| """Tests for FastAPI API endpoints using TestClient with mocked services.""" |
|
|
| from __future__ import annotations |
|
|
| from unittest.mock import AsyncMock, MagicMock, patch |
|
|
| import pytest |
| from fastapi.testclient import TestClient |
|
|
|
|
| @pytest.fixture |
| def client() -> TestClient: |
| """Create a FastAPI test client with mocked startup.""" |
| with patch("app.main.get_vector_store") as mock_vs: |
| mock_store = MagicMock() |
| mock_store.ensure_collection = AsyncMock() |
| mock_store.check_connection.return_value = True |
| mock_vs.return_value = mock_store |
|
|
| from app.main import app |
| return TestClient(app) |
|
|
|
|
| def test_health_endpoint(client: TestClient) -> None: |
| """Test the health check endpoint returns proper status.""" |
| with patch("app.main.get_vector_store") as mock_vs, \ |
| patch("app.main.get_llm_client") as mock_llm: |
| mock_store = MagicMock() |
| mock_store.check_connection.return_value = True |
| mock_vs.return_value = mock_store |
|
|
| mock_client = MagicMock() |
| mock_client.check_connection.return_value = True |
| mock_llm.return_value = mock_client |
|
|
| response = client.get("/api/v1/health") |
| assert response.status_code == 200 |
| data = response.json() |
| assert "status" in data |
| assert "qdrant_connected" in data |
| assert "groq_connected" in data |
| assert "timestamp" in data |
|
|
|
|
| def test_root_endpoint(client: TestClient) -> None: |
| """Test the root endpoint returns API info.""" |
| response = client.get("/") |
| assert response.status_code == 200 |
| data = response.json() |
| assert data["name"] == "Multi-Document RAG System" |
| assert data["version"] == "1.0.0" |
|
|
|
|
| @patch("app.api.routes.ingest.run_ingestion_pipeline") |
| def test_ingest_pdf_endpoint(mock_pipeline: MagicMock, client: TestClient) -> None: |
| """Test the ingest endpoint with a mock PDF file.""" |
| from app.models.schemas import IngestResult |
| mock_pipeline.return_value = IngestResult( |
| doc_id="test-doc-id", |
| filename="test.pdf", |
| chunks_created=10, |
| metadata={"doc_type": "research_paper", "source": "test"}, |
| status="success", |
| ) |
|
|
| import io |
| file_content = b"%PDF-1.4 fake pdf content" |
| response = client.post( |
| "/api/v1/ingest", |
| files={"file": ("test.pdf", io.BytesIO(file_content), "application/pdf")}, |
| data={"source": "test"}, |
| ) |
| assert response.status_code == 200 |
| data = response.json() |
| assert data["doc_id"] == "test-doc-id" |
| assert data["chunks_created"] == 10 |
| assert data["status"] == "success" |
|
|
|
|
| def test_ingest_rejects_unsupported_format(client: TestClient) -> None: |
| """Test that ingest rejects unsupported file formats.""" |
| import io |
| response = client.post( |
| "/api/v1/ingest", |
| files={"file": ("test.xlsx", io.BytesIO(b"data"), "application/xlsx")}, |
| data={"source": "test"}, |
| ) |
| assert response.status_code == 400 |
|
|
|
|
| @patch("app.api.routes.query.run_rag_chain") |
| def test_query_endpoint(mock_chain: MagicMock, client: TestClient) -> None: |
| """Test the query endpoint with a mock RAG chain response.""" |
| from app.models.schemas import QueryResponse, SourceChunk |
| mock_chain.return_value = QueryResponse( |
| answer="Machine learning is a subset of AI.", |
| sources=[SourceChunk( |
| doc_id="doc-1", filename="paper.pdf", |
| text="ML text", chunk_index=0, score=0.9, |
| )], |
| model_used="llama-3.3-70b-versatile", |
| latency_ms=500, |
| relevance_score=0.85, |
| ) |
|
|
| response = client.post( |
| "/api/v1/query", |
| json={"question": "What is ML?", "mode": "standard", "top_k": 5}, |
| ) |
| assert response.status_code == 200 |
| data = response.json() |
| assert "answer" in data |
| assert "sources" in data |
| assert data["model_used"] == "llama-3.3-70b-versatile" |
|
|
|
|
| @patch("app.api.routes.query.run_cross_doc_chain") |
| def test_compare_endpoint(mock_chain: MagicMock, client: TestClient) -> None: |
| """Test the compare endpoint with a mock cross-doc chain response.""" |
| from app.models.schemas import CompareResponse |
| mock_chain.return_value = CompareResponse( |
| comparison="Paper A uses CNN, Paper B uses RNN.", |
| agreements=["Both classify images"], |
| contradictions=["Different architectures"], |
| model_used="deepseek-r1-distill-llama-70b", |
| latency_ms=1200, |
| ) |
|
|
| response = client.post( |
| "/api/v1/query/compare", |
| json={ |
| "question": "Compare approaches", |
| "doc_ids": ["doc-1", "doc-2"], |
| "aspect": "methodology", |
| }, |
| ) |
| assert response.status_code == 200 |
| data = response.json() |
| assert "comparison" in data |
| assert "agreements" in data |
| assert "contradictions" in data |
|
|
|
|
| def test_compare_rejects_single_doc(client: TestClient) -> None: |
| """Test that compare rejects requests with less than 2 doc_ids.""" |
| response = client.post( |
| "/api/v1/query/compare", |
| json={"question": "Compare", "doc_ids": ["doc-1"], "aspect": "general"}, |
| ) |
| assert response.status_code == 422 |
|
|
|
|
| @patch("app.api.routes.evaluate.run_ragas_evaluation") |
| def test_evaluate_endpoint(mock_eval: MagicMock, client: TestClient) -> None: |
| """Test the evaluate endpoint with a mock RAGAS result.""" |
| from app.models.schemas import RAGASMetrics |
| mock_eval.return_value = RAGASMetrics( |
| faithfulness=0.85, |
| answer_relevancy=0.90, |
| context_precision=0.80, |
| context_recall=0.75, |
| answer_correctness=0.82, |
| eval_id="eval-test123", |
| timestamp="2024-01-01T00:00:00Z", |
| question_count=5, |
| ) |
|
|
| response = client.post( |
| "/api/v1/evaluate", |
| json={"sample_size": 3}, |
| ) |
| assert response.status_code == 200 |
| data = response.json() |
| assert data["status"] == "completed" |
| assert data["metrics"]["faithfulness"] == 0.85 |
|
|
