Testing Patterns - LangChain in Production

Learn advanced testing strategies for LangChain applications, including unit, integration, and end-to-end tests

🧪 Testing Patterns Overview

Testing ensures reliability and quality in LangChain systems. This guide covers test types, frameworks, mocking, and CI integration for robust production systems.

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🏛️ Test Types

• Unit Tests: Test individual functions and components
• Integration Tests: Test chains, LLM calls, and vector DBs together
• End-to-End (E2E) Tests: Test full workflows and user scenarios

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🧑‍💻 Testing Frameworks

• Use pytest, unittest, or nose for Python
• Integrate with CI/CD pipelines for automated testing
• Use coverage tools to measure test completeness

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🛠️ Mocking LLMs and APIs

• Use mock objects for LLMs and external APIs
• Simulate responses and errors for edge cases
• Test error handling and retries

import pytest

class MockLLM:
    def invoke(self, prompt):
        return "Mock response"

def test_chain_with_mock_llm():
    llm = MockLLM()
    result = llm.invoke("Hello!")
    assert result == "Mock response"

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🧩 Example: Integration Test with FastAPI

from fastapi.testclient import TestClient
from main import app

client = TestClient(app)

def test_chat_endpoint():
    response = client.post("/chat", json={"prompt": "Hello!"})
    assert response.status_code == 200
    assert "response" in response.json()

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🔗 Next Steps

• Error Handling
• Logging Patterns
• Monitoring Patterns

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Key Testing Takeaways:

• Use unit, integration, and E2E tests
• Mock LLMs and APIs for reliability
• Automate tests in CI/CD
• Measure coverage and improve continuously
• Test for edge cases and failures