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refactor(test): introduce --stack-config and simplify options (#1404)

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You now run the integration tests with these options:

```bash
Custom options:
  --stack-config=STACK_CONFIG
                        a 'pointer' to the stack. this can be either be:
                        (a) a template name like `fireworks`, or
                        (b) a path to a run.yaml file, or
                        (c) an adhoc config spec, e.g.
                        `inference=fireworks,safety=llama-guard,agents=meta-
                        reference`
  --env=ENV             Set environment variables, e.g. --env KEY=value
  --text-model=TEXT_MODEL
                        comma-separated list of text models. Fixture name:
                        text_model_id
  --vision-model=VISION_MODEL
                        comma-separated list of vision models. Fixture name:
                        vision_model_id
  --embedding-model=EMBEDDING_MODEL
                        comma-separated list of embedding models. Fixture name:
                        embedding_model_id
  --safety-shield=SAFETY_SHIELD
                        comma-separated list of safety shields. Fixture name:
                        shield_id
  --judge-model=JUDGE_MODEL
                        comma-separated list of judge models. Fixture name:
                        judge_model_id
  --embedding-dimension=EMBEDDING_DIMENSION
                        Output dimensionality of the embedding model to use for
                        testing. Default: 384
  --record-responses    Record new API responses instead of using cached ones.
  --report=REPORT       Path where the test report should be written, e.g.
                        --report=/path/to/report.md

```

Importantly, if you don't specify any of the models (text-model,
vision-model, etc.) the relevant tests will get **skipped!**

This will make running tests somewhat more annoying since all options
will need to be specified. We will make this easier by adding some easy
wrapper yaml configs.

## Test Plan

Example:

```bash
ashwin@ashwin-mbp ~/local/llama-stack/tests/integration (unify_tests) $ 
LLAMA_STACK_CONFIG=fireworks pytest -s -v inference/test_text_inference.py \
   --text-model meta-llama/Llama-3.2-3B-Instruct 
```
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Ashwin Bharambe 2025-03-05 17:02:02 -08:00 • committed by GitHub
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101
llama_stack/scripts/test_rag_via_curl.py
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@ -1,101 +0,0 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import json
from typing import List
import pytest
import requests
from pydantic import TypeAdapter
from llama_stack.apis.tools import (
DefaultRAGQueryGeneratorConfig,
RAGDocument,
RAGQueryConfig,
RAGQueryResult,
)
from llama_stack.apis.vector_dbs import VectorDB
from llama_stack.providers.utils.memory.vector_store import interleaved_content_as_str
class TestRAGToolEndpoints:
@pytest.fixture
def base_url(self) -> str:
return "http://localhost:8321/v1" # Adjust port if needed
@pytest.fixture
def sample_documents(self) -> List[RAGDocument]:
return [
RAGDocument(
document_id="doc1",
content="Python is a high-level programming language.",
metadata={"category": "programming", "difficulty": "beginner"},
),
RAGDocument(
document_id="doc2",
content="Machine learning is a subset of artificial intelligence.",
metadata={"category": "AI", "difficulty": "advanced"},
),
RAGDocument(
document_id="doc3",
content="Data structures are fundamental to computer science.",
metadata={"category": "computer science", "difficulty": "intermediate"},
),
]
@pytest.mark.asyncio
async def test_rag_workflow(self, base_url: str, sample_documents: List[RAGDocument]):
vector_db_payload = {
"vector_db_id": "test_vector_db",
"embedding_model": "all-MiniLM-L6-v2",
"embedding_dimension": 384,
}
response = requests.post(f"{base_url}/vector-dbs", json=vector_db_payload)
assert response.status_code == 200
vector_db = VectorDB(**response.json())
insert_payload = {
"documents": [json.loads(doc.model_dump_json()) for doc in sample_documents],
"vector_db_id": vector_db.identifier,
"chunk_size_in_tokens": 512,
}
response = requests.post(
f"{base_url}/tool-runtime/rag-tool/insert-documents",
json=insert_payload,
)
assert response.status_code == 200
query = "What is Python?"
query_config = RAGQueryConfig(
query_generator_config=DefaultRAGQueryGeneratorConfig(),
max_tokens_in_context=4096,
max_chunks=2,
)
query_payload = {
"content": query,
"query_config": json.loads(query_config.model_dump_json()),
"vector_db_ids": [vector_db.identifier],
}
response = requests.post(
f"{base_url}/tool-runtime/rag-tool/query-context",
json=query_payload,
)
assert response.status_code == 200
result = response.json()
result = TypeAdapter(RAGQueryResult).validate_python(result)
content_str = interleaved_content_as_str(result.content)
print(f"content: {content_str}")
assert len(content_str) > 0
assert "Python" in content_str
# Clean up: Delete the vector DB
response = requests.delete(f"{base_url}/vector-dbs/{vector_db.identifier}")
assert response.status_code == 200