# What does this PR do?
Client SDK fixes
## Test Plan
LLAMA_STACK_CONFIG="/Users/dineshyv/.llama/distributions/llamastack-fireworks/fireworks-run.yaml"
pytest -v tests/client-sdk/safety/test_safety.py
LLAMA_STACK_CONFIG="/Users/dineshyv/.llama/distributions/llamastack-fireworks/fireworks-run.yaml"
pytest -v tests/client-sdk/memory/test_memory.py
## 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.
# Context
For test automation, the end goal is to run a single pytest command from
root test directory (llama_stack/providers/tests/.) such that we execute
push-blocking tests
The work plan:
1) trigger pytest from llama_stack/providers/tests/.
2) use config file to determine what tests and parametrization we want
to run
# What does this PR do?
1) consolidates the "inference-models" / "embedding-model" /
"judge-model" ... options in root conftest.py. Without this change, we
will hit into error when trying to run `pytest
/Users/sxyi/llama-stack/llama_stack/providers/tests/.` because of
duplicated `addoptions` definitions across child conftest files.
2) Add a `config` option to specify test config in YAML. (see
[`ci_test_config.yaml`](https://gist.github.com/sixianyi0721/5b37fbce4069139445c2f06f6e42f87e)
for example config file)
For provider_fixtures, we allow users to use either a default fixture
combination or define their own {api:provider} combinations.
```
memory:
....
fixtures:
provider_fixtures:
- default_fixture_param_id: ollama // use default fixture combination with param_id="ollama" in [providers/tests/memory/conftest.py](https://fburl.com/mtjzwsmk)
- inference: sentence_transformers
memory: faiss
- default_fixture_param_id: chroma
```
3) generate tests according to the config. Logic lives in two places:
a) in `{api}/conftest.py::pytest_generate_tests`, we read from config to
do parametrization.
b) after test collection, in `pytest_collection_modifyitems`, we filter
the tests to include only functions listed in config.
## Test Plan
1) `pytest /Users/sxyi/llama-stack/llama_stack/providers/tests/.
--collect-only --config=ci_test_config.yaml`
Using `--collect-only` tag to print the pytests listed in the config
file (`ci_test_config.yaml`).
output:
[gist](https://gist.github.com/sixianyi0721/05145e60d4d085c17cfb304beeb1e60e)
2) sanity check on `--inference-model` option
```
pytest -v -s -k "ollama" --inference-model="meta-llama/Llama-3.1-8B-Instruct" ./llama_stack/providers/tests/inference/test_text_inference.py
```
## 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?
Since provider list returns a map grouping providers by API, we should
not be using data. This PR fixes the types to just be the plain dict,
basically reverting back to previous behavior
## Test Plan
llama-stack on fix-provider-list [$] 🅒 stack❯
LLAMA_STACK_CONFIG="/Users/dineshyv/.llama/distributions/llamastack-together/together-run.yaml"
pytest -v tests/client-sdk/safety/test_safety.py
# What does this PR do?
PR fixes couple of issues with telemetry:
1) The REST refactor changed the method from get_span_tree to
query_span_tree, which is causing the server side to return empty spans
2) Library client has introduced a new event loop, which required
changing the location of where start and end trace are called
## Test Plan
LLAMA_STACK_CONFIG="/Users/dineshyv/.llama/distributions/llamastack-fireworks/fireworks-run.yaml"
pytest -v tests/client-sdk/agents/test_agents.py -k
"test_builtin_tool_web_search"
And querying for spans from the agent run using the library client.
# What does this PR do?
Library client needs to match the impl based on both the path and
method. Since path is no longer static, this PR uses the inefficient way
of using regexes computed based on the annotated route path to match
against the incoming request path. The variables now also can come to
the impl from both path or the body, which is also handled cleanly by
finding all the regex matches.
## Test Plan
LLAMA_STACK_CONFIG="/Users/dineshyv/.llama/distributions/llamastack-together/together-run.yaml"
pytest -v tests/client-sdk/agents/test_agents.py
# 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?
- Add Github workflow for publishing docker images.
- Manual Inputs
- We can use a (1) TestPyPi version / (2) build via released PyPi
version
**Notes**
- Keep this workflow manually triggered as we don't want to publish
nightly docker images
**Additional Changes**
- Resolve issue with running llama stack build in non-terminal device
```
File "/home/runner/.local/lib/python3.12/site-packages/llama_stack/distribution/utils/exec.py", line 25, in run_with_pty
old_settings = termios.tcgetattr(sys.stdin)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
termios.error: (25, 'Inappropriate ioctl for device')
```
- Modified build_container.sh to work in non-terminal environment
## Test Plan
- Triggered workflow:
3562217878
<img width="1076" alt="image"
src="https://github.com/user-attachments/assets/f1b5cef6-05ab-49c7-b405-53abc9264734"
/>
- Tested published docker image
<img width="702" alt="image"
src="https://github.com/user-attachments/assets/e7135189-65c8-45d8-86f9-9f3be70e380b"
/>
- /tools API endpoints are served so that docker is correctly using the
TestPyPi package
<img width="296" alt="image"
src="https://github.com/user-attachments/assets/bbcaa7fe-c0a4-4d22-b600-90e3c254bbfd"
/>
- Published tagged images:
https://hub.docker.com/repositories/llamastack
<img width="947" alt="image"
src="https://github.com/user-attachments/assets/2a0a0494-4d45-4643-bc29-72154ecc54a5"
/>
## 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?
To build a conda env for specific Llama Stack version, e.g.
`PYPI_VERSION=0.0.58 llama stack build --template together --image-type
conda`
will install these in the llamastack-together env:
```
llama_models 0.0.58
llama_stack 0.0.58
llama_stack_client 0.0.58
```
Without `PYPI_VERSION=`, `llama stack build --template together
--image-type conda` installs the latest all.
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.
# What does this PR do?
#### Issue
- Using Jupyter notebook with LlamaStackAsLibraryClient + streaming
gives exception
```
Exception ignored in: <async_generator object HTTP11ConnectionByteStream.__aiter__ at 0x32a95a740>
Traceback (most recent call last):
File "/opt/anaconda3/envs/fresh/lib/python3.11/site-packages/httpcore/_async/connection_pool.py", line 404, in _aiter_
yield part
RuntimeError: async generator ignored GeneratorExit
```
- Reproduce w/
https://github.com/meta-llama/llama-stack/blob/notebook-streaming-debug/inline.ipynb
#### Fix
- Issue likely comes from stream_across_asyncio_run_boundary closing
connection too soon when interacting in jupyter environment
- This uses an alternative way to convert AsyncStream to SyncStream
return type by sync version of LlamaStackAsLibraryClient, which calls
AsyncLlamaStackAsLibraryClient calling async impls under the hood
#### Additional changes
- Moved tracing logic into AsyncLlamaStackAsLibraryClient.request s.t.
streaming / non-streaming request for LlamaStackAsLibraryClient shares
same code
## Test Plan
- Test w/ together & fireworks & ollama with streaming and non-streaming
using notebook in:
https://github.com/meta-llama/llama-stack/blob/notebook-streaming-debug/inline.ipynb
- Note: need to restart kernel and run pip install -e . in jupyter
interpreter for local code change to take effect
<img width="826" alt="image"
src="https://github.com/user-attachments/assets/5f90985d-1aee-452c-a599-2157f5654fea"
/>
## 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.
This was missed during a rebase in
https://github.com/meta-llama/llama-stack/pull/676.
Fixed the following error:
```
Error: crun: executable file `python` not found in $PATH: No such file or directory: OCI runtime attempted to invoke a command that was not found
++ error_handler 88
++ echo 'Error occurred in script at line: 88'
Error occurred in script at line: 88
```
cc @hardikjshah
Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>
# What does this PR do?
This PR adds the provider data passing for the library client and
changes the provider's api keys be unique
## Test Plan
LLAMA_STACK_CONFIG="/Users/dineshyv/.llama/distributions/llamastack-fireworks/fireworks-run.yaml"
pytest -v tests/client-sdk/agents/test_agents.py
run.yaml:
https://gist.github.com/dineshyv/0c10b5c7d0a2fb7ba4f0ecc8dcf860d1
This adds support for [UBI9 (Red Hat Universal Base Image
9)](615bcf606f).
Tested `registry.access.redhat.com/ubi9/ubi-minimal:9.5`.
Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>
# What does this PR do?
Rename environment var for consistency
## Test Plan
No regressions
## Sources
## Before submitting
- [X] 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.
- [ ] Wrote necessary unit or integration tests.
---------
Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>
Co-authored-by: Yuan Tang <terrytangyuan@gmail.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
When we bump up `major.minor` we want to make sure clients can
immediately detect a version change and appropriately error out. It is
not reasonable to keep checking for API-level backwards compatibility
across such version bumps. Over time, we will make the check based only
on the major version perhaps.
### Test Plan
Manually updated `__version__` in the client SDK to be "0.1.0" which is
incompatible with server's current version "0.0.63", got the following
error:
<img width="1077" alt="image"
src="https://github.com/user-attachments/assets/06ae4659-0a25-4c4c-a999-ce44678d4e6f"
/>
Without this update, the CLI worked correctly.
Add another header so client SDKs can identify their versions which can
be used for immediate detection of possible compatibility issues. A
semver mismatch against the wrong server should be immediately flagged
and requests should be denied.
Also change `X-LlamaStack-ProviderData` to `X-LlamaStack-Provider-Data`
since that hyphenation is better.
# 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
This fixes the pre-commit check when running locally (not sure why this
was not caught on CI check):
```
> pre-commit run --show-diff-on-failure --color=always --all-files
trim trailing whitespace.................................................Passed
check python ast.........................................................Passed
check for merge conflicts................................................Passed
check for added large files..............................................Passed
fix end of files.........................................................Passed
Insert license in comments...............................................Passed
flake8...................................................................Failed
- hook id: flake8
- exit code: 1
llama_stack/distribution/ui/page/evaluations/app_eval.py:132:65: E226 missing whitespace around arithmetic operator
llama_stack/distribution/ui/page/evaluations/native_eval.py:235:61: E226 missing whitespace around arithmetic operator
llama_stack/providers/utils/telemetry/trace_protocol.py:56:78: E226 missing whitespace around arithmetic operator
```
Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>
## 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
### Context
This is the 1st of series PRs that integrate torchtune with llama-stack
as meta reference post-training implementation. For MVP, we will focus
on single device LoRA SFT.
Though this PR is still WIP, we want to get early feedback on the high
level design of this skeleton while still working on several details
### Scope
To limit the scope of this PR, we focus on the skeleton of the
implementation.
**What are included?**
- refine the post-training SFT apis
- skeleton of supervised_fine_tune implementation. We verified that we
can call the supervised_fine_tune API successfully from llama stack
client SDK (client side PR:
https://github.com/meta-llama/llama-stack-client-python/pull/51)
- a very basic single device LoRA training recipe based on torchtune
core components
- parity check with torchtune library and post training api unit test
**What are not includes?**
- implementation of other job management, get training artifacts apis
(separate PR)
- refactor the meta reference inference logic to support eval on
finetuned model (separate PR)
- several necessary functionality in the training recipe such as
logging, validation etc (separate PR)
- interop with telemetry for tracing and metrics logging, currently
temporarily log to local disk (separate PR)
### Testing
**e2e test**
Although we haven't added detailed testing and numerical parity check
with torchtune yet, we did a simple E2E test from client to server
1. setup server with` llama stack build --template
experimental-post-training --image-type conda` and `llama stack run
experimental-post-training `
2. On client, run `llama-stack-client --endpoint
http://devgpu018.nha2.facebook.com:5000 post_training
supervised_fine_tune`
3. Training finishes successfully. On server side, get the finetune
checkpoints under output dir. On client side, get the job uuid
server
<img width="1110" alt="Screenshot 2024-12-02 at 5 52 32 PM"
src="https://github.com/user-attachments/assets/b548eb90-7a9b-4edc-a858-ee237cc4361d">
client
<img width="807" alt="Screenshot 2024-12-02 at 5 52 37 PM"
src="https://github.com/user-attachments/assets/1138ffa8-4698-40fa-b190-3d7b99646838">
**parity check**
torchtune dataloader output and llama-stack post training dataloader
output are same
<img width="1116" alt="Screenshot 2024-12-04 at 8 18 46 PM"
src="https://github.com/user-attachments/assets/5e295cdc-4c24-4ea6-82c0-ca96ef1bd6ee">
torchtune LoRA SFT and llama-stack post training LoRA SFT on alpaca
dataset with llama3.2 3B instruct model are numerical match
<img width="860" alt="Screenshot 2024-12-04 at 8 17 01 PM"
src="https://github.com/user-attachments/assets/c05cf0a8-c674-4d2e-9f0a-c5d01b2dca99">
<img width="1049" alt="Screenshot 2024-12-04 at 8 17 06 PM"
src="https://github.com/user-attachments/assets/b911d4e2-e7b1-41a9-b62c-d75529b6d443">
**unit test **
![Uploading Screenshot 2024-12-09 at 1.35.10 PM.png…]()
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.
