AFFAANhandClaude Opus 5
19e1e84fb7
Independent FastAPI backend for the MaskanX agentic growth platform. Includes the agent runtime, MCP client integrations (Meta Ads, LinkedIn, HubSpot, Tavily, Exa, xAI, Citedy, image generation), PostgreSQL storage for chats and cron jobs, provider and secret management, and the CLI. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
75 lines
2.5 KiB
Python
75 lines
2.5 KiB
Python
# -*- coding: utf-8 -*-
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"""Tests for EmbeddingPipeline and FakeEmbeddingPipeline."""
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import math
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from unittest.mock import AsyncMock, MagicMock, patch
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import pytest
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from adclaw.memory_agent.embeddings import EmbeddingPipeline, FakeEmbeddingPipeline
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class TestFakeEmbeddingPipeline:
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async def test_embed_returns_correct_dimensions(self):
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pipe = FakeEmbeddingPipeline(dimensions=32)
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vec = await pipe.embed("test text")
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assert len(vec) == 32
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async def test_embed_is_deterministic(self):
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pipe = FakeEmbeddingPipeline(dimensions=16)
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v1 = await pipe.embed("hello")
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v2 = await pipe.embed("hello")
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assert v1 == v2
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async def test_different_texts_different_vectors(self):
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pipe = FakeEmbeddingPipeline(dimensions=16)
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v1 = await pipe.embed("hello")
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v2 = await pipe.embed("world")
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assert v1 != v2
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async def test_embed_batch(self):
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pipe = FakeEmbeddingPipeline(dimensions=8)
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results = await pipe.embed_batch(["a", "b", "c"])
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assert len(results) == 3
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assert all(len(v) == 8 for v in results)
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async def test_embed_batch_empty(self):
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pipe = FakeEmbeddingPipeline(dimensions=8)
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assert await pipe.embed_batch([]) == []
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async def test_vectors_are_normalized(self):
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pipe = FakeEmbeddingPipeline(dimensions=32)
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vec = await pipe.embed("test normalization")
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norm = math.sqrt(sum(v * v for v in vec))
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assert abs(norm - 1.0) < 0.01
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class TestEmbeddingPipelineAPI:
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async def test_api_backend(self):
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pipe = EmbeddingPipeline(
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backend="api",
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model_name="test-model",
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api_url="http://fake:8080/v1/embeddings",
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dimensions=4,
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)
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mock_resp = MagicMock()
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mock_resp.json.return_value = {
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"data": [
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{"index": 0, "embedding": [0.1, 0.2, 0.3, 0.4]},
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{"index": 1, "embedding": [0.5, 0.6, 0.7, 0.8]},
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]
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}
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mock_resp.raise_for_status = MagicMock()
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with patch("adclaw.memory_agent.embeddings.httpx.AsyncClient") as mock_client_cls:
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mock_client = AsyncMock()
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mock_client.post.return_value = mock_resp
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mock_client.__aenter__ = AsyncMock(return_value=mock_client)
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mock_client.__aexit__ = AsyncMock(return_value=None)
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mock_client_cls.return_value = mock_client
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results = await pipe.embed_batch(["text1", "text2"])
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assert len(results) == 2
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assert results[0] == [0.1, 0.2, 0.3, 0.4]
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