15ea23d5d6
* feat: introduce hindsight-api-slim and hindsight-all-slim packages Closes #552 - Move all source code from hindsight-api/ to new hindsight-api-slim/ - hindsight-api-slim has heavy ML deps (torch, sentence-transformers, transformers, einops, flashrank, mlx, mlx-lm, safetensors) and pg0-embedded as optional extras: [local-ml], [embedded-db], [all] - hindsight-api becomes a zero-code meta-package depending on hindsight-api-slim[all] for full backward compatibility - Add hindsight-all-slim meta-package: hindsight-api-slim + client + embed - hindsight-all updated to depend on hindsight-api-slim[all] - pg0.py: lazy-import pg0 with clear ImportError pointing to [embedded-db] - Dockerfile: replace sed hack with proper uv sync --extra flags - Update release.yml, test.yml, lint.sh, release.sh, CLAUDE.md and all path references throughout the repo * refactor: rename hindsight/ directory to hindsight-all/ * docs: document hindsight-api-slim and hindsight-all-slim package variants Add package variants table and extras explanation to installation.md * docs: remove emojis from installation.md, use professional tone * docs: link Docker slim variant to pip package variants section * docs: consolidate Docker image variants into single table * ci: fix working-directory paths after package restructure - Replace all hindsight-api → hindsight-api-slim in test.yml - Replace hindsight → hindsight-all in test.yml - Add --extra embedded-db to test-embed API install step * ci: add local-ml and embedded-db extras to API sync steps These extras were previously implicit in the old hindsight-api package (which bundled everything). Now that hindsight-api-slim uses optional extras, we must explicitly request local-ml and embedded-db in CI. * ci: add API install step with embedded-db to test-embed smoke test The smoke test starts hindsight-api as a daemon, which requires pg0-embedded. Add a dedicated install step for hindsight-api-slim with embedded-db extra so the daemon can start successfully. * ci: remove --no-install-project when using optional extras When --no-install-project is combined with --extra, the optional deps are not installed because extras require the project to be active. Remove --no-install-project from steps that need local-ml or embedded-db. * ci: fix ordering of uv sync steps to preserve optional extras When uv sync runs for a different workspace member, it removes optional extras installed for other members. Fix by always running extra-requiring API sync last, after other workspace member syncs. Also remove --no-install-project from embedded-db sync in test-embed, as --no-install-project prevents optional extras from being active. * ci: add local-ml extra to test-embed API install for smoke test The smoke test starts the full API server which needs sentence-transformers for local embeddings (default provider). Add local-ml extra to the install. * ci: simplify extras with --all-extras and add slim pip smoke test - Replace explicit --extra local-ml --extra embedded-db with --all-extras for cleaner, more maintainable sync steps - Add test-pip-slim job: tests hindsight-api-slim[embedded-db] without local ML models, using Cohere for embeddings/reranking (mirrors Docker slim smoke test approach) * ci: simplify slim smoke test to health check only (mirrors Docker test)
741 lines
26 KiB
Python
741 lines
26 KiB
Python
"""
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Tests for custom embedding dimensions and automatic dimension detection.
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Uses isolated PostgreSQL schemas to avoid affecting other tests.
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Includes tests for:
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- Automatic embedding dimension detection and database schema adjustment
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- OpenAI embeddings provider with 1536 dimensions
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"""
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import asyncio
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import os
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from datetime import datetime
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import pytest
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from sqlalchemy import create_engine, text
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from hindsight_api import MemoryEngine, RequestContext
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from hindsight_api.engine.cross_encoder import CohereCrossEncoder, LocalSTCrossEncoder, ZeroEntropyCrossEncoder
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from hindsight_api.engine.embeddings import CohereEmbeddings, LocalSTEmbeddings, OpenAIEmbeddings
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from hindsight_api.engine.query_analyzer import DateparserQueryAnalyzer
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from hindsight_api.engine.task_backend import SyncTaskBackend
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from hindsight_api.extensions import TenantContext, TenantExtension
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from hindsight_api.migrations import ensure_embedding_dimension, run_migrations
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# =============================================================================
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# Shared Utilities
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# =============================================================================
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class SchemaTenantExtension(TenantExtension):
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"""Tenant extension that routes all requests to a specific schema (for testing)."""
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def __init__(self, schema_name: str):
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self.schema_name = schema_name
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async def authenticate(self, request_context: RequestContext) -> TenantContext:
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return TenantContext(schema_name=self.schema_name)
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async def list_tenants(self) -> list:
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from hindsight_api.extensions.tenant import Tenant
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return [Tenant(schema=self.schema_name)]
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def get_test_schema(prefix: str, worker_id: str) -> str:
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"""Get unique schema name per xdist worker."""
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if worker_id == "master" or not worker_id:
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return prefix
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return f"{prefix}_{worker_id}"
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def create_isolated_schema(db_url: str, schema_name: str, dimension: int | None = None):
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"""Create an isolated schema with migrations and optional dimension adjustment."""
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engine = create_engine(db_url)
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# Create schema (drop first if exists from previous failed run)
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with engine.connect() as conn:
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conn.execute(text(f"DROP SCHEMA IF EXISTS {schema_name} CASCADE"))
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conn.execute(text(f"CREATE SCHEMA {schema_name}"))
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conn.commit()
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# Run migrations in the isolated schema
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run_migrations(db_url, schema=schema_name)
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# Adjust embedding dimension if specified
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if dimension is not None:
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ensure_embedding_dimension(db_url, dimension, schema=schema_name)
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def drop_schema(db_url: str, schema_name: str):
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"""Drop an isolated schema."""
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engine = create_engine(db_url)
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with engine.connect() as conn:
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conn.execute(text(f"DROP SCHEMA IF EXISTS {schema_name} CASCADE"))
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conn.commit()
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def get_column_dimension(db_url: str, schema: str = "public", table: str = "memory_units") -> int | None:
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"""Get the current embedding column dimension from the database."""
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engine = create_engine(db_url)
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with engine.connect() as conn:
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result = conn.execute(
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text("""
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SELECT atttypmod
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FROM pg_attribute a
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JOIN pg_class c ON a.attrelid = c.oid
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JOIN pg_namespace n ON c.relnamespace = n.oid
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WHERE n.nspname = :schema
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AND c.relname = :table
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AND a.attname = 'embedding'
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"""),
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{"schema": schema, "table": table},
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).scalar()
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return result
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def get_row_count(db_url: str, schema: str = "public") -> int:
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"""Get the number of rows with embeddings in memory_units."""
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engine = create_engine(db_url)
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with engine.connect() as conn:
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return conn.execute(text(f"SELECT COUNT(*) FROM {schema}.memory_units WHERE embedding IS NOT NULL")).scalar()
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def insert_test_embedding(db_url: str, schema: str, dimension: int):
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"""Insert a test row with a dummy embedding."""
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engine = create_engine(db_url)
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embedding = [0.1] * dimension
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embedding_str = "[" + ",".join(str(x) for x in embedding) + "]"
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with engine.connect() as conn:
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conn.execute(
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text(f"""
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INSERT INTO {schema}.memory_units (bank_id, text, embedding, event_date, fact_type)
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VALUES ('test-bank', 'test text', '{embedding_str}'::vector, NOW(), 'world')
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""")
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)
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conn.commit()
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def clear_embeddings(db_url: str, schema: str):
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"""Clear all rows from memory_units."""
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engine = create_engine(db_url)
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with engine.connect() as conn:
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conn.execute(text(f"DELETE FROM {schema}.memory_units"))
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conn.commit()
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def insert_test_mental_model_embedding(db_url: str, schema: str, dimension: int):
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"""Insert a test mental model row with a dummy embedding."""
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engine = create_engine(db_url)
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embedding = [0.1] * dimension
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embedding_str = "[" + ",".join(str(x) for x in embedding) + "]"
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with engine.connect() as conn:
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# Ensure test bank exists
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conn.execute(
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text(f"""
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INSERT INTO {schema}.banks (bank_id, name)
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VALUES ('test-bank-mm', 'Test Bank')
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ON CONFLICT (bank_id) DO NOTHING
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""")
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)
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conn.execute(
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text(f"""
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INSERT INTO {schema}.mental_models (bank_id, name, source_query, content, embedding)
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VALUES ('test-bank-mm', 'test model', 'test query', 'test content', '{embedding_str}'::vector)
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""")
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)
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conn.commit()
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def clear_mental_model_embeddings(db_url: str, schema: str):
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"""Clear all rows from mental_models."""
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engine = create_engine(db_url)
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with engine.connect() as conn:
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conn.execute(text(f"DELETE FROM {schema}.mental_models"))
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conn.commit()
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# =============================================================================
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# Embedding Dimension Tests (Local Embeddings)
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# =============================================================================
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@pytest.fixture(scope="class")
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def dimension_test_schema(pg0_db_url, worker_id):
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"""Create an isolated schema for dimension tests."""
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schema_name = get_test_schema("test_embed_dim", worker_id)
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create_isolated_schema(pg0_db_url, schema_name)
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yield pg0_db_url, schema_name
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drop_schema(pg0_db_url, schema_name)
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class TestEmbeddingDimension:
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"""Tests for embedding dimension detection and adjustment."""
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def test_dimension_matches_no_change(self, dimension_test_schema):
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"""When dimension matches, no changes should be made."""
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db_url, schema = dimension_test_schema
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# Get initial dimension (should be 384 from migration)
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initial_dim = get_column_dimension(db_url, schema)
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assert initial_dim == 384, f"Expected 384, got {initial_dim}"
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# Call ensure_embedding_dimension with matching dimension
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ensure_embedding_dimension(db_url, 384, schema=schema)
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# Dimension should still be 384
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assert get_column_dimension(db_url, schema) == 384
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def test_dimension_change_empty_table(self, dimension_test_schema):
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"""When table is empty, dimension can be changed."""
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db_url, schema = dimension_test_schema
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# Ensure table is empty
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clear_embeddings(db_url, schema)
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assert get_row_count(db_url, schema) == 0
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# Change dimension to 768
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ensure_embedding_dimension(db_url, 768, schema=schema)
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# Verify dimension changed
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new_dim = get_column_dimension(db_url, schema)
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assert new_dim == 768, f"Expected 768, got {new_dim}"
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# Change back to 384 for other tests
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ensure_embedding_dimension(db_url, 384, schema=schema)
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assert get_column_dimension(db_url, schema) == 384
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def test_dimension_change_blocked_with_data(self, dimension_test_schema):
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"""When table has data, dimension change should be blocked."""
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db_url, schema = dimension_test_schema
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# Ensure table is empty first
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clear_embeddings(db_url, schema)
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# Insert a test row with 384-dim embedding
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insert_test_embedding(db_url, schema, 384)
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assert get_row_count(db_url, schema) == 1
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# Try to change dimension - should raise error
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with pytest.raises(RuntimeError) as exc_info:
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ensure_embedding_dimension(db_url, 768, schema=schema)
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assert "Cannot change embedding dimension" in str(exc_info.value)
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assert "1 rows with embeddings" in str(exc_info.value)
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# Dimension should be unchanged
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assert get_column_dimension(db_url, schema) == 384
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# Cleanup
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clear_embeddings(db_url, schema)
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def test_mental_models_dimension_matches_no_change(self, dimension_test_schema):
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"""When mental_models dimension matches, no changes should be made."""
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db_url, schema = dimension_test_schema
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initial_dim = get_column_dimension(db_url, schema, table="mental_models")
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assert initial_dim == 384, f"Expected 384, got {initial_dim}"
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ensure_embedding_dimension(db_url, 384, schema=schema)
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assert get_column_dimension(db_url, schema, table="mental_models") == 384
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def test_mental_models_dimension_change_empty_table(self, dimension_test_schema):
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"""When mental_models is empty, dimension can be changed."""
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db_url, schema = dimension_test_schema
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clear_mental_model_embeddings(db_url, schema)
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ensure_embedding_dimension(db_url, 768, schema=schema)
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assert get_column_dimension(db_url, schema, table="mental_models") == 768
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# Change back for other tests
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ensure_embedding_dimension(db_url, 384, schema=schema)
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assert get_column_dimension(db_url, schema, table="mental_models") == 384
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def test_mental_models_dimension_change_blocked_with_data(self, dimension_test_schema):
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"""When mental_models has data, dimension change should be blocked."""
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db_url, schema = dimension_test_schema
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clear_mental_model_embeddings(db_url, schema)
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insert_test_mental_model_embedding(db_url, schema, 384)
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with pytest.raises(RuntimeError) as exc_info:
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ensure_embedding_dimension(db_url, 768, schema=schema)
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assert "Cannot change embedding dimension" in str(exc_info.value)
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assert "mental_models" in str(exc_info.value)
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assert get_column_dimension(db_url, schema, table="mental_models") == 384
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# Cleanup
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clear_mental_model_embeddings(db_url, schema)
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def test_local_embeddings_dimension_detection(self, embeddings):
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"""Test that LocalSTEmbeddings correctly detects dimension."""
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# Initialize embeddings if not already done
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loop = asyncio.new_event_loop()
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try:
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loop.run_until_complete(embeddings.initialize())
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finally:
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loop.close()
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# bge-small-en-v1.5 produces 384-dim embeddings
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assert embeddings.dimension == 384
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# Verify by generating an actual embedding
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result = embeddings.encode(["test"])
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assert len(result) == 1
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assert len(result[0]) == 384
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# =============================================================================
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# OpenAI Embeddings Tests
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# =============================================================================
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def has_openai_api_key() -> bool:
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"""Check if OpenAI API key is available."""
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return bool(os.environ.get("HINDSIGHT_API_EMBEDDINGS_OPENAI_API_KEY"))
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def get_openai_api_key() -> str:
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"""Get OpenAI API key from environment."""
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return os.environ.get("HINDSIGHT_API_EMBEDDINGS_OPENAI_API_KEY", "")
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|
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@pytest.fixture(scope="module")
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def openai_embeddings():
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"""Create OpenAI embeddings instance."""
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if not has_openai_api_key():
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pytest.skip("OpenAI API key not available (set HINDSIGHT_API_EMBEDDINGS_OPENAI_API_KEY)")
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embeddings = OpenAIEmbeddings(
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api_key=get_openai_api_key(),
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model="text-embedding-3-small",
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)
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loop = asyncio.new_event_loop()
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try:
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loop.run_until_complete(embeddings.initialize())
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finally:
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loop.close()
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return embeddings
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|
|
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@pytest.fixture(scope="module")
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def openai_test_schema(pg0_db_url, worker_id, openai_embeddings):
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"""Create an isolated schema for OpenAI embedding tests."""
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schema_name = get_test_schema("test_openai_embed", worker_id)
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create_isolated_schema(pg0_db_url, schema_name, dimension=openai_embeddings.dimension)
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yield pg0_db_url, schema_name
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drop_schema(pg0_db_url, schema_name)
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|
|
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@pytest.fixture
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def cross_encoder():
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"""Provide a cross encoder for tests."""
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return LocalSTCrossEncoder()
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|
|
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@pytest.fixture
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def query_analyzer():
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"""Provide a query analyzer for tests."""
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return DateparserQueryAnalyzer()
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|
|
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@pytest.fixture
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def test_bank_id():
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"""Provide a unique bank ID for this test run."""
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return f"openai_test_{datetime.now().timestamp()}"
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|
|
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@pytest.fixture
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def request_context():
|
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"""Provide a default RequestContext for tests."""
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return RequestContext()
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|
|
|
|
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class TestOpenAIEmbeddings:
|
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"""Tests for OpenAI embeddings provider."""
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def test_openai_embeddings_initialization(self, openai_embeddings):
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"""Test that OpenAI embeddings initializes correctly."""
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assert openai_embeddings.dimension == 1536
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assert openai_embeddings.provider_name == "openai"
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|
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def test_openai_embeddings_encode(self, openai_embeddings):
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"""Test that OpenAI embeddings can encode text."""
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texts = ["Hello, world!", "This is a test."]
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embeddings = openai_embeddings.encode(texts)
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assert len(embeddings) == 2
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assert len(embeddings[0]) == 1536
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assert len(embeddings[1]) == 1536
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assert all(isinstance(x, float) for x in embeddings[0])
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|
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@pytest.mark.asyncio
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async def test_openai_embeddings_retain_recall(
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self,
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openai_test_schema,
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openai_embeddings,
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cross_encoder,
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query_analyzer,
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test_bank_id,
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request_context,
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):
|
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"""Test retain and recall operations with OpenAI embeddings."""
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db_url, schema_name = openai_test_schema
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|
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memory = MemoryEngine(
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db_url=db_url,
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memory_llm_provider=os.getenv("HINDSIGHT_API_LLM_PROVIDER", "groq"),
|
|
memory_llm_api_key=os.getenv("HINDSIGHT_API_LLM_API_KEY"),
|
|
memory_llm_model=os.getenv("HINDSIGHT_API_LLM_MODEL", "openai/gpt-oss-120b"),
|
|
memory_llm_base_url=os.getenv("HINDSIGHT_API_LLM_BASE_URL") or None,
|
|
embeddings=openai_embeddings,
|
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cross_encoder=cross_encoder,
|
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query_analyzer=query_analyzer,
|
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pool_min_size=1,
|
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pool_max_size=3,
|
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run_migrations=False,
|
|
tenant_extension=SchemaTenantExtension(schema_name),
|
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task_backend=SyncTaskBackend(),
|
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)
|
|
|
|
try:
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await memory.initialize()
|
|
|
|
# Store some memories
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|
await memory.retain_async(
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|
bank_id=test_bank_id,
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|
content="Alice works as a software engineer at Google.",
|
|
context="career discussion",
|
|
request_context=request_context,
|
|
)
|
|
|
|
await memory.retain_async(
|
|
bank_id=test_bank_id,
|
|
content="Bob is a data scientist specializing in machine learning.",
|
|
context="team introductions",
|
|
request_context=request_context,
|
|
)
|
|
|
|
# Recall memories
|
|
result = await memory.recall_async(
|
|
bank_id=test_bank_id,
|
|
query="Who works in technology?",
|
|
request_context=request_context,
|
|
)
|
|
|
|
assert result is not None
|
|
assert len(result.results) > 0
|
|
|
|
memory_texts = [m.text for m in result.results]
|
|
assert any(
|
|
"Alice" in text or "Bob" in text or "software" in text or "data scientist" in text
|
|
for text in memory_texts
|
|
), f"Expected to find relevant memories, got: {memory_texts}"
|
|
|
|
finally:
|
|
try:
|
|
if memory._pool and not memory._pool._closing:
|
|
await memory.close()
|
|
except Exception:
|
|
pass
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_openai_embeddings_batch_retain(
|
|
self,
|
|
openai_test_schema,
|
|
openai_embeddings,
|
|
cross_encoder,
|
|
query_analyzer,
|
|
test_bank_id,
|
|
request_context,
|
|
):
|
|
"""Test batch retain with OpenAI embeddings."""
|
|
db_url, schema_name = openai_test_schema
|
|
|
|
memory = MemoryEngine(
|
|
db_url=db_url,
|
|
memory_llm_provider=os.getenv("HINDSIGHT_API_LLM_PROVIDER", "groq"),
|
|
memory_llm_api_key=os.getenv("HINDSIGHT_API_LLM_API_KEY"),
|
|
memory_llm_model=os.getenv("HINDSIGHT_API_LLM_MODEL", "openai/gpt-oss-120b"),
|
|
memory_llm_base_url=os.getenv("HINDSIGHT_API_LLM_BASE_URL") or None,
|
|
embeddings=openai_embeddings,
|
|
cross_encoder=cross_encoder,
|
|
query_analyzer=query_analyzer,
|
|
pool_min_size=1,
|
|
pool_max_size=3,
|
|
run_migrations=False,
|
|
tenant_extension=SchemaTenantExtension(schema_name),
|
|
task_backend=SyncTaskBackend(),
|
|
)
|
|
|
|
try:
|
|
await memory.initialize()
|
|
|
|
contents = [
|
|
{"content": "Python is my favorite programming language.", "context": "preferences"},
|
|
{"content": "I prefer dark mode for all my applications.", "context": "preferences"},
|
|
{"content": "Coffee is essential for morning productivity.", "context": "habits"},
|
|
]
|
|
|
|
result = await memory.retain_batch_async(
|
|
bank_id=test_bank_id,
|
|
contents=contents,
|
|
request_context=request_context,
|
|
)
|
|
|
|
assert len(result) == 3
|
|
|
|
recall_result = await memory.recall_async(
|
|
bank_id=test_bank_id,
|
|
query="What are my preferences?",
|
|
request_context=request_context,
|
|
)
|
|
|
|
assert recall_result is not None
|
|
assert len(recall_result.results) > 0
|
|
|
|
finally:
|
|
try:
|
|
if memory._pool and not memory._pool._closing:
|
|
await memory.close()
|
|
except Exception:
|
|
pass
|
|
|
|
|
|
# =============================================================================
|
|
# Cohere Embeddings Tests
|
|
# =============================================================================
|
|
|
|
|
|
def has_cohere_api_key() -> bool:
|
|
"""Check if Cohere API key is available."""
|
|
return bool(os.environ.get("COHERE_API_KEY"))
|
|
|
|
|
|
def get_cohere_api_key() -> str:
|
|
"""Get Cohere API key from environment."""
|
|
return os.environ.get("COHERE_API_KEY", "")
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def cohere_embeddings():
|
|
"""Create Cohere embeddings instance."""
|
|
if not has_cohere_api_key():
|
|
pytest.skip("Cohere API key not available (set COHERE_API_KEY)")
|
|
|
|
embeddings = CohereEmbeddings(
|
|
api_key=get_cohere_api_key(),
|
|
model="embed-english-v3.0",
|
|
)
|
|
loop = asyncio.new_event_loop()
|
|
try:
|
|
loop.run_until_complete(embeddings.initialize())
|
|
finally:
|
|
loop.close()
|
|
return embeddings
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def cohere_cross_encoder():
|
|
"""Create Cohere cross-encoder instance."""
|
|
if not has_cohere_api_key():
|
|
pytest.skip("Cohere API key not available (set COHERE_API_KEY)")
|
|
|
|
cross_encoder = CohereCrossEncoder(
|
|
api_key=get_cohere_api_key(),
|
|
model="rerank-english-v3.0",
|
|
)
|
|
loop = asyncio.new_event_loop()
|
|
try:
|
|
loop.run_until_complete(cross_encoder.initialize())
|
|
finally:
|
|
loop.close()
|
|
return cross_encoder
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def cohere_test_schema(pg0_db_url, worker_id, cohere_embeddings):
|
|
"""Create an isolated schema for Cohere embedding tests."""
|
|
schema_name = get_test_schema("test_cohere_embed", worker_id)
|
|
create_isolated_schema(pg0_db_url, schema_name, dimension=cohere_embeddings.dimension)
|
|
yield pg0_db_url, schema_name
|
|
drop_schema(pg0_db_url, schema_name)
|
|
|
|
|
|
class TestCohereEmbeddings:
|
|
"""Tests for Cohere embeddings provider."""
|
|
|
|
def test_cohere_embeddings_initialization(self, cohere_embeddings):
|
|
"""Test that Cohere embeddings initializes correctly."""
|
|
assert cohere_embeddings.dimension == 1024
|
|
assert cohere_embeddings.provider_name == "cohere"
|
|
|
|
def test_cohere_embeddings_encode(self, cohere_embeddings):
|
|
"""Test that Cohere embeddings can encode text."""
|
|
texts = ["Hello, world!", "This is a test."]
|
|
embeddings = cohere_embeddings.encode(texts)
|
|
|
|
assert len(embeddings) == 2
|
|
assert len(embeddings[0]) == 1024
|
|
assert len(embeddings[1]) == 1024
|
|
assert all(isinstance(x, float) for x in embeddings[0])
|
|
|
|
|
|
class TestCohereCrossEncoder:
|
|
"""Tests for Cohere cross-encoder/reranker."""
|
|
|
|
def test_cohere_cross_encoder_initialization(self, cohere_cross_encoder):
|
|
"""Test that Cohere cross-encoder initializes correctly."""
|
|
assert cohere_cross_encoder.provider_name == "cohere"
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_cohere_cross_encoder_predict(self, cohere_cross_encoder):
|
|
"""Test that Cohere cross-encoder can score pairs."""
|
|
pairs = [
|
|
("What is the capital of France?", "Paris is the capital of France."),
|
|
("What is the capital of France?", "The Eiffel Tower is in Paris."),
|
|
("What is the capital of France?", "Python is a programming language."),
|
|
]
|
|
scores = await cohere_cross_encoder.predict(pairs)
|
|
|
|
assert len(scores) == 3
|
|
assert all(isinstance(s, float) for s in scores)
|
|
# The first result should be most relevant
|
|
assert scores[0] > scores[2], "Direct answer should score higher than unrelated text"
|
|
|
|
|
|
class TestCohereIntegration:
|
|
"""Integration tests for Cohere embeddings with memory engine."""
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_cohere_embeddings_retain_recall(
|
|
self,
|
|
cohere_test_schema,
|
|
cohere_embeddings,
|
|
cohere_cross_encoder,
|
|
query_analyzer,
|
|
request_context,
|
|
):
|
|
"""Test retain and recall operations with Cohere embeddings."""
|
|
db_url, schema_name = cohere_test_schema
|
|
test_bank_id = f"cohere_test_{datetime.now().timestamp()}"
|
|
|
|
memory = MemoryEngine(
|
|
db_url=db_url,
|
|
memory_llm_provider=os.getenv("HINDSIGHT_API_LLM_PROVIDER", "groq"),
|
|
memory_llm_api_key=os.getenv("HINDSIGHT_API_LLM_API_KEY"),
|
|
memory_llm_model=os.getenv("HINDSIGHT_API_LLM_MODEL", "openai/gpt-oss-120b"),
|
|
memory_llm_base_url=os.getenv("HINDSIGHT_API_LLM_BASE_URL") or None,
|
|
embeddings=cohere_embeddings,
|
|
cross_encoder=cohere_cross_encoder,
|
|
query_analyzer=query_analyzer,
|
|
pool_min_size=1,
|
|
pool_max_size=3,
|
|
run_migrations=False,
|
|
tenant_extension=SchemaTenantExtension(schema_name),
|
|
task_backend=SyncTaskBackend(),
|
|
)
|
|
|
|
try:
|
|
await memory.initialize()
|
|
|
|
# Store some memories
|
|
await memory.retain_async(
|
|
bank_id=test_bank_id,
|
|
content="Alice works as a software engineer at Google.",
|
|
context="career discussion",
|
|
request_context=request_context,
|
|
)
|
|
|
|
await memory.retain_async(
|
|
bank_id=test_bank_id,
|
|
content="Bob is a data scientist specializing in machine learning.",
|
|
context="team introductions",
|
|
request_context=request_context,
|
|
)
|
|
|
|
# Recall memories
|
|
result = await memory.recall_async(
|
|
bank_id=test_bank_id,
|
|
query="Who works in technology?",
|
|
request_context=request_context,
|
|
)
|
|
|
|
assert result is not None
|
|
assert len(result.results) > 0
|
|
|
|
memory_texts = [m.text for m in result.results]
|
|
assert any(
|
|
"Alice" in text or "Bob" in text or "software" in text or "data scientist" in text
|
|
for text in memory_texts
|
|
), f"Expected to find relevant memories, got: {memory_texts}"
|
|
|
|
finally:
|
|
try:
|
|
if memory._pool and not memory._pool._closing:
|
|
await memory.close()
|
|
except Exception:
|
|
pass
|
|
|
|
|
|
# =============================================================================
|
|
# ZeroEntropy Reranker Tests
|
|
# =============================================================================
|
|
|
|
|
|
def has_zeroentropy_api_key() -> bool:
|
|
"""Check if ZeroEntropy API key is available."""
|
|
return bool(os.environ.get("ZEROENTROPY_API_KEY"))
|
|
|
|
|
|
def get_zeroentropy_api_key() -> str:
|
|
"""Get ZeroEntropy API key from environment."""
|
|
return os.environ.get("ZEROENTROPY_API_KEY", "")
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def zeroentropy_cross_encoder():
|
|
"""Create ZeroEntropy cross-encoder instance."""
|
|
if not has_zeroentropy_api_key():
|
|
pytest.skip("ZeroEntropy API key not available (set ZEROENTROPY_API_KEY)")
|
|
|
|
cross_encoder = ZeroEntropyCrossEncoder(
|
|
api_key=get_zeroentropy_api_key(),
|
|
model="zerank-2",
|
|
)
|
|
loop = asyncio.new_event_loop()
|
|
try:
|
|
loop.run_until_complete(cross_encoder.initialize())
|
|
finally:
|
|
loop.close()
|
|
return cross_encoder
|
|
|
|
|
|
class TestZeroEntropyCrossEncoder:
|
|
"""Tests for ZeroEntropy cross-encoder/reranker."""
|
|
|
|
def test_zeroentropy_cross_encoder_initialization(self, zeroentropy_cross_encoder):
|
|
"""Test that ZeroEntropy cross-encoder initializes correctly."""
|
|
assert zeroentropy_cross_encoder.provider_name == "zeroentropy"
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_zeroentropy_cross_encoder_predict(self, zeroentropy_cross_encoder):
|
|
"""Test that ZeroEntropy cross-encoder can score pairs."""
|
|
pairs = [
|
|
("What is the capital of France?", "Paris is the capital of France."),
|
|
("What is the capital of France?", "The Eiffel Tower is in Paris."),
|
|
("What is the capital of France?", "Python is a programming language."),
|
|
]
|
|
scores = await zeroentropy_cross_encoder.predict(pairs)
|
|
|
|
assert len(scores) == 3
|
|
assert all(isinstance(s, float) for s in scores)
|
|
# The first result should be most relevant
|
|
assert scores[0] > scores[2], "Direct answer should score higher than unrelated text"
|