# What does this PR do?
Some templates were still using the old environment variable substition
syntax instead of the new one and were not getting substituted properly.
Also, some places didn't handle the new None vs old empty string ("")
values that come from the conditional environment variable substitution.
This gets the starter and remote-vllm distributions starting again, and
I tested various permutations of the starter as chroma and pgvector
needed some adjustments to their config classes to handle the new
possible `None` values. And, I had to tweak our `Provider` class to also
handle `None` values, for cases where we disable providers in the
starter config via environment variables.
This may not have caught everything that was missed, but I did grep
around quite a bit to try and find anything lingering.
## Test Plan
The following permutations now all run (or attempt to run to the point
of complaining that they can't connect to chroma, vllm, etc) when before
they failed immediately on startup because of bad environment variable
substitions:
```
uv run llama stack run llama_stack/templates/starter/run.yaml
ENABLE_SQLITE_VEC=true uv run llama stack run llama_stack/templates/starter/run.yaml
ENABLE_PGVECTOR=true uv run llama stack run llama_stack/templates/starter/run.yaml
ENABLE_CHROMADB=true uv run llama stack run llama_stack/templates/starter/run.yaml
uv run llama stack run llama_stack/templates/remote-vllm/run.yaml
```
<!-- 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: Ben Browning <bbrownin@redhat.com>
Co-authored-by: raghotham <rsm@meta.com>
# What does this PR do?
This commit significantly improves the environment variable substitution
functionality in Llama Stack configuration files:
* The version field in configuration files has been changed from string
to integer type for better type consistency across build and run
configurations.
* The environment variable substitution system for ${env.FOO:} was fixed
and properly returns an error
* The environment variable substitution system for ${env.FOO+} returns
None instead of an empty strings, it better matches type annotations in
config fields
* The system includes automatic type conversion for boolean, integer,
and float values.
* The error messages have been enhanced to provide clearer guidance when
environment variables are missing, including suggestions for using
default values or conditional syntax.
* Comprehensive documentation has been added to the configuration guide
explaining all supported syntax patterns, best practices, and runtime
override capabilities.
* Multiple provider configurations have been updated to use the new
conditional syntax for optional API keys, making the system more
flexible for different deployment scenarios. The telemetry configuration
has been improved to properly handle optional endpoints with appropriate
validation, ensuring that required endpoints are specified when their
corresponding sinks are enabled.
* There were many instances of ${env.NVIDIA_API_KEY:} that should have
caused the code to fail. However, due to a bug, the distro server was
still being started, and early validation wasn’t triggered. As a result,
failures were likely being handled downstream by the providers. I’ve
maintained similar behavior by using ${env.NVIDIA_API_KEY:+}, though I
believe this is incorrect for many configurations. I’ll leave it to each
provider to correct it as needed.
* Environment variable substitution now uses the same syntax as Bash
parameter expansion.
Signed-off-by: Sébastien Han <seb@redhat.com>
# What does this PR do?
* Given that our API packages use "import *" in `__init.py__` we don't
need to do `from llama_stack.apis.models.models` but simply from
llama_stack.apis.models. The decision to use `import *` is debatable and
should probably be revisited at one point.
* Remove unneeded Ruff F401 rule
* Consolidate Ruff F403 rule in the pyprojectfrom
llama_stack.apis.models.models
Signed-off-by: Sébastien Han <seb@redhat.com>
# What does this PR do?
Our starter distro required Ollama to be running (and a large list of
models available in that Ollama) to successfully start. This adjusts
things so that Ollama does not have to be running to use the starter
template / distro.
To accomplish this, a few changes were needed:
* The Ollama provider is now configurable whether it raises an Exception
or just logs a warning when it cannot reach the Ollama server on
startup. The default is to raise an exception (same as previous
behavior), but in the starter template we adjust this to just log a
warning so that we can bring the stack up without needing a running
Ollama server.
* The starter template no longer specifies a default list of models for
Ollama, as any models specified there need to actually be pulled and
available in Ollama. Instead, it adds a new
`OLLAMA_INFERENCE_MODEL` environment variable where users can provide an
optional model to register with the Ollama provider on startup.
Additional models can also be registered via the typical
`models.register(...)` at runtime.
* The vLLM template was adjusted to also allow an optional
`VLLM_INFERENCE_MODEL` specified on startup, so that the behavior
between vLLM and Ollama was consistent here to make it easy to get up
and running quickly.
* The default vector store was changed from sqlite-vec to faiss.
sqlite-vec can enabled via setting the `ENABLE_SQLITE_VEC` environment
variable, like we do for chromadb and pgvector. This is due to
sqlite-vec not shipping proper arm64 binaries, like we previously fixed
in #1530 for the ollama distribution.
## Test Plan
With this change, the following scenarios now work with the starter
template that did not before:
* no Ollama running
* Ollama running but not all of the Llama models pulled locally
* Ollama running with a custom model registered on startup
* vLLM running with a custom model registered on startup
* running the starter template on linux/arm64, like when running
containers on Mac without rosetta emulation
---------
Signed-off-by: Ben Browning <bbrownin@redhat.com>
feat: Add Gemini 2.0 and 2.5 models
This commit expands the set of known Gemini models by introducing:
- `gemini/gemini-2.0-flash`
- `gemini/gemini-2.5-flash`
- `gemini/gemini-2.5-pro`
These new models are added to `LLM_MODEL_IDS` for broader compatibility
and updated in `run.yaml` to allow for their immediate use in starter
configurations.
Signed-off-by: Eran Cohen <eranco@redhat.com>
# What does this PR do?
This is an initial working prototype of wiring up the `file_search`
builtin tool for the Responses API to our existing rag knowledge search
tool.
This is me seeing what I could pull together on top of the bits we
already have merged. This may not be the ideal way to implement this,
and things like how I shuffle the vector store ids from the original
response API tool request to the actual tool execution feel a bit hacky
(grep for `tool_kwargs["vector_db_ids"]` in `_execute_tool_call` to see
what I mean).
## Test Plan
I stubbed in some new tests to exercise this using text and pdf
documents.
Note that this is currently under tests/verification only because it
sometimes flakes with tool calling of the small Llama-3.2-3B model we
run in CI (and that I use as an example below). We'd want to make the
test a bit more robust in some way if we moved this over to
tests/integration and ran it in CI.
### OpenAI SaaS (to verify test correctness)
```
pytest -sv tests/verifications/openai_api/test_responses.py \
-k 'file_search' \
--base-url=https://api.openai.com/v1 \
--model=gpt-4o
```
### Fireworks with faiss vector store
```
llama stack run llama_stack/templates/fireworks/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-3.3-70B-Instruct
```
### Ollama with faiss vector store
This sometimes flakes on Ollama because the quantized small model
doesn't always choose to call the tool to answer the user's question.
But, it often works.
```
ollama run llama3.2:3b
INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct" \
llama stack run ./llama_stack/templates/ollama/run.yaml \
--image-type venv \
--env OLLAMA_URL="http://0.0.0.0:11434"
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
```
### OpenAI provider with sqlite-vec vector store
```
llama stack run ./llama_stack/templates/starter/run.yaml --image-type venv
pytest -sv tests/verifications/openai_api/test_responses.py \
-k 'file_search' \
--base-url=http://localhost:8321/v1/openai/v1 \
--model=openai/gpt-4o-mini
```
### Ensure existing vector store integration tests still pass
```
ollama run llama3.2:3b
INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct" \
llama stack run ./llama_stack/templates/ollama/run.yaml \
--image-type venv \
--env OLLAMA_URL="http://0.0.0.0:11434"
LLAMA_STACK_CONFIG=http://localhost:8321 \
pytest -sv tests/integration/vector_io \
--text-model "meta-llama/Llama-3.2-3B-Instruct" \
--embedding-model=all-MiniLM-L6-v2
```
---------
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?
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.
# 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?
It may not always be desirable to listen on all interfaces, which is the
default. As an example, by listening instead only on a loopback
interface, the server cannot be reached except from within the host it
is run on. This PR makes this configurable, through a CLI option, an env
var or an entry on the config file.
## Test Plan
I ran a server with and without the added CLI argument to verify that
the argument is used if provided, but the default is as it was before if
not.
Signed-off-by: Gordon Sim <gsim@redhat.com>
We want this to be a "flagship" distribution we can advertize to a
segment of users to get started quickly. This distro should package a
bunch of remote providers and some cheap inline providers so they get a
solid "AI Platform in a box" setup instantly.
