This brings an interesting aspect -- we need to maintain session-level
tempdir state (!) since the model was told there was some resource at a
given location that it needs to maintain
# What does this PR do?
Addresses issue (#391)
- Adds json structured output for vLLM
- Enables structured output tests for vLLM
> Give me a recipe for Spaghetti Bolognaise:
```json
{
"recipe_name": "Spaghetti Bolognaise",
"preamble": "Ah, spaghetti bolognaise - the quintessential Italian dish that fills my kitchen with the aromas of childhood nostalgia. As a child, I would watch my nonna cook up a big pot of spaghetti bolognaise every Sunday, filling our small Italian household with the savory scent of simmering meat and tomatoes. The way the sauce would thicken and the spaghetti would al dente - it was love at first bite. And now, as a chef, I want to share that same love with you, so you can recreate these warm, comforting memories at home.",
"ingredients": [
"500g minced beef",
"1 medium onion, finely chopped",
"2 cloves garlic, minced",
"1 carrot, finely chopped",
" celery, finely chopped",
"1 (28 oz) can whole peeled tomatoes",
"1 tbsp tomato paste",
"1 tsp dried basil",
"1 tsp dried oregano",
"1 tsp salt",
"1/2 tsp black pepper",
"1/2 tsp sugar",
"1 lb spaghetti",
"Grated Parmesan cheese, for serving",
"Extra virgin olive oil, for serving"
],
"steps": [
"Heat a large pot over medium heat and add a generous drizzle of extra virgin olive oil.",
"Add the chopped onion, garlic, carrot, and celery and cook until the vegetables are soft and translucent, about 5-7 minutes.",
"Add the minced beef and cook until browned, breaking it up with a spoon as it cooks.",
"Add the tomato paste and cook for 1-2 minutes, stirring constantly.",
"Add the canned tomatoes, dried basil, dried oregano, salt, black pepper, and sugar. Stir well to combine.",
"Bring the sauce to a simmer and let it cook for 20-30 minutes, stirring occasionally, until the sauce has thickened and the flavors have melded together.",
"While the sauce cooks, bring a large pot of salted water to a boil and cook the spaghetti according to the package instructions until al dente. Reserve 1 cup of pasta water before draining the spaghetti.",
"Add the reserved pasta water to the sauce and stir to combine.",
"Combine the cooked spaghetti and sauce, tossing to coat the pasta evenly.",
"Serve hot, topped with grated Parmesan cheese and a drizzle of extra virgin olive oil.",
"Enjoy!"
]
}
```
Generated with Llama-3.2-3B-Instruct model - pretty good for a 3B
parameter model 👍
## Test Plan
`pytest -v -s
llama_stack/providers/tests/inference/test_text_inference.py -k
llama_3b-vllm_remote`
With the following setup:
```bash
# Environment
export INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
export INFERENCE_PORT=8000
export VLLM_URL=http://localhost:8000/v1
# vLLM server
sudo docker run --gpus all \
-v $STORAGE_DIR/.cache/huggingface:/root/.cache/huggingface \
--env "HUGGING_FACE_HUB_TOKEN=$(cat ~/.cache/huggingface/token)" \
-p 8000:$INFERENCE_PORT \
--ipc=host \
--net=host \
vllm/vllm-openai:v0.6.3.post1 \
--model $INFERENCE_MODEL
# llama-stack server
llama stack build --template remote-vllm --image-type conda && llama stack run distributions/remote-vllm/run.yaml \
--port 5001 \
--env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
```
Results:
```
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_model_list[llama_3b-vllm_remote] PASSED
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_completion[llama_3b-vllm_remote] SKIPPED
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_completions_structured_output[llama_3b-vllm_remote] SKIPPED
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_chat_completion_non_streaming[llama_3b-vllm_remote] PASSED
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_structured_output[llama_3b-vllm_remote] PASSED
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_chat_completion_streaming[llama_3b-vllm_remote] PASSED
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_chat_completion_with_tool_calling[llama_3b-vllm_remote] PASSED
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_chat_completion_with_tool_calling_streaming[llama_3b-vllm_remote] PASSED
================================ 6 passed, 2 skipped, 120 deselected, 2 warnings in 13.26s ================================
```
## Sources
- https://github.com/vllm-project/vllm/discussions/8300
- By default, vLLM uses https://github.com/dottxt-ai/outlines for
structured outputs
[[1](32e7db2536/vllm/engine/arg_utils.py (L279-L280))]
## Before submitting
[N/A] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case)
- [x] Ran pre-commit to handle lint / formatting issues.
- [x] Read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
Pull Request section?
[N/A?] Updated relevant documentation. Couldn't find any relevant
documentation. Lmk if I've missed anything.
- [x] Wrote necessary unit or integration tests.
This PR does a few things:
- it moves "direct client" to llama-stack repo instead of being in the
llama-stack-client-python repo
- renames it to `LlamaStackLibraryClient`
- actually makes synchronous generators work
- makes streaming and non-streaming work properly
In many ways, this PR makes things finally "work"
## Test Plan
See a `library_client_test.py` I added. This isn't really quite a test
yet but it demonstrates that this mode now works. Here's the invocation
and the response:
```
INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct python llama_stack/distribution/tests/library_client_test.py ollama
```
# What does this PR do?
#525 introduced a telemetry configuration named jaeger, but what it
really is pointing to is an OTLP HTTP endpoint which is supported by
most servers in the ecosystem, including raw opentelemetry collectors,
several APMs, and even https://github.com/ymtdzzz/otel-tui
I chose to rename this to "otel" as it will bring in more people to the
ecosystem vs feeling it only works with jaeger. Later, we can use the
[standard
ENV](https://opentelemetry.io/docs/specs/otel/protocol/exporter/) to
configure this if we like so that you can override things with variables
people might expect.
Note: I also added to the README that you have to install conda.
Depending on experience level of the user, and especially with miniforge
vs other ways, I felt this helps.
## Test Plan
I would like to test this, but actually got a little lost. The previous
PRs referenced yaml which doesn't seem published anywhere. It would be
nice to have a pre-canned setup that uses ollama and turns on otel, but
would also appreciate a hand on instructions meanwhile.
## Sources
https://github.com/meta-llama/llama-stack/pull/525
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [x] Ran pre-commit to handle lint / formatting issues.
- [x] Read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
Pull Request section?
- [ ] Updated relevant documentation.
- [ ] Wrote necessary unit or integration tests.
---------
Signed-off-by: Adrian Cole <adrian.cole@elastic.co>
This PR adds two new methods to the telemetry API:
1) Gives the ability to query spans directly instead of first querying
traces and then using that to get spans
2) Another method save_spans_to_dataset, which builds on the query spans
to save it on dataset.
This give the ability to saves spans that are part of an agent session
to a dataset.
The unique aspect of this API is that we dont require each provider of
telemetry to implement this method. Hence, its implemented in the
protocol class itself. This required the protocol check to be slightly
modified.
When running:
python -m llama_stack.apis.safety.client localhost 5000
The API server was logging:
INFO: ::1:57176 - "POST /safety/run_shield HTTP/1.1" 404 Not Found
This patch uses the versioned API, uses the updated safety endpoint, and
updates the model name to what's being served. The above python command
now demonstrates a passing and failing example.
# What does this PR do?
Change the Telemetry API to be able to support different use cases like
returning traces for the UI and ability to export for Evals.
Other changes:
* Add a new trace_protocol decorator to decorate all our API methods so
that any call to them will automatically get traced across all impls.
* There is some issue with the decorator pattern of span creation when
using async generators, where there are multiple yields with in the same
context. I think its much more explicit by using the explicit context
manager pattern using with. I moved the span creations in agent instance
to be using with
* Inject session id at the turn level, which should quickly give us all
traces across turns for a given session
Addresses #509
## Test Plan
```
llama stack run /Users/dineshyv/.llama/distributions/llamastack-together/together-run.yaml
PYTHONPATH=. python -m examples.agents.rag_with_memory_bank localhost 5000
curl -X POST 'http://localhost:5000/alpha/telemetry/query-traces' \
-H 'Content-Type: application/json' \
-d '{
"attribute_filters": [
{
"key": "session_id",
"op": "eq",
"value": "dd667b87-ca4b-4d30-9265-5a0de318fc65" }],
"limit": 100,
"offset": 0,
"order_by": ["start_time"]
}' | jq .
[
{
"trace_id": "6902f54b83b4b48be18a6f422b13e16f",
"root_span_id": "5f37b85543afc15a",
"start_time": "2024-12-04T08:08:30.501587",
"end_time": "2024-12-04T08:08:36.026463"
},
{
"trace_id": "92227dac84c0615ed741be393813fb5f",
"root_span_id": "af7c5bb46665c2c8",
"start_time": "2024-12-04T08:08:36.031170",
"end_time": "2024-12-04T08:08:41.693301"
},
{
"trace_id": "7d578a6edac62f204ab479fba82f77b6",
"root_span_id": "1d935e3362676896",
"start_time": "2024-12-04T08:08:41.695204",
"end_time": "2024-12-04T08:08:47.228016"
},
{
"trace_id": "dbd767d76991bc816f9f078907dc9ff2",
"root_span_id": "f5a7ee76683b9602",
"start_time": "2024-12-04T08:08:47.234578",
"end_time": "2024-12-04T08:08:53.189412"
}
]
curl -X POST 'http://localhost:5000/alpha/telemetry/get-span-tree' \
-H 'Content-Type: application/json' \
-d '{ "span_id" : "6cceb4b48a156913", "max_depth": 2, "attributes_to_return": ["input"] }' | jq .
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 875 100 790 100 85 18462 1986 --:--:-- --:--:-- --:--:-- 20833
{
"span_id": "6cceb4b48a156913",
"trace_id": "dafa796f6aaf925f511c04cd7c67fdda",
"parent_span_id": "892a66d726c7f990",
"name": "retrieve_rag_context",
"start_time": "2024-12-04T09:28:21.781995",
"end_time": "2024-12-04T09:28:21.913352",
"attributes": {
"input": [
"{\"role\":\"system\",\"content\":\"You are a helpful assistant\"}",
"{\"role\":\"user\",\"content\":\"What are the top 5 topics that were explained in the documentation? Only list succinct bullet points.\",\"context\":null}"
]
},
"children": [
{
"span_id": "1a2df181854064a8",
"trace_id": "dafa796f6aaf925f511c04cd7c67fdda",
"parent_span_id": "6cceb4b48a156913",
"name": "MemoryRouter.query_documents",
"start_time": "2024-12-04T09:28:21.787620",
"end_time": "2024-12-04T09:28:21.906512",
"attributes": {
"input": null
},
"children": [],
"status": "ok"
}
],
"status": "ok"
}
```
<img width="1677" alt="Screenshot 2024-12-04 at 9 42 56 AM"
src="https://github.com/user-attachments/assets/4d3cea93-05ce-415a-93d9-4b1628631bf8">
# What does this PR do?
1) Implement `unregister_dataset(dataset_id)` API in both llama stack
routing table and providers: It removes {dataset_id -> Dataset} mapping
from routing table and removes the dataset_id references in provider as
well (ex. for huggingface, we use a KV store to store the dataset id =>
dataset. we delete it during unregistering as well)
2) expose the datasets/unregister_dataset api endpoint
## Test Plan
**Unit test:**
`
pytest llama_stack/providers/tests/datasetio/test_datasetio.py -m
"huggingface" -v -s --tb=short --disable-warnings
`
**Test on endpoint:**
tested llama stack using an ollama distribution template:
1) start an ollama server
2) Start a llama stack server with the default ollama distribution
config + dataset/datasetsio APIs + datasetio provider
```
---- .../ollama-run.yaml
...
apis:
- agents
- inference
- memory
- safety
- telemetry
- datasetio
- datasets
providers:
datasetio:
- provider_id: localfs
provider_type: inline::localfs
config: {}
...
```
saw that the new API showed up in startup script
```
Serving API datasets
GET /alpha/datasets/get
GET /alpha/datasets/list
POST /alpha/datasets/register
POST /alpha/datasets/unregister
```
3) query `/alpha/datasets/unregister` through curl (since we have not implemented unregister api in llama stack client)
```
(base) sxyi@sxyi-mbp llama-stack % llama-stack-client datasets register
--dataset-id sixian --url
https://raw.githubusercontent.com/pytorch/torchtune/main/docs/source/tutorials/chat.rst
--schema {}
(base) sxyi@sxyi-mbp llama-stack % llama-stack-client datasets list
┏━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━┓
┃ identifier ┃ provider_id ┃ metadata ┃ type ┃
┡━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━┩
│ sixian │ localfs │ {} │ dataset │
└────────────┴─────────────┴──────────┴─────────┘
(base) sxyi@sxyi-mbp llama-stack % llama-stack-client datasets register
--dataset-id sixian2 --url
https://raw.githubusercontent.com/pytorch/torchtune/main/docs/source/tutorials/chat.rst
--schema {}
(base) sxyi@sxyi-mbp llama-stack % llama-stack-client datasets list
┏━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━┓
┃ identifier ┃ provider_id ┃ metadata ┃ type ┃
┡━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━┩
│ sixian │ localfs │ {} │ dataset │
│ sixian2 │ localfs │ {} │ dataset │
└────────────┴─────────────┴──────────┴─────────┘
(base) sxyi@sxyi-mbp llama-stack % curl
http://localhost:5001/alpha/datasets/unregister \
-H "Content-Type: application/json" \
-d '{"dataset_id": "sixian"}'
null%
(base) sxyi@sxyi-mbp llama-stack % llama-stack-client datasets list
┏━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━┓
┃ identifier ┃ provider_id ┃ metadata ┃ type ┃
┡━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━┩
│ sixian2 │ localfs │ {} │ dataset │
└────────────┴─────────────┴──────────┴─────────┘
(base) sxyi@sxyi-mbp llama-stack % curl
http://localhost:5001/alpha/datasets/unregister \
-H "Content-Type: application/json" \
-d '{"dataset_id": "sixian2"}'
null%
(base) sxyi@sxyi-mbp llama-stack % llama-stack-client datasets list
```
## Sources
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Ran pre-commit to handle lint / formatting issues.
- [ ] Read the [contributor guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
Pull Request section?
- [ ] Updated relevant documentation.
- [ ] Wrote necessary unit or integration tests.
i find `test_structured_output` to be flakey. it's both a functionality
and accuracy test -
```
answer = AnswerFormat.model_validate_json(response.completion_message.content)
assert answer.first_name == "Michael"
assert answer.last_name == "Jordan"
assert answer.year_of_birth == 1963
assert answer.num_seasons_in_nba == 15
```
it's an accuracy test because it checks the value of first/last name,
birth year, and num seasons.
i find that -
- llama-3.1-8b-instruct and llama-3.2-3b-instruct pass the functionality
portion
- llama-3.2-3b-instruct consistently fails the accuracy portion
(thinking MJ was in the NBA for 14 seasons)
- llama-3.1-8b-instruct occasionally fails the accuracy portion
suggestions (not mutually exclusive) -
1. turn the test into functionality only, skip the value checks
2. split the test into a functionality version and an xfail accuracy
version
3. add context to the prompt so the llm can answer without accessing
embedded memory
# What does this PR do?
implements option (3) by adding context to the system prompt.
## Test Plan
`pytest -s -v ... llama_stack/providers/tests/inference/ ... -k
structured_output`
## Before submitting
- [x] Ran pre-commit to handle lint / formatting issues.
- [x] Read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
Pull Request section?
- [x] Updated relevant documentation.
- [x] Wrote necessary unit or integration tests.
# What does this PR do?
Many of the URLs pointing to the Llama Stack's Read The Docs webpages
were broken, presumably due to recent refactor of the documentation.
This PR fixes all effected URLs throughout the repository.
# What does this PR do?
- Move Llama Stack Playground UI to llama-stack repo under
llama_stack/distribution
- Original PR in llama-stack-apps:
https://github.com/meta-llama/llama-stack-apps/pull/127
## Test Plan
```
cd llama-stack/llama_stack/distribution/ui
streamlit run app.py
```
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [ ] Ran pre-commit to handle lint / formatting issues.
- [ ] Read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
Pull Request section?
- [ ] Updated relevant documentation.
- [ ] Wrote necessary unit or integration tests.
# What does this PR do?
this allows setting an NVIDIA_BASE_URL variable to control the
NVIDIAConfig.url option
## Test Plan
`pytest -s -v --providers inference=nvidia
llama_stack/providers/tests/inference/ --env
NVIDIA_BASE_URL=http://localhost:8000`
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [x] Ran pre-commit to handle lint / formatting issues.
- [x] Read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
Pull Request section?
- [ ] Updated relevant documentation.
- [ ] Wrote necessary unit or integration tests.
# What does this PR do?
- braintrust scoring provider requires OPENAI_API_KEY env variable to be
set
- move this to be able to be set as request headers (e.g. like together
/ fireworks api keys)
- fixes pytest with agents dependency
## Test Plan
**E2E**
```
llama stack run
```
```yaml
scoring:
- provider_id: braintrust-0
provider_type: inline::braintrust
config: {}
```
**Client**
```python
self.client = LlamaStackClient(
base_url=os.environ.get("LLAMA_STACK_ENDPOINT", "http://localhost:5000"),
provider_data={
"openai_api_key": os.environ.get("OPENAI_API_KEY", ""),
},
)
```
- run `llama-stack-client eval run_scoring`
**Unit Test**
```
pytest -v -s -m meta_reference_eval_together_inference eval/test_eval.py
```
```
pytest -v -s -m braintrust_scoring_together_inference scoring/test_scoring.py --env OPENAI_API_KEY=$OPENAI_API_KEY
```
<img width="745" alt="image"
src="https://github.com/user-attachments/assets/68f5cdda-f6c8-496d-8b4f-1b3dabeca9c2">
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [ ] Ran pre-commit to handle lint / formatting issues.
- [ ] Read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
Pull Request section?
- [ ] Updated relevant documentation.
- [ ] Wrote necessary unit or integration tests.
# What does this PR do?
* Add a test fixture for tgi
* Fixes the logic to correctly pass the llama model for chat completion
Fixes#514
## Test Plan
pytest -k "tgi"
llama_stack/providers/tests/inference/test_text_inference.py --env
TGI_URL=http://localhost:$INFERENCE_PORT --env TGI_API_TOKEN=$HF_TOKEN
# What does this PR do?
this PR adds a basic inference adapter to NVIDIA NIMs
what it does -
- chat completion api
- tool calls
- streaming
- structured output
- logprobs
- support hosted NIM on integrate.api.nvidia.com
- support downloaded NIM containers
what it does not do -
- completion api
- embedding api
- vision models
- builtin tools
- have certainty that sampling strategies are correct
## Feature/Issue validation/testing/test plan
`pytest -s -v --providers inference=nvidia
llama_stack/providers/tests/inference/ --env NVIDIA_API_KEY=...`
all tests should pass. there are pydantic v1 warnings.
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [x] Did you read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes?
- [x] Did you write any new necessary tests?
Thanks for contributing 🎉!
# What does this PR do?
Update the llama model supported list for Ollama.
- [x] Addresses issue (#462)
Signed-off-by: Martin Hickey <martin.hickey@ie.ibm.com>
# What does this PR do?
This PR fixes some of the issues with our telemetry setup to enable logs
to be delivered to opentelemetry and jaeger. Main fixes
1) Updates the open telemetry provider to use the latest oltp exports
instead of deprected ones.
2) Adds a tracing middleware, which injects traces into each HTTP
request that the server recieves and this is going to be the root trace.
Previously, we did this in the create_dynamic_route method, which is
actually not the actual exectuion flow, but more of a config and this
causes the traces to end prematurely. Through middleware, we plugin the
trace start and end at the right location.
3) We manage our own methods to create traces and spans and this does
not fit well with Opentelemetry SDK since it does not support provide a
way to take in traces and spans that are already created. it expects us
to use the SDK to create them. For now, I have a hacky approach of just
maintaining a map from our internal telemetry objects to the open
telemetry specfic ones. This is not the ideal solution. I will explore
other ways to get around this issue. for now, to have something that
works, i am going to keep this as is.
Addresses: #509
# What does this PR do?
Safety provider `inline::meta-reference` is now deprecated. However, we
* aren't checking / printing the deprecation message in `llama stack
build`
* make the deprecated (unusable) provider
So I (1) added checking and (2) made `inline::llama-guard` the default
## Test Plan
Before
```
Traceback (most recent call last):
File "/home/dalton/.conda/envs/nov22/bin/llama", line 8, in <module>
sys.exit(main())
File "/home/dalton/all/llama-stack/llama_stack/cli/llama.py", line 46, in main
parser.run(args)
File "/home/dalton/all/llama-stack/llama_stack/cli/llama.py", line 40, in run
args.func(args)
File "/home/dalton/all/llama-stack/llama_stack/cli/stack/build.py", line 177, in _run_stack_build_command
self._run_stack_build_command_from_build_config(build_config)
File "/home/dalton/all/llama-stack/llama_stack/cli/stack/build.py", line 305, in _run_stack_build_command_from_build_config
self._generate_run_config(build_config, build_dir)
File "/home/dalton/all/llama-stack/llama_stack/cli/stack/build.py", line 226, in _generate_run_config
config_type = instantiate_class_type(
File "/home/dalton/all/llama-stack/llama_stack/distribution/utils/dynamic.py", line 12, in instantiate_class_type
module = importlib.import_module(module_name)
File "/home/dalton/.conda/envs/nov22/lib/python3.10/importlib/__init__.py", line 126, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "<frozen importlib._bootstrap>", line 1050, in _gcd_import
File "<frozen importlib._bootstrap>", line 1027, in _find_and_load
File "<frozen importlib._bootstrap>", line 1004, in _find_and_load_unlocked
ModuleNotFoundError: No module named 'llama_stack.providers.inline.safety.meta_reference'
```
After
```
Traceback (most recent call last):
File "/home/dalton/.conda/envs/nov22/bin/llama", line 8, in <module>
sys.exit(main())
File "/home/dalton/all/llama-stack/llama_stack/cli/llama.py", line 46, in main
parser.run(args)
File "/home/dalton/all/llama-stack/llama_stack/cli/llama.py", line 40, in run
args.func(args)
File "/home/dalton/all/llama-stack/llama_stack/cli/stack/build.py", line 177, in _run_stack_build_command
self._run_stack_build_command_from_build_config(build_config)
File "/home/dalton/all/llama-stack/llama_stack/cli/stack/build.py", line 309, in _run_stack_build_command_from_build_config
self._generate_run_config(build_config, build_dir)
File "/home/dalton/all/llama-stack/llama_stack/cli/stack/build.py", line 228, in _generate_run_config
raise InvalidProviderError(p.deprecation_error)
llama_stack.distribution.resolver.InvalidProviderError:
Provider `inline::meta-reference` for API `safety` does not work with the latest Llama Stack.
- if you are using Llama Guard v3, please use the `inline::llama-guard` provider instead.
- if you are using Prompt Guard, please use the `inline::prompt-guard` provider instead.
- if you are using Code Scanner, please use the `inline::code-scanner` provider instead.
```
<img width="469" alt="Screenshot 2024-11-22 at 4 10 24 PM"
src="https://github.com/user-attachments/assets/8c2e09fe-379a-4504-b246-7925f80a6ed6">
## Sources
Please link relevant resources if necessary.
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [x] Ran pre-commit to handle lint / formatting issues.
- [ ] Read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
Pull Request section?
- [ ] Updated relevant documentation.
- [ ] Wrote necessary unit or integration tests.
