29dc1e138c
Add EMBEDDING_MODEL_CONFIG__DIMENSIONS_MODE (auto|always|never) controlling
whether the dimensions= parameter is forwarded on OpenAI embeddings.create
calls. auto (default) sends it when the operator explicitly set
EMBEDDING_VECTOR_DIMENSIONS and the configured model is not on the
known-rejecting allowlist (currently text-embedding-ada-002).
The provenance check (was VECTOR_DIMENSIONS explicitly set?) lives as
EmbeddingSettings.resolve_send_dimensions() because it needs access to
model_fields_set, which the standalone resolver does not have. The
resolved boolean is passed into _EmbeddingClient at construction time;
the client never inspects mode or provenance.
Also pins cloudevents <2.0 — 2.0.0 reorganized the package and dropped
cloudevents.conversion and cloudevents.http, which src/telemetry/emitter.py
imports. The original `>=1.12.0` constraint allowed the broken 2.0 resolve.
With the pin, the imports resolve cleanly and the basedpyright warning
cascade (37+ warnings about unknown types) disappears.
Drive-by cleanups (all unnecessary cast/ignore comments flagged by
basedpyright after the cloudevents downgrade):
- vector_store/lancedb.py, tests/conftest.py, and
tests/deriver/test_vector_reconciliation.py — drop dead pyright ignores
- sdks/python/src/honcho/http/{async_,}client.py — drop unnecessary
cast(datetime, ...) (parsedate_to_datetime already returns datetime)
- vector_store/turbopuffer.py — cast(Any, rows) for the upsert_rows
TypedDict that the SDK exposes but our row builder doesn't satisfy
- tests/test_datetime_parsing.py — ignore reportArgumentType on the
test that deliberately passes wrong types to assert raises
|
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|---|---|---|
| .. | ||
| README.md | ||
| __init__.py | ||
| conftest.py | ||
| test_deriver_processing.py | ||
| test_enqueue_dream.py | ||
| test_queue_operations.py | ||
| test_queue_processing.py | ||
| test_representation_crud.py | ||
| test_vector_reconciliation.py | ||
README.md
Deriver Testing
This directory contains tests for the deriver system, which handles background processing of messages to extract insights and update working representations.
Structure
conftest.py- Shared fixtures for deriver testingtest_queue_operations.py- Tests for basic queue operationstest_deriver_processing.py- Tests for deriver processing logictest_queue_processing.py- Tests for queue manager and work unit processing
Key Fixtures
Database Fixtures
sample_session_with_peers- Creates a session with multiple peers having different observation configurationssample_messages- Creates sample messages for testingsample_queue_items- Creates queue items with various payload types (representation, summary)
Queue Fixtures
create_queue_payload- Helper to create queue payloads for testingadd_queue_items- Helper to add queue items to the databasecreate_active_queue_session- Helper to create active queue sessions for work unit tracking
Mocking Fixtures
mock_critical_analysis_call- Mocks the critical analysis LLM callmock_queue_manager- Mocks the queue manager for testingmock_representation_manager- Mocks the representation manager operations
Testing Patterns
Creating Queue Items
# Create representation payloads
payload = create_queue_payload(
message=message,
task_type="representation",
observer=observer_peer.name,
observed=message.peer_name
)
# Add to queue
queue_items = await add_queue_items([payload], session.id)
Testing Work Units
# Create a work unit
work_unit = WorkUnit(
session_id=session.id,
task_type="representation",
observer=observer,
observed=observed
)
# Test string representation
assert str(work_unit) == f"({session.id}, {observed.name}, {observer.name}, representation)"