Extracts common OpenAI vector-store code into its own mixin so that all
providers can share the same core logic.
This also makes it easy for Llama Stack to support both vector-stores
and Llama Stack APIs in the interim so that both share the same
underlying vector-dbs.
Each provider contains storage specific logic to `create / edit / delete
/ list` vector dbs while the plumbing logic is standardized in the
common code.
Ensured that this works well with both faiss and sqllite-vec.
### Test Plan
```
llama stack run starter
pytest -sv --stack-config http://localhost:8321 tests/integration/vector_io/test_openai_vector_stores.py --embedding-model all-MiniLM-L6-v2
```
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?
Fixes provider pgvector `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="pgvector",
)
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")
```
# What does this PR do?
Adds try-catch to faiss `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 appends `(1.0
/ sys.float_info.min)` to `scores` to 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="faiss",
)
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")
```
### Running unit tests
`uv run pytest tests/unit/rag/test_rag_query.py -v`
---------
Signed-off-by: Ben Browning <bbrownin@redhat.com>
Co-authored-by: Ben Browning <bbrownin@redhat.com>
# What does this PR do?
<!-- Provide a short summary of what this PR does and why. Link to
relevant issues if applicable. -->
To add health status check for remote VLLM
<!-- 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.* -->
PR includes the unit test to test the added health check implementation
feature.
The non-streaming version is just a small layer on top of the streaming
version - just pluck off the final `response.completed` event and return
that as the response!
This PR also includes a couple other changes which I ended up making
while working on it on a flight:
- changes to `ollama` so it does not pull embedding models
unconditionally
- a small fix to library client to make the stream and non-stream cases
a bit more symmetric
This PR fixes a runtime import error caused by missing OpenTelemetry
dependencies during `llama stack run`.
Specifically, the following imports fail if `opentelemetry-sdk` and
`opentelemetry-exporter-otlp-proto-http` are not present in the
environment:
```python
from opentelemetry import metrics, trace
from opentelemetry.exporter.otlp.proto.http.metric_exporter import OTLPMetricExporter
```
See
[llama\_stack/providers/inline/telemetry/meta\_reference/telemetry.py#L10-L19](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/inline/telemetry/meta_reference/telemetry.py#L10-L19)
This PR resolves the issue by adding both packages to the
`SERVER_DEPENDENCIES` list:
```python
"opentelemetry-sdk",
"opentelemetry-exporter-otlp-proto-http",
```
### Reproduction Steps
```bash
llama stack build --config llama.yaml --image-type venv --image-name fun-with-lamas
llama stack run ~/.llama/distributions/fun-with-lamas/fun-with-lamas-run.yaml
```
Results in:
```
ModuleNotFoundError: No module named 'opentelemetry'
```
or
```
ModuleNotFoundError: No module named 'opentelemetry.exporter'
```
Signed-off-by: Jose Angel Morena <jmorenas@redhat.com>
Co-authored-by: raghotham <rsm@meta.com>
This allows a set of rules to be defined for determining access to
resources. The rules are (loosely) based on the cedar policy format.
A rule defines a list of action either to permit or to forbid. It may
specify a principal or a resource that must match for the rule to take
effect. It may also specify a condition, either a 'when' or an 'unless',
with additional constraints as to where the rule applies.
A list of rules is held for each type to be protected and tried in order
to find a match. If a match is found, the request is permitted or
forbidden depening on the type of rule. If no match is found, the
request is denied. If no rules are specified for a given type, a rule
that allows any action as long as the resource attributes match the user
attributes is added (i.e. the previous behaviour is the default.
Some examples in yaml:
```
model:
- permit:
principal: user-1
actions: [create, read, delete]
comment: user-1 has full access to all models
- permit:
principal: user-2
actions: [read]
resource: model-1
comment: user-2 has read access to model-1 only
- permit:
actions: [read]
when:
user_in: resource.namespaces
comment: any user has read access to models with matching attributes
vector_db:
- forbid:
actions: [create, read, delete]
unless:
user_in: role::admin
comment: only user with admin role can use vector_db resources
```
---------
Signed-off-by: Gordon Sim <gsim@redhat.com>
# What does this PR do?
This adds the missing `text` parameter to the Responses API that is how
users control structured outputs. All we do with that parameter is map
it to the corresponding chat completion response_format.
## Test Plan
The new unit tests exercise the various permutations allowed for this
property, while a couple of new verification tests actually use it for
real to verify the model outputs are following the format as expected.
Unit tests:
`python -m pytest -s -v
tests/unit/providers/agents/meta_reference/test_openai_responses.py`
Verification tests:
```
llama stack run llama_stack/templates/together/run.yaml
pytest -s -vv 'tests/verifications/openai_api/test_responses.py' \
--base-url=http://localhost:8321/v1/openai/v1 \
--model meta-llama/Llama-4-Scout-17B-16E-Instruct
```
Note that the verification tests can only be run with a real Llama Stack
server (as opposed to using the library client via
`--provider=stack:together`) because the Llama Stack python client is
not yet updated to accept this text field.
Signed-off-by: Ben Browning <bbrownin@redhat.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 a bug where running a known template by name using:
`llama stack run ollama`
would fail with the following error:
`ValueError: Config file ollama does not exist`
<!-- If resolving an issue, uncomment and update the line below -->
Closes#2291
## 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.* -->
`llama stack run ollama` should work
# 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?
The chat completion ids generated by Ollama are not unique enough to use
with stored chat completions as they rely on only 3 numbers of
randomness to give unique values - ie `chatcmpl-373`. This causes
frequent collisions in id values of chat completions in Ollama, which
creates issues in our SQL storage of chat completions by id where it
expects ids to actually be unique.
So, this adjusts Ollama responses to use uuids as unique ids. This does
mean we're replacing the ids generated natively by Ollama. If we don't
wish to do this, we'll either need to relax the unique constraint on our
chat completions id field in the inference storage or convince Ollama
upstream to use something closer to uuid values here.
Closes#2315
## Test Plan
I tested by running the openai completion / chat completion integration
tests in a loop. Without this change, I regularly get unique id
collisions. With this change, I do not. We sometimes see flakes from
these unique id collisions in our CI tests, and this will resolve those.
```
INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct" \
llama stack run llama_stack/templates/ollama/run.yaml
while true; do; \
INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct" \
pytest -s -v \
tests/integration/inference/test_openai_completion.py \
--stack-config=http://localhost:8321 \
--text-model="meta-llama/Llama-3.2-3B-Instruct"; \
done
```
Signed-off-by: Ben Browning <bbrownin@redhat.com>
I think the implementation needs more simplification. Spent way too much
time trying to get the tests pass with models not co-operating :(
Finally had to switch claude-sonnet to get things to pass reliably.
### Test Plan
```
export TAVILY_SEARCH_API_KEY=...
export OPENAI_API_KEY=...
uv run pytest -p no:warnings \
-s -v tests/verifications/openai_api/test_responses.py \
--provider=stack:starter \
--model openai/gpt-4o
```
# What does this PR do?
1. remove openai dep
2. temporarily update llama-stack-client to stainless sync'd branch as
the responses/inputitems API wasn't included in the last push. This will
automatically be updated to the next version in the release.
## Test Plan
npm run dev
go to any responses details page
fixes provider to use stream var correctly
Before
```
curl --request POST \
--url http://localhost:8321/v1/openai/v1/chat/completions \
--header 'content-type: application/json' \
--data '{
"model": "meta-llama/Llama-4-Scout-17B-16E-Instruct",
"messages": [
{
"role": "user",
"content": "Who are you?"
}
]
}'
{"detail":"Internal server error: An unexpected error occurred."}
```
After
```
llama-stack % curl --request POST \
--url http://localhost:8321/v1/openai/v1/chat/completions \
--header 'content-type: application/json' \
--data '{
"model": "accounts/fireworks/models/llama4-scout-instruct-basic",
"messages": [
{
"role": "user",
"content": "Who are you?"
}
]
}'
{"id":"chatcmpl-97978538-271d-4c73-8d4d-c509bfb6c87e","choices":[{"message":{"role":"assistant","content":"I'm an AI assistant designed by Meta. I'm here to answer your questions, share interesting ideas and maybe even surprise you with a fresh perspective. What's on your mind?","name":null,"tool_calls":null},"finish_reason":"stop","index":0,"logprobs":null}],"object":"chat.completion","created":1748896403,"model":"accounts/fireworks/models/llama4-scout-instruct-basic"}%
```
# What does this PR do?
[Provide a short summary of what this PR does and why. Link to relevant
issues if applicable.]
Removes the ability to run llama stack container images through the
llama stack CLI
Closes#2110
## 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:
```
llama stack run /path/to/run.yaml --image-type container
```
Expected outcome:
```
llama stack run: error: argument --image-type: invalid choice: 'container' (choose from 'conda', 'venv')
```
[//]: # (## Documentation)
# 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?
This adds a check to ensure we don't attempt to concatenate `None + str`
or `str + None` when building up our arguments for streaming tool calls
in the Responses API.
## Test Plan
All existing tests pass with this change.
Unit tests:
```
python -m pytest -s -v \
tests/unit/providers/agents/meta_reference/test_openai_responses.py
```
Integration tests:
```
llama stack run llama_stack/templates/together/run.yaml
LLAMA_STACK_CONFIG=http://localhost:8321 \
python -m pytest -s -v \
tests/integration/agents/test_openai_responses.py \
--text-model meta-llama/Llama-4-Scout-17B-16E-Instruct
```
Verification tests:
```
llama stack run llama_stack/templates/together/run.yaml
pytest -s -v 'tests/verifications/openai_api/test_responses.py' \
--base-url=http://localhost:8321/v1/openai/v1 \
--model meta-llama/Llama-4-Scout-17B-16E-Instruct
```
Additionally, the manual example using Codex CLI from #2325 now succeeds
instead of throwing a 500 error.
Closes#2325
Signed-off-by: Ben Browning <bbrownin@redhat.com>
# 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?
Updates sambanova inference to use strict as false in json_schema
structured output
## Test Plan
pytest -s -v tests/integration/inference/test_text_inference.py
--stack-config=sambanova
--text-model=sambanova/Meta-Llama-3.3-70B-Instruct
We must store the full (re-hydrated) input not just the original input
in the Response object. Of course, this is not very space efficient and
we should likely find a better storage scheme so that we can only store
unique entries in the database and then re-hydrate them efficiently
later. But that can be done safely later.
Closes https://github.com/meta-llama/llama-stack/issues/2299
## Test Plan
Unit test
# What does this PR do?
Previously prompt guard was hard coded to require cuda which prevented
it from being used on an instance without a cuda support.
This PR allows prompt guard to be configured to use either cpu or cuda.
[//]: # (If resolving an issue, uncomment and update the line below)
Closes [#2133](https://github.com/meta-llama/llama-stack/issues/2133)
## Test Plan (Edited after incorporating suggestion)
1) started stack configured with prompt guard as follows on a system
without a GPU
and validated prompt guard could be used through the APIs
2) validated on a system with a gpu (but without llama stack) that the
python selecting between cpu and cuda support returned the right value
when a cuda device was available.
3) ran the unit tests as per -
https://github.com/meta-llama/llama-stack/blob/main/tests/unit/README.md
[//]: # (## Documentation)
---------
Signed-off-by: Michael Dawson <mdawson@devrus.com>
# What does this PR do?
Use a more common pattern and known terminology from the ecosystem,
where Route is more approved than Endpoint.
Signed-off-by: Sébastien Han <seb@redhat.com>
# 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
This adds initial streaming support to the Responses API.
This PR makes sure that the _first_ inference call made to chat
completions streams out.
There's more to be done:
- tool call output tokens need to stream out when possible
- we need to loop through multiple rounds of inference and they all need
to stream out.
## Test Plan
Added a test. Executed as:
```
FIREWORKS_API_KEY=... \
pytest -s -v 'tests/verifications/openai_api/test_responses.py' \
--provider=stack:fireworks --model meta-llama/Llama-4-Scout-17B-16E-Instruct
```
Then, started a llama stack fireworks distro and tested against it like
this:
```
OPENAI_API_KEY=blah \
pytest -s -v 'tests/verifications/openai_api/test_responses.py' \
--base-url http://localhost:8321/v1/openai/v1 \
--model meta-llama/Llama-4-Scout-17B-16E-Instruct
```
# What does this PR do?
Handles the case where the vllm config `tls_verify` is set to `false` or
`true`.
Closes: https://github.com/meta-llama/llama-stack/issues/2283
Signed-off-by: Sébastien Han <seb@redhat.com>
# What does this PR do?
It's not used anywhere in the build process. Ancient artifact from an
old attempt of using sub packages to build distros.
## 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.* -->
N/A
Signed-off-by: Sébastien Han <seb@redhat.com>
# What does this PR do?
Fixes an issue where running `llama stack build --template ollama
--image-type venv --run` fails with a TypeError when validating external
providers directory paths.
The error occurs because `os.path.exists()` is called with `Path(None)`
instead of converting it to a string first. This change ensures
consistent handling of `None` values for `external_providers_dir` across
both build and
[run](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/cli/stack/run.py#L134)
commands by using `str()` conversion before path validation.
[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])
## Test Plan
```bash
INFERENCE_MODEL=llama3.2:3b uv run --with llama-stack llama stack build --template ollama --image-type venv --run
```
Command completes successfully without TypeError
[//]: # (## Documentation)
Two somewhat annoying fixes:
- we are going to index tools for non-MCP toolgroups always (like we
used to do). because there are just random assumptions in our tests,
etc. and I don't want to fix them right now
- we need to handle the funny case of toolgroups like
`builtin::rag/knowledge_search` where we added the tool name to use in
the toolgroup itself.
# What does this PR do?
The `tls_verify` can now receive a path to a certificate file if the
endpoint requires it.
Signed-off-by: Sébastien Han <seb@redhat.com>
