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)
60 lines
2.1 KiB
Python
60 lines
2.1 KiB
Python
"""Test automatic batch chunking based on character count."""
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import asyncio
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import pytest
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from hindsight_api import MemoryEngine
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import os
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@pytest.mark.asyncio
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async def test_large_batch_auto_chunks(memory, request_context):
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bank_id = "test_chunking_agent"
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# Create a large batch that should trigger chunking
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# Each item is ~2000 chars, so 30 items = 60k chars (exceeds 50k threshold)
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large_content = "Alice met with Bob at the coffee shop. " * 50 # ~2000 chars
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contents = [
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{"content": large_content, "context": f"conversation_{i}"}
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for i in range(30)
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]
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# Calculate total chars
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total_chars = sum(len(item["content"]) for item in contents)
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print(f"\nTotal characters: {total_chars:,}")
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print(f"Should trigger chunking: {total_chars > 50_000}")
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# Ingest the large batch (should auto-chunk)
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result = await memory.retain_batch_async(
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bank_id=bank_id,
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contents=contents,
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request_context=request_context,
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)
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# Verify we got results back
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assert len(result) == 30, f"Expected 30 results, got {len(result)}"
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print(f"Successfully ingested {len(result)} items (auto-chunked)")
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@pytest.mark.asyncio
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async def test_small_batch_no_chunking(memory, request_context):
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bank_id = "test_no_chunking_agent"
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# Create a small batch that should NOT trigger chunking
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contents = [
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{"content": "Alice works at Google", "context": "conversation_1"},
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{"content": "Bob loves Python", "context": "conversation_2"}
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]
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# Calculate total chars
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total_chars = sum(len(item["content"]) for item in contents)
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print(f"\nTotal characters: {total_chars:,}")
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print(f"Should NOT trigger chunking: {total_chars <= 50_000}")
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# Ingest the small batch (should NOT auto-chunk)
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result = await memory.retain_batch_async(
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bank_id=bank_id,
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contents=contents,
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request_context=request_context,
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)
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# Verify we got results back
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assert len(result) == 2, f"Expected 2 results, got {len(result)}"
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print(f"Successfully ingested {len(result)} items (no chunking)")
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