# What does this PR do?
* Removes a bunch of distros
* Removed distros were added into the "starter" distribution
* Doc for "starter" has been added
* Partially reverts https://github.com/meta-llama/llama-stack/pull/2482
since inference providers are disabled by default and can be turned on
manually via env variable.
* Disables safety in starter distro
Closes: https://github.com/meta-llama/llama-stack/issues/2502.
~Needs: https://github.com/meta-llama/llama-stack/pull/2482 for Ollama
to work properly in the CI.~
TODO:
- [ ] We can only update `install.sh` when we get a new release.
- [x] Update providers documentation
- [ ] Update notebooks to reference starter instead of ollama
Signed-off-by: Sébastien Han <seb@redhat.com>
# What does this PR do?
### Summary
This pull request implements support for the OpenAI Vector Store Files
API for the Milvus vector store provider in `llama_stack`. It enables
storing, loading, updating, and deleting file metadata and file contents
in Milvus collections, allowing OpenAI vector store files to be managed
directly within Milvus.
### Main Changes
- **Milvus Vector Store Files API Implementation**
- Implements all required methods for storing, loading, updating, and
deleting vector store file metadata and contents
(`_save_openai_vector_store_file`, `_load_openai_vector_store_file`,
`_load_openai_vector_store_file_contents`,
`_update_openai_vector_store_file`,
`_delete_openai_vector_store_file_from_storage`).
- Uses two Milvus collections: `openai_vector_store_files` (for
metadata) and `openai_vector_store_files_contents` (for chunked file
contents).
- Collections are created dynamically if they do not exist, with
appropriate schema definitions.
- **Collection Name Sanitization**
- Adds a `sanitize_collection_name` utility to ensure Milvus collection
names only contain valid characters (letters, numbers, underscores).
- **Testing**
- Updates test skip logic to include `"inline::milvus"` for cases where
the OpenAI Vector Store Files API is not supported, improving
integration test accuracy.
- **Other Improvements**
- Passes `kvstore` to `MilvusIndex` for consistency.
- Removes obsolete NotImplementedErrors and legacy code for file
storage.
## Test Plan
CI and tested via a test script
## Notes
- `VectorDB` currently uses the `name` as the `identifier` in
`openai_create_vector_store`. We need to add `name` as a field to
`VectorDB` and generate the `identifier` upon creation. OpenAI is not
idempotent with respect to the `name` field that they pass (i.e., you
can pass the same name multiple times and OpenAI will generate a new
identifier). I'll add a follow up PR for this.
- The `Files` api needs to use `files-` as a prefix in the identifier. I
have updated the Vector Store to use the OpenAI prefix `vs_*`.
---------
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
# What does this PR do?
* Use #2580 functionality to auto-start the server with the tests
* Reduce timeout to 30sec
* Print server logs on errors
* Pytest logs are collected to a file pytest.log
Signed-off-by: Sébastien Han <seb@redhat.com>
## Summary
Add support for `server:<config>` format in `--stack-config` option to
enable seamless one-step integration testing. This eliminates the need
to manually start servers in separate terminals before running tests.
## Key Features
- **Auto-start server**: Automatically launches `llama stack run
<config>` if target port is available
- **Smart reuse**: Reuses existing server if port is already occupied
- **Health check polling**: Waits up to 2 minutes for server readiness
via `/v1/health` endpoint
- **Custom port support**: Use `server:<config>:<port>` for non-default
ports
- **Clean output**: Server runs quietly in background without cluttering
test output
- **Backward compatibility**: All existing `--stack-config` formats
continue to work
## Usage Examples
```bash
# Auto-start server with default port 8321
pytest tests/integration/inference/ --stack-config=server:fireworks
# Use custom port
pytest tests/integration/safety/ --stack-config=server:together:8322
# Run multiple test suites seamlessly
pytest tests/integration/inference/ tests/integration/agents/ --stack-config=server:starter
```
## Implementation Details
- Enhanced `llama_stack_client` fixture with server management
- Updated documentation with cleaner organization and comprehensive
examples
- Added utility functions for port checking, server startup, and health
verification
## Test Plan
- Verified server auto-start when port 8321 is available
- Verified server reuse when port 8321 is occupied
- Tested health check polling via `/v1/health` endpoint
- Confirmed custom port configuration works correctly
- Verified backward compatibility with existing config formats
## Before/After Comparison
**Before (2 steps):**
```bash
# Terminal 1: Start server manually
llama stack run fireworks --port 8321
# Terminal 2: Wait for startup, then run tests
pytest tests/integration/inference/ --stack-config=http://localhost:8321
```
**After (1 step):**
```bash
# Single command handles everything
pytest tests/integration/inference/ --stack-config=server:fireworks
```
# What does this PR do?
This PR creates a webmethod for deleting open AI responses, adds and
implementation for it and makes an integration test for the OpenAI
delete response method.
[//]: # (If resolving an issue, uncomment and update the line below)
# (Closes#2077)
## Test Plan
Ran the standard tests and the pre-commit hooks and the unit tests.
# (## Documentation)
For this pr I made the routes and implementation based on the current
get and create methods. The unit tests were not able to handle this test
due to the mock interface in use, which did not allow for effective CRUD
to be tested. I instead created an integration test to match the
existing ones in the test_openai_responses.
# What does this PR do?
Closes https://github.com/meta-llama/llama-stack/issues/2461
## Test Plan
Tested with the `ollama` distriubtion template and updated the vector_io
provider to:
```yaml
vector_io:
- provider_id: milvus
provider_type: inline::milvus
config:
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ollama}/milvus_store.db
kvstore:
type: sqlite
db_name: milvus_registry.db
```
Ran the stack
```bash
llama stack run ./llama_stack/templates/ollama/run.yaml --image-type venv --env OLLAMA_URL="http://0.0.0.0:11434"
```
Ran the tests:
```
pytest -sv --stack-config=http://localhost:8321 tests/integration/vector_io/test_openai_vector_stores.py --embedding-model all-MiniLM-L6-v2
```
Output passed.
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
# What does this PR do?
closes#2522
## Test Plan
added integration test
LLAMA_STACK_CONFIG=http://localhost:8321 pytest -v
tests/integration/agents/test_openai_responses.py --text-model
"accounts/fireworks/models/llama-v3p3-70b-instruct" -vv -k
'function_call'
# What does this PR do?
Add search_mode parameter (vector/keyword/hybrid) to
openai_search_vector_store method. Fixes OpenAPI
code generation by using str instead of Literal type.
Closes: #2459
## 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>
# What does this PR do?
This adds the ability to list, retrieve, update, and delete Vector Store
Files. It implements these new APIs for the faiss and sqlite-vec
providers, since those are the two that also have the rest of the vector
store files implementation.
Closes#2445
## Test Plan
### test_openai_vector_stores Integration Tests
There are a number of new integration tests added, which I ran for each
provider as outlined below.
faiss (from ollama distro):
```
INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct" \
llama stack run llama_stack/templates/ollama/run.yaml
LLAMA_STACK_CONFIG=http://localhost:8321 \
pytest -sv tests/integration/vector_io/test_openai_vector_stores.py \
--embedding-model=all-MiniLM-L6-v2
```
sqlite-vec (from starter distro):
```
llama stack run llama_stack/templates/starter/run.yaml
LLAMA_STACK_CONFIG=http://localhost:8321 \
pytest -sv tests/integration/vector_io/test_openai_vector_stores.py \
--embedding-model=all-MiniLM-L6-v2
```
### file_search verification tests
I also ensured the file_search verification tests continue to work, both
for faiss and sqlite-vec.
faiss (ollama distro):
```
INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct" \
llama stack run llama_stack/templates/ollama/run.yaml
pytest -sv tests/verifications/openai_api/test_responses.py \
-k'file_search' \
--base-url=http://localhost:8321/v1/openai/v1 \
--model=meta-llama/Llama-3.2-3B-Instruct
```
sqlite-vec (starter distro):
```
llama stack run llama_stack/templates/starter/run.yaml
pytest -sv tests/verifications/openai_api/test_responses.py \
-k'file_search' \
--base-url=http://localhost:8321/v1/openai/v1 \
--model=together/meta-llama/Llama-3.2-3B-Instruct-Turbo
```
---------
Signed-off-by: Ben Browning <bbrownin@redhat.com>
# What does this PR do?
Do not force 384 for the embedding dimension, use the one provided by
the test run.
## Test Plan
```
pytest -s -vvv tests/integration/vector_io/test_vector_io.py --stack-config=http://localhost:8321 \
-k "not(builtin_tool or safety_with_image or code_interpreter or test_rag)" \
--text-model="meta-llama/Llama-3.2-3B-Instruct" \
--embedding-model=granite-embedding-125m --embedding-dimension=768
Uninstalled 1 package in 16ms
Installed 1 package in 11ms
INFO 2025-06-18 10:52:03,314 tests.integration.conftest:59 tests: Setting DISABLE_CODE_SANDBOX=1 for macOS
/Users/leseb/Documents/AI/llama-stack/.venv/lib/python3.10/site-packages/pytest_asyncio/plugin.py:207: PytestDeprecationWarning: The configuration option "asyncio_default_fixture_loop_scope" is unset.
The event loop scope for asynchronous fixtures will default to the fixture caching scope. Future versions of pytest-asyncio will default the loop scope for asynchronous fixtures to function scope. Set the default fixture loop scope explicitly in order to avoid unexpected behavior in the future. Valid fixture loop scopes are: "function", "class", "module", "package", "session"
warnings.warn(PytestDeprecationWarning(_DEFAULT_FIXTURE_LOOP_SCOPE_UNSET))
================================================= test session starts =================================================
platform darwin -- Python 3.10.16, pytest-8.3.4, pluggy-1.5.0 -- /Users/leseb/Documents/AI/llama-stack/.venv/bin/python
cachedir: .pytest_cache
metadata: {'Python': '3.10.16', 'Platform': 'macOS-15.5-arm64-arm-64bit', 'Packages': {'pytest': '8.3.4', 'pluggy': '1.5.0'}, 'Plugins': {'cov': '6.0.0', 'html': '4.1.1', 'json-report': '1.5.0', 'timeout': '2.4.0', 'metadata': '3.1.1', 'asyncio': '0.25.3', 'anyio': '4.8.0', 'nbval': '0.11.0'}}
rootdir: /Users/leseb/Documents/AI/llama-stack
configfile: pyproject.toml
plugins: cov-6.0.0, html-4.1.1, json-report-1.5.0, timeout-2.4.0, metadata-3.1.1, asyncio-0.25.3, anyio-4.8.0, nbval-0.11.0
asyncio: mode=strict, asyncio_default_fixture_loop_scope=None
collected 8 items
tests/integration/vector_io/test_vector_io.py::test_vector_db_retrieve[emb=granite-embedding-125m:dim=768] PASSED
tests/integration/vector_io/test_vector_io.py::test_vector_db_register[emb=granite-embedding-125m:dim=768] PASSED
tests/integration/vector_io/test_vector_io.py::test_insert_chunks[emb=granite-embedding-125m:dim=768-test_case0] PASSED
tests/integration/vector_io/test_vector_io.py::test_insert_chunks[emb=granite-embedding-125m:dim=768-test_case1] PASSED
tests/integration/vector_io/test_vector_io.py::test_insert_chunks[emb=granite-embedding-125m:dim=768-test_case2] PASSED
tests/integration/vector_io/test_vector_io.py::test_insert_chunks[emb=granite-embedding-125m:dim=768-test_case3] PASSED
tests/integration/vector_io/test_vector_io.py::test_insert_chunks[emb=granite-embedding-125m:dim=768-test_case4] PASSED
tests/integration/vector_io/test_vector_io.py::test_insert_chunks_with_precomputed_embeddings[emb=granite-embedding-125m:dim=768] PASSED
================================================== 8 passed in 5.50s ==================================================
```
Signed-off-by: Sébastien Han <seb@redhat.com>
For code completion apps need "fill in the middle" capabilities.
Added option of `suffix` to `openai_completion` to enable this.
Updated ollama provider to showcase the same.
### Test Plan
```
pytest -sv --stack-config="inference=ollama" tests/integration/inference/test_openai_completion.py --text-model qwen2.5-coder:1.5b -k test_openai_completion_non_streaming_suffix
```
### OpenAI Sample script
```
from openai import OpenAI
client = OpenAI(base_url="http://localhost:8321/v1/openai/v1")
response = client.completions.create(
model="qwen2.5-coder:1.5b",
prompt="The capital of ",
suffix="is Paris.",
max_tokens=10,
)
print(response.choices[0].text)
```
### Output
```
France is ____.
To answer this question, we
```
# What does this PR do?
This PR adds OpenAI compatibility for Ollama embeddings. Closes
https://github.com/meta-llama/llama-stack/issues/2428
Summary of changes:
- `llama_stack/providers/remote/inference/ollama/ollama.py`
- Implements the OpenAI embeddings endpoint for Ollama, replacing the
NotImplementedError with a full function that validates the model,
prepares parameters, calls the client, encodes embedding data
(optionally in base64), and returns a correctly structured response.
- Updates import statements to include the new embedding response
utilities.
- `llama_stack/providers/utils/inference/litellm_openai_mixin.py`
- Refactors the embedding data encoding logic to use a new shared
utility (`b64_encode_openai_embeddings_response`) instead of inline
base64 encoding and packing logic.
- Cleans up imports accordingly.
- `llama_stack/providers/utils/inference/openai_compat.py`
- Adds `b64_encode_openai_embeddings_response` to handle encoding OpenAI
embedding outputs (including base64 support) in a reusable way.
- Adds `prepare_openai_embeddings_params` utility for standardizing
embedding parameter preparation.
- Updates imports to include the new embedding data class.
- `tests/integration/inference/test_openai_embeddings.py`
- Removes `"remote::ollama"` from the list of providers that skip OpenAI
embeddings tests, since support is now implemented.
## Note
There was one minor issue, which required me to override the
`OpenAIEmbeddingsResponse.model` name with
`self._get_model(model).identifier` name, which is very unsatisfying.
## Test Plan
Unit Tests and integration tests
---------
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
https://github.com/meta-llama/llama-stack-client-python/pull/238 updated
llama-stack-client to also support Open AI endpoints for embeddings,
files, vector-stores. This updates the test to test all configs --
openai sdk, llama stack sdk and library-as-client.
Updated the `search` functionality return response to match openai.
## Test Plan
```
pytest -sv --stack-config=http://localhost:8321 tests/integration/vector_io/test_openai_vector_stores.py --embedding-model all-MiniLM-L6-v2
```
# What does this PR do?
Fixes provider weaviate `query_vector` function for when the distance
between the query embedding and an embedding within the vector db is 0
(identical vectors). Catches `ZeroDivisionError` and then sets `score`
to infinity, which represent maximum similarity.
<!-- If resolving an issue, uncomment and update the line below -->
Closes [#2381]
## Test Plan
Checkout this PR
Execute this code and there will no longer be a `ZeroDivisionError`
exception
```
from llama_stack_client import LlamaStackClient
base_url = "http://localhost:8321"
client = LlamaStackClient(base_url=base_url)
models = client.models.list()
embedding_model = (
em := next(m for m in models if m.model_type == "embedding")
).identifier
embedding_dimension = 384
_ = client.vector_dbs.register(
vector_db_id="foo_db",
embedding_model=embedding_model,
embedding_dimension=embedding_dimension,
provider_id="weaviate",
)
chunk = {
"content": "foo",
"mime_type": "text/plain",
"metadata": {
"document_id": "foo-id"
}
}
client.vector_io.insert(vector_db_id="foo_db", chunks=[chunk])
client.vector_io.query(vector_db_id="foo_db", query="foo")
```
Adding OpenAI compat `/v1/vector-store` apis.
This PR implements the `faiss` provider with followup PRs coming up for
other providers.
Added routes to create, update, delete, list vector stores.
Also added route to search a vector store
Inserting into vector stores is missing and will be a follow up diff.
### Test Plan
- Added new integration test for testing the faiss provider
```
pytest -sv --stack-config http://localhost:8321 tests/integration/vector_io/test_openai_vector_stores.py --embedding-model all-MiniLM-L6-v2
```
# What does this PR do?
This loosens up the tool call function name and arguments checks in
`tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls`
because the small models we use in CI cannot reliably get the tool call
function name or arguments exactly right.
Closes#2345
## Test Plan
I ran this flaking test in a loop, let it run many dozens of times, and
didn't observe any flakes after the changes. Previously it flaked quite
regularly.
```
while uv run pytest -s -v \
'tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[llama_stack_client-txt=3B-False]' \
--stack-config=http://localhost:8321 \
--text-model="meta-llama/Llama-3.2-3B-Instruct" \
--embedding-model=all-MiniLM-L6-v2; do; sleep 0.1; done
```
Signed-off-by: Ben Browning <bbrownin@redhat.com>
# What does this PR do?
## Test Plan
LLAMA_STACK_CONFIG=fireworks pytest -s -v
tests/integration/files/test_files.py::test_openai_client_basic_operations
# What does this PR do?
TSIA
Added Files provider to the fireworks template. Might want to add to all
templates as a follow-up.
## Test Plan
llama-stack pytest tests/unit/files/test_files.py
llama-stack llama stack build --template fireworks --image-type conda
--run
LLAMA_STACK_CONFIG=http://localhost:8321 pytest -s -v
tests/integration/files/
# What does this PR do?
Adds a new endpoint that is compatible with OpenAI for embeddings api.
`/openai/v1/embeddings`
Added providers for OpenAI, LiteLLM and SentenceTransformer.
## Test Plan
```
LLAMA_STACK_CONFIG=http://localhost:8321 pytest -sv tests/integration/inference/test_openai_embeddings.py --embedding-model all-MiniLM-L6-v2,text-embedding-3-small,gemini/text-embedding-004
```
# What does this PR do?
* Added support postgresql inference store
* Added 'oracle' template that demos how to config postgresql stores
(except for telemetry, which is not supported currently)
## Test Plan
llama stack build --template oracle --image-type conda --run
LLAMA_STACK_CONFIG=http://localhost:8321 pytest -s -v tests/integration/
--text-model accounts/fireworks/models/llama-v3p3-70b-instruct -k
'inference_store'
# What does this PR do?
Fix a bug in openai_compat where choices are not indexed correctly.
## Test Plan
Added a new test.
Rerun the failed inference_store tests:
llama stack run fireworks --image-type conda
pytest -s -v tests/integration/ --stack-config http://localhost:8321 -k
'test_inference_store' --text-model meta-llama/Llama-3.3-70B-Instruct
--count 10
# What does this PR do?
Changed the test to not require tool_call in output, but still keeping
the tools params there as a smoke test.
## Test Plan
Used llama3.3 from fireworks (same as CI)
<img width="1433" alt="image"
src="https://github.com/user-attachments/assets/1e5fca98-9b4f-402e-a0bc-d9f910f2c207"
/>
Run with ollama distro and 3b model.
When registering a MCP endpoint, we cannot list tools (like we used to)
since the MCP endpoint may be behind an auth wall. Registration can
happen much sooner (via run.yaml).
Instead, we do listing only when the _user_ actually calls listing.
Furthermore, we cache the list in-memory in the server. Currently, the
cache is not invalidated -- we may want to periodically re-list for MCP
servers. Note that they must call `list_tools` before calling
`invoke_tool` -- we use this critically.
This will enable us to list MCP servers in run.yaml
## Test Plan
Existing tests, updated tests accordingly.
The test depends on llama's tool calling ability. In the CI, we run with
a small ollama model.
The fix might be to check for either message or function_call because
the model is flaky and we aren't really testing that behavior?
# What does this PR do?
This is not part of the official OpenAI API, but we'll use this for the
logs UI.
In order to support more filtering options, I'm adopting the newly
introduced sql store in in place of the kv store.
## Test Plan
Added integration/unit tests.
The most interesting MCP servers are those with an authorization wall in
front of them. This PR uses the existing `provider_data` mechanism of
passing provider API keys for passing MCP access tokens (in fact,
arbitrary headers in the style of the OpenAI Responses API) from the
client through to the MCP server.
```
class MCPProviderDataValidator(BaseModel):
# mcp_endpoint => list of headers to send
mcp_headers: dict[str, list[str]] | None = None
```
Note how we must stuff the headers for all MCP endpoints into a single
"MCPProviderDataValidator". Unlike existing providers (e.g., Together
and Fireworks for inference) where we could name the provider api keys
clearly (`together_api_key`, `fireworks_api_key`), we cannot name these
keys for MCP. We have a single generic MCP provider which can serve
multiple "toolgroups". So we use a dict to combine all the headers for
all MCP endpoints you may want to use in an agentic call.
## Test Plan
See the added integration test for usage.
# What does this PR do?
* Provide sqlite implementation of the APIs introduced in
https://github.com/meta-llama/llama-stack/pull/2145.
* Introduced a SqlStore API: llama_stack/providers/utils/sqlstore/api.py
and the first Sqlite implementation
* Pagination support will be added in a future PR.
## Test Plan
Unit test on sql store:
<img width="1005" alt="image"
src="https://github.com/user-attachments/assets/9b8b7ec8-632b-4667-8127-5583426b2e29"
/>
Integration test:
```
INFERENCE_MODEL="llama3.2:3b-instruct-fp16" llama stack build --template ollama --image-type conda --run
```
```
LLAMA_STACK_CONFIG=http://localhost:5001 INFERENCE_MODEL="llama3.2:3b-instruct-fp16" python -m pytest -v tests/integration/inference/test_openai_completion.py --text-model "llama3.2:3b-instruct-fp16" -k 'inference_store and openai'
```
# What does this PR do?
adds an inline HF SFTTrainer provider. Alongside touchtune -- this is a
super popular option for running training jobs. The config allows a user
to specify some key fields such as a model, chat_template, device, etc
the provider comes with one recipe `finetune_single_device` which works
both with and without LoRA.
any model that is a valid HF identifier can be given and the model will
be pulled.
this has been tested so far with CPU and MPS device types, but should be
compatible with CUDA out of the box
The provider processes the given dataset into the proper format,
establishes the various steps per epoch, steps per save, steps per eval,
sets a sane SFTConfig, and runs n_epochs of training
if checkpoint_dir is none, no model is saved. If there is a checkpoint
dir, a model is saved every `save_steps` and at the end of training.
## Test Plan
re-enabled post_training integration test suite with a singular test
that loads the simpleqa dataset:
https://huggingface.co/datasets/llamastack/simpleqa and a tiny granite
model: https://huggingface.co/ibm-granite/granite-3.3-2b-instruct. The
test now uses the llama stack client and the proper post_training API
runs one step with a batch_size of 1. This test runs on CPU on the
Ubuntu runner so it needs to be a small batch and a single step.
[//]: # (## Documentation)
---------
Signed-off-by: Charlie Doern <cdoern@redhat.com>
# What does this PR do?
## Test Plan
LLAMA_STACK_CONFIG=http://localhost:8321 pytest
tests/integration/tool_runtime/test_rag_tool.py --embedding-model
text-embedding-3-small
# What does this PR do?
This fixes an issue in how we used the tool_call_buf from streaming tool
calls in the remote-vllm provider where it would end up concatenating
parameters from multiple different tool call results instead of
aggregating the results from each tool call separately.
It also fixes an issue found while digging into that where we were
accidentally mixing the json string form of tool call parameters with
the string representation of the python form, which mean we'd end up
with single quotes in what should be double-quoted json strings.
Closes#1120
## Test Plan
The following tests are now passing 100% for the remote-vllm provider,
where some of the test_text_inference were failing before this change:
```
VLLM_URL="http://localhost:8000/v1" INFERENCE_MODEL="RedHatAI/Llama-4-Scout-17B-16E-Instruct-FP8-dynamic" LLAMA_STACK_CONFIG=remote-vllm python -m pytest -v tests/integration/inference/test_text_inference.py --text-model "RedHatAI/Llama-4-Scout-17B-16E-Instruct-FP8-dynamic"
VLLM_URL="http://localhost:8000/v1" INFERENCE_MODEL="RedHatAI/Llama-4-Scout-17B-16E-Instruct-FP8-dynamic" LLAMA_STACK_CONFIG=remote-vllm python -m pytest -v tests/integration/inference/test_vision_inference.py --vision-model "RedHatAI/Llama-4-Scout-17B-16E-Instruct-FP8-dynamic"
```
All but one of the agent tests are passing (including the multi-tool
one). See the PR at https://github.com/vllm-project/vllm/pull/17917 and
a gist at
https://gist.github.com/bbrowning/4734240ce96b4264340caa9584e47c9e for
changes needed there, which will have to get made upstream in vLLM.
Agent tests:
```
VLLM_URL="http://localhost:8000/v1" INFERENCE_MODEL="RedHatAI/Llama-4-Scout-17B-16E-Instruct-FP8-dynamic" LLAMA_STACK_CONFIG=remote-vllm python -m pytest -v tests/integration/agents/test_agents.py --text-model "RedHatAI/Llama-4-Scout-17B-16E-Instruct-FP8-dynamic"
````
---------
Signed-off-by: Ben Browning <bbrownin@redhat.com>
# What does this PR do?
his PR allows users to customize the template used for chunks when
inserted into the context. Additionally, this enables metadata injection
into the context of an LLM for RAG. This makes a naive and crude
assumption that each chunk should include the metadata, this is
obviously redundant when multiple chunks are returned from the same
document. In order to remove any sort of duplication of chunks, we'd
have to make much more significant changes so this is a reasonable first
step that unblocks users requesting this enhancement in
https://github.com/meta-llama/llama-stack/issues/1767.
In the future, this can be extended to support citations.
List of Changes:
- `llama_stack/apis/tools/rag_tool.py`
- Added `chunk_template` field in `RAGQueryConfig`.
- Added `field_validator` to validate the `chunk_template` field in
`RAGQueryConfig`.
- Ensured the `chunk_template` field includes placeholders `{index}` and
`{chunk.content}`.
- Updated the `query` method to use the `chunk_template` for formatting
chunk text content.
- `llama_stack/providers/inline/tool_runtime/rag/memory.py`
- Modified the `insert` method to pass `doc.metadata` for chunk
creation.
- Enhanced the `query` method to format results using `chunk_template`
and exclude unnecessary metadata fields like `token_count`.
- `llama_stack/providers/utils/memory/vector_store.py`
- Updated `make_overlapped_chunks` to include metadata serialization and
token count for both content and metadata.
- Added error handling for metadata serialization issues.
- `pyproject.toml`
- Added `pydantic.field_validator` as a recognized `classmethod`
decorator in the linting configuration.
- `tests/integration/tool_runtime/test_rag_tool.py`
- Refactored test assertions to separate `assert_valid_chunk_response`
and `assert_valid_text_response`.
- Added integration tests to validate `chunk_template` functionality
with and without metadata inclusion.
- Included a test case to ensure `chunk_template` validation errors are
raised appropriately.
- `tests/unit/rag/test_vector_store.py`
- Added unit tests for `make_overlapped_chunks`, verifying chunk
creation with overlapping tokens and metadata integrity.
- Added tests to handle metadata serialization errors, ensuring proper
exception handling.
- `docs/_static/llama-stack-spec.html`
- Added a new `chunk_template` field of type `string` with a default
template for formatting retrieved chunks in RAGQueryConfig.
- Updated the `required` fields to include `chunk_template`.
- `docs/_static/llama-stack-spec.yaml`
- Introduced `chunk_template` field with a default value for
RAGQueryConfig.
- Updated the required configuration list to include `chunk_template`.
- `docs/source/building_applications/rag.md`
- Documented the `chunk_template` configuration, explaining how to
customize metadata formatting in RAG queries.
- Added examples demonstrating the usage of the `chunk_template` field
in RAG tool queries.
- Highlighted default values for `RAG` agent configurations.
# Resolves https://github.com/meta-llama/llama-stack/issues/1767
## Test Plan
Updated both `test_vector_store.py` and `test_rag_tool.py` and tested
end-to-end with a script.
I also tested the quickstart to enable this and specified this metadata:
```python
document = RAGDocument(
document_id="document_1",
content=source,
mime_type="text/html",
metadata={"author": "Paul Graham", "title": "How to do great work"},
)
```
Which produced the output below:
This highlights the usefulness of the additional metadata. Notice how
the metadata is redundant for different chunks of the same document. I
think we can update that in a subsequent PR.
# Documentation
I've added a brief comment about this in the documentation to outline
this to users and updated the API documentation.
---------
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
# What does this PR do?
reduces duplication and centralizes information to be easier to find for
contributors
Signed-off-by: Nathan Weinberg <nweinber@redhat.com>
# What does this PR do?
While adding other tests, I came across this and wasn’t sure how useful
it is. It doesn’t seem to be exercised anywhere in CI, but I figured I’d
fix it anyway. Happy to remove it if preferred. :)
## Test Plan
Run:
```
uv run pytest tests/integration/inference --stack-config=ollama --report=test_report.md -v --text-model="llama3.2:3b" --embedding-model=all-MiniLM-L6-v2
```
Look at the produced `test_report.md`.
Signed-off-by: Sébastien Han <seb@redhat.com>
# What does this PR do?
switch sambanova inference adaptor to LiteLLM usage to simplify
integration and solve issues with current adaptor when streaming and
tool calling, models and templates updated
## Test Plan
pytest -s -v tests/integration/inference/test_text_inference.py
--stack-config=sambanova
--text-model=sambanova/Meta-Llama-3.3-70B-Instruct
pytest -s -v tests/integration/inference/test_vision_inference.py
--stack-config=sambanova
--vision-model=sambanova/Llama-3.2-11B-Vision-Instruct
# What does this PR do?
**Fixes** #1959
HuggingFace provides several loading paths that the datasets library can
use. My theory on why the test would previously fail intermittently is
because when calling `load_dataset(...)`, it may be trying several
options such as local cache, Hugging Face Hub, or a dataset script, or
other. There's one of these options that seem to work inconsistently in
the CI.
The HuggingFace datasets library relies on the `transformers` package to
load certain datasets such as `llamastack/simpleqa`, and by adding the
package, we can see the dataset is loaded consistently via the Hugging
Face Hub.
Please see PR in my fork demonstrating over 7 consecutive passes:
https://github.com/ChristianZaccaria/llama-stack/pull/1
**Some References:**
- https://github.com/huggingface/transformers/issues/8690
- https://huggingface.co/docs/datasets/en/loading
[//]: # (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.*]
[//]: # (## Documentation)
# What does this PR do?
We've disabled it for a while given that this hasn't worked as well as
expected given the frequent changes of llama_stack_client and how this
requires both repos to be in sync.
## Test Plan
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
# What does this PR do?
The builtin implementation of code interpreter is not robust and has a
really weak sandboxing shell (the `bubblewrap` container). Given the
availability of better MCP code interpreter servers coming up, we should
use them instead of baking an implementation into the Stack and
expanding the vulnerability surface to the rest of the Stack.
This PR only does the removal. We will add examples with how to
integrate with MCPs in subsequent ones.
## Test Plan
Existing tests.
