# What does this PR do?
This fixes an issue when running the e2e agent example:
https://github.com/meta-llama/llama-stack-apps/blob/main/examples/agents/e2e_loop_with_client_tools.py
```
| File "/home/yutang/repos/llama-stack/llama_stack/providers/remote/inference/vllm/vllm.py", line 175, in _process_vllm_chat_completion_stream_response
| tool_call = convert_tool_call(choice.delta.tool_calls[0])
| File "/home/yutang/repos/llama-stack/llama_stack/providers/utils/inference/openai_compat.py", line 441, in convert_tool_call
| return ToolCall(
| File "/home/yutang/.conda/envs/distribution-myenv/lib/python3.10/site-packages/pydantic/main.py", line 214, in __init__
| validated_self = self.__pydantic_validator__.validate_python(data, self_instance=self)
| pydantic_core._pydantic_core.ValidationError: 4 validation errors for ToolCall
| call_id
| Input should be a valid string [type=string_type, input_value=None, input_type=NoneType]
| For further information visit https://errors.pydantic.dev/2.10/v/string_type
| tool_name.enum[BuiltinTool]
| Input should be 'brave_search', 'wolfram_alpha', 'photogen' or 'code_interpreter' [type=enum, input_value=None, input_type=NoneType]
| For further information visit https://errors.pydantic.dev/2.10/v/enum
| tool_name.str
| Input should be a valid string [type=string_type, input_value=None, input_type=NoneType]
| For further information visit https://errors.pydantic.dev/2.10/v/string_type
| arguments
| Input should be a valid dictionary [type=dict_type, input_value=202, input_type=int]
| For further information visit https://errors.pydantic.dev/2.10/v/dict_type
```
This issue happened because not all arguments have been appended to the
tool call buffer yet. The current code assumes that we are ready to
convert the tool call whenever args can be converted to JSON
successfully. In this case, `json.loads("202")` would succeed but the
rest of the arguments have not been properly parsed yet.
[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])
## Test Plan
The e2e example worked successfully (although note that I ran the script
twice with each function call separately due to
https://github.com/meta-llama/llama-stack/issues/1120):
```
tool_execution> Tool:get_ticker_data Args:{'ticker_symbol': 'GOOG', 'start': '2023-01-01', 'end': '2023-12-31'}
tool_execution> Tool:get_ticker_data Response:"[{\"('Year', '')\":2023,\"('Close', 'GOOG')\":140.4254455566}]"
tool_execution> Tool:web_search Args:{'query': '42nd president of the United States'}
tool_execution> Tool:web_search Response:"{\"query\": \"42nd president of the United States\", \"top_k\": [{\"title\": \"William J. Clinton | whitehouse.gov\", \"url\": \"https://obamawhitehouse.archives.gov/1600/presidents/williamjclinton\", \"description\": \"<strong>Bill Clinton</strong> is an American politician from Arkansas who served as the 42nd President of the United States (1993-2001). He took office at the end of the Cold War, and was the first baby-boomer generation President.\", \"type\": \"search_result\"}, {\"title\": \"Bill Clinton - Wikipedia\", \"url\": \"https://en.wikipedia.org/wiki/Bill_Clinton\", \"description\": \"<strong>William Jefferson Clinton</strong> (n\\u00e9 Blythe; born August 19, 1946) is an American politician and lawyer who served as the 42nd president of the United States from 1993 to 2001. A member of the Democratic Party, he previously served as the attorney general of Arkansas from 1977 to 1979 and as the ...\", \"type\": \"search_result\"}, [{\"type\": \"video_result\", \"url\": \"https://www.youtube.com/watch?v=eR2z_1-v87Y\", \"title\": \"A Conversation with Bill Clinton, 42nd President of the United ...\", \"description\": \"William Jefferson Clinton, the first Democratic president in six decades to be elected twice, led the United States to the longest economic expansion in Amer...\"}, {\"type\": \"video_result\", \"url\": \"4484174096/\", \"title\": \"January 20, 1993, President Clinton was sworn in as the 42nd ...\", \"description\": \"WATCH: On January 20, 1993, President Bill Clinton was sworn in as the 42nd President of the United States. #InaugurationDay Video courtesy of the...\"}, {\"type\": \"video_result\", \"url\": \"https://www.youtube.com/watch?v=vI0HGQqEJh0\", \"title\": \"42nd President of the United States, Bill Clinton, shared thoughts ...\", \"description\": \"AboutPressCopyrightContact usCreatorsAdvertiseDevelopersTermsPrivacyPolicy & SafetyHow YouTube worksTest new features \\u00b7 \\u00a9 2024 Google LLC\"}, {\"type\": \"video_result\", \"url\": \"https://www.youtube.com/shorts/vI0HGQqEJh0\", \"title\": \"42nd President of the United States, Bill Clinton, shared ...\", \"description\": \"Enjoy the videos and music you love, upload original content, and share it all with friends, family, and the world on YouTube.\"}, {\"type\": \"video_result\", \"url\": \"https://www.youtube.com/watch?v=PHihhihVth0\", \"title\": \"Bill & Hillary Clinton returning to Little Rock for 20th ...\", \"description\": \"Enjoy the videos and music you love, upload original content, and share it all with friends, family, and the world on YouTube.\"}]]}"
```
All text inference tests passed.
[//]: # (## Documentation)
Signed-off-by: Yuan Tang <terrytangyuan@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?
- 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?
[Provide a short summary of what this PR does and why. Link to relevant
issues if applicable.]
Closes https://github.com/meta-llama/llama-stack/issues/1046.
## 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_BASE_URL=http://localhost:5002 pytest -v tests/client-sdk/inference/test_text_inference.py
================================================================= test session starts =================================================================
platform linux -- Python 3.10.16, pytest-8.3.4, pluggy-1.5.0 -- /home/yutang/.conda/envs/distribution-myenv/bin/python3.10
cachedir: .pytest_cache
rootdir: /home/yutang/repos/llama-stack
configfile: pyproject.toml
plugins: anyio-4.8.0
collected 14 items
tests/client-sdk/inference/test_text_inference.py::test_text_completion_non_streaming[meta-llama/Llama-3.1-8B-Instruct] PASSED [ 7%]
tests/client-sdk/inference/test_text_inference.py::test_text_completion_streaming[meta-llama/Llama-3.1-8B-Instruct] PASSED [ 14%]
tests/client-sdk/inference/test_text_inference.py::test_completion_log_probs_non_streaming[meta-llama/Llama-3.1-8B-Instruct] XFAIL (remote:...) [ 21%]
tests/client-sdk/inference/test_text_inference.py::test_completion_log_probs_streaming[meta-llama/Llama-3.1-8B-Instruct] XFAIL (remote::vll...) [ 28%]
tests/client-sdk/inference/test_text_inference.py::test_text_completion_structured_output[meta-llama/Llama-3.1-8B-Instruct] PASSED [ 35%]
tests/client-sdk/inference/test_text_inference.py::test_text_chat_completion_non_streaming[meta-llama/Llama-3.1-8B-Instruct-Which planet do humans live on?-Earth] PASSED [ 42%]
tests/client-sdk/inference/test_text_inference.py::test_text_chat_completion_non_streaming[meta-llama/Llama-3.1-8B-Instruct-Which planet has rings around it with a name starting with letter S?-Saturn] PASSED [ 50%]
tests/client-sdk/inference/test_text_inference.py::test_text_chat_completion_streaming[meta-llama/Llama-3.1-8B-Instruct-What's the name of the Sun in latin?-Sol] PASSED [ 57%]
tests/client-sdk/inference/test_text_inference.py::test_text_chat_completion_streaming[meta-llama/Llama-3.1-8B-Instruct-What is the name of the US captial?-Washington] PASSED [ 64%]
tests/client-sdk/inference/test_text_inference.py::test_text_chat_completion_with_tool_calling_and_non_streaming[meta-llama/Llama-3.1-8B-Instruct] PASSED [ 71%]
tests/client-sdk/inference/test_text_inference.py::test_text_chat_completion_with_tool_calling_and_streaming[meta-llama/Llama-3.1-8B-Instruct] PASSED [ 78%]
tests/client-sdk/inference/test_text_inference.py::test_text_chat_completion_structured_output[meta-llama/Llama-3.1-8B-Instruct] PASSED [ 85%]
tests/client-sdk/inference/test_text_inference.py::test_text_chat_completion_tool_calling_tools_not_in_request[meta-llama/Llama-3.1-8B-Instruct-True] PASSED [ 92%]
tests/client-sdk/inference/test_text_inference.py::test_text_chat_completion_tool_calling_tools_not_in_request[meta-llama/Llama-3.1-8B-Instruct-False] PASSED [100%]
=============================================== 12 passed, 2 xfailed, 1 warning in 366.56s (0:06:06) ================================================
```
---------
Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>
# What does this PR do?
This PR adds support for tool calling for non-streaming chat completion.
Prior to this, tool calls were not passed to chat completion requests
and the tools object needs to be restructured properly to be compatible
with vLLM provider.
## Test Plan
```
LLAMA_STACK_BASE_URL=http://localhost:5002 pytest -v tests/client-sdk/inference/test_text_inference.py
================================================================= test session starts =================================================================
platform linux -- Python 3.10.16, pytest-8.3.4, pluggy-1.5.0 -- /home/yutang/.conda/envs/distribution-myenv/bin/python3.10
cachedir: .pytest_cache
rootdir: /home/yutang/repos/llama-stack
configfile: pyproject.toml
plugins: anyio-4.8.0
collected 12 items
tests/client-sdk/inference/test_text_inference.py::test_text_completion_non_streaming[meta-llama/Llama-3.1-8B-Instruct] PASSED [ 8%]
tests/client-sdk/inference/test_text_inference.py::test_text_completion_streaming[meta-llama/Llama-3.1-8B-Instruct] PASSED [ 16%]
tests/client-sdk/inference/test_text_inference.py::test_completion_log_probs_non_streaming[meta-llama/Llama-3.1-8B-Instruct] XFAIL (remote:...) [ 25%]
tests/client-sdk/inference/test_text_inference.py::test_completion_log_probs_streaming[meta-llama/Llama-3.1-8B-Instruct] XFAIL (remote::vll...) [ 33%]
tests/client-sdk/inference/test_text_inference.py::test_text_completion_structured_output[meta-llama/Llama-3.1-8B-Instruct] PASSED [ 41%]
tests/client-sdk/inference/test_text_inference.py::test_text_chat_completion_non_streaming[meta-llama/Llama-3.1-8B-Instruct-Which planet do humans live on?-Earth] PASSED [ 50%]
tests/client-sdk/inference/test_text_inference.py::test_text_chat_completion_non_streaming[meta-llama/Llama-3.1-8B-Instruct-Which planet has rings around it with a name starting with letter S?-Saturn] PASSED [ 58%]
tests/client-sdk/inference/test_text_inference.py::test_text_chat_completion_streaming[meta-llama/Llama-3.1-8B-Instruct-What's the name of the Sun in latin?-Sol] PASSED [ 66%]
tests/client-sdk/inference/test_text_inference.py::test_text_chat_completion_streaming[meta-llama/Llama-3.1-8B-Instruct-What is the name of the US captial?-Washington] PASSED [ 75%]
tests/client-sdk/inference/test_text_inference.py::test_text_chat_completion_with_tool_calling_and_non_streaming[meta-llama/Llama-3.1-8B-Instruct] PASSED [ 83%]
tests/client-sdk/inference/test_text_inference.py::test_text_chat_completion_with_tool_calling_and_streaming[meta-llama/Llama-3.1-8B-Instruct] FAILED [ 91%]
tests/client-sdk/inference/test_text_inference.py::test_text_chat_completion_structured_output[meta-llama/Llama-3.1-8B-Instruct] PASSED [100%]
```
---------
Signed-off-by: Yuan Tang <terrytangyuan@gmail.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
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
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>
# What does this PR do?
add support to the NVIDIA Inference provider for image inputs
## Test Plan
1. Run local [Llama 3.2 11b vision
instruct](https://build.nvidia.com/meta/llama-3.2-11b-vision-instruct?snippet_tab=Docker)
NIM
2. Start a stack, e.g. `llama stack run
llama_stack/templates/nvidia/run.yaml --env
NVIDIA_BASE_URL=http://localhost:8000`
3. Run image tests, e.g. `LLAMA_STACK_BASE_URL=http://localhost:8321
pytest -v tests/client-sdk/inference/test_inference.py
--vision-inference-model meta-llama/Llama-3.2-11B-Vision-Instruct -k
image`
## 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.
- [x] Wrote necessary unit or integration tests.
# What does this PR do?
- Discussion in
https://github.com/meta-llama/llama-stack/pull/906#discussion_r1936260819
- image.data should accept base64 string as input instead of binary
bytes, change prompt_adapter to account for that.
## Test Plan
```
pytest -v tests/client-sdk/inference/test_inference.py
```
with test in https://github.com/meta-llama/llama-stack/pull/906
## 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?
1) As per @mattf's suggestion, we want to mark the pytest as xfail for
providers that do not support the functionality. In this diff, we xfail
the logProbs inference tests for providers who does not support log
probs.
( log probs is only supported by together, fireworks and vllm)
2) Added logProbs support for together according to their developer
[doc](https://docs.together.ai/docs/logprobs).
## Test Plan
1) Together & Fireworks
```
export LLAMA_STACK_CONFIG=/Users/sxyi/llama-stack/llama_stack/templates/together/run.yaml
/opt/miniconda3/envs/stack/bin/pytest -s -v /Users/sxyi/llama-stack/tests/client-sdk/inference/test_inference.py
```
```
tests/client-sdk/inference/test_inference.py::test_text_completion_streaming[meta-llama/Llama-3.1-8B-Instruct] PASSED
tests/client-sdk/inference/test_inference.py::test_completion_log_probs_non_streaming[meta-llama/Llama-3.1-8B-Instruct] PASSED
tests/client-sdk/inference/test_inference.py::test_completion_log_probs_streaming[meta-llama/Llama-3.1-8B-Instruct] PASSED
tests/client-sdk/inference/test_inference.py::test_text_completion_structured_output[meta-llama/Llama-3.1-8B-Instruct] PASSED
tests/client-sdk/inference/test_inference.py::test_text_chat_completion_non_streaming[meta-llama/Llama-3.1-8B-Instruct-What are the names of planets in our solar system?-Earth] PASSED
tests/client-sdk/inference/test_inference.py::test_text_chat_completion_non_streaming[meta-llama/Llama-3.1-8B-Instruct-What are the names of the planets that have rings around them?-Saturn] PASSED
tests/client-sdk/inference/test_inference.py::test_text_chat_completion_streaming[meta-llama/Llama-3.1-8B-Instruct-What's the name of the Sun in latin?-Sol] PASSED
tests/client-sdk/inference/test_inference.py::test_text_chat_completion_streaming[meta-llama/Llama-3.1-8B-Instruct-What is the name of the US captial?-Washington] PASSED
tests/client-sdk/inference/test_inference.py::test_text_chat_completion_with_tool_calling_and_non_streaming[meta-llama/Llama-3.1-8B-Instruct] PASSED
tests/client-sdk/inference/test_inference.py::test_text_chat_completion_with_tool_calling_and_streaming[meta-llama/Llama-3.1-8B-Instruct] PASSED
tests/client-sdk/inference/test_inference.py::test_text_chat_completion_structured_output[meta-llama/Llama-3.1-8B-Instruct] PASSED
tests/client-sdk/inference/test_inference.py::test_image_chat_completion_non_streaming[meta-llama/Llama-3.2-11B-Vision-Instruct] PASSED
tests/client-sdk/inference/test_inference.py::test_image_chat_completion_streaming[meta-llama/Llama-3.2-11B-Vision-Instruct] PASSED
tests/client-sdk/inference/test_inference.py::test_image_chat_completion_base64_url[meta-llama/Llama-3.2-11B-Vision-Instruct] PASSED
========================================================================================== 15 passed, 2 warnings in 19.46s ===========================================================================================
```
```
export LLAMA_STACK_CONFIG=/Users/sxyi/llama-stack/llama_stack/templates/fireworks/run.yaml
/opt/miniconda3/envs/stack/bin/pytest -s -v /Users/sxyi/llama-stack/tests/client-sdk/inference/test_inference.py
```
All tests passed
2) Ollama - LogProbs tests are marked as xfailed.
```
tests/client-sdk/inference/test_inference.py::test_completion_log_probs_non_streaming[meta-llama/Llama-3.1-8B-Instruct] XFAIL (remote::ollama doesn't support log probs yet)
tests/client-sdk/inference/test_inference.py::test_completion_log_probs_streaming[meta-llama/Llama-3.1-8B-Instruct] XFAIL (remote::ollama doesn't support log probs yet)
```
## 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.
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=<...>)`
# What does this PR do?
- previous fix introduced regression for non base64 image
- add back download, and base64 check
## Test Plan
<img width="835" alt="image"
src="https://github.com/user-attachments/assets/b70bf725-035a-4b42-b492-53daaf71458a"
/>
## 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?
Fixes error when running an provider using openai_compat.py
```python
Traceback (most recent call last):
File "/home/ubuntu/miniconda3/envs/llamastack-vllm/lib/python3.10/runpy.py", line 196, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/home/ubuntu/miniconda3/envs/llamastack-vllm/lib/python3.10/runpy.py", line 86, in _run_code
exec(code, run_globals)
File "/home/ubuntu/us-south-2/llama-stack/llama_stack/distribution/server/server.py", line 426, in <module>
main()
File "/home/ubuntu/us-south-2/llama-stack/llama_stack/distribution/server/server.py", line 349, in main
impls = asyncio.run(construct_stack(config))
File "/home/ubuntu/miniconda3/envs/llamastack-vllm/lib/python3.10/asyncio/runners.py", line 44, in run
return loop.run_until_complete(main)
File "/home/ubuntu/miniconda3/envs/llamastack-vllm/lib/python3.10/asyncio/base_events.py", line 649, in run_until_complete
return future.result()
File "/home/ubuntu/us-south-2/llama-stack/llama_stack/distribution/stack.py", line 207, in construct_stack
impls = await resolve_impls(
File "/home/ubuntu/us-south-2/llama-stack/llama_stack/distribution/resolver.py", line 239, in resolve_impls
impl = await instantiate_provider(
File "/home/ubuntu/us-south-2/llama-stack/llama_stack/distribution/resolver.py", line 330, in instantiate_provider
impl = await fn(*args)
File "/home/ubuntu/us-south-2/llama-stack/llama_stack/providers/remote/inference/vllm/__init__.py", line 11, in get_adapter_impl
from .vllm import VLLMInferenceAdapter
File "/home/ubuntu/us-south-2/llama-stack/llama_stack/providers/remote/inference/vllm/vllm.py", line 39, in <module>
from llama_stack.providers.utils.inference.openai_compat import (
File "/home/ubuntu/us-south-2/llama-stack/llama_stack/providers/utils/inference/openai_compat.py", line 11, in <module>
from llama_models.llama3.api.datatypes import (
ImportError: cannot import name 'GreedySamplingStrategy' from 'llama_models.llama3.api.datatypes' (/home/ubuntu/miniconda3/envs/llamastack-vllm/lib/python3.10/site-packages/llama_models/llama3/api/datatypes.py)
++ error_handler 61
++ echo 'Error occurred in script at line: 61'
Error occurred in script at line: 61
++ exit 1
```
## Test Plan
```bash
conda create --name llamastack-vllm python=3.10
conda activate llamastack-vllm
# To sync with the current llama-models repo
pip install -e git+https://github.com/meta-llama/llama-models.git#egg=llama-models
export INFERENCE_MODEL=unsloth/Llama-3.3-70B-Instruct-bnb-4bit && \
pip install -e . && \
llama stack build --template remote-vllm --image-type conda && \
llama stack run ./distributions/remote-vllm/run.yaml \
--port 5000 \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env VLLM_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).
- [ ] 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?
- add completion log probs for fireworks
## Test Plan
<img width="849" alt="image"
src="https://github.com/user-attachments/assets/5aa1f27f-02a6-422c-8478-94dd1e345342"
/>
## 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?
Cleans up how we provide sampling params. Earlier, strategy was an enum
and all params (top_p, temperature, top_k) across all strategies were
grouped. We now have a strategy union object with each strategy (greedy,
top_p, top_k) having its corresponding params.
Earlier,
```
class SamplingParams:
strategy: enum ()
top_p, temperature, top_k and other params
```
However, the `strategy` field was not being used in any providers making
it confusing to know the exact sampling behavior purely based on the
params since you could pass temperature, top_p, top_k and how the
provider would interpret those would not be clear.
Hence we introduced -- a union where the strategy and relevant params
are all clubbed together to avoid this confusion.
Have updated all providers, tests, notebooks, readme and otehr places
where sampling params was being used to use the new format.
## Test Plan
`pytest llama_stack/providers/tests/inference/groq/test_groq_utils.py`
// inference on ollama, fireworks and together
`with-proxy pytest -v -s -k "ollama"
--inference-model="meta-llama/Llama-3.1-8B-Instruct"
llama_stack/providers/tests/inference/test_text_inference.py `
// agents on fireworks
`pytest -v -s -k 'fireworks and create_agent'
--inference-model="meta-llama/Llama-3.1-8B-Instruct"
llama_stack/providers/tests/agents/test_agents.py
--safety-shield="meta-llama/Llama-Guard-3-8B"`
## 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?
- [X] Updated relevant documentation.
- [X] Wrote necessary unit or integration tests.
---------
Co-authored-by: Hardik Shah <hjshah@fb.com>
# What does this PR do?
We are setting a default value of json for tool prompt format, which
conflicts with llama 3.2/3.3 models since they use python list. This PR
changes the defaults to None and in the code, we infer default based on
the model.
Addresses: #695
Tests:
❯ LLAMA_STACK_BASE_URL=http://localhost:5000 pytest -v
tests/client-sdk/inference/test_inference.py -k
"test_text_chat_completion"
pytest llama_stack/providers/tests/inference/test_prompt_adapter.py
# What does this PR do?
PR #639 introduced the notion of Tools API and ability to invoke tools
through API just as any resource. This PR changes the Agents to start
using the Tools API to invoke tools. Major changes include:
1) Ability to specify tool groups with AgentConfig
2) Agent gets the corresponding tool definitions for the specified tools
and pass along to the model
3) Attachements are now named as Documents and their behavior is mostly
unchanged from user perspective
4) You can specify args that can be injected to a tool call through
Agent config. This is especially useful in case of memory tool, where
you want the tool to operate on a specific memory bank.
5) You can also register tool groups with args, which lets the agent
inject these as well into the tool call.
6) All tests have been migrated to use new tools API and fixtures
including client SDK tests
7) Telemetry just works with tools API because of our trace protocol
decorator
## Test Plan
```
pytest -s -v -k fireworks llama_stack/providers/tests/agents/test_agents.py \
--safety-shield=meta-llama/Llama-Guard-3-8B \
--inference-model=meta-llama/Llama-3.1-8B-Instruct
pytest -s -v -k together llama_stack/providers/tests/tools/test_tools.py \
--safety-shield=meta-llama/Llama-Guard-3-8B \
--inference-model=meta-llama/Llama-3.1-8B-Instruct
LLAMA_STACK_CONFIG="/Users/dineshyv/.llama/distributions/llamastack-together/together-run.yaml" pytest -v tests/client-sdk/agents/test_agents.py
```
run.yaml:
https://gist.github.com/dineshyv/0365845ad325e1c2cab755788ccc5994
Notebook:
https://colab.research.google.com/drive/1ck7hXQxRl6UvT-ijNRZ-gMZxH1G3cN2d?usp=sharing
# What does this PR do?
- fix interleaved_content_convert_to_raw in prompt_adapter to correctly
convert ImageContentItem to RawMediaItem with raw data bytes
## Test Plan
```
torchrun $CONDA_PREFIX/bin/pytest -v -s -k "meta_reference" --inference-model="meta-llama/Llama-3.2-11B-Vision-Instruct" ./llama_stack/providers/tests/inference/test_vision_inference.py
```
**Before**
<img width="844" alt="image"
src="https://github.com/user-attachments/assets/f2784b42-2e36-4477-9041-903d5d628a68"
/>
**After**
<img width="836" alt="image"
src="https://github.com/user-attachments/assets/362b6e47-29f7-4119-bcf3-f75db842735f"
/>
## 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?
- Makes Llama 70B 3.3 available for fireworks
## Test Plan
```shell
pip install -e . \
&& llama stack build --config distributions/fireworks/build.yaml --image-type conda \
&& llama stack run distributions/fireworks/run.yaml \
--port 5000
```
```python
response = client.inference.chat_completion(
model_id="Llama3.3-70B-Instruct",
messages=[
{"role": "user", "content": "hello world"},
],
)
```
## 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?
This is a long-pending change and particularly important to get done
now.
Specifically:
- we cannot "localize" (aka download) any URLs from media attachments
anywhere near our modeling code. it must be done within llama-stack.
- `PIL.Image` is infesting all our APIs via `ImageMedia ->
InterleavedTextMedia` and that cannot be right at all. Anything in the
API surface must be "naturally serializable". We need a standard `{
type: "image", image_url: "<...>" }` which is more extensible
- `UserMessage`, `SystemMessage`, etc. are moved completely to
llama-stack from the llama-models repository.
See https://github.com/meta-llama/llama-models/pull/244 for the
corresponding PR in llama-models.
## Test Plan
```bash
cd llama_stack/providers/tests
pytest -s -v -k "fireworks or ollama or together" inference/test_vision_inference.py
pytest -s -v -k "(fireworks or ollama or together) and llama_3b" inference/test_text_inference.py
pytest -s -v -k chroma memory/test_memory.py \
--env EMBEDDING_DIMENSION=384 --env CHROMA_DB_PATH=/tmp/foobar
pytest -s -v -k fireworks agents/test_agents.py \
--safety-shield=meta-llama/Llama-Guard-3-8B \
--inference-model=meta-llama/Llama-3.1-8B-Instruct
```
Updated the client sdk (see PR ...), installed the SDK in the same
environment and then ran the SDK tests:
```bash
cd tests/client-sdk
LLAMA_STACK_CONFIG=together pytest -s -v agents/test_agents.py
LLAMA_STACK_CONFIG=ollama pytest -s -v memory/test_memory.py
# this one needed a bit of hacking in the run.yaml to ensure I could register the vision model correctly
INFERENCE_MODEL=llama3.2-vision:latest LLAMA_STACK_CONFIG=ollama pytest -s -v inference/test_inference.py
```
# What does this PR do?
Adds the sentence transformer provider and the `all-MiniLM-L6-v2`
embedding model to the default models to register in the run.yaml for
all providers.
## Test Plan
llama stack build --template together --image-type conda
llama stack run
~/.llama/distributions/llamastack-together/together-run.yaml
This PR does the following:
1) adds the ability to generate embeddings in all supported inference
providers.
2) Moves all the memory providers to use the inference API and improved
the memory tests to setup the inference stack correctly and use the
embedding models
This is a merge from #589 and #598
# What does this PR do?
This PR adds a new model type field to support embedding models to be
registered. Summary of changes:
1) Each registered model by default is an llm model.
2) User can specify an embedding model type, while registering.If
specified, the model bypass the llama model checks since embedding
models can by of any type and based on llama.
3) User needs to include the required embedding dimension in metadata.
This will be used by embedding generation to generate the requried size
of embeddings.
## Test Plan
This PR will go together will need to be merged with two follow up PRs
that will include test plans.
# 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?
This PR moves all print statements to use logging. Things changed:
- Had to add `await start_trace("sse_generator")` to server.py to
actually get tracing working. else was not seeing any logs
- If no telemetry provider is provided in the run.yaml, we will write to
stdout
- by default, the logs are going to be in JSON, but we expose an option
to configure to output in a human readable way.
# What does this PR do?
As the title says.
## Test Plan
This needs
8752149f58
to also land. So the next package (0.0.54) will make this work properly.
The test is:
```bash
pytest -v -s -m "llama_3b and meta_reference" test_model_registration.py
```
# What does this PR do?
adds a new method build_model_alias_with_just_llama_model which is
needed for cases like ollama's quantized models which do not really have
a repo in hf and an entry in SKU list.
## Test Plan
pytest -v -s -m "ollama"
llama_stack/providers/tests/inference/test_text_inference.py
---------
Co-authored-by: Dinesh Yeduguru <dineshyv@fb.com>
This PR allows models to be registered with provider as long as the user
specifies a llama model, even though the model does not match our
prebuilt provider specific mapping.
Test:
pytest -v -s
llama_stack/providers/tests/inference/test_model_registration.py -m
"together" --env TOGETHER_API_KEY=<KEY>
---------
Co-authored-by: Dinesh Yeduguru <dineshyv@fb.com>
This PR changes the way model id gets translated to the final model name
that gets passed through the provider.
Major changes include:
1) Providers are responsible for registering an object and as part of
the registration returning the object with the correct provider specific
name of the model provider_resource_id
2) To help with the common look ups different names a new ModelLookup
class is created.
Tested all inference providers including together, fireworks, vllm,
ollama, meta reference and bedrock
* Enable vision models for Together and Fireworks
* Works with ollama 0.4.0 pre-release with the vision model
* localize media for meta_reference inference
* Fix
Added support for structured output in the API and added a reference implementation for meta-reference.
A few notes:
* Two formats are specified in the API: Json schema and EBNF based grammar
* Implementation only supports Json for now
We use lm-format-enhancer to provide the implementation right now but may change this especially because BNF grammars aren't supported by that library.
Fireworks has support for structured output and Together has limited supported for it too. Subsequent PRs will add these changes. We would like all our inference providers to provide structured output for llama models since it is an extremely important and highly sought-after need by the developers.
