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"""
Integration tests for the complete RAG system.
"""
import pytest
import tempfile
from pathlib import Path
from unittest.mock import Mock, patch, MagicMock
import json
from src.rag.embedding_system import EmbeddingSystem, EmbeddingResult, RerankResult
from src.rag.vector_store import QdrantVectorStore, SearchResult
from src.rag.groq_client import GroqClient, LLMResponse, LLMSystem
from src.rag.rag_engine import RAGEngine, RAGResponse, Citation
from src.rag.metadata_manager import MetadataManager, DocumentMetadata
from src.rag.ingestion_pipeline import DocumentIngestionPipeline, IngestionResult
from src.rag.document_processor import DocumentChunk, ChunkMetadata, ProcessingStatus
class TestEmbeddingSystem:
"""Test cases for EmbeddingSystem."""
def setup_method(self):
"""Set up test fixtures."""
self.config = {
'siliconflow_api_key': 'test_key',
'embedding_model': 'test-model',
'reranker_model': 'test-reranker',
'batch_size': 2,
'max_retries': 2,
'enable_embedding_cache': True
}
@patch('src.rag.embedding_system.SiliconFlowEmbeddingClient')
def test_embedding_system_initialization(self, mock_client_class):
"""Test embedding system initialization."""
mock_client = Mock()
mock_client_class.return_value = mock_client
embedding_system = EmbeddingSystem(self.config)
assert embedding_system.api_key == 'test_key'
assert embedding_system.embedding_model == 'test-model'
assert embedding_system.batch_size == 2
assert embedding_system.cache_enabled is True
mock_client_class.assert_called_once_with('test_key')
@patch('src.rag.embedding_system.SiliconFlowEmbeddingClient')
def test_generate_embeddings_success(self, mock_client_class):
"""Test successful embedding generation."""
mock_client = Mock()
mock_client_class.return_value = mock_client
# Mock successful embedding response
mock_client.generate_embeddings.return_value = EmbeddingResult(
embeddings=[[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]],
model_name='test-model',
processing_time=1.0,
token_count=10,
success=True
)
embedding_system = EmbeddingSystem(self.config)
embeddings = embedding_system.generate_embeddings(['text1', 'text2'])
assert len(embeddings) == 2
assert embeddings[0] == [0.1, 0.2, 0.3]
assert embeddings[1] == [0.4, 0.5, 0.6]
@patch('src.rag.embedding_system.SiliconFlowEmbeddingClient')
def test_rerank_results(self, mock_client_class):
"""Test reranking functionality."""
mock_client = Mock()
mock_client_class.return_value = mock_client
# Mock reranking response
mock_client.rerank_documents.return_value = [
RerankResult(text='doc2', score=0.9, index=1),
RerankResult(text='doc1', score=0.7, index=0)
]
embedding_system = EmbeddingSystem(self.config)
results = embedding_system.rerank_results('query', ['doc1', 'doc2'])
assert len(results) == 2
assert results[0].text == 'doc2'
assert results[0].score == 0.9
assert results[1].text == 'doc1'
assert results[1].score == 0.7
class TestGroqClient:
"""Test cases for GroqClient."""
def setup_method(self):
"""Set up test fixtures."""
self.api_key = 'test_groq_key'
self.client = GroqClient(self.api_key)
@patch('requests.Session.post')
def test_generate_response_success(self, mock_post):
"""Test successful response generation."""
# Mock successful API response
mock_response = Mock()
mock_response.status_code = 200
mock_response.json.return_value = {
'choices': [{
'message': {'content': 'Test response'},
'finish_reason': 'stop'
}],
'usage': {'total_tokens': 50}
}
mock_post.return_value = mock_response
messages = [{'role': 'user', 'content': 'Test question'}]
result = self.client.generate_response(messages)
assert result.success is True
assert result.text == 'Test response'
assert result.token_count == 50
assert result.finish_reason == 'stop'
@patch('requests.Session.post')
def test_answer_question(self, mock_post):
"""Test question answering functionality."""
# Mock successful API response
mock_response = Mock()
mock_response.status_code = 200
mock_response.json.return_value = {
'choices': [{
'message': {'content': 'Based on the context, the answer is...'},
'finish_reason': 'stop'
}],
'usage': {'total_tokens': 75}
}
mock_post.return_value = mock_response
result = self.client.answer_question('What is the yield?', 'Context: Yield is 95%')
assert result.success is True
assert 'answer is' in result.text
assert result.token_count == 75
class TestRAGEngine:
"""Test cases for RAGEngine."""
def setup_method(self):
"""Set up test fixtures."""
self.config = {
'siliconflow_api_key': 'test_key',
'groq_api_key': 'test_groq_key',
'qdrant_url': 'http://localhost:6333',
'qdrant_api_key': 'test_qdrant_key',
'qdrant_collection': 'test_collection',
'max_context_chunks': 3,
'similarity_threshold': 0.7,
'rerank_top_k': 10,
'final_top_k': 3
}
@patch('src.rag.rag_engine.EmbeddingSystem')
@patch('src.rag.rag_engine.QdrantVectorStore')
@patch('src.rag.rag_engine.LLMSystem')
def test_rag_engine_initialization(self, mock_llm, mock_vector, mock_embedding):
"""Test RAG engine initialization."""
rag_engine = RAGEngine(self.config)
assert rag_engine.max_context_chunks == 3
assert rag_engine.similarity_threshold == 0.7
assert rag_engine.rerank_top_k == 10
assert rag_engine.final_top_k == 3
mock_embedding.assert_called_once()
mock_vector.assert_called_once()
mock_llm.assert_called_once()
@patch('src.rag.rag_engine.EmbeddingSystem')
@patch('src.rag.rag_engine.QdrantVectorStore')
@patch('src.rag.rag_engine.LLMSystem')
def test_answer_question_success(self, mock_llm, mock_vector, mock_embedding):
"""Test successful question answering."""
# Mock components
mock_embedding_instance = Mock()
mock_vector_instance = Mock()
mock_llm_instance = Mock()
mock_embedding.return_value = mock_embedding_instance
mock_vector.return_value = mock_vector_instance
mock_llm.return_value = mock_llm_instance
# Mock embedding generation
mock_embedding_instance.generate_query_embedding.return_value = [0.1, 0.2, 0.3]
# Mock search results
mock_chunk = DocumentChunk(
content="Test content about manufacturing",
metadata=ChunkMetadata(
chunk_id="test_chunk_1",
document_id="test_doc_1",
chunk_index=0,
page_number=1
)
)
mock_search_result = SearchResult(
chunk=mock_chunk,
similarity_score=0.9
)
mock_vector_instance.similarity_search.return_value = [mock_search_result]
# Mock reranking
mock_embedding_instance.rerank_results.return_value = [
RerankResult(text="Test content about manufacturing", score=0.95, index=0)
]
# Mock LLM response
mock_llm_instance.answer_question.return_value = "The manufacturing process shows good results."
# Test question answering
rag_engine = RAGEngine(self.config)
response = rag_engine.answer_question("What are the manufacturing results?")
assert response.success is True
assert "manufacturing process" in response.answer
assert len(response.citations) == 1
assert response.citations[0].confidence == 0.9
assert response.total_chunks_retrieved == 1
class TestMetadataManager:
"""Test cases for MetadataManager."""
def setup_method(self):
"""Set up test fixtures."""
self.temp_dir = tempfile.mkdtemp()
self.config = {
'metadata_db_path': str(Path(self.temp_dir) / 'test_metadata.db')
}
self.metadata_manager = MetadataManager(self.config)
def test_store_and_retrieve_document_metadata(self):
"""Test storing and retrieving document metadata."""
from datetime import datetime
# Create test metadata
metadata = DocumentMetadata(
document_id='test_doc_1',
filename='test.pdf',
file_path='/path/to/test.pdf',
file_type='pdf',
upload_timestamp=datetime.now(),
processing_status=ProcessingStatus.COMPLETED,
total_chunks=10,
file_size=1024,
checksum='abc123',
processing_time=5.5
)
# Store metadata
success = self.metadata_manager.store_document_metadata('test_doc_1', metadata)
assert success is True
# Retrieve metadata
retrieved = self.metadata_manager.get_document_metadata('test_doc_1')
assert retrieved is not None
assert retrieved.document_id == 'test_doc_1'
assert retrieved.filename == 'test.pdf'
assert retrieved.processing_status == ProcessingStatus.COMPLETED
assert retrieved.total_chunks == 10
assert retrieved.processing_time == 5.5
def test_update_document_status(self):
"""Test updating document status."""
from datetime import datetime
# First store a document
metadata = DocumentMetadata(
document_id='test_doc_2',
filename='test2.pdf',
file_path='/path/to/test2.pdf',
file_type='pdf',
upload_timestamp=datetime.now(),
processing_status=ProcessingStatus.PENDING,
total_chunks=0,
file_size=2048,
checksum='def456'
)
self.metadata_manager.store_document_metadata('test_doc_2', metadata)
# Update status
success = self.metadata_manager.update_document_status(
'test_doc_2',
ProcessingStatus.COMPLETED,
processing_time=3.2
)
assert success is True
# Verify update
retrieved = self.metadata_manager.get_document_metadata('test_doc_2')
assert retrieved.processing_status == ProcessingStatus.COMPLETED
assert retrieved.processing_time == 3.2
def test_list_documents(self):
"""Test listing documents with filters."""
from datetime import datetime
# Store multiple documents
for i in range(3):
metadata = DocumentMetadata(
document_id=f'test_doc_{i}',
filename=f'test{i}.pdf',
file_path=f'/path/to/test{i}.pdf',
file_type='pdf',
upload_timestamp=datetime.now(),
processing_status=ProcessingStatus.COMPLETED if i < 2 else ProcessingStatus.FAILED,
total_chunks=i * 5,
file_size=1024 * (i + 1),
checksum=f'hash{i}'
)
self.metadata_manager.store_document_metadata(f'test_doc_{i}', metadata)
# List all documents
all_docs = self.metadata_manager.list_documents()
assert len(all_docs) == 3
# List only completed documents
completed_docs = self.metadata_manager.list_documents(status=ProcessingStatus.COMPLETED)
assert len(completed_docs) == 2
# List by file type
pdf_docs = self.metadata_manager.list_documents(file_type='pdf')
assert len(pdf_docs) == 3
def test_get_statistics(self):
"""Test getting database statistics."""
from datetime import datetime
# Store some test documents
for i in range(2):
metadata = DocumentMetadata(
document_id=f'stats_doc_{i}',
filename=f'stats{i}.pdf',
file_path=f'/path/to/stats{i}.pdf',
file_type='pdf',
upload_timestamp=datetime.now(),
processing_status=ProcessingStatus.COMPLETED,
total_chunks=5,
file_size=1000,
checksum=f'stats_hash{i}'
)
self.metadata_manager.store_document_metadata(f'stats_doc_{i}', metadata)
# Get statistics
stats = self.metadata_manager.get_statistics()
assert stats['total_documents'] >= 2
assert stats['total_chunks'] >= 10
assert stats['total_file_size'] >= 2000
assert 'pdf' in stats['documents_by_type']
assert ProcessingStatus.COMPLETED.value in stats['documents_by_status']
class TestIngestionPipeline:
"""Test cases for DocumentIngestionPipeline."""
def setup_method(self):
"""Set up test fixtures."""
self.temp_dir = tempfile.mkdtemp()
self.config = {
'siliconflow_api_key': 'test_key',
'groq_api_key': 'test_groq_key',
'qdrant_url': 'http://localhost:6333',
'qdrant_api_key': 'test_qdrant_key',
'qdrant_collection': 'test_collection',
'metadata_db_path': str(Path(self.temp_dir) / 'test_metadata.db'),
'chunk_size': 100,
'chunk_overlap': 20,
'max_workers': 2,
'image_processing': True
}
@patch('src.rag.ingestion_pipeline.EmbeddingSystem')
@patch('src.rag.ingestion_pipeline.QdrantVectorStore')
@patch('src.rag.ingestion_pipeline.MetadataManager')
@patch('src.rag.ingestion_pipeline.ImageProcessor')
def test_pipeline_initialization(self, mock_image, mock_metadata, mock_vector, mock_embedding):
"""Test pipeline initialization."""
pipeline = DocumentIngestionPipeline(self.config)
assert pipeline.chunk_size == 100
assert pipeline.chunk_overlap == 20
assert pipeline.max_workers == 2
assert pipeline.enable_ocr is True
mock_embedding.assert_called_once()
mock_vector.assert_called_once()
mock_metadata.assert_called_once()
mock_image.assert_called_once()
if __name__ == "__main__":
pytest.main([__file__]) |