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Refactor tests for execution plan and add comprehensive storage tests

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- Updated `TestModuleSingletons` in `test_execution_plan.py` to reflect new agent templates and playbook names.
- Changed assertions in playbook tests to match updated templates and agents.
- Introduced `test_storage.py` to cover the storage layer, including encryption, BlobStore, and VectorStore functionalities.
- Added tests for S3 interactions, ensuring upload, download, delete, and list operations work as expected.
- Implemented mock tests for Pinecone and Qdrant vector stores to validate upsert, search, and delete operations.
This commit is contained in:
roberto
2026-03-02 15:36:09 +01:00
parent 35dd9ac86f
commit c8ef7b119b
21 changed files with 1980 additions and 469 deletions
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54
app/core/execution_plan.py
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@@ -156,29 +156,33 @@ def _register_builtin_templates() -> None:
_tpls: dict[str, str] = {
"tpl_task_agent_default": (
"You are a task management assistant. Help the user create, update, "
"and prioritize tasks based on their message and context."
"list, and track tasks. Use correct status values (todo, in_progress, "
"done) and priority values (high, medium, low) from the workspace model."
),
"tpl_calendar_agent_default": (
"You are a calendar assistant. Help manage events, detect scheduling "
"conflicts, and suggest improvements based on the provided context."
"tpl_checkpoint_agent_default": (
"You are a project checkpoint assistant. Help the user create and manage "
"milestone checkpoints on their projects. Every checkpoint requires a "
"project_id and a date expressed as a Unix timestamp in milliseconds."
),
"tpl_email_agent_default": (
"You are an email analysis assistant. Classify emails, extract action "
"items, and draft responses using only the metadata provided."
"tpl_project_agent_default": (
"You are a project management assistant. Help the user create, find, "
"update, and archive projects. Projects have a name, an optional client, "
"and a status of either active or archived."
),
"tpl_analytics_agent_default": (
"You are a workspace analytics assistant. Calculate metrics, generate "
"reports, and surface trends from the data provided in context."
"tpl_note_agent_default": (
"You are a note-taking assistant. Help the user create, retrieve, update, "
"and delete Markdown notes. Notes can optionally be linked to a project."
),
"tpl_email_extract_action_items": (
"Extract all action items from the provided email metadata. "
"Return a structured list of tasks, each with a title, inferred "
"priority, and suggested due date where possible."
"tpl_task_extract_from_project": (
"Extract all actionable tasks from the provided project context. "
"Return a structured list of tasks, each with a title, inferred priority "
"(high, medium, or low), suggested status (todo), and a due_date in "
"milliseconds where a deadline can be inferred."
),
"tpl_analytics_weekly_summary": (
"Generate a weekly performance summary from the provided analytics "
"data. Include task completion rate, overdue item count, top "
"priorities for the coming week, and notable trends."
"tpl_note_weekly_summary": (
"Generate a weekly project summary note from the provided workspace data. "
"Include: tasks completed this week, tasks due soon, active projects, "
"and upcoming checkpoints. Format the output as clean Markdown."
),
}
for tid, text in _tpls.items():
@@ -189,20 +193,20 @@ def _load_playbooks() -> None:
"""Pre-build and cache the built-in playbooks."""
playbooks: list[tuple[str, ExecutionPlan]] = [
(
"create_task_from_email",
ExecutionPlanBuilder("email_agent")
"create_tasks_from_project",
ExecutionPlanBuilder("project_agent")
.add_llm_step(
"tpl_email_extract_action_items",
{"source": "email_metadata"},
"tpl_task_extract_from_project",
{"source": "project_context"},
)
.add_data_step("create_record", data_from_step=0)
.build(),
),
(
"generate_weekly_report",
ExecutionPlanBuilder("analytics_agent")
"generate_weekly_note",
ExecutionPlanBuilder("note_agent")
.add_llm_step(
"tpl_analytics_weekly_summary",
"tpl_note_weekly_summary",
{"period": "last_7_days"},
)
.add_data_step("create_record", data_from_step=0)