Joseph Doherty
92 lines
3.3 KiB
Python
92 lines
3.3 KiB
Python
"""Tests for the embedding generation service (T91, Phase 4).
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Phase 4's first cut ships a deterministic local pseudo-embedding so the
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vector retrieval pipeline can land without an external embeddings API
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or a heavy local model dependency. These tests pin the contract:
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* the result has the right shape (vector length, ``dim`` metadata),
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* the default ``model`` string is reported back unchanged,
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* output is byte-identical for the same input (deterministic),
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* distinct inputs produce distinct vectors (so cosine actually
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discriminates),
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* empty / whitespace-only input collapses to the ``"fallback"`` sentinel
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with a zero vector — callers detect this and skip indexing,
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* the vector is unit-normalized so cosine similarity behaves.
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The pseudo path doesn't touch the LLMClient, so we pass an empty
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``MockLLMClient`` — any accidental call into it would raise
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``IndexError`` and surface as a regression.
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"""
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from __future__ import annotations
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import math
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import pytest
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from chat.llm.mock import MockLLMClient
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from chat.services.embeddings import (
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DEFAULT_EMBEDDING_DIM,
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DEFAULT_EMBEDDING_MODEL,
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FALLBACK_EMBEDDING_MODEL,
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EmbeddingResult,
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generate_embedding,
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)
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def _client() -> MockLLMClient:
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# Pseudo path never calls the client — empty canned list ensures any
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# accidental call raises and surfaces the regression loudly.
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return MockLLMClient(canned=[])
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@pytest.mark.asyncio
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async def test_generate_embedding_returns_vector_of_correct_dim():
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result = await generate_embedding(_client(), text="hello")
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assert isinstance(result, EmbeddingResult)
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assert isinstance(result.vector, list)
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assert len(result.vector) == DEFAULT_EMBEDDING_DIM == 384
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assert result.dim == 384
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assert all(isinstance(x, float) for x in result.vector)
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@pytest.mark.asyncio
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async def test_generate_embedding_returns_correct_model_metadata():
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result = await generate_embedding(_client(), text="hello")
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assert result.model == DEFAULT_EMBEDDING_MODEL == "pseudo-sha256-384"
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@pytest.mark.asyncio
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async def test_generate_embedding_is_deterministic():
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a = await generate_embedding(_client(), text="hello world")
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b = await generate_embedding(_client(), text="hello world")
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assert a.vector == b.vector
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@pytest.mark.asyncio
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async def test_generate_embedding_distinct_text_produces_distinct_vectors():
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a = await generate_embedding(_client(), text="hello world")
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b = await generate_embedding(_client(), text="totally different content")
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assert a.vector != b.vector
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# Sanity-check cosine similarity — both vectors are unit-normalized,
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# so this reduces to a plain dot product.
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cosine = sum(x * y for x, y in zip(a.vector, b.vector))
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assert cosine < 0.99
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@pytest.mark.asyncio
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async def test_generate_embedding_empty_text_returns_fallback():
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for empty in ("", " ", "\n\t"):
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result = await generate_embedding(_client(), text=empty)
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assert result.model == FALLBACK_EMBEDDING_MODEL == "fallback"
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assert result.dim == DEFAULT_EMBEDDING_DIM
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assert len(result.vector) == DEFAULT_EMBEDDING_DIM
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assert all(x == 0.0 for x in result.vector)
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@pytest.mark.asyncio
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async def test_generate_embedding_unit_normalized():
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result = await generate_embedding(_client(), text="some non-empty text")
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norm_sq = sum(x * x for x in result.vector)
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assert math.isclose(norm_sq, 1.0, abs_tol=1e-6)
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