283 lines
8.3 KiB
Python
283 lines
8.3 KiB
Python
"""
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Pytest configuration and shared fixtures for Claude OS tests.
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"""
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import os
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import pytest
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import psycopg2
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from typing import Generator, Dict, Any
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from unittest.mock import Mock, MagicMock
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import numpy as np
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# Set test environment variables
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os.environ["POSTGRES_HOST"] = os.getenv("TEST_POSTGRES_HOST", "localhost")
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os.environ["POSTGRES_PORT"] = os.getenv("TEST_POSTGRES_PORT", "5432")
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os.environ["POSTGRES_DB"] = os.getenv("TEST_POSTGRES_DB", "codeforge_test")
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os.environ["POSTGRES_USER"] = os.getenv("TEST_POSTGRES_USER", os.getenv("USER", "postgres"))
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os.environ["OLLAMA_HOST"] = os.getenv("TEST_OLLAMA_HOST", "http://localhost:11434")
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@pytest.fixture(scope="session")
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def test_db_config() -> Dict[str, str]:
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"""Database configuration for tests."""
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return {
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"host": os.getenv("POSTGRES_HOST"),
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"port": int(os.getenv("POSTGRES_PORT")),
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"database": os.getenv("POSTGRES_DB"),
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"user": os.getenv("POSTGRES_USER"),
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}
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@pytest.fixture(scope="session")
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def db_connection(test_db_config):
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"""
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Create a test database connection.
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This is a session-scoped fixture that creates the test database once.
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"""
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# Connect to default postgres database to create test database
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conn = psycopg2.connect(
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host=test_db_config["host"],
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port=test_db_config["port"],
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database="postgres",
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user=test_db_config["user"],
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)
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conn.autocommit = True
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with conn.cursor() as cur:
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# Drop and recreate test database
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cur.execute(f"DROP DATABASE IF EXISTS {test_db_config['database']}")
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cur.execute(f"CREATE DATABASE {test_db_config['database']}")
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conn.close()
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# Connect to test database
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test_conn = psycopg2.connect(**test_db_config)
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# Create pgvector extension
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with test_conn.cursor() as cur:
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cur.execute("CREATE EXTENSION IF NOT EXISTS vector")
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test_conn.commit()
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# Load schema
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with open("app/core/schema_pgvector.sql", "r") as f:
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schema_sql = f.read()
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with test_conn.cursor() as cur:
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cur.execute(schema_sql)
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test_conn.commit()
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yield test_conn
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# Cleanup
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test_conn.close()
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# Drop test database
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conn = psycopg2.connect(
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host=test_db_config["host"],
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port=test_db_config["port"],
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database="postgres",
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user=test_db_config["user"],
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)
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conn.autocommit = True
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with conn.cursor() as cur:
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cur.execute(f"DROP DATABASE IF EXISTS {test_db_config['database']}")
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conn.close()
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@pytest.fixture
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def clean_db(db_connection):
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"""
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Clean database before each test.
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This is a function-scoped fixture that cleans tables before each test.
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Works with NEW PGVectorStore schema (data_{kb_name} tables).
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"""
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with db_connection.cursor() as cur:
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# Get all KB table names
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cur.execute("SELECT table_name FROM knowledge_bases WHERE table_name IS NOT NULL")
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kb_tables = cur.fetchall()
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# Truncate each KB data table
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for (table_name,) in kb_tables:
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try:
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cur.execute(f"TRUNCATE TABLE {table_name} CASCADE")
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except Exception:
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# Table might not exist, skip it
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pass
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# Truncate knowledge_bases table
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cur.execute("TRUNCATE TABLE knowledge_bases CASCADE")
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db_connection.commit()
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yield db_connection
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@pytest.fixture
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def sample_kb(clean_db):
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"""Create a sample knowledge base for testing with NEW PGVectorStore schema."""
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import json
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table_name = "data_test_kb"
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embed_dim = 768
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with clean_db.cursor() as cur:
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# Insert KB metadata with NEW schema columns
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# Note: metadata must be JSON string, not dict
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cur.execute(
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"""
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INSERT INTO knowledge_bases (name, kb_type, description, metadata, table_name, embed_dim)
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VALUES (%s, %s, %s, %s, %s, %s)
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RETURNING id
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""",
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("test_kb", "GENERIC", "Test knowledge base", json.dumps({}), table_name, embed_dim)
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)
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kb_id = cur.fetchone()[0]
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# Create the actual data table using helper function
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cur.execute("SELECT create_kb_table(%s, %s, %s)", ("test_kb", table_name, embed_dim))
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clean_db.commit()
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return {
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"id": kb_id,
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"name": "test_kb",
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"kb_type": "GENERIC",
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"table_name": table_name,
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"embed_dim": embed_dim
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}
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@pytest.fixture
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def sample_embedding() -> list:
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"""Generate a sample 768-dimensional embedding."""
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np.random.seed(42) # For reproducibility
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return np.random.randn(768).tolist()
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@pytest.fixture
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def sample_documents(sample_kb, sample_embedding, clean_db):
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"""Create sample documents with embeddings in NEW PGVectorStore schema."""
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import json
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documents = []
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table_name = sample_kb["table_name"] # Should be 'data_test_kb'
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for i in range(5):
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# Generate slightly different embeddings
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np.random.seed(42 + i)
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embedding = np.random.randn(768).tolist()
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node_id = f"node_doc_{i}"
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text_content = f"This is test document {i} with some content about testing."
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metadata = {"filename": f"test_{i}.txt", "chunk_index": i}
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with clean_db.cursor() as cur:
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# Insert into data_test_kb table with NEW schema columns
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# Note: metadata_ is JSON type, needs JSON string
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cur.execute(
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f"""
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INSERT INTO {table_name} (text, metadata_, node_id, embedding)
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VALUES (%s, %s, %s, %s)
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RETURNING id
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""",
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(text_content, json.dumps(metadata), node_id, embedding)
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)
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doc_id = cur.fetchone()[0]
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documents.append({
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"id": doc_id,
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"node_id": node_id,
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"text": text_content,
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"metadata": metadata,
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"embedding": embedding
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})
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clean_db.commit()
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return documents
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@pytest.fixture
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def mock_ollama_embedding():
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"""Mock Ollama embedding model."""
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mock = MagicMock()
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mock.get_text_embedding.return_value = np.random.randn(768).tolist()
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return mock
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@pytest.fixture
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def mock_ollama_llm():
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"""Mock Ollama LLM."""
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mock = MagicMock()
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mock_response = MagicMock()
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mock_response.message.content = "This is a test response from the LLM."
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mock.chat.return_value = mock_response
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return mock
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@pytest.fixture
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def sample_text_file(tmp_path):
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"""Create a sample text file for testing."""
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file_path = tmp_path / "test_document.txt"
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file_path.write_text("This is a test document.\n\nIt has multiple paragraphs.\n\nFor testing purposes.")
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return file_path
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@pytest.fixture
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def sample_pdf_file(tmp_path):
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"""Create a sample PDF file for testing (requires PyMuPDF)."""
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try:
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import fitz # PyMuPDF
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file_path = tmp_path / "test_document.pdf"
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doc = fitz.open()
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page = doc.new_page()
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page.insert_text((72, 72), "This is a test PDF document.\n\nIt has multiple paragraphs.\n\nFor testing purposes.")
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doc.save(str(file_path))
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doc.close()
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return file_path
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except ImportError:
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pytest.skip("PyMuPDF not installed")
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@pytest.fixture
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def sample_markdown_file(tmp_path):
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"""Create a sample Markdown file for testing."""
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file_path = tmp_path / "test_document.md"
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content = """# Test Document
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This is a test document.
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## Section 1
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Some content here.
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## Section 2
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More content here.
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"""
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file_path.write_text(content)
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return file_path
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@pytest.fixture
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def api_client():
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"""Create a test client for FastAPI."""
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from fastapi.testclient import TestClient
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from mcp_server.server import app
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return TestClient(app)
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# Markers for test categorization
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def pytest_configure(config):
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"""Configure custom markers."""
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config.addinivalue_line("markers", "unit: Unit tests (fast, no external dependencies)")
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config.addinivalue_line("markers", "integration: Integration tests (require database, Ollama)")
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config.addinivalue_line("markers", "slow: Slow tests (may take several seconds)")
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config.addinivalue_line("markers", "embeddings: Tests involving embedding generation")
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config.addinivalue_line("markers", "vector: Tests involving vector operations")
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config.addinivalue_line("markers", "rag: Tests involving RAG engine")
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config.addinivalue_line("markers", "api: Tests involving API endpoints")
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