# What does this PR do?
- Add new Vertex AI remote inference provider with litellm integration
- Support for Gemini models through Google Cloud Vertex AI platform
- Uses Google Cloud Application Default Credentials (ADC) for
authentication
- Added VertexAI models: gemini-2.5-flash, gemini-2.5-pro,
gemini-2.0-flash.
- Updated provider registry to include vertexai provider
- Updated starter template to support Vertex AI configuration
- Added comprehensive documentation and sample configuration
<!-- If resolving an issue, uncomment and update the line below -->
relates to https://github.com/meta-llama/llama-stack/issues/2747
## Test Plan
<!-- Describe the tests you ran to verify your changes with result
summaries. *Provide clear instructions so the plan can be easily
re-executed.* -->
Signed-off-by: Eran Cohen <eranco@redhat.com>
Co-authored-by: Francisco Arceo <arceofrancisco@gmail.com>
This PR kills the verifications infrastructure which is no longer used.
It was relocated to the `llama-stack-evals`
(https://github.com/meta-llama/llama-stack-evals) repository previously.
Responses tests used this infrastructure but that wasn't quite
necessary, just a little useful back when @bbrownin introduced the
tests. On Discord, we agreed that tests can be moved to our regular
integrations test infra.
## Test Plan
Some tests currently do fail (although they run!) I will send a
follow-up PR which makes them all pass.
# What does this PR do?
This PR implements hybrid search for Milvus DB based on the inbuilt
milvus support.
To test:
```
pytest tests/unit/providers/vector_io/remote/test_milvus.py -v -s
--tb=long --disable-warnings --asyncio-mode=auto
```
Signed-off-by: Varsha Prasad Narsing <varshaprasad96@gmail.com>
# What does this PR do?
This PR adds Open AI Compatible moderations api. Currently only
implementing for llama guard safety provider
Image support, expand to other safety providers and Deprecation of
run_shield will be next steps.
## Test Plan
Added 2 new tests for safe/ unsafe text prompt examples for the new open
ai compatible moderations api usage
`SAFETY_MODEL=llama-guard3:8b LLAMA_STACK_CONFIG=starter uv run pytest
-v tests/integration/safety/test_safety.py
--text-model=llama3.2:3b-instruct-fp16
--embedding-model=all-MiniLM-L6-v2 --safety-shield=ollama`
(Had some issue with previous PR
https://github.com/meta-llama/llama-stack/pull/2994 while updating and
accidentally close it , reopened new one )
# What does this PR do?
I found a few issues while adding new metrics for various APIs:
currently metrics are only propagated in `chat_completion` and
`completion`
since most providers use the `openai_..` routes as the default in
`llama-stack-client inference chat-completion`, metrics are currently
not working as expected.
in order to get them working the following had to be done:
1. get the completion as usual
2. use new `openai_` versions of the metric gathering functions which
use `.usage` from the `OpenAI..` response types to gather the metrics
which are already populated.
3. define a `stream_generator` which counts the tokens and computes the
metrics (only for stream=True)
5. add metrics to response
NOTE: I could not add metrics to `openai_completion` where stream=True
because that ONLY returns an `OpenAICompletion` not an AsyncGenerator
that we can manipulate.
acquire the lock, and add event to the span as the other `_log_...`
methods do
some new output:
`llama-stack-client inference chat-completion --message hi`
<img width="2416" height="425" alt="Screenshot 2025-07-16 at 8 28 20 AM"
src="https://github.com/user-attachments/assets/ccdf1643-a184-4ddd-9641-d426c4d51326"
/>
and in the client:
<img width="763" height="319" alt="Screenshot 2025-07-16 at 8 28 32 AM"
src="https://github.com/user-attachments/assets/6bceb811-5201-47e9-9e16-8130f0d60007"
/>
these were not previously being recorded nor were they being printed to
the server due to the improper console sink handling
---------
Signed-off-by: Charlie Doern <cdoern@redhat.com>
# What does this PR do?
1. Introduce new base custom exception class `ResourceNotFoundError`
2. All other "not found" exception classes now inherit from
`ResourceNotFoundError`
Closes#3030
Signed-off-by: Nathan Weinberg <nweinber@redhat.com>
A bunch of miscellaneous cleanup focusing on tests, but ended up
speeding up starter distro substantially.
- Pulled llama stack client init for tests into `pytest_sessionstart` so
it does not clobber output
- Profiling of that told me where we were doing lots of heavy imports
for starter, so lazied them
- starter now starts 20seconds+ faster on my Mac
- A few other smallish refactors for `compat_client`
As the title says. Distributions is in, Templates is out.
`llama stack build --template` --> `llama stack build --distro`. For
backward compatibility, the previous option is kept but results in a
warning.
Updated `server.py` to remove the "config_or_template" backward
compatibility since it has been a couple releases since that change.
# What does this PR do?
Implement vector store search test
<!-- If resolving an issue, uncomment and update the line below -->
<!-- Closes #[issue-number] -->
## Test Plan
```
pytest tests/integration/vector_io/test_openai_vector_stores.py::test_openai_vector_store_search_modes --stack-config=http://localhost:8321 --embedding-model=all-MiniLM-L6-v2 -v
```
Signed-off-by: Varsha Prasad Narsing <varshaprasad96@gmail.com>
# What does this PR do?
Adds support to Vector store Open AI APIs in Qdrant.
<!-- If resolving an issue, uncomment and update the line below -->
Closes#2463
## Test Plan
<!-- Describe the tests you ran to verify your changes with result
summaries. *Provide clear instructions so the plan can be easily
re-executed.* -->
Signed-off-by: Varsha Prasad Narsing <varshaprasad96@gmail.com>
Co-authored-by: ehhuang <ehhuang@users.noreply.github.com>
Co-authored-by: Francisco Arceo <arceofrancisco@gmail.com>
# What does this PR do?
This should be more robust as sometimes its run without running build
first.
## Test Plan
OLLAMA_URL=http://localhost:11434 LLAMA_STACK_TEST_INFERENCE_MODE=replay
LLAMA_STACK_TEST_RECORDING_DIR=tests/integration/recordings
LLAMA_STACK_CONFIG=server:starter uv run --with pytest-repeat pytest
tests/integration/telemetry
--text-model="ollama/llama3.2:3b-instruct-fp16" -vvs
# What does this PR do?
This PR (1) enables the files API for Weaviate and (2) enables
integration tests for Weaviate, which adds a docker container to the
github action.
This PR also handles a couple of edge cases for in creating the
collection and ensuring the tests all pass.
## Test Plan
CI enabled
---------
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
This PR significantly refactors the Integration Tests workflow. The main
goal behind the PR was to enable recording of vision tests which were
never run as part of our CI ever before. During debugging, I ended up
making several other changes refactoring and hopefully increasing the
robustness of the workflow.
After doing the experiments, I have updated the trigger event to be
`pull_request_target` so this workflow can get write permissions by
default but it will run with source code from the base (main) branch in
the source repository only. If you do change the workflow, you'd need to
experiment using the `workflow_dispatch` triggers. This should not be
news to anyone using Github Actions (except me!)
It is likely to be a little rocky though while I learn more about GitHub
Actions, etc. Please be patient :)
---------
Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
What does this PR do?
This PR adds support for Direct Preference Optimization (DPO) training
via the existing HuggingFace inline provider. It introduces a new DPO
training recipe, config schema updates, dataset integration, and
end-to-end testing to support preference-based fine-tuning with TRL.
Test Plan
Added integration test:
tests/integration/post_training/test_post_training.py::TestPostTraining::test_preference_optimize
Ran tests on both CPU and CUDA environments
---------
Co-authored-by: Ubuntu <ubuntu@ip-172-31-43-83.ec2.internal>
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
This PR makes setting up Ollama optional for CI. By default, we use
`replay` mode for inference requests and use the stored results from the
`tests/integration/recordings/` directory.
Every so often, users will update tests which will need us to re-record.
To do this, we check for the existence of a label `re-record-tests` on
the PR. If detected,
- ollama is spun up
- inference mode is set to record
- after the tests are done, if any new changes are detected, they are
pushed back to the PR
## Test Plan
This is GitHub CI. Gotta test it live.
Continuing with https://github.com/meta-llama/llama-stack/pull/2952
This also includes a "fix" to inference store related tests so that we
pull a large number of inference responses from the DB so as to always
find the one we just wrote.
Post training tests need _much_ better thinking before we can re-enable
them to be run on every single PR. Running periodically should be
approached only when it is shown that the tests are reliable and as
light-weight as can be; otherwise, it is just kicking the can down the
road.
Continue to build on top of
https://github.com/meta-llama/llama-stack/pull/2941
## Test Plan
Run server with `LLAMA_STACK_TEST_INFERENCE_MODE=record` and then run
the integration tests with `--stack-config=server:starter`. Then restart
the server with `LLAMA_STACK_TEST_INFERENCE_MODE=replay` and re-run the
tests. Verify that no request hit Ollama at any point.
Implements a comprehensive recording and replay system for inference API
calls that eliminates dependency on online inference providers during
testing. The system treats inference as deterministic by recording real
API responses and replaying them in subsequent test runs. Applies to
OpenAI clients (which should cover many inference requests) as well as
Ollama AsyncClient.
For storing, we use a hybrid system: Sqlite for fast lookups and JSON
files for easy greppability / debuggability.
As expected, tests become much much faster (more than 3x in just
inference testing.)
```bash
LLAMA_STACK_TEST_INFERENCE_MODE=record LLAMA_STACK_TEST_RECORDING_DIR=<...> \
uv run pytest -s -v tests/integration/inference \
--stack-config=starter \
-k "not( builtin_tool or safety_with_image or code_interpreter or test_rag )" \
--text-model="ollama/llama3.2:3b-instruct-fp16" \
--embedding-model=sentence-transformers/all-MiniLM-L6-v2
```
```bash
LLAMA_STACK_TEST_INFERENCE_MODE=replay LLAMA_STACK_TEST_RECORDING_DIR=<...> \
uv run pytest -s -v tests/integration/inference \
--stack-config=starter \
-k "not( builtin_tool or safety_with_image or code_interpreter or test_rag )" \
--text-model="ollama/llama3.2:3b-instruct-fp16" \
--embedding-model=sentence-transformers/all-MiniLM-L6-v2
```
- `LLAMA_STACK_TEST_INFERENCE_MODE`: `live` (default), `record`, or
`replay`
- `LLAMA_STACK_TEST_RECORDING_DIR`: Storage location (must be specified
for record or replay modes)
# What does this PR do?
OpenAI Chat Completions supports passing a base64 encoded PDF file to a
model, but Llama Stack currently does not allow for this behavior. This
PR extends our implementation of the OpenAI API spec to change that.
Closes#2129
## Test Plan
A new functional test has been added to test the validity of such a
request
Signed-off-by: Nathan Weinberg <nweinber@redhat.com>
Add support for deleting individual chunks from vector stores
- Add abstract remove_chunk() method to EmbeddingIndex base class
- Implement chunk deletion for Faiss provider, SQLite Vec, Milvus,
PGVector
- Placeholder implementations with NotImplementedError for
Chroma/Qdrant/Weaviate
- Integrate chunk deletion into OpenAI vector store file deletion flow
- removed xfail from
test_openai_vector_store_delete_file_removes_from_vector_store
Closes: #2477
---------
Signed-off-by: Derek Higgins <derekh@redhat.com>
Co-authored-by: Francisco Arceo <arceofrancisco@gmail.com>
# What does this PR do?
Enable Chroma inline unit tests and fix integration tests.
<!-- If resolving an issue, uncomment and update the line below -->
<!-- Closes #[issue-number] -->
## Test Plan
<!-- Describe the tests you ran to verify your changes with result
summaries. *Provide clear instructions so the plan can be easily
re-executed.* -->
---------
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
- Add setup-vllm GitHub action to start VLLM container
- Extend integration test matrix to support both ollama and vllm
providers
- Make test setup conditional based on provider type
- Add provider-specific environment variables and configurations
- vllm tests setup to run weekly or can be triggered manually (only
ollama on PR)
TODO:
investigate failing tests for vllm provider (safety and post_training)
Also need a proper fix for #2713 (tmp fix for this in the first commit
in this PR)
Closes: #1648
---------
Signed-off-by: Derek Higgins <derekh@redhat.com>
# What does this PR do?
This PR implements the openai compatible endpoints for chromadb
Closes#2462
## Test Plan
Ran ollama llama stack server and ran the command
`pytest -sv --stack-config=http://localhost:8321
tests/integration/vector_io/test_openai_vector_stores.py
--embedding-model all-MiniLM-L6-v2`
8 failed, 27 passed, 8 skipped, 1 xfailed
The failed ones are regarding files api
---------
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
Co-authored-by: sarthakdeshpande <sarthak.deshpande@engati.com>
Co-authored-by: Francisco Javier Arceo <farceo@redhat.com>
Co-authored-by: Francisco Arceo <arceofrancisco@gmail.com>
# What does this PR do?
<!-- Provide a short summary of what this PR does and why. Link to
relevant issues if applicable. -->
This PR fixes flaky telemetry tests
<!-- If resolving an issue, uncomment and update the line below -->
<!-- Closes #[issue-number] -->
See https://github.com/meta-llama/llama-stack/pull/2814
## Test Plan
<!-- Describe the tests you ran to verify your changes with result
summaries. *Provide clear instructions so the plan can be easily
re-executed.* -->
Signed-off-by: Mustafa Elbehery <melbeher@redhat.com>
# What does this PR do?
some async test markers are in the codebase causing pre-commit to fail
due to #2744
remove these pytest fixtures
## Test Plan
pre-commit passes
Signed-off-by: Charlie Doern <cdoern@redhat.com>
# What does this PR do?
## Test Plan
ENABLE_OLLAMA=ollama LLAMA_STACK_CONFIG=starter uv run pytest
tests/integration/telemetry
--text-model="ollama/llama3.2:3b-instruct-fp16"
# What does this PR do?
Adding OpenAI Vector Stores Files API compatibility for PGVector
<!-- If resolving an issue, uncomment and update the line below -->
<!-- Closes #[issue-number] -->
## Test Plan
Updated CI to include PGVector
---------
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
# What does this PR do?
Resolves https://github.com/meta-llama/llama-stack/issues/2735
Currently, if you test against OpenAI's Vector Stores API the
`client.vector_stores.search` call fails with an invalid vector_db
during routing (see the script referenced in the clickable item under
the Test Plan section).
This PR ensures that `client.vector_stores.search()` is compatible with
OpenAI's Vector Stores API.
Two biggest changes:
1. The `name`, which was previously used as the `vector_db_id`, has been
changed to be consistent with OpenAI's `vs_{uuid}` format.
2. The vector store ID has to be referenced by the ID, the name is not
reliable as every `client.vector_stores.create` results in a new vector
store.
NOTE: I believe this is a breaking change for end users as they'll need
to update their VectorDB identifiers.
## Test Plan
Unit tests:
```bash
./scripts/unit-tests.sh tests/unit/providers/vector_io/ -v
```
Integration tests:
```bash
ENABLE_MILVUS=milvus llama stack run /Users/farceo/dev/llama-stack/llama_stack/templates/starter/run.yaml --image-type venv
LLAMA_STACK_CONFIG=http://localhost:8321 pytest -sv tests/integration/vector_io/test_openai_vector_stores.py --embedding-model=all-MiniLM-L6-v2 -vv
```
Unit tests and test script below 👇
<details>
<summary>Click here for script used to test OpenAI and Llama Stack
Vector Store implementation</summary>
```python
import json
import argparse
from openai import OpenAI, pagination
import logging
from colorama import Fore, Style, init
import traceback
import os
# Initialize colorama for color support in terminal
init(autoreset=True)
# Setup basic logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
DEMO_VECTOR_STORE_NAME = "Support FAQ FJA"
global DEMO_VECTOR_STORE_ID
global DEMO_VECTOR_STORE_ID2
def colored_print(color, text):
"""Prints text to the console with the specified color."""
print(f"{color}{text}{Style.RESET_ALL}")
def log_and_print(color, message, level=logging.INFO):
"""Logs a message and prints it to the console with the specified color."""
logging.log(level, message)
colored_print(color, message)
def run_tests(client, prefix="openai"):
"""
Runs all tests using the provided OpenAI client and saves the output
to JSON files with the given prefix.
"""
# Create the directory if it doesn't exist
os.makedirs('openai_testing', exist_ok=True)
# Default values in case tests fail
global DEMO_VECTOR_STORE_ID, DEMO_VECTOR_STORE_ID2
DEMO_VECTOR_STORE_ID = None
DEMO_VECTOR_STORE_ID2 = None
def test_idempotent_vector_store_creation():
"""
Test that creating a vector store with the same name is idempotent.
"""
log_and_print(Fore.BLUE, "Starting vector store creation test...")
try:
vector_store = client.vector_stores.create(
name=DEMO_VECTOR_STORE_NAME,
)
# Attempt to create the same vector store again
vector_store2 = client.vector_stores.create(
name=DEMO_VECTOR_STORE_NAME,
)
# Check instead of assert
if vector_store2.id != vector_store.id:
log_and_print(Fore.YELLOW, f"FAILED IDEMPOTENCY: the same VectorStore name for {prefix.upper()} does not return the same ID",
level=logging.WARNING)
else:
log_and_print(Fore.GREEN, f"PASSED IDEMPOTENCY: f{vector_store2.id} == {vector_store.id} the same VectorStore name for {prefix.upper()} returns the same ID")
vector_store_data = vector_store.to_dict()
log_and_print(Fore.WHITE, f"vector_stores.create = {json.dumps(vector_store_data, indent=2)}")
with open(f'openai_testing/{prefix}_vector_store_create.json', 'w') as f:
json.dump(vector_store_data, f, indent=2)
global DEMO_VECTOR_STORE_ID, DEMO_VECTOR_STORE_ID2
DEMO_VECTOR_STORE_ID = vector_store.id
DEMO_VECTOR_STORE_ID2 = vector_store2.id
return DEMO_VECTOR_STORE_ID, DEMO_VECTOR_STORE_ID2
except Exception as e:
log_and_print(Fore.RED, f"Idempotent vector store creation test failed: {e}", level=logging.ERROR)
logging.error(traceback.format_exc())
# Create a fallback vector store ID if needed
if 'vector_store' in locals() and vector_store:
DEMO_VECTOR_STORE_ID = vector_store.id
return DEMO_VECTOR_STORE_ID, DEMO_VECTOR_STORE_ID2
def test_vector_store_list():
"""
Test listing vector stores.
"""
log_and_print(Fore.BLUE, "Starting vector store list test...")
try:
vector_stores = client.vector_stores.list()
# Check instead of assert
if not isinstance(vector_stores, pagination.SyncCursorPage):
log_and_print(Fore.YELLOW, f"FAILED: Expected a list of vector stores, got {type(vector_stores)}",
level=logging.WARNING)
else:
log_and_print(Fore.GREEN, "Vector store list test passed!")
vector_stores_data = vector_stores.to_dict()
log_and_print(Fore.WHITE, f"vector_stores.list = {json.dumps(vector_stores_data, indent=2)}")
with open(f'openai_testing/{prefix}_vector_store_list.json', 'w') as f:
json.dump(vector_stores_data, f, indent=2)
except Exception as e:
log_and_print(Fore.RED, f"Vector store list test failed: {e}", level=logging.ERROR)
logging.error(traceback.format_exc())
def test_retrieve_vector_store():
"""
Test retrieving a specific vector store.
"""
log_and_print(Fore.BLUE, "Starting retrieve vector store test...")
if not DEMO_VECTOR_STORE_ID:
log_and_print(Fore.YELLOW, "Skipping retrieve vector store test - no vector store ID available",
level=logging.WARNING)
return
try:
vector_store = client.vector_stores.retrieve(
vector_store_id=DEMO_VECTOR_STORE_ID,
)
# Check instead of assert
if vector_store.id != DEMO_VECTOR_STORE_ID:
log_and_print(Fore.YELLOW, "FAILED: Retrieved vector store ID does not match", level=logging.WARNING)
else:
log_and_print(Fore.GREEN, "Retrieve vector store test passed!")
vector_store_data = vector_store.to_dict()
log_and_print(Fore.WHITE, f"vector_stores.retrieve = {json.dumps(vector_store_data, indent=2)}")
with open(f'openai_testing/{prefix}_vector_store_retrieve.json', 'w') as f:
json.dump(vector_store_data, f, indent=2)
except Exception as e:
log_and_print(Fore.RED, f"Retrieve vector store test failed: {e}", level=logging.ERROR)
logging.error(traceback.format_exc())
def test_modify_vector_store():
"""
Test modifying a vector store.
"""
log_and_print(Fore.BLUE, "Starting modify vector store test...")
if not DEMO_VECTOR_STORE_ID:
log_and_print(Fore.YELLOW, "Skipping modify vector store test - no vector store ID available",
level=logging.WARNING)
return
try:
updated_vector_store = client.vector_stores.update(
vector_store_id=DEMO_VECTOR_STORE_ID,
name="Updated Support FAQ FJA",
)
# Check instead of assert
if updated_vector_store.name != "Updated Support FAQ FJA":
log_and_print(Fore.YELLOW, "FAILED: Vector store name was not updated correctly", level=logging.WARNING)
else:
log_and_print(Fore.GREEN, "Modify vector store test passed!")
updated_vector_store_data = updated_vector_store.to_dict()
log_and_print(Fore.WHITE, f"vector_stores.modify = {json.dumps(updated_vector_store_data, indent=2)}")
with open(f'openai_testing/{prefix}_vector_store_modify.json', 'w') as f:
json.dump(updated_vector_store_data, f, indent=2)
except Exception as e:
log_and_print(Fore.RED, f"Modify vector store test failed: {e}", level=logging.ERROR)
logging.error(traceback.format_exc())
def test_delete_vector_store():
"""
Test deleting a vector store.
"""
log_and_print(Fore.BLUE, "Starting delete vector store test...")
if not DEMO_VECTOR_STORE_ID2:
log_and_print(Fore.YELLOW, "Skipping delete vector store test - no second vector store ID available",
level=logging.WARNING)
return
try:
response = client.vector_stores.delete(
vector_store_id=DEMO_VECTOR_STORE_ID2,
)
log_and_print(Fore.GREEN, "Delete vector store test passed!")
response_data = response.to_dict()
log_and_print(Fore.WHITE, f"Vector store delete response = {json.dumps(response_data, indent=2)}")
with open(f'openai_testing/{prefix}_vector_store_delete.json', 'w') as f:
json.dump(response_data, f, indent=2)
except Exception as e:
log_and_print(Fore.RED, f"Delete vector store test failed: {e}", level=logging.ERROR)
logging.error(traceback.format_exc())
def test_create_vector_store_file():
log_and_print(Fore.BLUE, "Starting create vector store file test...")
if not DEMO_VECTOR_STORE_ID:
log_and_print(Fore.YELLOW, "Skipping create vector store file test - no vector store ID available",
level=logging.WARNING)
return
try:
# create jsonl of files as an example
with open("mydata.jsonl", "w") as f:
f.write('{"text": "What is the return policy?", "metadata": {"category": "support"}}\n')
f.write('{"text": "How do I reset my password?", "metadata": {"category": "support"}}\n')
f.write('{"text": "Where can I find my order history?", "metadata": {"category": "support"}}\n')
f.write('{"text": "What are the shipping options?", "metadata": {"category": "support"}}\n')
f.write('{"text": "What is your favorite banana?", "metadata": {"category": "support"}}\n')
# Create a simple text file if my_data_small.txt doesn't exist
if not os.path.exists("my_data_small.txt"):
with open("my_data_small.txt", "w") as f:
f.write("This is a test file for vector store testing.\n")
created_file = client.files.create(
file=open("my_data_small.txt", "rb"),
purpose="assistants",
)
created_file_data = created_file.to_dict()
log_and_print(Fore.WHITE, f"Created file {json.dumps(created_file_data, indent=2)}")
with open(f'openai_testing/{prefix}_file_create.json', 'w') as f:
json.dump(created_file_data, f, indent=2)
retrieved_files = client.files.retrieve(created_file.id)
retrieved_files_data = retrieved_files.to_dict()
log_and_print(Fore.WHITE, f"Retrieved file {json.dumps(retrieved_files_data, indent=2)}")
with open(f'openai_testing/{prefix}_file_retrieve.json', 'w') as f:
json.dump(retrieved_files_data, f, indent=2)
vector_store_file = client.vector_stores.files.create(
vector_store_id=DEMO_VECTOR_STORE_ID,
file_id=created_file.id,
)
log_and_print(Fore.GREEN, "Create vector store file test passed!")
except Exception as e:
log_and_print(Fore.RED, f"Create vector store file test failed: {e}", level=logging.ERROR)
logging.error(traceback.format_exc())
def test_search_vector_store():
"""
Test searching a vector store.
"""
log_and_print(Fore.BLUE, "Starting search vector store test...")
if not DEMO_VECTOR_STORE_ID:
log_and_print(Fore.YELLOW, "Skipping search vector store test - no vector store ID available",
level=logging.WARNING)
return
try:
query = "What is the banana policy?"
search_results = client.vector_stores.search(
vector_store_id=DEMO_VECTOR_STORE_ID,
query=query,
max_num_results=10,
ranking_options={
'ranker': 'default-2024-11-15',
'score_threshold': 0.0,
},
rewrite_query=False,
)
# Check instead of assert
if not isinstance(search_results, pagination.SyncPage):
log_and_print(Fore.YELLOW, f"FAILED: Expected a list of search results, got {type(search_results)}",
level=logging.WARNING)
else:
log_and_print(Fore.GREEN, "Search vector store test passed!")
search_results_dict = search_results.to_dict()
log_and_print(Fore.WHITE, f"Search results = {search_results_dict}")
with open(f'openai_testing/{prefix}_vector_store_search.json', 'w') as f:
json.dump(search_results_dict, f, indent=2)
log_and_print(Fore.WHITE, f"vector_stores.search = {search_results.to_json()}")
except Exception as e:
log_and_print(Fore.RED, f"Search vector store test failed: {e}", level=logging.ERROR)
logging.error(traceback.format_exc())
# Run all tests in sequence, even if some fail
test_results = []
try:
result = test_idempotent_vector_store_creation()
if result and len(result) == 2:
DEMO_VECTOR_STORE_ID, DEMO_VECTOR_STORE_ID2 = result
test_results.append(True)
except Exception as e:
log_and_print(Fore.RED, f"Vector store creation test failed: {e}", level=logging.ERROR)
logging.error(traceback.format_exc())
test_results.append(False)
for test_func in [
test_vector_store_list,
test_retrieve_vector_store,
test_modify_vector_store,
test_delete_vector_store,
test_create_vector_store_file,
test_search_vector_store
]:
try:
test_func()
test_results.append(True)
except Exception as e:
log_and_print(Fore.RED, f"{test_func.__name__} failed: {e}", level=logging.ERROR)
logging.error(traceback.format_exc())
test_results.append(False)
if all(test_results):
log_and_print(Fore.GREEN, f"All {prefix} tests completed successfully!")
else:
failed_count = test_results.count(False)
log_and_print(Fore.YELLOW, f"{failed_count} {prefix} test(s) failed, but script completed.")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Run OpenAI and/or LlamaStack tests.")
parser.add_argument(
"--provider",
type=str,
default="llama",
choices=["openai", "llama", "both"],
help="Specify which environment to test: openai, llama, or both. Default is both.",
)
args = parser.parse_args()
try:
if args.provider in ("openai", "both"):
openai_client = OpenAI()
run_tests(openai_client, prefix="openai")
if args.provider in ("llama", "both"):
llama_client = OpenAI(base_url="http://localhost:8321/v1/openai/v1", api_key="none")
run_tests(llama_client, prefix="llama")
log_and_print(Fore.GREEN, "All tests completed!")
except Exception as e:
log_and_print(Fore.RED, f"Tests failed to complete: {e}", level=logging.ERROR)
logging.error(traceback.format_exc())
```
</details>
---------
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
- fireworks, together do not support Llama-guard 3 8b model anymore
- Need to default to ollama
- current safety shields logic was not correct since the shield_id was
the provider ( which had duplicates )
- Followed similar logic to models
Note: Seems a bit over-engineered but this can now be extended to other
providers and fits in the overall mechanism of how env_vars are used to
manage starter.
### How to test
```
ENABLE_OLLAMA=ollama ENABLE_FIREWORKS=fireworks SAFETY_MODEL=llama-guard3:1b pytest -s -v tests/integration/ --stack-config starter -k 'not(supervised_fine_tune or builtin_tool_code or safety_with_image or code_interpreter_for or rag_and_code or truncation or register_and_unregister)' --text-model fireworks/meta-llama/Llama-3.3-70B-Instruct --vision-model fireworks/meta-llama/Llama-4-Scout-17B-16E-Instruct --safety-shield llama-guard3:1b --embedding-model all-MiniLM-L6-v2
```
### Related but not obvious in this PR
In the llama-stack-ops repo, we run tests before publishing packages and
docker containers.
The actions in that repo were using the fireworks / together distros (
which are non-existent )
So need to update that to run with `starter` and use `ollama`
specifically for safety.
# What does this PR do?
Update the shield register validation of Sambanova not to raise, but
only warn when a model is not available in the base url endpoint used,
also added warnings when model is not available in the base url endpoint
used
<!-- If resolving an issue, uncomment and update the line below -->
<!-- Closes #[issue-number] -->
## Test Plan
<!-- Describe the tests you ran to verify your changes with result
summaries. *Provide clear instructions so the plan can be easily
re-executed.* -->
run starter distro with Sambanova enabled
# What does this PR do?
previously, developers who ran `./scripts/unit-tests.sh` would get
`asyncio-mode=auto`, which meant `@pytest.mark.asyncio` and
`@pytest_asyncio.fixture` were redundent. developers who ran `pytest`
directly would get pytest's default (strict mode), would run into errors
leading them to add `@pytest.mark.asyncio` / `@pytest_asyncio.fixture`
to their code.
with this change -
- `asyncio_mode=auto` is included in `pyproject.toml` making behavior
consistent for all invocations of pytest
- removes all redundant `@pytest_asyncio.fixture` and
`@pytest.mark.asyncio`
- for good measure, requires `pytest>=8.4` and `pytest-asyncio>=1.0`
## Test Plan
- `./scripts/unit-tests.sh`
- `uv run pytest tests/unit`
# What does this PR do?
The current authorized sql store implementation does not respect
user.principal (only checks attributes). This PR addresses that.
## Test Plan
Added test cases to integration tests.
