asyncio.run(run())
|
|
|
|
|
|
def test_finish_does_not_checkpoint_failed_changed_paths():
|
|
"""Finish does not checkpoint failed changed paths."""
|
|
|
|
async def run():
|
|
with tempfile.TemporaryDirectory() as tmp:
|
|
workspace = Path(tmp)
|
|
ok = _touch(workspace / "daily" / "2026-05-28" / "ok.md")
|
|
failed = _touch(workspace / "daily" / "2026-05-28" / "failed.md")
|
|
day_index = _touch(workspace / "daily" / "2026-05-28.md")
|
|
interests = _touch(workspace / "daily" / "2026-05-28" / "interests.yaml")
|
|
state = DreamState(
|
|
date="2026-05-28",
|
|
dates=["2026-05-26", "2026-05-27", "2026-05-28"],
|
|
workspace=str(workspace),
|
|
daily_dir="daily",
|
|
changed_paths=[str(ok.relative_to(workspace)), str(failed.relative_to(workspace))],
|
|
failed_paths=[str(failed.relative_to(workspace))],
|
|
interests_paths=[str(interests.relative_to(workspace))],
|
|
integrate_results=[
|
|
{
|
|
"action": "CREATE",
|
|
"target_path": "digest/procedure/example.md",
|
|
"note": "Created a concise procedure node.",
|
|
},
|
|
],
|
|
)
|
|
step, catalog = DreamFinishStep(), _Catalog()
|
|
resp = await step(RuntimeContext(dream=state.model_dump(), file_catalog=catalog))
|
|
|
|
upserted = [n.path for n in catalog.upserts]
|
|
assert resp.success is True
|
|
assert resp.answer.startswith("AutoDream completed\n\n")
|
|
assert "action:" not in resp.answer
|
|
assert "topics:" not in resp.answer
|
|
assert "Changes:" in resp.answer
|
|
assert "- [digest/procedure/example.md][CREATE]: Created a concise procedure node." in resp.answer
|
|
assert str(ok.relative_to(workspace)) in upserted
|
|
assert str(failed.relative_to(workspace)) not in upserted
|
|
assert str(interests.relative_to(workspace)) in upserted
|
|
assert str(day_index.relative_to(workspace)) in upserted
|
|
assert catalog.dumps == 1
|
|
|
|
asyncio.run(run())
|