See Issue #922
The change is slightly backwards incompatible but no callsite (in our
client codebases or stack-apps) every passes a depth-2
`List[List[InterleavedContentItem]]` (which is now disallowed.)
## Test Plan
```bash
$ cd llama_stack/providers/tests/inference
$ pytest -s -v -k fireworks test_embeddings.py \
--inference-model nomic-ai/nomic-embed-text-v1.5 --env EMBEDDING_DIMENSION=784
$ pytest -s -v -k together test_embeddings.py \
--inference-model togethercomputer/m2-bert-80M-8k-retrieval --env EMBEDDING_DIMENSION=784
$ pytest -s -v -k ollama test_embeddings.py \
--inference-model all-minilm:latest --env EMBEDDING_DIMENSION=784
```
Also ran `tests/client-sdk/inference/test_embeddings.py`
# What does this PR do?
- Fully deprecate eval/tasks
[//]: # (If resolving an issue, uncomment and update the line below)
Closes#1088
NOTE: this will be a breaking change. We have introduced the new API in
0.1.3 .
Notebook has been updated to use the new endpoints.
## Test Plan
```
pytest -v -s --nbval-lax ./docs/notebooks/Llama_Stack_Benchmark_Evals.ipynb
```
<img width="611" alt="image"
src="https://github.com/user-attachments/assets/79f6efe1-81ba-494e-bf36-1fc0c2b9bc6f"
/>
cc @SLR722 for awareness
[//]: # (## Documentation)
# What does this PR do?
You are now able to run a training cycle on CPU. This is useful for
debugging and testing purposes.
[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])
## Test Plan
On a Mac machine without CUDA devices:
```
17:00:24.417 [START] /v1/post-training/supervised-fine-tune
DEBUG 2025-02-18 12:00:24,419 torchtune.utils._logging:60: Setting manual seed to local seed 3268931494. Local seed is seed + rank = 3268931494 + 0
INFO 2025-02-18 12:00:24,463 torchtune.utils._logging:64: Identified model_type = Llama3_2. Ignoring output.weight in checkpoint in favor of the tok_embedding.weight tied weights.
INFO 2025-02-18 12:00:46,699 llama_stack.providers.inline.post_training.torchtune.recipes.lora_finetuning_single_device:182: Model is initialized with precision torch.bfloat16.
INFO 2025-02-18 12:00:46,784 llama_stack.providers.inline.post_training.torchtune.recipes.lora_finetuning_single_device:185: Tokenizer is initialized.
INFO 2025-02-18 12:00:46,786 llama_stack.providers.inline.post_training.torchtune.recipes.lora_finetuning_single_device:188: Optimizer is initialized.
INFO 2025-02-18 12:00:46,786 llama_stack.providers.inline.post_training.torchtune.recipes.lora_finetuning_single_device:192: Loss is initialized.
INFO 2025-02-18 12:00:48,997 llama_stack.providers.inline.post_training.torchtune.recipes.lora_finetuning_single_device:209: Dataset and Sampler are initialized.
INFO 2025-02-18 12:00:48,998 llama_stack.providers.inline.post_training.torchtune.recipes.lora_finetuning_single_device:227: Learning rate scheduler is initialized.
Writing logs to /Users/ihrachys/.llama/checkpoints/meta-llama/Llama-3.2-3B-Instruct-sft-0/log_1739898049.txt
1|1|Loss: 1.7414989471435547: 100% 1/1 [03:46<00:00, 226.21s/it]INFO 2025-02-18 12:04:35,227 llama_stack.providers.inline.post_training.torchtune.recipes.lora_finetuning_single_device:528: Starting checkpoint save...
INFO 2025-02-18 12:04:49,974 torchtune.utils._logging:121: Model checkpoint of size 6.43 GB saved to /Users/ihrachys/.llama/checkpoints/meta-llama/Llama-3.2-3B-Instruct-sft-0/consolidated.00.pth
INFO 2025-02-18 12:04:49,981 torchtune.utils._logging:132: Adapter checkpoint of size 0.00 GB saved to /Users/ihrachys/.llama/checkpoints/meta-llama/Llama-3.2-3B-Instruct-sft-0/adapter/adapter.pth
model_file_path /Users/ihrachys/.llama/checkpoints/meta-llama/Llama-3.2-3B-Instruct-sft-0
1|1|Loss: 1.7414989471435547: 100% 1/1 [04:01<00:00, 241.18s/it]
INFO: ::1:64990 - "POST /v1/post-training/supervised-fine-tune HTTP/1.1" 200 OK
17:04:50.364 [END] /v1/post-training/supervised-fine-tune [StatusCode.OK] (265947.01ms)
17:00:24.419 [DEBUG] Setting manual seed to local seed 3268931494. Local seed is seed + rank = 3268931494 + 0
17:00:24.463 [INFO] Identified model_type = Llama3_2. Ignoring output.weight in checkpoint in favor of the tok_embedding.weight tied weights.
17:00:46.700 [INFO] Model is initialized with precision torch.bfloat16.
17:00:46.784 [INFO] Tokenizer is initialized.
17:00:46.786 [INFO] Optimizer is initialized.
17:00:46.786 [INFO] Loss is initialized.
17:00:48.997 [INFO] Dataset and Sampler are initialized.
17:00:48.998 [INFO] Learning rate scheduler is initialized.
17:04:35.227 [INFO] Starting checkpoint save...
17:04:49.974 [INFO] Model checkpoint of size 6.43 GB saved to /Users/ihrachys/.llama/checkpoints/meta-llama/Llama-3.2-3B-Instruct-sft-0/consolidated.00.pth
17:04:49.981 [INFO] Adapter checkpoint of size 0.00 GB saved to /Users/ihrachys/.llama/checkpoints/meta-llama/Llama-3.2-3B-Instruct-sft-0/adapter/adapter.pth
```
[//]: # (## Documentation)
Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>
as title, to let scoring function llm_as_judge_405b_simpleqa output
aggregated_results.
We can leverage categorical_count to calculate the % of correctness as
eval benchmark metrics
- **refactor: simplify job status extraction a bit**
- **torchtune: save job status on schedule**
- **refactor: get rid of job_list in torchtune job management code**
# What does this PR do?
A failed job is now registered in API, and one can consult its status.
[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])
## Test Plan
```
$ llama-stack-client post_training status --job-uuid test-jobe244b5b0-5053-4892-a4d9-d8fc8b116e73
JobStatusResponse(checkpoints=[], job_uuid='test-jobe244b5b0-5053-4892-a4d9-d8fc8b116e73', status='failed', completed_at=None, resources_allocated=None, scheduled_at=datetime.datetime(2025, 2, 18, 9, 4, 34, 3252), started_at=datetime.datetime(2025, 2, 18, 9, 4, 34, 10688))
```
[//]: # (## Documentation)
---------
Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>
# What does this PR do?
1. This PR adds batch inserts into sqlite-vec as requested in
https://github.com/meta-llama/llama-stack/pull/1040
- Note: the inserts uses a uuid generated from the hash of the document
id and chunk content.
2. This PR also adds unit tests for sqlite-vec. In a follow up PR, I can
add similar tests to Faiss.
## Test Plan
1. Integration tests:
```python
INFERENCE_MODEL=llama3.2:3b-instruct-fp16 LLAMA_STACK_CONFIG=ollama pytest -s -v tests/client-sdk/vector_io/test_vector_io.py
...
PASSED
tests/client-sdk/vector_io/test_vector_io.py::test_vector_db_retrieve[all-MiniLM-L6-v2-sqlite_vec] PASSED
tests/client-sdk/vector_io/test_vector_io.py::test_vector_db_list PASSED
tests/client-sdk/vector_io/test_vector_io.py::test_vector_db_register[all-MiniLM-L6-v2-faiss] PASSED
tests/client-sdk/vector_io/test_vector_io.py::test_vector_db_register[all-MiniLM-L6-v2-sqlite_vec] PASSED
tests/client-sdk/vector_io/test_vector_io.py::test_vector_db_unregister[faiss] PASSED
tests/client-sdk/vector_io/test_vector_io.py::test_vector_db_unregister[sqlite_vec] PASSED
```
3. Unit tests:
```python
pytest llama_stack/providers/tests/vector_io/test_sqlite_vec.py -v -s --tb=short --disable-warnings --asyncio-mode=auto
...
llama_stack/providers/tests/vector_io/test_sqlite_vec.py::test_add_chunks PASSED
llama_stack/providers/tests/vector_io/test_sqlite_vec.py::test_query_chunks PASSED
llama_stack/providers/tests/vector_io/test_sqlite_vec.py::test_chunk_id_conflict PASSED
llama_stack/providers/tests/vector_io/test_sqlite_vec.py::test_register_vector_db PASSED
llama_stack/providers/tests/vector_io/test_sqlite_vec.py::test_unregister_vector_db PASSED
llama_stack/providers/tests/vector_io/test_sqlite_vec.py::test_generate_chunk_id PASSED
```
I also tested using the same example RAG script in
https://github.com/meta-llama/llama-stack/pull/1040 and received the
output.
---------
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
There should be a choke-point for llama3.api imports -- this is the
prompt adapter. Creating a ChatFormat() object on demand is inexpensive.
The underlying Tokenizer is a singleton anyway.
# What does this PR do?
Before this change, `distro_codegen.py` would only work if the user
manually installed multiple provider-specific dependencies (see #1122).
Now, users can run `distro_codegen.py` without any provider-specific
dependencies because we avoid importing the entire provider
implementations just to get the config needed to build the provider
template.
Concretely, this mostly means moving the
MODEL_ALIASES (and related variants) definitions to a new models.py
class within the provider implementation for those providers that
require additional dependencies. It also meant moving a couple of
imports from top-level imports to inside `get_adapter_impl` for some
providers, which follows the pattern used by multiple existing
providers.
To ensure we don't regress and accidentally add new imports that cause
distro_codegen.py to fail, the stubbed-in pre-commit hook for
distro_codegen.py was uncommented and slightly tweaked to run via `uv
run python ...` to ensure it runs with only the project's default
dependencies and to run automatically instead of manually.
Lastly, this updates distro_codegen.py itself to keep track of paths it
might have changed and to only `git diff` those specific paths when
checking for changed files instead of doing a diff on the entire working
tree. The latter was overly broad and would require a user have no other
unstaged changes in their working tree, even if those unstaged changes
were unrelated to generated code. Now it only flags uncommitted changes
for paths distro_codegen.py actually writes to.
Our generated code was also out-of-date, presumably because of these
issues, so this commit also has some updates to the generated code
purely because it was out of sync, and the pre-commit hook now enforces
things to be updated.
(Closes#1122)
## Test Plan
I manually tested distro_codegen.py and the pre-commit hook to verify
those work as expected, flagging any uncommited changes and catching any
imports that attempt to pull in provider-specific dependencies.
However, I do not have valid api keys to the impacted provider
implementations, and am unable to easily run the inference tests against
each changed provider. There are no functional changes to the provider
implementations here, but I'd appreciate a second set of eyes on the
changed import statements and moving of MODEL_ALIASES type code to a
separate models.py to ensure I didn't make any obvious errors.
---------
Signed-off-by: Ben Browning <bbrownin@redhat.com>
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
llama-models should have extremely minimal cruft. Its sole purpose
should be didactic -- show the simplest implementation of the llama
models and document the prompt formats, etc.
This PR is the complement to
https://github.com/meta-llama/llama-models/pull/279
## Test Plan
Ensure all `llama` CLI `model` sub-commands work:
```bash
llama model list
llama model download --model-id ...
llama model prompt-format -m ...
```
Ran tests:
```bash
cd tests/client-sdk
LLAMA_STACK_CONFIG=fireworks pytest -s -v inference/
LLAMA_STACK_CONFIG=fireworks pytest -s -v vector_io/
LLAMA_STACK_CONFIG=fireworks pytest -s -v agents/
```
Create a fresh venv `uv venv && source .venv/bin/activate` and run
`llama stack build --template fireworks --image-type venv` followed by
`llama stack run together --image-type venv` <-- the server runs
Also checked that the OpenAPI generator can run and there is no change
in the generated files as a result.
```bash
cd docs/openapi_generator
sh run_openapi_generator.sh
```
# What does this PR do?
- Remove hardcoded configurations from pre-commit.
- Allow configuration to be set via pyproject.toml.
- Merge .ruff.toml settings into pyproject.toml.
- Ensure the linter and formatter use the defined configuration instead
of being overridden by pre-commit.
Signed-off-by: Sébastien Han <seb@redhat.com>
[//]: # (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)
Signed-off-by: Sébastien Han <seb@redhat.com>
# What does this PR do?
- Update `/eval-tasks` to `/benchmarks`
- ⚠️ Remove differentiation between `app` v.s. `benchmark` eval task
config. Now we only have `BenchmarkConfig`. The overloaded `benchmark`
is confusing and do not add any value. Backward compatibility is being
kept as the "type" is not being used anywhere.
[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])
## Test Plan
- This change is backward compatible
- Run notebook test with
```
pytest -v -s --nbval-lax ./docs/getting_started.ipynb
pytest -v -s --nbval-lax ./docs/notebooks/Llama_Stack_Benchmark_Evals.ipynb
```
<img width="846" alt="image"
src="https://github.com/user-attachments/assets/d2fc06a7-593a-444f-bc1f-10ab9b0c843d"
/>
[//]: # (## Documentation)
[//]: # (- [ ] Added a Changelog entry if the change is significant)
---------
Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>
Signed-off-by: Ben Browning <bbrownin@redhat.com>
Signed-off-by: Sébastien Han <seb@redhat.com>
Signed-off-by: reidliu <reid201711@gmail.com>
Co-authored-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>
Co-authored-by: Ben Browning <ben324@gmail.com>
Co-authored-by: Sébastien Han <seb@redhat.com>
Co-authored-by: Reid <61492567+reidliu41@users.noreply.github.com>
Co-authored-by: reidliu <reid201711@gmail.com>
Co-authored-by: Yuan Tang <terrytangyuan@gmail.com>
# What does this PR do?
This changes all VectorIO providers classes to follow the pattern
`<ProviderName>VectorIOConfig` and `<ProviderName>VectorIOAdapter`. All
API endpoints for VectorIOs are currently consistent with `/vector-io`.
Note that API endpoint for VectorDB stay unchanged as `/vector-dbs`.
## Test Plan
I don't have a way to test all providers. This is a simple renaming so
things should work as expected.
---------
Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>
# What does this PR do?
- Configured ruff linter to automatically fix import sorting issues.
- Set --exit-non-zero-on-fix to ensure non-zero exit code when fixes are
applied.
- Enabled the 'I' selection to focus on import-related linting rules.
- Ran the linter, and formatted all codebase imports accordingly.
- Removed the black dep from the "dev" group since we use ruff
Signed-off-by: Sébastien Han <seb@redhat.com>
[//]: # (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)
[//]: # (- [ ] Added a Changelog entry if the change is significant)
Signed-off-by: Sébastien Han <seb@redhat.com>
# What does this PR do?
This commit enhances the signal handling mechanism in the server by
improving the `handle_signal` (previously handle_sigint) function. It
now properly retrieves the signal name, ensuring clearer logging when a
termination signal is received. Additionally, it cancels all running
tasks and waits for their completion before stopping the event loop,
allowing for a more graceful shutdown. Support for handling
SIGTERM has also been added alongside SIGINT.
Before the changes, handle_sigint used asyncio.run(run_shutdown()).
However, asyncio.run() is meant to start a new event loop, and calling
it inside an existing one (like when running Uvicorn) raises an error.
The fix replaces asyncio.run(run_shutdown()) with an async function
scheduled on the existing loop using loop.create_task(shutdown()). This
ensures that the shutdown coroutine runs within the current event loop
instead of trying to create a new one.
Furthermore, this commit updates the project dependencies. `fastapi` and
`uvicorn` have been added to the development dependencies in
`pyproject.toml` and `uv.lock`, ensuring that the necessary packages are
available for development and execution.
Closes: https://github.com/meta-llama/llama-stack/issues/1043
Signed-off-by: Sébastien Han <seb@redhat.com>
[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])
## Test Plan
Run a server and send SIGINT:
```
INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct" python -m llama_stack.distribution.server.server --yaml-config ./llama_stack/templates/ollama/run.yaml
Using config file: llama_stack/templates/ollama/run.yaml
Run configuration:
apis:
- agents
- datasetio
- eval
- inference
- safety
- scoring
- telemetry
- tool_runtime
- vector_io
container_image: null
datasets: []
eval_tasks: []
image_name: ollama
metadata_store:
db_path: /Users/leseb/.llama/distributions/ollama/registry.db
namespace: null
type: sqlite
models:
- metadata: {}
model_id: meta-llama/Llama-3.2-3B-Instruct
model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType
- llm
provider_id: ollama
provider_model_id: null
- metadata:
embedding_dimension: 384
model_id: all-MiniLM-L6-v2
model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType
- embedding
provider_id: sentence-transformers
provider_model_id: null
providers:
agents:
- config:
persistence_store:
db_path: /Users/leseb/.llama/distributions/ollama/agents_store.db
namespace: null
type: sqlite
provider_id: meta-reference
provider_type: inline::meta-reference
datasetio:
- config: {}
provider_id: huggingface
provider_type: remote::huggingface
- config: {}
provider_id: localfs
provider_type: inline::localfs
eval:
- config: {}
provider_id: meta-reference
provider_type: inline::meta-reference
inference:
- config:
url: http://localhost:11434
provider_id: ollama
provider_type: remote::ollama
- config: {}
provider_id: sentence-transformers
provider_type: inline::sentence-transformers
safety:
- config: {}
provider_id: llama-guard
provider_type: inline::llama-guard
scoring:
- config: {}
provider_id: basic
provider_type: inline::basic
- config: {}
provider_id: llm-as-judge
provider_type: inline::llm-as-judge
- config:
openai_api_key: '********'
provider_id: braintrust
provider_type: inline::braintrust
telemetry:
- config:
service_name: llama-stack
sinks: console,sqlite
sqlite_db_path: /Users/leseb/.llama/distributions/ollama/trace_store.db
provider_id: meta-reference
provider_type: inline::meta-reference
tool_runtime:
- config:
api_key: '********'
max_results: 3
provider_id: brave-search
provider_type: remote::brave-search
- config:
api_key: '********'
max_results: 3
provider_id: tavily-search
provider_type: remote::tavily-search
- config: {}
provider_id: code-interpreter
provider_type: inline::code-interpreter
- config: {}
provider_id: rag-runtime
provider_type: inline::rag-runtime
vector_io:
- config:
kvstore:
db_path: /Users/leseb/.llama/distributions/ollama/faiss_store.db
namespace: null
type: sqlite
provider_id: faiss
provider_type: inline::faiss
scoring_fns: []
server:
port: 8321
tls_certfile: null
tls_keyfile: null
shields: []
tool_groups:
- args: null
mcp_endpoint: null
provider_id: tavily-search
toolgroup_id: builtin::websearch
- args: null
mcp_endpoint: null
provider_id: rag-runtime
toolgroup_id: builtin::rag
- args: null
mcp_endpoint: null
provider_id: code-interpreter
toolgroup_id: builtin::code_interpreter
vector_dbs: []
version: '2'
INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:213: Resolved 31 providers
INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: inner-inference => ollama
INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: inner-inference => sentence-transformers
INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: models => __routing_table__
INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: inference => __autorouted__
INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: inner-vector_io => faiss
INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: inner-safety => llama-guard
INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: shields => __routing_table__
INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: safety => __autorouted__
INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: vector_dbs => __routing_table__
INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: vector_io => __autorouted__
INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: inner-tool_runtime => brave-search
INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: inner-tool_runtime => tavily-search
INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: inner-tool_runtime => code-interpreter
INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: inner-tool_runtime => rag-runtime
INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: tool_groups => __routing_table__
INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: tool_runtime => __autorouted__
INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: agents => meta-reference
INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: inner-datasetio => huggingface
INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: inner-datasetio => localfs
INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: datasets => __routing_table__
INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: datasetio => __autorouted__
INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: telemetry => meta-reference
INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: inner-scoring => basic
INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: inner-scoring => llm-as-judge
INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: inner-scoring => braintrust
INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: scoring_functions => __routing_table__
INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: scoring => __autorouted__
INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: inner-eval => meta-reference
INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: eval_tasks => __routing_table__
INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: eval => __autorouted__
INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: inspect => __builtin__
INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:216:
INFO 2025-02-12 10:21:03,723 llama_stack.providers.remote.inference.ollama.ollama:148: checking connectivity to Ollama at `http://localhost:11434`...
INFO 2025-02-12 10:21:03,734 httpx:1740: HTTP Request: GET http://localhost:11434/api/ps "HTTP/1.1 200 OK"
INFO 2025-02-12 10:21:03,843 faiss.loader:148: Loading faiss.
INFO 2025-02-12 10:21:03,865 faiss.loader:150: Successfully loaded faiss.
INFO 2025-02-12 10:21:03,868 faiss:173: Failed to load GPU Faiss: name 'GpuIndexIVFFlat' is not defined. Will not load constructor refs for GPU indexes.
Warning: `bwrap` is not available. Code interpreter tool will not work correctly.
INFO 2025-02-12 10:21:04,315 datasets:54: PyTorch version 2.6.0 available.
INFO 2025-02-12 10:21:04,556 httpx:1740: HTTP Request: GET http://localhost:11434/api/ps "HTTP/1.1 200 OK"
INFO 2025-02-12 10:21:04,557 llama_stack.providers.utils.inference.embedding_mixin:42: Loading sentence transformer for all-MiniLM-L6-v2...
INFO 2025-02-12 10:21:07,202 sentence_transformers.SentenceTransformer:210: Use pytorch device_name: mps
INFO 2025-02-12 10:21:07,202 sentence_transformers.SentenceTransformer:218: Load pretrained SentenceTransformer: all-MiniLM-L6-v2
INFO 2025-02-12 10:21:09,500 llama_stack.distribution.stack:102: Models: all-MiniLM-L6-v2 served by sentence-transformers
INFO 2025-02-12 10:21:09,500 llama_stack.distribution.stack:102: Models: meta-llama/Llama-3.2-3B-Instruct served by ollama
INFO 2025-02-12 10:21:09,501 llama_stack.distribution.stack:102: Scoring_fns: basic::equality served by basic
INFO 2025-02-12 10:21:09,501 llama_stack.distribution.stack:102: Scoring_fns: basic::regex_parser_multiple_choice_answer served by basic
INFO 2025-02-12 10:21:09,501 llama_stack.distribution.stack:102: Scoring_fns: basic::subset_of served by basic
INFO 2025-02-12 10:21:09,501 llama_stack.distribution.stack:102: Scoring_fns: braintrust::answer-correctness served by braintrust
INFO 2025-02-12 10:21:09,501 llama_stack.distribution.stack:102: Scoring_fns: braintrust::answer-relevancy served by braintrust
INFO 2025-02-12 10:21:09,501 llama_stack.distribution.stack:102: Scoring_fns: braintrust::answer-similarity served by braintrust
INFO 2025-02-12 10:21:09,501 llama_stack.distribution.stack:102: Scoring_fns: braintrust::context-entity-recall served by braintrust
INFO 2025-02-12 10:21:09,501 llama_stack.distribution.stack:102: Scoring_fns: braintrust::context-precision served by braintrust
INFO 2025-02-12 10:21:09,501 llama_stack.distribution.stack:102: Scoring_fns: braintrust::context-recall served by braintrust
INFO 2025-02-12 10:21:09,501 llama_stack.distribution.stack:102: Scoring_fns: braintrust::context-relevancy served by braintrust
INFO 2025-02-12 10:21:09,501 llama_stack.distribution.stack:102: Scoring_fns: braintrust::factuality served by braintrust
INFO 2025-02-12 10:21:09,501 llama_stack.distribution.stack:102: Scoring_fns: braintrust::faithfulness served by braintrust
INFO 2025-02-12 10:21:09,501 llama_stack.distribution.stack:102: Scoring_fns: llm-as-judge::405b-simpleqa served by llm-as-judge
INFO 2025-02-12 10:21:09,501 llama_stack.distribution.stack:102: Scoring_fns: llm-as-judge::base served by llm-as-judge
INFO 2025-02-12 10:21:09,501 llama_stack.distribution.stack:102: Tool_groups: builtin::code_interpreter served by code-interpreter
INFO 2025-02-12 10:21:09,501 llama_stack.distribution.stack:102: Tool_groups: builtin::rag served by rag-runtime
INFO 2025-02-12 10:21:09,501 llama_stack.distribution.stack:102: Tool_groups: builtin::websearch served by tavily-search
INFO 2025-02-12 10:21:09,501 llama_stack.distribution.stack:106:
Serving API eval
POST /v1/eval/tasks/{task_id}/evaluations
DELETE /v1/eval/tasks/{task_id}/jobs/{job_id}
GET /v1/eval/tasks/{task_id}/jobs/{job_id}/result
GET /v1/eval/tasks/{task_id}/jobs/{job_id}
POST /v1/eval/tasks/{task_id}/jobs
Serving API agents
POST /v1/agents
POST /v1/agents/{agent_id}/session
POST /v1/agents/{agent_id}/session/{session_id}/turn
DELETE /v1/agents/{agent_id}
DELETE /v1/agents/{agent_id}/session/{session_id}
GET /v1/agents/{agent_id}/session/{session_id}
GET /v1/agents/{agent_id}/session/{session_id}/turn/{turn_id}/step/{step_id}
GET /v1/agents/{agent_id}/session/{session_id}/turn/{turn_id}
Serving API scoring_functions
GET /v1/scoring-functions/{scoring_fn_id}
GET /v1/scoring-functions
POST /v1/scoring-functions
Serving API safety
POST /v1/safety/run-shield
Serving API inspect
GET /v1/health
GET /v1/inspect/providers
GET /v1/inspect/routes
GET /v1/version
Serving API tool_runtime
POST /v1/tool-runtime/invoke
GET /v1/tool-runtime/list-tools
POST /v1/tool-runtime/rag-tool/insert
POST /v1/tool-runtime/rag-tool/query
Serving API datasetio
POST /v1/datasetio/rows
GET /v1/datasetio/rows
Serving API shields
GET /v1/shields/{identifier}
GET /v1/shields
POST /v1/shields
Serving API eval_tasks
GET /v1/eval-tasks/{eval_task_id}
GET /v1/eval-tasks
POST /v1/eval-tasks
Serving API models
GET /v1/models/{model_id}
GET /v1/models
POST /v1/models
DELETE /v1/models/{model_id}
Serving API datasets
GET /v1/datasets/{dataset_id}
GET /v1/datasets
POST /v1/datasets
DELETE /v1/datasets/{dataset_id}
Serving API vector_io
POST /v1/vector-io/insert
POST /v1/vector-io/query
Serving API inference
POST /v1/inference/chat-completion
POST /v1/inference/completion
POST /v1/inference/embeddings
Serving API tool_groups
GET /v1/tools/{tool_name}
GET /v1/toolgroups/{toolgroup_id}
GET /v1/toolgroups
GET /v1/tools
POST /v1/toolgroups
DELETE /v1/toolgroups/{toolgroup_id}
Serving API vector_dbs
GET /v1/vector-dbs/{vector_db_id}
GET /v1/vector-dbs
POST /v1/vector-dbs
DELETE /v1/vector-dbs/{vector_db_id}
Serving API scoring
POST /v1/scoring/score
POST /v1/scoring/score-batch
Serving API telemetry
GET /v1/telemetry/traces/{trace_id}/spans/{span_id}
GET /v1/telemetry/spans/{span_id}/tree
GET /v1/telemetry/traces/{trace_id}
POST /v1/telemetry/events
GET /v1/telemetry/spans
GET /v1/telemetry/traces
POST /v1/telemetry/spans/export
Listening on ['::', '0.0.0.0']:5001
INFO: Started server process [65372]
INFO: Waiting for application startup.
INFO: ASGI 'lifespan' protocol appears unsupported.
INFO: Application startup complete.
INFO: Uvicorn running on http://['::', '0.0.0.0']:5001 (Press CTRL+C to quit)
^CINFO: Shutting down
INFO: Finished server process [65372]
Received signal SIGINT (2). Exiting gracefully...
INFO 2025-02-12 10:21:11,215 __main__:151: Shutting down ModelsRoutingTable
INFO 2025-02-12 10:21:11,216 __main__:151: Shutting down InferenceRouter
INFO 2025-02-12 10:21:11,216 __main__:151: Shutting down ShieldsRoutingTable
INFO 2025-02-12 10:21:11,216 __main__:151: Shutting down SafetyRouter
INFO 2025-02-12 10:21:11,216 __main__:151: Shutting down VectorDBsRoutingTable
INFO 2025-02-12 10:21:11,216 __main__:151: Shutting down VectorIORouter
INFO 2025-02-12 10:21:11,216 __main__:151: Shutting down ToolGroupsRoutingTable
INFO 2025-02-12 10:21:11,216 __main__:151: Shutting down ToolRuntimeRouter
INFO 2025-02-12 10:21:11,216 __main__:151: Shutting down MetaReferenceAgentsImpl
INFO 2025-02-12 10:21:11,216 __main__:151: Shutting down DatasetsRoutingTable
INFO 2025-02-12 10:21:11,216 __main__:151: Shutting down DatasetIORouter
INFO 2025-02-12 10:21:11,216 __main__:151: Shutting down TelemetryAdapter
INFO 2025-02-12 10:21:11,216 __main__:151: Shutting down ScoringFunctionsRoutingTable
INFO 2025-02-12 10:21:11,216 __main__:151: Shutting down ScoringRouter
INFO 2025-02-12 10:21:11,216 __main__:151: Shutting down EvalTasksRoutingTable
INFO 2025-02-12 10:21:11,216 __main__:151: Shutting down EvalRouter
INFO 2025-02-12 10:21:11,216 __main__:151: Shutting down DistributionInspectImpl
```
[//]: # (## Documentation)
[//]: # (- [ ] Added a Changelog entry if the change is significant)
Signed-off-by: Sébastien Han <seb@redhat.com>
# What does this PR do?
This PR adds `sqlite_vec` as an additional inline vectordb.
Tested with `ollama` by adding the `vector_io` object in
`./llama_stack/templates/ollama/run.yaml` :
```yaml
vector_io:
- provider_id: sqlite_vec
provider_type: inline::sqlite_vec
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/ollama}/sqlite_vec.db
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/ollama}/sqlite_vec.db
```
I also updated the `./tests/client-sdk/vector_io/test_vector_io.py` test
file with:
```python
INLINE_VECTOR_DB_PROVIDERS = ["faiss", "sqlite_vec"]
```
And parameterized the relevant tests.
[//]: # (If resolving an issue, uncomment and update the line below)
# Closes
https://github.com/meta-llama/llama-stack/issues/1005
## Test Plan
I ran the tests with:
```bash
INFERENCE_MODEL=llama3.2:3b-instruct-fp16 LLAMA_STACK_CONFIG=ollama pytest -s -v tests/client-sdk/vector_io/test_vector_io.py
```
Which outputs:
```python
...
PASSED
tests/client-sdk/vector_io/test_vector_io.py::test_vector_db_retrieve[all-MiniLM-L6-v2-sqlite_vec] PASSED
tests/client-sdk/vector_io/test_vector_io.py::test_vector_db_list PASSED
tests/client-sdk/vector_io/test_vector_io.py::test_vector_db_register[all-MiniLM-L6-v2-faiss] PASSED
tests/client-sdk/vector_io/test_vector_io.py::test_vector_db_register[all-MiniLM-L6-v2-sqlite_vec] PASSED
tests/client-sdk/vector_io/test_vector_io.py::test_vector_db_unregister[faiss] PASSED
tests/client-sdk/vector_io/test_vector_io.py::test_vector_db_unregister[sqlite_vec] PASSED
```
In addition, I ran the `rag_with_vector_db.py`
[example](https://github.com/meta-llama/llama-stack-apps/blob/main/examples/agents/rag_with_vector_db.py)
using the script below with `uv run rag_example.py`.
<details>
<summary>CLICK TO SHOW SCRIPT 👋 </summary>
```python
#!/usr/bin/env python3
import os
import uuid
from termcolor import cprint
# Set environment variables
os.environ['INFERENCE_MODEL'] = 'llama3.2:3b-instruct-fp16'
os.environ['LLAMA_STACK_CONFIG'] = 'ollama'
# Import libraries after setting environment variables
from llama_stack.distribution.library_client import LlamaStackAsLibraryClient
from llama_stack_client.lib.agents.agent import Agent
from llama_stack_client.lib.agents.event_logger import EventLogger
from llama_stack_client.types.agent_create_params import AgentConfig
from llama_stack_client.types import Document
def main():
# Initialize the client
client = LlamaStackAsLibraryClient("ollama")
vector_db_id = f"test-vector-db-{uuid.uuid4().hex}"
_ = client.initialize()
model_id = 'llama3.2:3b-instruct-fp16'
# Define the list of document URLs and create Document objects
urls = [
"chat.rst",
"llama3.rst",
"memory_optimizations.rst",
"lora_finetune.rst",
]
documents = [
Document(
document_id=f"num-{i}",
content=f"https://raw.githubusercontent.com/pytorch/torchtune/main/docs/source/tutorials/{url}",
mime_type="text/plain",
metadata={},
)
for i, url in enumerate(urls)
]
# (Optional) Use the documents as needed with your client here
client.vector_dbs.register(
provider_id='sqlite_vec',
vector_db_id=vector_db_id,
embedding_model="all-MiniLM-L6-v2",
embedding_dimension=384,
)
client.tool_runtime.rag_tool.insert(
documents=documents,
vector_db_id=vector_db_id,
chunk_size_in_tokens=512,
)
# Create agent configuration
agent_config = AgentConfig(
model=model_id,
instructions="You are a helpful assistant",
enable_session_persistence=False,
toolgroups=[
{
"name": "builtin::rag",
"args": {
"vector_db_ids": [vector_db_id],
}
}
],
)
# Instantiate the Agent
agent = Agent(client, agent_config)
# List of user prompts
user_prompts = [
"What are the top 5 topics that were explained in the documentation? Only list succinct bullet points.",
"Was anything related to 'Llama3' discussed, if so what?",
"Tell me how to use LoRA",
"What about Quantization?",
]
# Create a session for the agent
session_id = agent.create_session("test-session")
# Process each prompt and display the output
for prompt in user_prompts:
cprint(f"User> {prompt}", "green")
response = agent.create_turn(
messages=[
{
"role": "user",
"content": prompt,
}
],
session_id=session_id,
)
# Log and print events from the response
for log in EventLogger().log(response):
log.print()
if __name__ == "__main__":
main()
```
</details>
Which outputs a large summary of RAG generation.
# Documentation
Will handle documentation updates in follow-up PR.
# (- [ ] Added a Changelog entry if the change is significant)
---------
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
# What does this PR do?
The current default system prompt for llama3.2 tends to overindex on
tool calling and doesn't work well when the prompt does not require tool
calling.
This PR adds an option to override the default system prompt, and
organizes tool-related configs into a new config object.
- [ ] Addresses issue (#issue)
## Test Plan
LLAMA_STACK_CONFIG=together pytest
\-\-inference\-model=meta\-llama/Llama\-3\.3\-70B\-Instruct -s -v
tests/client-sdk/agents/test_agents.py::test_override_system_message_behavior
## 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).
- [ ] 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?
The current default system prompt for llama3.2 tends to overindex on
tool calling and doesn't work well when the prompt does not require tool
calling.
This PR adds an option to override the default system prompt, and
organizes tool-related configs into a new config object.
- [ ] Addresses issue (#issue)
## Test Plan
python -m unittest
llama_stack.providers.tests.inference.test_prompt_adapter
## 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).
- [ ] 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.
---
[//]: # (BEGIN SAPLING FOOTER)
Stack created with [Sapling](https://sapling-scm.com). Best reviewed
with
[ReviewStack](https://reviewstack.dev/meta-llama/llama-stack/pull/937).
* #938
* __->__ #937
# What does this PR do?
To work with the updated iOSCalendarAssistantWithLocalInf
[here](https://github.com/meta-llama/llama-stack-apps/compare/ios_local).
In short, provide a summary of what this PR does and why. Usually, the
relevant context should be present in a linked issue.
- [ ] Addresses issue (#issue)
## Test Plan
Please describe:
- tests you ran to verify your changes with result summaries.
- provide instructions so it can be reproduced.
## 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).
- [ ] 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.
Lint check in main branch is failing. This fixes the lint check after we
moved to ruff in https://github.com/meta-llama/llama-stack/pull/921. We
need to move to a `ruff.toml` file as well as fixing and ignoring some
additional checks.
Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>
This commit adds support for XPU and CPU devices into meta-reference
stack for text models. On creation stack automatically identifies which
device to use checking available accelerate capabilities in the
following order: CUDA, then XPU, finally CPU. This behaviour can be
overwritten with the `DEVICE` environment variable. In this case
explicitly specified device will be used.
Tested with:
```
torchrun pytest llama_stack/providers/tests/inference/test_text_inference.py -k meta_reference
```
Results:
* Tested on: system with single CUDA device, system with single XPU
device and on pure CPU system
* Results: all test pass except `test_completion_logprobs`
* `test_completion_logprobs` fails in the same way as on a baseline,
i.e. unrelated with this change: `AssertionError: Unexpected top_k=3`
Requires: https://github.com/meta-llama/llama-models/pull/233
Signed-off-by: Dmitry Rogozhkin <dmitry.v.rogozhkin@intel.com>
# What does this PR do?
Fixes a bug where agents were not working when both rag and
code-interpreter were added as tools.
## Test Plan
Added a new client_sdk test which tests for this scenario
```
LLAMA_STACK_CONFIG=together pytest -s -v tests/client-sdk -k 'test_rag_and_code_agent'
```
---------
Co-authored-by: Hardik Shah <hjshah@fb.com>
# What does this PR do?
Add response format for agents structured output.
- [ ] Using structured output for agents (interior_design app as an
example) (#issue)
https://github.com/meta-llama/llama-stack-apps/issues/122
## Test Plan
E2E test plan with llama-stack-apps interior_design
Please describe:
Test ran:
- provide instructions so it can be reproduced.
Start your distro:
llama stack run llama_stack/templates/fireworks/run.yaml --env
FIREWORKS_API_KEY=<API_KEY>
Run api test:
```PYTHONPATH=. python examples/interior_design_assistant/api.py localhost 5000 examples/interior_design_assistant/resources/documents/ examples/interior_design_assistant/resources/images/fireplaces```
## Sources
Results:
https://github.com/meta-llama/llama-stack-client-python/pull/72
## 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.
- [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?
When you re-initialize the library client in a notebook, we were seeing
this error:
```
Getting traces for session_id=5c8d1969-0957-49d2-b852-32cbb8ef8caf
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
[<ipython-input-11-d74bb6cdd3ab>](https://localhost:8080/#) in <cell line: 0>()
7 agent_logs = []
8
----> 9 for span in client.telemetry.query_spans(
10 attribute_filters=[
11 {"key": "session_id", "op": "eq", "value": session_id},
10 frames
[/usr/local/lib/python3.11/dist-packages/llama_stack/providers/inline/telemetry/meta_reference/telemetry.py](https://localhost:8080/#) in query_traces(self, attribute_filters, limit, offset, order_by)
246 ) -> QueryTracesResponse:
247 return QueryTracesResponse(
--> 248 data=await self.trace_store.query_traces(
249 attribute_filters=attribute_filters,
250 limit=limit,
AttributeError: 'TelemetryAdapter' object has no attribute 'trace_store'
```
This is happening because the we were skipping some required steps for
the object state as part of the global _TRACE_PROVIDER check. This PR
moves the initialization of the object state out of the TRACE_PROVIDER
init.
Some small updates to the inference types to make them more standard
Specifically:
- image data is now located in a "image" subkey
- similarly tool call data is located in a "tool_call" subkey
The pattern followed is `dict(type="foo", foo=<...>)`
Making a few small naming changes as per feedback:
- RAGToolRuntime methods are called `insert` and `query` to keep them
more general
- The tool names are changed to non-namespaced forms
`insert_into_memory` and `query_from_memory`
- The REST endpoints are more REST-ful
See https://github.com/meta-llama/llama-stack/issues/827 for the broader
design.
Third part:
- we need to make `tool_runtime.rag_tool.query_context()` and
`tool_runtime.rag_tool.insert_documents()` methods work smoothly with
complete type safety. To that end, we introduce a sub-resource path
`tool-runtime/rag-tool/` and make changes to the resolver to make things
work.
- the PR updates the agents implementation to directly call these typed
APIs for memory accesses rather than going through the complex, untyped
"invoke_tool" API. the code looks much nicer and simpler (expectedly.)
- there are a number of hacks in the server resolver implementation
still, we will live with some and fix some
Note that we must make sure the client SDKs are able to handle this
subresource complexity also. Stainless has support for subresources, so
this should be possible but beware.
## Test Plan
Our RAG test is sad (doesn't actually test for actual RAG output) but I
verified that the implementation works. I will work on fixing the RAG
test afterwards.
```bash
pytest -s -v tests/agents/test_agents.py -k "rag and together" --safety-shield=meta-llama/Llama-Guard-3-8B
```
See https://github.com/meta-llama/llama-stack/issues/827 for the broader
design.
Second part:
- updates routing table / router code
- updates the faiss implementation
## Test Plan
```
pytest -s -v -k sentence test_vector_io.py --env EMBEDDING_DIMENSION=384
```
See https://github.com/meta-llama/llama-stack/issues/827 for the broader
design.
This is the first part:
- delete other kinds of memory banks (keyvalue, keyword, graph) for now;
we will introduce a keyvalue store API as part of this design but not
use it in the RAG tool yet.
- renaming of the APIs
## What does this PR do?
So far `llama stack build` has always created a separate conda
environment for packaging the dependencies of a distribution. The main
reason to do so is isolation -- distributions are composed of providers
which can have a variety of potentially conflicting dependencies. That
said, this has created significant annoyance for new users since it is
not at all transparent. The fact that `llama stack run` is actually
running the code in some other conda is very surprising.
This PR tries to make things better.
- Both `llama stack build` and `llama stack run` now accept an
`--image-name` argument which represents the (conda, docker, virtualenv)
image you want to operate upon.
- For the default (conda) mode, the script checks if a current conda
environment exists. If one exists, it uses it.
- If `--image-name` is provided, that option is used. In this case, an
environment is created if needed.
- There is no automatic `llamastack-` prefixing of the environment names
done anymore.
## Test Plan
Start in a conda environment, run `llama stack build --template
fireworks`; verify that it successfully built into the current
environment and stored the build file at
`$CONDA_PREFIX/llamastack-build.yaml`. Run `llama stack run fireworks`
which started correctly in the current environment.
Ran the same build command outside of conda. It failed asking for
`--image-name`. Ran it with `llama stack build --template fireworks
--image-name foo`. This successfully created a conda environment called
`foo` and installed deps. Ran `llama stack run fireworks` outside conda
which failed. Activated a different conda, ran again, it failed saying
it did not find the `llamastack-build.yaml` file. Then used
`--image-name foo` option and it ran successfully.
# What does this PR do?
Changes Telemetry API to follow more idiomatic REST
- [ ] Addresses issue (#issue)
## Test Plan
TBD, once i get an approval for rest endpoints
# What does this PR do?
This PR changes our API to follow more idiomatic REST API approaches of
having paths being resources and methods indicating the action being
performed.
Changes made to generator:
1) removed the prefix check of "get" as its not required and is actually
needed for other method types too
2) removed _ check on path since variables can have "_"
## Test Plan
LLAMA_STACK_BASE_URL=http://localhost:5000 pytest -v
tests/client-sdk/agents/test_agents.py
# What does this PR do?
- fix eval tests to include tool_runtime fixtures
- rebase eval for extracting memory retrieval context
## Test Plan
```
pytest -v -s -m meta_reference_eval_together_inference_huggingface_datasetio llama_stack/providers/tests/eval/test_eval.py
pytest -v -s -m braintrust_scoring_together_inference llama_stack/providers/tests/scoring/test_scoring.py
```
- With notebook:
https://gist.github.com/yanxi0830/1260a6cb7ec42498a195b88422462a34
## 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).
- [ ] 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.
