# Integration Testing Guide

Integration tests verify complete workflows across different providers using Llama Stack's record-replay system.

## Quick Start

```bash
# Run all integration tests with existing recordings
uv run pytest tests/integration/

# Test against live APIs with auto-server
export FIREWORKS_API_KEY=your_key
pytest tests/integration/inference/ \
    --stack-config=server:fireworks \
    --text-model=meta-llama/Llama-3.1-8B-Instruct
```

## Configuration Options

You can see all options with:
```bash
cd tests/integration

# this will show a long list of options, look for "Custom options:"
pytest --help
```

Here are the most important options:
- `--stack-config`: specify the stack config to use. You have four ways to point to a stack:
  - **`server:<config>`** - automatically start a server with the given config (e.g., `server:fireworks`). This provides one-step testing by auto-starting the server if the port is available, or reusing an existing server if already running.
  - **`server:<config>:<port>`** - same as above but with a custom port (e.g., `server:together:8322`)
  - a URL which points to a Llama Stack distribution server
  - a template (e.g., `starter`) or a path to a `run.yaml` file
  - a comma-separated list of api=provider pairs, e.g. `inference=fireworks,safety=llama-guard,agents=meta-reference`. This is most useful for testing a single API surface.
- `--env`: set environment variables, e.g. --env KEY=value. this is a utility option to set environment variables required by various providers.

Model parameters can be influenced by the following options:
- `--text-model`: comma-separated list of text models.
- `--vision-model`: comma-separated list of vision models.
- `--embedding-model`: comma-separated list of embedding models.
- `--safety-shield`: comma-separated list of safety shields.
- `--judge-model`: comma-separated list of judge models.
- `--embedding-dimension`: output dimensionality of the embedding model to use for testing. Default: 384

Each of these are comma-separated lists and can be used to generate multiple parameter combinations. Note that tests will be skipped
if no model is specified.

## Examples

### Testing against a Server

Run all text inference tests by auto-starting a server with the `fireworks` config:

```bash
pytest -s -v tests/integration/inference/test_text_inference.py \
   --stack-config=server:fireworks \
   --text-model=meta-llama/Llama-3.1-8B-Instruct
```

Run tests with auto-server startup on a custom port:

```bash
pytest -s -v tests/integration/inference/ \
   --stack-config=server:together:8322 \
   --text-model=meta-llama/Llama-3.1-8B-Instruct
```

Run multiple test suites with auto-server (eliminates manual server management):

```bash
# Auto-start server and run all integration tests
export FIREWORKS_API_KEY=<your_key>

pytest -s -v tests/integration/inference/ tests/integration/safety/ tests/integration/agents/ \
   --stack-config=server:fireworks \
   --text-model=meta-llama/Llama-3.1-8B-Instruct
```

### Testing with Library Client

Run all text inference tests with the `starter` distribution using the `together` provider:

```bash
ENABLE_TOGETHER=together pytest -s -v tests/integration/inference/test_text_inference.py \
   --stack-config=starter \
   --text-model=meta-llama/Llama-3.1-8B-Instruct
```

Run all text inference tests with the `starter` distribution using the `together` provider and `meta-llama/Llama-3.1-8B-Instruct`:

```bash
ENABLE_TOGETHER=together pytest -s -v tests/integration/inference/test_text_inference.py \
   --stack-config=starter \
   --text-model=meta-llama/Llama-3.1-8B-Instruct
```

Running all inference tests for a number of models using the `together` provider:

```bash
TEXT_MODELS=meta-llama/Llama-3.1-8B-Instruct,meta-llama/Llama-3.1-70B-Instruct
VISION_MODELS=meta-llama/Llama-3.2-11B-Vision-Instruct
EMBEDDING_MODELS=all-MiniLM-L6-v2
ENABLE_TOGETHER=together
export TOGETHER_API_KEY=<together_api_key>

pytest -s -v tests/integration/inference/ \
   --stack-config=together \
   --text-model=$TEXT_MODELS \
   --vision-model=$VISION_MODELS \
   --embedding-model=$EMBEDDING_MODELS
```

Same thing but instead of using the distribution, use an adhoc stack with just one provider (`fireworks` for inference):

```bash
export FIREWORKS_API_KEY=<fireworks_api_key>

pytest -s -v tests/integration/inference/ \
   --stack-config=inference=fireworks \
   --text-model=$TEXT_MODELS \
   --vision-model=$VISION_MODELS \
   --embedding-model=$EMBEDDING_MODELS
```

Running Vector IO tests for a number of embedding models:

```bash
EMBEDDING_MODELS=all-MiniLM-L6-v2

pytest -s -v tests/integration/vector_io/ \
   --stack-config=inference=sentence-transformers,vector_io=sqlite-vec \
   --embedding-model=$EMBEDDING_MODELS
```

## Recording Modes

The testing system supports three modes controlled by environment variables:

### LIVE Mode (Default)
Tests make real API calls:
```bash
LLAMA_STACK_TEST_INFERENCE_MODE=live pytest tests/integration/
```

### RECORD Mode
Captures API interactions for later replay:
```bash
LLAMA_STACK_TEST_INFERENCE_MODE=record \
LLAMA_STACK_TEST_RECORDING_DIR=./recordings \
pytest tests/integration/inference/test_new_feature.py
```

### REPLAY Mode
Uses cached responses instead of making API calls:
```bash
LLAMA_STACK_TEST_INFERENCE_MODE=replay \
LLAMA_STACK_TEST_RECORDING_DIR=./recordings \
pytest tests/integration/
```

## Managing Recordings

### Viewing Recordings
```bash
# See what's recorded
sqlite3 recordings/index.sqlite "SELECT endpoint, model, timestamp FROM recordings;"

# Inspect specific response
cat recordings/responses/abc123.json | jq '.'
```

### Re-recording Tests
```bash
# Re-record specific tests
rm -rf recordings/
LLAMA_STACK_TEST_INFERENCE_MODE=record pytest tests/integration/test_modified.py
```

## Writing Tests

### Basic Test Pattern
```python
def test_basic_completion(llama_stack_client, text_model_id):
    response = llama_stack_client.inference.completion(
        model_id=text_model_id,
        content=CompletionMessage(role="user", content="Hello"),
    )

    # Test structure, not AI output quality
    assert response.completion_message is not None
    assert isinstance(response.completion_message.content, str)
    assert len(response.completion_message.content) > 0
```

### Provider-Specific Tests
```python
def test_asymmetric_embeddings(llama_stack_client, embedding_model_id):
    if embedding_model_id not in MODELS_SUPPORTING_TASK_TYPE:
        pytest.skip(f"Model {embedding_model_id} doesn't support task types")

    query_response = llama_stack_client.inference.embeddings(
        model_id=embedding_model_id,
        contents=["What is machine learning?"],
        task_type="query"
    )

    assert query_response.embeddings is not None
```

## Best Practices

- **Test API contracts, not AI output quality** - Focus on response structure, not content
- **Use existing recordings for development** - Fast iteration without API costs
- **Record new interactions only when needed** - Adding new functionality
- **Test across providers** - Ensure compatibility
- **Commit recordings to version control** - Deterministic CI builds