mirror of
https://github.com/paperless-ngx/paperless-ngx.git
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351 lines
13 KiB
Python
351 lines
13 KiB
Python
from typing import TYPE_CHECKING
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from typing import cast
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from unittest.mock import ANY
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from unittest.mock import MagicMock
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from unittest.mock import patch
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import pytest
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from django.conf import settings
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from pytest_mock import MockerFixture
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from documents.models import Document
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from paperless.models import LLMEmbeddingBackend
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from paperless_ai.client import PLACEHOLDER_API_KEY
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from paperless_ai.embedding import _normalize_llm_index_text
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from paperless_ai.embedding import build_llm_index_text
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from paperless_ai.embedding import get_configured_model_name
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from paperless_ai.embedding import get_embedding_model
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from paperless_testing.outbound import guard_of
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if TYPE_CHECKING:
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from llama_index.embeddings.ollama import OllamaEmbedding
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@pytest.fixture
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def mock_ai_config():
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with patch("paperless_ai.embedding.AIConfig") as MockAIConfig:
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MockAIConfig.return_value.llm_embedding_endpoint = None
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MockAIConfig.return_value.llm_allow_internal_endpoints = True
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MockAIConfig.return_value.llm_context_size = 8192
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MockAIConfig.return_value.llm_request_timeout = 120
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yield MockAIConfig
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@pytest.fixture
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def mock_document():
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doc = MagicMock(spec=Document)
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doc.title = "Test Title"
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doc.filename = "test_file.pdf"
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doc.created = "2023-01-01"
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doc.added = "2023-01-02"
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doc.modified = "2023-01-03"
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tag1 = MagicMock()
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tag1.name = "Tag1"
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tag2 = MagicMock()
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tag2.name = "Tag2"
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doc.tags.all = MagicMock(return_value=[tag1, tag2])
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doc.document_type = MagicMock()
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doc.document_type.name = "Invoice"
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doc.correspondent = MagicMock()
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doc.correspondent.name = "Test Correspondent"
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doc.archive_serial_number = "12345"
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doc.content = "This is the document content."
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cf1 = MagicMock(__str__=lambda x: "Value1")
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cf1.field = MagicMock()
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cf1.field.name = "Field1"
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cf1.value = "Value1"
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cf2 = MagicMock(__str__=lambda x: "Value2")
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cf2.field = MagicMock()
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cf2.field.name = "Field2"
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cf2.value = "Value2"
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doc.custom_fields.all = MagicMock(return_value=[cf1, cf2])
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doc.notes.all = MagicMock(return_value=[])
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return doc
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def test_get_embedding_model_openai(mock_ai_config):
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mock_ai_config.return_value.llm_embedding_backend = LLMEmbeddingBackend.OPENAI_LIKE
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mock_ai_config.return_value.llm_embedding_model = "text-embedding-3-small"
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mock_ai_config.return_value.llm_api_key = "test_api_key"
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mock_ai_config.return_value.llm_endpoint = "http://test-url"
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with patch(
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"llama_index.embeddings.openai_like.OpenAILikeEmbedding",
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) as MockOpenAIEmbedding:
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model = get_embedding_model(mock_ai_config.return_value)
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MockOpenAIEmbedding.assert_called_once_with(
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model_name="text-embedding-3-small",
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api_key="test_api_key",
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api_base="http://test-url",
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timeout=120,
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http_client=ANY,
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async_http_client=ANY,
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)
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assert model == MockOpenAIEmbedding.return_value
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@pytest.mark.parametrize("configured_key", [None, ""])
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def test_get_embedding_model_openai_without_api_key_sends_placeholder(
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mock_ai_config,
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configured_key,
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):
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"""Same required key handling as the LLM client, see #13831."""
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mock_ai_config.return_value.llm_embedding_backend = LLMEmbeddingBackend.OPENAI_LIKE
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mock_ai_config.return_value.llm_embedding_model = "text-embedding-3-small"
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mock_ai_config.return_value.llm_api_key = configured_key
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mock_ai_config.return_value.llm_endpoint = "http://test-url"
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with patch(
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"llama_index.embeddings.openai_like.OpenAILikeEmbedding",
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) as MockOpenAIEmbedding:
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get_embedding_model(mock_ai_config.return_value)
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assert MockOpenAIEmbedding.call_args.kwargs["api_key"] == PLACEHOLDER_API_KEY
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def test_get_embedding_model_openai_prefers_embedding_endpoint(mock_ai_config):
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mock_ai_config.return_value.llm_embedding_backend = LLMEmbeddingBackend.OPENAI_LIKE
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mock_ai_config.return_value.llm_embedding_model = "text-embedding-3-small"
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mock_ai_config.return_value.llm_api_key = "test_api_key"
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mock_ai_config.return_value.llm_embedding_endpoint = "http://embedding-url"
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mock_ai_config.return_value.llm_endpoint = "http://test-url"
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with patch(
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"llama_index.embeddings.openai_like.OpenAILikeEmbedding",
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) as MockOpenAIEmbedding:
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model = get_embedding_model(mock_ai_config.return_value)
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MockOpenAIEmbedding.assert_called_once_with(
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model_name="text-embedding-3-small",
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api_key="test_api_key",
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api_base="http://embedding-url",
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timeout=120,
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http_client=ANY,
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async_http_client=ANY,
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)
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assert model == MockOpenAIEmbedding.return_value
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def test_get_embedding_model_openai_blocks_internal_endpoint_when_disallowed(
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mock_ai_config,
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):
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mock_ai_config.return_value.llm_embedding_backend = LLMEmbeddingBackend.OPENAI_LIKE
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mock_ai_config.return_value.llm_embedding_model = "text-embedding-3-small"
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mock_ai_config.return_value.llm_api_key = "test_api_key"
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mock_ai_config.return_value.llm_endpoint = "http://127.0.0.1:11434"
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mock_ai_config.return_value.llm_allow_internal_endpoints = False
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with pytest.raises(ValueError, match="non-public address"):
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get_embedding_model(mock_ai_config.return_value)
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def test_get_embedding_model_huggingface(mock_ai_config):
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mock_ai_config.return_value.llm_embedding_backend = LLMEmbeddingBackend.HUGGINGFACE
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mock_ai_config.return_value.llm_embedding_model = (
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"sentence-transformers/all-MiniLM-L6-v2"
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)
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with patch(
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"llama_index.embeddings.huggingface.HuggingFaceEmbedding",
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) as MockHuggingFaceEmbedding:
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model = get_embedding_model(mock_ai_config.return_value)
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MockHuggingFaceEmbedding.assert_called_once_with(
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model_name="sentence-transformers/all-MiniLM-L6-v2",
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cache_folder=str(settings.DATA_DIR / "hf_cache"),
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)
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assert model == MockHuggingFaceEmbedding.return_value
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def test_get_embedding_model_ollama(mock_ai_config):
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mock_ai_config.return_value.llm_embedding_backend = LLMEmbeddingBackend.OLLAMA
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mock_ai_config.return_value.llm_embedding_model = "embeddinggemma"
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mock_ai_config.return_value.llm_endpoint = "http://test-url"
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with patch(
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"llama_index.embeddings.ollama.OllamaEmbedding",
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) as MockOllamaEmbedding:
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model = get_embedding_model(mock_ai_config.return_value)
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MockOllamaEmbedding.assert_called_once_with(
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model_name="embeddinggemma",
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base_url="http://test-url",
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ollama_additional_kwargs={"num_ctx": 8192},
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)
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assert model == MockOllamaEmbedding.return_value
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def test_get_embedding_model_ollama_prefers_embedding_endpoint(mock_ai_config):
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mock_ai_config.return_value.llm_embedding_backend = LLMEmbeddingBackend.OLLAMA
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mock_ai_config.return_value.llm_embedding_model = "embeddinggemma"
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mock_ai_config.return_value.llm_embedding_endpoint = "http://embedding-url"
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mock_ai_config.return_value.llm_endpoint = "http://test-url"
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with patch(
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"llama_index.embeddings.ollama.OllamaEmbedding",
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) as MockOllamaEmbedding:
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model = get_embedding_model(mock_ai_config.return_value)
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MockOllamaEmbedding.assert_called_once_with(
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model_name="embeddinggemma",
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base_url="http://embedding-url",
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ollama_additional_kwargs={"num_ctx": 8192},
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)
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assert model == MockOllamaEmbedding.return_value
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def test_get_embedding_model_ollama_blocks_internal_endpoint_when_disallowed(
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mock_ai_config,
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):
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mock_ai_config.return_value.llm_embedding_backend = LLMEmbeddingBackend.OLLAMA
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mock_ai_config.return_value.llm_embedding_model = "embeddinggemma"
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mock_ai_config.return_value.llm_endpoint = "http://127.0.0.1:11434"
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mock_ai_config.return_value.llm_allow_internal_endpoints = False
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with pytest.raises(ValueError, match="non-public address"):
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get_embedding_model(mock_ai_config.return_value)
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def test_get_embedding_model_invalid_backend(mock_ai_config):
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mock_ai_config.return_value.llm_embedding_backend = "INVALID_BACKEND"
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with pytest.raises(
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ValueError,
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match="Unsupported embedding backend: INVALID_BACKEND",
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):
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get_embedding_model(mock_ai_config.return_value)
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@pytest.mark.parametrize(
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("backend", "expected_default"),
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[
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(LLMEmbeddingBackend.OPENAI_LIKE, "text-embedding-3-small"),
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(LLMEmbeddingBackend.HUGGINGFACE, "sentence-transformers/all-MiniLM-L6-v2"),
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(LLMEmbeddingBackend.OLLAMA, "embeddinggemma"),
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],
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)
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def test_get_configured_model_name_falls_back_to_backend_default(
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mock_ai_config,
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backend,
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expected_default,
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):
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"""When no model is explicitly configured, each backend has a distinct default."""
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config = mock_ai_config.return_value
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config.llm_embedding_backend = backend
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config.llm_embedding_model = None
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assert get_configured_model_name(config) == expected_default
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def test_get_configured_model_name_explicit_overrides_default(mock_ai_config):
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"""An explicit model name overrides the backend default for all backends."""
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config = mock_ai_config.return_value
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config.llm_embedding_backend = LLMEmbeddingBackend.OPENAI_LIKE
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config.llm_embedding_model = "my-custom-model"
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# The backend default for OPENAI_LIKE is "text-embedding-3-small", so if
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# the explicit name was ignored we'd get the wrong result.
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assert get_configured_model_name(config) == "my-custom-model"
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def test_build_llm_index_text(mock_document):
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mock_document.notes.all = MagicMock(
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return_value=[MagicMock(note="Note1"), MagicMock(note="Note2")],
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)
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result = build_llm_index_text(mock_document)
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# Structured fields live in node.metadata for LLM context -- not body text
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assert "Title: Test Title" not in result
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assert "Created: 2023-01-01" not in result
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assert "Tags: Tag1, Tag2" not in result
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assert "Document Type: Invoice" not in result
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assert "Correspondent: Test Correspondent" not in result
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assert "Filename:" not in result
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assert "Storage Path:" not in result
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assert "Archive Serial Number:" not in result
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# Fields without a metadata equivalent stay in body text
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assert "Notes: Note1,Note2" in result
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assert "Content:\n\nThis is the document content." in result
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assert "Custom Field - Field1: Value1\nCustom Field - Field2: Value2" in result
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def test_build_llm_index_text_normalizes_ocr_punctuation_runs(mock_document):
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mock_document.content = (
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"Introduction ................................................ 7\n"
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"Hardware Limitation ________________________________________ 9\n"
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"Keep short punctuation like INV-100 and ellipses..."
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)
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result = build_llm_index_text(mock_document)
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assert "Introduction 7" in result
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assert "Hardware Limitation 9" in result
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assert "INV-100" in result
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assert "ellipses..." in result
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def test_normalize_llm_index_text_collapses_ocr_leaders_without_joining_lines():
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assert _normalize_llm_index_text("A........B\nC____D----E") == "A B\nC D E"
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def test_normalize_llm_index_text_collapses_non_breaking_spaces():
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assert _normalize_llm_index_text("A\u00a0........\u00a0B") == "A B"
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class TestGuardedEmbeddingClients:
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def test_ollama_embedding_clients_are_guarded(
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self,
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mocker: MockerFixture,
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mock_ai_config: MagicMock,
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) -> None:
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"""
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GIVEN:
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- The Ollama embedding backend
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WHEN:
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- The embedding model is built
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THEN:
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- The clients swapped onto it use guarded transports
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"""
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config = mock_ai_config.return_value
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config.llm_embedding_backend = LLMEmbeddingBackend.OLLAMA
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config.llm_embedding_model = "embeddinggemma"
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config.llm_endpoint = "http://93.184.216.34:11434"
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config.llm_allow_internal_endpoints = False
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mocker.patch("llama_index.embeddings.ollama.OllamaEmbedding")
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model = cast("OllamaEmbedding", get_embedding_model(config))
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assert guard_of(model._client._client)._allow_internal is False
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assert guard_of(model._async_client._client)._allow_internal is False
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def test_openai_like_embedding_clients_are_guarded(
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self,
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mocker: MockerFixture,
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mock_ai_config: MagicMock,
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) -> None:
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"""
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GIVEN:
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- The OpenAI-like embedding backend with an endpoint
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WHEN:
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- The embedding model is built
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THEN:
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- Its http clients use guarded transports
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"""
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config = mock_ai_config.return_value
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config.llm_embedding_backend = LLMEmbeddingBackend.OPENAI_LIKE
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config.llm_embedding_model = "text-embedding-3-small"
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config.llm_api_key = "key"
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config.llm_endpoint = "http://93.184.216.34:8080"
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config.llm_allow_internal_endpoints = False
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embedding_class = mocker.patch(
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"llama_index.embeddings.openai_like.OpenAILikeEmbedding",
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)
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get_embedding_model(config)
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kwargs = embedding_class.call_args.kwargs
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assert guard_of(kwargs["http_client"])._allow_internal is False
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assert guard_of(kwargs["async_http_client"])._allow_internal is False
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