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https://github.com/trustgraph-ai/trustgraph.git
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Fixing tests
This commit is contained in:
parent
2503791b9a
commit
6ebddb85d0
3 changed files with 100 additions and 55 deletions
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@ -11,6 +11,14 @@ from unittest.mock import AsyncMock, MagicMock
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from trustgraph.retrieval.document_rag.document_rag import DocumentRag
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from trustgraph.retrieval.document_rag.document_rag import DocumentRag
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# Sample chunk content for testing - maps chunk_id to content
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CHUNK_CONTENT = {
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"doc/c1": "Machine learning is a subset of artificial intelligence that focuses on algorithms that learn from data.",
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"doc/c2": "Deep learning uses neural networks with multiple layers to model complex patterns in data.",
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"doc/c3": "Supervised learning algorithms learn from labeled training data to make predictions on new data.",
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}
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@pytest.mark.integration
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@pytest.mark.integration
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class TestDocumentRagIntegration:
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class TestDocumentRagIntegration:
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"""Integration tests for DocumentRAG system coordination"""
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"""Integration tests for DocumentRAG system coordination"""
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@ -27,15 +35,19 @@ class TestDocumentRagIntegration:
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@pytest.fixture
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@pytest.fixture
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def mock_doc_embeddings_client(self):
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def mock_doc_embeddings_client(self):
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"""Mock document embeddings client that returns realistic document chunks"""
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"""Mock document embeddings client that returns chunk IDs"""
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client = AsyncMock()
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client = AsyncMock()
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client.query.return_value = [
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# Now returns chunk_ids instead of actual content
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"Machine learning is a subset of artificial intelligence that focuses on algorithms that learn from data.",
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client.query.return_value = ["doc/c1", "doc/c2", "doc/c3"]
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"Deep learning uses neural networks with multiple layers to model complex patterns in data.",
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"Supervised learning algorithms learn from labeled training data to make predictions on new data."
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]
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return client
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return client
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@pytest.fixture
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def mock_fetch_chunk(self):
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"""Mock fetch_chunk function that retrieves chunk content from librarian"""
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async def fetch(chunk_id, user):
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return CHUNK_CONTENT.get(chunk_id, f"Content for {chunk_id}")
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return fetch
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@pytest.fixture
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@pytest.fixture
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def mock_prompt_client(self):
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def mock_prompt_client(self):
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"""Mock prompt client that generates realistic responses"""
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"""Mock prompt client that generates realistic responses"""
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@ -48,12 +60,14 @@ class TestDocumentRagIntegration:
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return client
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return client
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@pytest.fixture
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@pytest.fixture
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def document_rag(self, mock_embeddings_client, mock_doc_embeddings_client, mock_prompt_client):
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def document_rag(self, mock_embeddings_client, mock_doc_embeddings_client,
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mock_prompt_client, mock_fetch_chunk):
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"""Create DocumentRag instance with mocked dependencies"""
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"""Create DocumentRag instance with mocked dependencies"""
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return DocumentRag(
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return DocumentRag(
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embeddings_client=mock_embeddings_client,
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embeddings_client=mock_embeddings_client,
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doc_embeddings_client=mock_doc_embeddings_client,
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doc_embeddings_client=mock_doc_embeddings_client,
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prompt_client=mock_prompt_client,
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prompt_client=mock_prompt_client,
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fetch_chunk=mock_fetch_chunk,
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verbose=True
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verbose=True
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)
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)
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@ -85,6 +99,7 @@ class TestDocumentRagIntegration:
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collection=collection
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collection=collection
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)
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)
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# Documents are fetched from librarian using chunk_ids
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mock_prompt_client.document_prompt.assert_called_once_with(
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mock_prompt_client.document_prompt.assert_called_once_with(
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query=query,
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query=query,
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documents=[
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documents=[
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@ -102,16 +117,18 @@ class TestDocumentRagIntegration:
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@pytest.mark.asyncio
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@pytest.mark.asyncio
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async def test_document_rag_with_no_documents_found(self, mock_embeddings_client,
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async def test_document_rag_with_no_documents_found(self, mock_embeddings_client,
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mock_doc_embeddings_client, mock_prompt_client):
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mock_doc_embeddings_client, mock_prompt_client,
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mock_fetch_chunk):
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"""Test DocumentRAG behavior when no documents are retrieved"""
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"""Test DocumentRAG behavior when no documents are retrieved"""
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# Arrange
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# Arrange
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mock_doc_embeddings_client.query.return_value = [] # No documents found
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mock_doc_embeddings_client.query.return_value = [] # No chunk_ids found
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mock_prompt_client.document_prompt.return_value = "I couldn't find any relevant documents for your query."
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mock_prompt_client.document_prompt.return_value = "I couldn't find any relevant documents for your query."
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document_rag = DocumentRag(
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document_rag = DocumentRag(
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embeddings_client=mock_embeddings_client,
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embeddings_client=mock_embeddings_client,
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doc_embeddings_client=mock_doc_embeddings_client,
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doc_embeddings_client=mock_doc_embeddings_client,
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prompt_client=mock_prompt_client,
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prompt_client=mock_prompt_client,
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fetch_chunk=mock_fetch_chunk,
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verbose=False
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verbose=False
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)
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)
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@ -130,7 +147,8 @@ class TestDocumentRagIntegration:
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@pytest.mark.asyncio
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@pytest.mark.asyncio
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async def test_document_rag_embeddings_service_failure(self, mock_embeddings_client,
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async def test_document_rag_embeddings_service_failure(self, mock_embeddings_client,
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mock_doc_embeddings_client, mock_prompt_client):
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mock_doc_embeddings_client, mock_prompt_client,
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mock_fetch_chunk):
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"""Test DocumentRAG error handling when embeddings service fails"""
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"""Test DocumentRAG error handling when embeddings service fails"""
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# Arrange
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# Arrange
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mock_embeddings_client.embed.side_effect = Exception("Embeddings service unavailable")
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mock_embeddings_client.embed.side_effect = Exception("Embeddings service unavailable")
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@ -139,6 +157,7 @@ class TestDocumentRagIntegration:
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embeddings_client=mock_embeddings_client,
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embeddings_client=mock_embeddings_client,
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doc_embeddings_client=mock_doc_embeddings_client,
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doc_embeddings_client=mock_doc_embeddings_client,
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prompt_client=mock_prompt_client,
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prompt_client=mock_prompt_client,
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fetch_chunk=mock_fetch_chunk,
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verbose=False
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verbose=False
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)
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)
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@ -153,7 +172,8 @@ class TestDocumentRagIntegration:
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@pytest.mark.asyncio
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@pytest.mark.asyncio
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async def test_document_rag_document_service_failure(self, mock_embeddings_client,
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async def test_document_rag_document_service_failure(self, mock_embeddings_client,
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mock_doc_embeddings_client, mock_prompt_client):
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mock_doc_embeddings_client, mock_prompt_client,
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mock_fetch_chunk):
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"""Test DocumentRAG error handling when document service fails"""
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"""Test DocumentRAG error handling when document service fails"""
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# Arrange
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# Arrange
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mock_doc_embeddings_client.query.side_effect = Exception("Document service connection failed")
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mock_doc_embeddings_client.query.side_effect = Exception("Document service connection failed")
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@ -162,6 +182,7 @@ class TestDocumentRagIntegration:
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embeddings_client=mock_embeddings_client,
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embeddings_client=mock_embeddings_client,
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doc_embeddings_client=mock_doc_embeddings_client,
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doc_embeddings_client=mock_doc_embeddings_client,
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prompt_client=mock_prompt_client,
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prompt_client=mock_prompt_client,
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fetch_chunk=mock_fetch_chunk,
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verbose=False
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verbose=False
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)
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)
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@ -176,7 +197,8 @@ class TestDocumentRagIntegration:
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@pytest.mark.asyncio
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@pytest.mark.asyncio
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async def test_document_rag_prompt_service_failure(self, mock_embeddings_client,
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async def test_document_rag_prompt_service_failure(self, mock_embeddings_client,
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mock_doc_embeddings_client, mock_prompt_client):
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mock_doc_embeddings_client, mock_prompt_client,
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mock_fetch_chunk):
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"""Test DocumentRAG error handling when prompt service fails"""
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"""Test DocumentRAG error handling when prompt service fails"""
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# Arrange
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# Arrange
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mock_prompt_client.document_prompt.side_effect = Exception("LLM service rate limited")
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mock_prompt_client.document_prompt.side_effect = Exception("LLM service rate limited")
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@ -185,6 +207,7 @@ class TestDocumentRagIntegration:
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embeddings_client=mock_embeddings_client,
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embeddings_client=mock_embeddings_client,
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doc_embeddings_client=mock_doc_embeddings_client,
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doc_embeddings_client=mock_doc_embeddings_client,
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prompt_client=mock_prompt_client,
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prompt_client=mock_prompt_client,
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fetch_chunk=mock_fetch_chunk,
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verbose=False
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verbose=False
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)
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)
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@ -247,6 +270,7 @@ class TestDocumentRagIntegration:
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@pytest.mark.asyncio
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@pytest.mark.asyncio
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async def test_document_rag_verbose_logging(self, mock_embeddings_client,
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async def test_document_rag_verbose_logging(self, mock_embeddings_client,
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mock_doc_embeddings_client, mock_prompt_client,
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mock_doc_embeddings_client, mock_prompt_client,
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mock_fetch_chunk,
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caplog):
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caplog):
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"""Test DocumentRAG verbose logging functionality"""
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"""Test DocumentRAG verbose logging functionality"""
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import logging
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import logging
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@ -258,6 +282,7 @@ class TestDocumentRagIntegration:
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embeddings_client=mock_embeddings_client,
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embeddings_client=mock_embeddings_client,
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doc_embeddings_client=mock_doc_embeddings_client,
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doc_embeddings_client=mock_doc_embeddings_client,
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prompt_client=mock_prompt_client,
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prompt_client=mock_prompt_client,
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fetch_chunk=mock_fetch_chunk,
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verbose=True
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verbose=True
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)
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)
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@ -269,7 +294,7 @@ class TestDocumentRagIntegration:
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assert "DocumentRag initialized" in log_messages
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assert "DocumentRag initialized" in log_messages
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assert "Constructing prompt..." in log_messages
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assert "Constructing prompt..." in log_messages
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assert "Computing embeddings..." in log_messages
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assert "Computing embeddings..." in log_messages
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assert "Getting documents..." in log_messages
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assert "chunk_ids" in log_messages.lower()
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assert "Invoking LLM..." in log_messages
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assert "Invoking LLM..." in log_messages
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assert "Query processing complete" in log_messages
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assert "Query processing complete" in log_messages
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@ -278,9 +303,9 @@ class TestDocumentRagIntegration:
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async def test_document_rag_performance_with_large_document_set(self, document_rag,
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async def test_document_rag_performance_with_large_document_set(self, document_rag,
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mock_doc_embeddings_client):
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mock_doc_embeddings_client):
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"""Test DocumentRAG performance with large document retrieval"""
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"""Test DocumentRAG performance with large document retrieval"""
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# Arrange - Mock large document set (100 documents)
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# Arrange - Mock large chunk_id set (100 chunks)
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large_doc_set = [f"Document {i} content about machine learning and AI" for i in range(100)]
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large_chunk_ids = [f"doc/c{i}" for i in range(100)]
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mock_doc_embeddings_client.query.return_value = large_doc_set
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mock_doc_embeddings_client.query.return_value = large_chunk_ids
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# Act
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# Act
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import time
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import time
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@ -14,6 +14,14 @@ from tests.utils.streaming_assertions import (
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)
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)
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# Sample chunk content for testing - maps chunk_id to content
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CHUNK_CONTENT = {
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"doc/c1": "Machine learning is a subset of AI.",
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"doc/c2": "Deep learning uses neural networks.",
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"doc/c3": "Supervised learning needs labeled data.",
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}
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@pytest.mark.integration
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@pytest.mark.integration
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class TestDocumentRagStreaming:
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class TestDocumentRagStreaming:
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"""Integration tests for DocumentRAG streaming"""
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"""Integration tests for DocumentRAG streaming"""
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@ -27,15 +35,19 @@ class TestDocumentRagStreaming:
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@pytest.fixture
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@pytest.fixture
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def mock_doc_embeddings_client(self):
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def mock_doc_embeddings_client(self):
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"""Mock document embeddings client"""
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"""Mock document embeddings client that returns chunk IDs"""
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client = AsyncMock()
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client = AsyncMock()
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client.query.return_value = [
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# Now returns chunk_ids instead of actual content
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"Machine learning is a subset of AI.",
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client.query.return_value = ["doc/c1", "doc/c2", "doc/c3"]
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"Deep learning uses neural networks.",
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"Supervised learning needs labeled data."
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]
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return client
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return client
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@pytest.fixture
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def mock_fetch_chunk(self):
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"""Mock fetch_chunk function that retrieves chunk content from librarian"""
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async def fetch(chunk_id, user):
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return CHUNK_CONTENT.get(chunk_id, f"Content for {chunk_id}")
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return fetch
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@pytest.fixture
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@pytest.fixture
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def mock_streaming_prompt_client(self, mock_streaming_llm_response):
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def mock_streaming_prompt_client(self, mock_streaming_llm_response):
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"""Mock prompt client with streaming support"""
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"""Mock prompt client with streaming support"""
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@ -66,12 +78,13 @@ class TestDocumentRagStreaming:
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@pytest.fixture
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@pytest.fixture
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def document_rag_streaming(self, mock_embeddings_client, mock_doc_embeddings_client,
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def document_rag_streaming(self, mock_embeddings_client, mock_doc_embeddings_client,
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mock_streaming_prompt_client):
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mock_streaming_prompt_client, mock_fetch_chunk):
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"""Create DocumentRag instance with streaming support"""
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"""Create DocumentRag instance with streaming support"""
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return DocumentRag(
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return DocumentRag(
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embeddings_client=mock_embeddings_client,
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embeddings_client=mock_embeddings_client,
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doc_embeddings_client=mock_doc_embeddings_client,
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doc_embeddings_client=mock_doc_embeddings_client,
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prompt_client=mock_streaming_prompt_client,
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prompt_client=mock_streaming_prompt_client,
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fetch_chunk=mock_fetch_chunk,
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verbose=True
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verbose=True
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)
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)
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@ -190,7 +203,7 @@ class TestDocumentRagStreaming:
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mock_doc_embeddings_client):
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mock_doc_embeddings_client):
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"""Test streaming with no documents found"""
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"""Test streaming with no documents found"""
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# Arrange
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# Arrange
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mock_doc_embeddings_client.query.return_value = [] # No documents
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mock_doc_embeddings_client.query.return_value = [] # No chunk_ids
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callback = AsyncMock()
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callback = AsyncMock()
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# Act
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# Act
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@ -202,11 +202,18 @@ class TestDocumentRagStreamingProtocol:
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@pytest.fixture
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@pytest.fixture
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def mock_doc_embeddings_client(self):
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def mock_doc_embeddings_client(self):
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"""Mock document embeddings client"""
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"""Mock document embeddings client that returns chunk IDs"""
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client = AsyncMock()
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client = AsyncMock()
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client.query.return_value = ["doc1", "doc2"]
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client.query.return_value = ["doc/c1", "doc/c2"]
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return client
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return client
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@pytest.fixture
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def mock_fetch_chunk(self):
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"""Mock fetch_chunk function that retrieves chunk content from librarian"""
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async def fetch(chunk_id, user):
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return f"Content for {chunk_id}"
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return fetch
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@pytest.fixture
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@pytest.fixture
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def mock_streaming_prompt_client(self):
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def mock_streaming_prompt_client(self):
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"""Mock prompt client with streaming support"""
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"""Mock prompt client with streaming support"""
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