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"""
Integration Tests for RAG Pipeline
Tests the full RAG workflow:
- Vector store operations
- Embedding generation
- Document retrieval
- Answer generation
"""
import pytest
from pathlib import Path
from unittest.mock import Mock, patch, MagicMock
import json
class TestVectorStore:
"""Test vector store functionality."""
def test_vector_store_config(self):
"""Test VectorStoreConfig creation."""
from src.rag.store import VectorStoreConfig
config = VectorStoreConfig(
collection_name="test_collection",
default_top_k=10,
similarity_threshold=0.8,
)
assert config.collection_name == "test_collection"
assert config.default_top_k == 10
def test_vector_search_result(self):
"""Test VectorSearchResult model."""
from src.rag.store import VectorSearchResult
result = VectorSearchResult(
chunk_id="chunk_1",
document_id="doc_1",
text="Sample text",
metadata={"page": 0},
similarity=0.85,
page=0,
chunk_type="text",
)
assert result.similarity == 0.85
assert result.chunk_id == "chunk_1"
@pytest.mark.skipif(
not pytest.importorskip("chromadb", reason="ChromaDB not installed"),
reason="ChromaDB not available"
)
def test_chromadb_store_creation(self, tmp_path):
"""Test ChromaDB store creation."""
from src.rag.store import ChromaVectorStore, VectorStoreConfig
config = VectorStoreConfig(
persist_directory=str(tmp_path / "vectorstore"),
collection_name="test_collection",
)
store = ChromaVectorStore(config)
assert store.count() == 0
class TestEmbeddings:
"""Test embedding functionality."""
def test_embedding_config(self):
"""Test EmbeddingConfig creation."""
from src.rag.embeddings import EmbeddingConfig
config = EmbeddingConfig(
adapter_type="ollama",
ollama_model="nomic-embed-text",
batch_size=16,
)
assert config.adapter_type == "ollama"
assert config.batch_size == 16
def test_embedding_cache_creation(self, tmp_path):
"""Test EmbeddingCache creation."""
from src.rag.embeddings import EmbeddingCache
cache = EmbeddingCache(str(tmp_path), "test_model")
assert cache.cache_dir.exists()
def test_embedding_cache_operations(self, tmp_path):
"""Test EmbeddingCache get/put operations."""
from src.rag.embeddings import EmbeddingCache
cache = EmbeddingCache(str(tmp_path), "test_model")
# Test put and get
test_text = "Hello world"
test_embedding = [0.1, 0.2, 0.3, 0.4]
cache.put(test_text, test_embedding)
retrieved = cache.get(test_text)
assert retrieved == test_embedding
def test_ollama_embedding_dimensions(self):
"""Test OllamaEmbedding model dimensions mapping."""
from src.rag.embeddings import OllamaEmbedding
assert OllamaEmbedding.MODEL_DIMENSIONS["nomic-embed-text"] == 768
assert OllamaEmbedding.MODEL_DIMENSIONS["mxbai-embed-large"] == 1024
class TestRetriever:
"""Test retriever functionality."""
def test_retriever_config(self):
"""Test RetrieverConfig creation."""
from src.rag.retriever import RetrieverConfig
config = RetrieverConfig(
default_top_k=10,
similarity_threshold=0.75,
include_evidence=True,
)
assert config.default_top_k == 10
assert config.include_evidence is True
def test_retrieved_chunk(self):
"""Test RetrievedChunk model."""
from src.rag.retriever import RetrievedChunk
chunk = RetrievedChunk(
chunk_id="chunk_1",
document_id="doc_1",
text="Sample retrieved text",
similarity=0.9,
page=0,
chunk_type="text",
)
assert chunk.similarity == 0.9
class TestGenerator:
"""Test generator functionality."""
def test_generator_config(self):
"""Test GeneratorConfig creation."""
from src.rag.generator import GeneratorConfig
config = GeneratorConfig(
llm_provider="ollama",
ollama_model="llama3.2:3b",
temperature=0.1,
require_citations=True,
)
assert config.llm_provider == "ollama"
assert config.require_citations is True
def test_citation_model(self):
"""Test Citation model."""
from src.rag.generator import Citation
citation = Citation(
index=1,
chunk_id="chunk_1",
page=0,
text_snippet="Sample snippet",
confidence=0.85,
)
assert citation.index == 1
assert citation.confidence == 0.85
def test_generated_answer_model(self):
"""Test GeneratedAnswer model."""
from src.rag.generator import GeneratedAnswer, Citation
answer = GeneratedAnswer(
answer="This is the generated answer.",
citations=[
Citation(
index=1,
chunk_id="chunk_1",
page=0,
text_snippet="Evidence text",
confidence=0.9,
)
],
confidence=0.85,
abstained=False,
num_chunks_used=3,
query="What is the answer?",
)
assert answer.answer == "This is the generated answer."
assert len(answer.citations) == 1
assert answer.abstained is False
def test_abstention(self):
"""Test abstention behavior."""
from src.rag.generator import GeneratedAnswer
answer = GeneratedAnswer(
answer="I cannot provide a confident answer.",
citations=[],
confidence=0.3,
abstained=True,
abstain_reason="Low confidence",
num_chunks_used=2,
query="Complex question",
)
assert answer.abstained is True
assert answer.abstain_reason == "Low confidence"
class TestIndexer:
"""Test indexer functionality."""
def test_indexer_config(self):
"""Test IndexerConfig creation."""
from src.rag.indexer import IndexerConfig
config = IndexerConfig(
batch_size=64,
include_bbox=True,
skip_empty_chunks=True,
)
assert config.batch_size == 64
def test_indexing_result(self):
"""Test IndexingResult model."""
from src.rag.indexer import IndexingResult
result = IndexingResult(
document_id="doc_1",
source_path="/path/to/doc.pdf",
num_chunks_indexed=10,
num_chunks_skipped=2,
success=True,
)
assert result.success is True
assert result.num_chunks_indexed == 10
class TestRAGIntegration:
"""Integration tests for full RAG pipeline."""
@pytest.fixture
def mock_chunks(self):
"""Create mock document chunks."""
from src.rag.retriever import RetrievedChunk
return [
RetrievedChunk(
chunk_id=f"chunk_{i}",
document_id="doc_1",
text=f"This is sample text from chunk {i}.",
similarity=0.9 - (i * 0.1),
page=i,
chunk_type="text",
)
for i in range(3)
]
def test_context_building(self, mock_chunks):
"""Test building context from chunks."""
from src.rag.retriever import DocumentRetriever
retriever = DocumentRetriever()
context = retriever.build_context(mock_chunks, include_metadata=True)
assert "chunk 0" in context.lower()
assert "Page 1" in context # Page numbers are 1-indexed in display
def test_citation_extraction(self):
"""Test citation extraction from text."""
from src.rag.generator import GroundedGenerator
from src.rag.retriever import RetrievedChunk
generator = GroundedGenerator()
chunks = [
RetrievedChunk(
chunk_id="chunk_1",
document_id="doc_1",
text="First chunk content",
similarity=0.9,
page=0,
),
RetrievedChunk(
chunk_id="chunk_2",
document_id="doc_1",
text="Second chunk content",
similarity=0.85,
page=1,
),
]
answer_text = "The answer is based on [1] and [2]."
citations = generator._extract_citations(answer_text, chunks)
assert len(citations) == 2
assert citations[0].index == 1
assert citations[1].index == 2
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