# 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?
In short, provide a summary of what this PR does and why. Usually, the
relevant context should be present in a linked issue.
- [x] Addresses issue (#issue)
```
from .nvidia import NVIDIAInferenceAdapter
File "/localhome/local-cdgamarose/llama-stack/llama_stack/providers/remote/inference/nvidia/nvidia.py", line 37, in <module>
from .openai_utils import (
File "/localhome/local-cdgamarose/llama-stack/llama_stack/providers/remote/inference/nvidia/openai_utils.py", line 11, in <module>
from llama_models.llama3.api.datatypes import (
ImportError: cannot import name 'CompletionMessage' from 'llama_models.llama3.api.datatypes' (/localhome/local-cdgamarose/.local/lib/python3.10/site-packages/llama_models/llama3/api/datatypes.py)
++ error_handler 62
```
## Test Plan
Deploy NIM using docker from
https://build.nvidia.com/meta/llama-3_1-8b-instruct?snippet_tab=Docker
```
(lsmyenv) local-cdgamarose@a4u8g-0006:~/llama-stack$ python3 -m pytest -s -v --providers inference=nvidia llama_stack/providers/tests/inference/ --env NVIDIA_BASE_URL=http://localhost:8000 -k test_completion --inference-model Llama3.1-8B-Instruct
======================================================================================== test session starts =========================================================================================
platform linux -- Python 3.10.16, pytest-8.3.4, pluggy-1.5.0 -- /localhome/local-cdgamarose/anaconda3/envs/lsmyenv/bin/python3
cachedir: .pytest_cache
rootdir: /localhome/local-cdgamarose/llama-stack
configfile: pyproject.toml
plugins: anyio-4.7.0, asyncio-0.25.0
asyncio: mode=strict, asyncio_default_fixture_loop_scope=None
collected 24 items / 21 deselected / 3 selected
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_completion[-nvidia] Initializing NVIDIAInferenceAdapter(http://localhost:8000)...
Checking NVIDIA NIM health...
Checking NVIDIA NIM health...
PASSED
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_completion_logprobs[-nvidia] SKIPPED (Other inference providers don't support completion() yet)
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_completion_structured_output[-nvidia] SKIPPED (This test is not quite robust)
====================================================================== 1 passed, 2 skipped, 21 deselected, 2 warnings in 1.57s =======================================================================
```
## 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?
Cerebras is rolling out support for llama 3.3 70b and deprecating llama
3.1 70b. This PR updates the documentation, config, and internal mapping
to reflect this change.
cc: @ashwinb @raghotham
## 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?
**Why**
- When AgentConfig has no `input_shields` / `output_shields` defined, we
still outputs a shield_call step with violation=None. This is impossible
to distinguish the case b/w (1) no violation from running shields v.s.
(2) no shields call
**What**
- We should not have a shield_call step when no `input_shields` /
`output_shields` are defined.
- Also removes a never reached try/catch code block in agent loop.
`run_multiple_shields` is never called in the try block (verified by
stacktrace print)
**Side Note**
- pre-commit fix
## Test Plan
Tested w/ DirectClient via:
https://gist.github.com/yanxi0830/b48f2a53b6f5391b9ff1e39992bc05b3
**No Shields**
<img width="858" alt="image"
src="https://github.com/user-attachments/assets/67319370-329f-4954-bd16-d21ce54c6ebf"
/>
**With Input + Output Shields**
<img width="854" alt="image"
src="https://github.com/user-attachments/assets/75ab1bee-3ba9-4549-ab51-23210be83da7"
/>
**Input Shields Only**
<img width="858" alt="image"
src="https://github.com/user-attachments/assets/1897206b-13dd-4ea5-92c2-b39bf68e9286"
/>
E2E pytest
```
LLAMA_STACK_BASE_URL=http://localhost:5000 pytest -v ./tests/client-sdk/agents/test_agents.py
```
## 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?
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?
**Why**
- huggingface datasets could have extra unused columns, some of these
columns (e.g. images) is unable to be casted as JSON over http requests
for datasetio.
- it is also inefficient to create a new dataset that's a subset of
columns
**Solution**
- drop columns not specified by dataset schema
## Test Plan
Tested with script:
https://gist.github.com/yanxi0830/23be5725e0d82d79e24cc5dd1d21b571
## 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.
The same code is used (inside providers/remote/memory/chroma/chroma.py)
but it is driven by separate configurations and changes which Chroma
client to use. Note that the dependencies are separate
(`chromadb-client` vs `chromadb` -- the latter is a _much_ heavier
package.)
```
pytest -s -v -m chroma memory/test_memory.py --env CHROMA_DB_PATH=/tmp/chroma_test
pytest -s -v -m chroma memory/test_memory.py --env CHROMA_URL=http://localhost:6001
```
# What does this PR do?
add the completion api to the nvidia inference provider
## Test Plan
while running the meta/llama-3.1-8b-instruct NIM from
https://build.nvidia.com/meta/llama-3_1-8b-instruct?snippet_tab=Docker
```
➜ pytest -s -v --providers inference=nvidia llama_stack/providers/tests/inference/ --env NVIDIA_BASE_URL=http://localhost:8000 -k test_completion --inference-model Llama3.1-8B-Instruct
=============================================== test session starts ===============================================
platform linux -- Python 3.10.15, pytest-8.3.3, pluggy-1.5.0 -- /home/matt/.conda/envs/stack/bin/python
cachedir: .pytest_cache
rootdir: /home/matt/Documents/Repositories/meta-llama/llama-stack
configfile: pyproject.toml
plugins: anyio-4.6.2.post1, asyncio-0.24.0, httpx-0.34.0
asyncio: mode=strict, default_loop_scope=None
collected 20 items / 18 deselected / 2 selected
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_completion[-nvidia] PASSED
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_completion_structured_output[-nvidia] SKIPPED
============================= 1 passed, 1 skipped, 18 deselected, 6 warnings in 5.40s =============================
```
the structured output functionality works but the accuracy fails
## 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?
Addresses issue (#391)
- Adds json structured output for vLLM
- Enables structured output tests for vLLM
> Give me a recipe for Spaghetti Bolognaise:
```json
{
"recipe_name": "Spaghetti Bolognaise",
"preamble": "Ah, spaghetti bolognaise - the quintessential Italian dish that fills my kitchen with the aromas of childhood nostalgia. As a child, I would watch my nonna cook up a big pot of spaghetti bolognaise every Sunday, filling our small Italian household with the savory scent of simmering meat and tomatoes. The way the sauce would thicken and the spaghetti would al dente - it was love at first bite. And now, as a chef, I want to share that same love with you, so you can recreate these warm, comforting memories at home.",
"ingredients": [
"500g minced beef",
"1 medium onion, finely chopped",
"2 cloves garlic, minced",
"1 carrot, finely chopped",
" celery, finely chopped",
"1 (28 oz) can whole peeled tomatoes",
"1 tbsp tomato paste",
"1 tsp dried basil",
"1 tsp dried oregano",
"1 tsp salt",
"1/2 tsp black pepper",
"1/2 tsp sugar",
"1 lb spaghetti",
"Grated Parmesan cheese, for serving",
"Extra virgin olive oil, for serving"
],
"steps": [
"Heat a large pot over medium heat and add a generous drizzle of extra virgin olive oil.",
"Add the chopped onion, garlic, carrot, and celery and cook until the vegetables are soft and translucent, about 5-7 minutes.",
"Add the minced beef and cook until browned, breaking it up with a spoon as it cooks.",
"Add the tomato paste and cook for 1-2 minutes, stirring constantly.",
"Add the canned tomatoes, dried basil, dried oregano, salt, black pepper, and sugar. Stir well to combine.",
"Bring the sauce to a simmer and let it cook for 20-30 minutes, stirring occasionally, until the sauce has thickened and the flavors have melded together.",
"While the sauce cooks, bring a large pot of salted water to a boil and cook the spaghetti according to the package instructions until al dente. Reserve 1 cup of pasta water before draining the spaghetti.",
"Add the reserved pasta water to the sauce and stir to combine.",
"Combine the cooked spaghetti and sauce, tossing to coat the pasta evenly.",
"Serve hot, topped with grated Parmesan cheese and a drizzle of extra virgin olive oil.",
"Enjoy!"
]
}
```
Generated with Llama-3.2-3B-Instruct model - pretty good for a 3B
parameter model 👍
## Test Plan
`pytest -v -s
llama_stack/providers/tests/inference/test_text_inference.py -k
llama_3b-vllm_remote`
With the following setup:
```bash
# Environment
export INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
export INFERENCE_PORT=8000
export VLLM_URL=http://localhost:8000/v1
# vLLM server
sudo docker run --gpus all \
-v $STORAGE_DIR/.cache/huggingface:/root/.cache/huggingface \
--env "HUGGING_FACE_HUB_TOKEN=$(cat ~/.cache/huggingface/token)" \
-p 8000:$INFERENCE_PORT \
--ipc=host \
--net=host \
vllm/vllm-openai:v0.6.3.post1 \
--model $INFERENCE_MODEL
# llama-stack server
llama stack build --template remote-vllm --image-type conda && llama stack run distributions/remote-vllm/run.yaml \
--port 5001 \
--env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
```
Results:
```
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_model_list[llama_3b-vllm_remote] PASSED
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_completion[llama_3b-vllm_remote] SKIPPED
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_completions_structured_output[llama_3b-vllm_remote] SKIPPED
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_chat_completion_non_streaming[llama_3b-vllm_remote] PASSED
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_structured_output[llama_3b-vllm_remote] PASSED
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_chat_completion_streaming[llama_3b-vllm_remote] PASSED
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_chat_completion_with_tool_calling[llama_3b-vllm_remote] PASSED
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_chat_completion_with_tool_calling_streaming[llama_3b-vllm_remote] PASSED
================================ 6 passed, 2 skipped, 120 deselected, 2 warnings in 13.26s ================================
```
## Sources
- https://github.com/vllm-project/vllm/discussions/8300
- By default, vLLM uses https://github.com/dottxt-ai/outlines for
structured outputs
[[1](32e7db2536/vllm/engine/arg_utils.py (L279-L280))]
## Before submitting
[N/A] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case)
- [x] Ran pre-commit to handle lint / formatting issues.
- [x] Read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
Pull Request section?
[N/A?] Updated relevant documentation. Couldn't find any relevant
documentation. Lmk if I've missed anything.
- [x] Wrote necessary unit or integration tests.
This PR does a few things:
- it moves "direct client" to llama-stack repo instead of being in the
llama-stack-client-python repo
- renames it to `LlamaStackLibraryClient`
- actually makes synchronous generators work
- makes streaming and non-streaming work properly
In many ways, this PR makes things finally "work"
## Test Plan
See a `library_client_test.py` I added. This isn't really quite a test
yet but it demonstrates that this mode now works. Here's the invocation
and the response:
```
INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct python llama_stack/distribution/tests/library_client_test.py ollama
```
# What does this PR do?
Change the Telemetry API to be able to support different use cases like
returning traces for the UI and ability to export for Evals.
Other changes:
* Add a new trace_protocol decorator to decorate all our API methods so
that any call to them will automatically get traced across all impls.
* There is some issue with the decorator pattern of span creation when
using async generators, where there are multiple yields with in the same
context. I think its much more explicit by using the explicit context
manager pattern using with. I moved the span creations in agent instance
to be using with
* Inject session id at the turn level, which should quickly give us all
traces across turns for a given session
Addresses #509
## Test Plan
```
llama stack run /Users/dineshyv/.llama/distributions/llamastack-together/together-run.yaml
PYTHONPATH=. python -m examples.agents.rag_with_memory_bank localhost 5000
curl -X POST 'http://localhost:5000/alpha/telemetry/query-traces' \
-H 'Content-Type: application/json' \
-d '{
"attribute_filters": [
{
"key": "session_id",
"op": "eq",
"value": "dd667b87-ca4b-4d30-9265-5a0de318fc65" }],
"limit": 100,
"offset": 0,
"order_by": ["start_time"]
}' | jq .
[
{
"trace_id": "6902f54b83b4b48be18a6f422b13e16f",
"root_span_id": "5f37b85543afc15a",
"start_time": "2024-12-04T08:08:30.501587",
"end_time": "2024-12-04T08:08:36.026463"
},
{
"trace_id": "92227dac84c0615ed741be393813fb5f",
"root_span_id": "af7c5bb46665c2c8",
"start_time": "2024-12-04T08:08:36.031170",
"end_time": "2024-12-04T08:08:41.693301"
},
{
"trace_id": "7d578a6edac62f204ab479fba82f77b6",
"root_span_id": "1d935e3362676896",
"start_time": "2024-12-04T08:08:41.695204",
"end_time": "2024-12-04T08:08:47.228016"
},
{
"trace_id": "dbd767d76991bc816f9f078907dc9ff2",
"root_span_id": "f5a7ee76683b9602",
"start_time": "2024-12-04T08:08:47.234578",
"end_time": "2024-12-04T08:08:53.189412"
}
]
curl -X POST 'http://localhost:5000/alpha/telemetry/get-span-tree' \
-H 'Content-Type: application/json' \
-d '{ "span_id" : "6cceb4b48a156913", "max_depth": 2, "attributes_to_return": ["input"] }' | jq .
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 875 100 790 100 85 18462 1986 --:--:-- --:--:-- --:--:-- 20833
{
"span_id": "6cceb4b48a156913",
"trace_id": "dafa796f6aaf925f511c04cd7c67fdda",
"parent_span_id": "892a66d726c7f990",
"name": "retrieve_rag_context",
"start_time": "2024-12-04T09:28:21.781995",
"end_time": "2024-12-04T09:28:21.913352",
"attributes": {
"input": [
"{\"role\":\"system\",\"content\":\"You are a helpful assistant\"}",
"{\"role\":\"user\",\"content\":\"What are the top 5 topics that were explained in the documentation? Only list succinct bullet points.\",\"context\":null}"
]
},
"children": [
{
"span_id": "1a2df181854064a8",
"trace_id": "dafa796f6aaf925f511c04cd7c67fdda",
"parent_span_id": "6cceb4b48a156913",
"name": "MemoryRouter.query_documents",
"start_time": "2024-12-04T09:28:21.787620",
"end_time": "2024-12-04T09:28:21.906512",
"attributes": {
"input": null
},
"children": [],
"status": "ok"
}
],
"status": "ok"
}
```
<img width="1677" alt="Screenshot 2024-12-04 at 9 42 56 AM"
src="https://github.com/user-attachments/assets/4d3cea93-05ce-415a-93d9-4b1628631bf8">
# What does this PR do?
1) Implement `unregister_dataset(dataset_id)` API in both llama stack
routing table and providers: It removes {dataset_id -> Dataset} mapping
from routing table and removes the dataset_id references in provider as
well (ex. for huggingface, we use a KV store to store the dataset id =>
dataset. we delete it during unregistering as well)
2) expose the datasets/unregister_dataset api endpoint
## Test Plan
**Unit test:**
`
pytest llama_stack/providers/tests/datasetio/test_datasetio.py -m
"huggingface" -v -s --tb=short --disable-warnings
`
**Test on endpoint:**
tested llama stack using an ollama distribution template:
1) start an ollama server
2) Start a llama stack server with the default ollama distribution
config + dataset/datasetsio APIs + datasetio provider
```
---- .../ollama-run.yaml
...
apis:
- agents
- inference
- memory
- safety
- telemetry
- datasetio
- datasets
providers:
datasetio:
- provider_id: localfs
provider_type: inline::localfs
config: {}
...
```
saw that the new API showed up in startup script
```
Serving API datasets
GET /alpha/datasets/get
GET /alpha/datasets/list
POST /alpha/datasets/register
POST /alpha/datasets/unregister
```
3) query `/alpha/datasets/unregister` through curl (since we have not implemented unregister api in llama stack client)
```
(base) sxyi@sxyi-mbp llama-stack % llama-stack-client datasets register
--dataset-id sixian --url
https://raw.githubusercontent.com/pytorch/torchtune/main/docs/source/tutorials/chat.rst
--schema {}
(base) sxyi@sxyi-mbp llama-stack % llama-stack-client datasets list
┏━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━┓
┃ identifier ┃ provider_id ┃ metadata ┃ type ┃
┡━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━┩
│ sixian │ localfs │ {} │ dataset │
└────────────┴─────────────┴──────────┴─────────┘
(base) sxyi@sxyi-mbp llama-stack % llama-stack-client datasets register
--dataset-id sixian2 --url
https://raw.githubusercontent.com/pytorch/torchtune/main/docs/source/tutorials/chat.rst
--schema {}
(base) sxyi@sxyi-mbp llama-stack % llama-stack-client datasets list
┏━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━┓
┃ identifier ┃ provider_id ┃ metadata ┃ type ┃
┡━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━┩
│ sixian │ localfs │ {} │ dataset │
│ sixian2 │ localfs │ {} │ dataset │
└────────────┴─────────────┴──────────┴─────────┘
(base) sxyi@sxyi-mbp llama-stack % curl
http://localhost:5001/alpha/datasets/unregister \
-H "Content-Type: application/json" \
-d '{"dataset_id": "sixian"}'
null%
(base) sxyi@sxyi-mbp llama-stack % llama-stack-client datasets list
┏━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━┓
┃ identifier ┃ provider_id ┃ metadata ┃ type ┃
┡━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━┩
│ sixian2 │ localfs │ {} │ dataset │
└────────────┴─────────────┴──────────┴─────────┘
(base) sxyi@sxyi-mbp llama-stack % curl
http://localhost:5001/alpha/datasets/unregister \
-H "Content-Type: application/json" \
-d '{"dataset_id": "sixian2"}'
null%
(base) sxyi@sxyi-mbp llama-stack % llama-stack-client datasets list
```
## Sources
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Ran pre-commit to handle lint / formatting issues.
- [ ] Read the [contributor guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
Pull Request section?
- [ ] Updated relevant documentation.
- [ ] Wrote necessary unit or integration tests.
# What does this PR do?
this allows setting an NVIDIA_BASE_URL variable to control the
NVIDIAConfig.url option
## Test Plan
`pytest -s -v --providers inference=nvidia
llama_stack/providers/tests/inference/ --env
NVIDIA_BASE_URL=http://localhost:8000`
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [x] Ran pre-commit to handle lint / formatting issues.
- [x] Read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
Pull Request section?
- [ ] Updated relevant documentation.
- [ ] Wrote necessary unit or integration tests.
# What does this PR do?
* Add a test fixture for tgi
* Fixes the logic to correctly pass the llama model for chat completion
Fixes#514
## Test Plan
pytest -k "tgi"
llama_stack/providers/tests/inference/test_text_inference.py --env
TGI_URL=http://localhost:$INFERENCE_PORT --env TGI_API_TOKEN=$HF_TOKEN
# What does this PR do?
this PR adds a basic inference adapter to NVIDIA NIMs
what it does -
- chat completion api
- tool calls
- streaming
- structured output
- logprobs
- support hosted NIM on integrate.api.nvidia.com
- support downloaded NIM containers
what it does not do -
- completion api
- embedding api
- vision models
- builtin tools
- have certainty that sampling strategies are correct
## Feature/Issue validation/testing/test plan
`pytest -s -v --providers inference=nvidia
llama_stack/providers/tests/inference/ --env NVIDIA_API_KEY=...`
all tests should pass. there are pydantic v1 warnings.
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [x] Did you read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes?
- [x] Did you write any new necessary tests?
Thanks for contributing 🎉!
# What does this PR do?
Update the llama model supported list for Ollama.
- [x] Addresses issue (#462)
Signed-off-by: Martin Hickey <martin.hickey@ie.ibm.com>
# What does this PR do?
This PR fixes some of the issues with our telemetry setup to enable logs
to be delivered to opentelemetry and jaeger. Main fixes
1) Updates the open telemetry provider to use the latest oltp exports
instead of deprected ones.
2) Adds a tracing middleware, which injects traces into each HTTP
request that the server recieves and this is going to be the root trace.
Previously, we did this in the create_dynamic_route method, which is
actually not the actual exectuion flow, but more of a config and this
causes the traces to end prematurely. Through middleware, we plugin the
trace start and end at the right location.
3) We manage our own methods to create traces and spans and this does
not fit well with Opentelemetry SDK since it does not support provide a
way to take in traces and spans that are already created. it expects us
to use the SDK to create them. For now, I have a hacky approach of just
maintaining a map from our internal telemetry objects to the open
telemetry specfic ones. This is not the ideal solution. I will explore
other ways to get around this issue. for now, to have something that
works, i am going to keep this as is.
Addresses: #509
# What does this PR do?
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?
The chroma provider maintains a cache but does not sync up with chroma
on a cold start. this change adds a fallback to read from chroma on a
cache miss.
## Test Plan
```bash
#start stack
llama stack run /Users/dineshyv/.llama/distributions/llamastack-together/together-run.yaml
# Add documents
PYTHONPATH=. python -m examples.agents.rag_with_memory_bank localhost 5000
No available shields. Disable safety.
Using model: Llama3.1-8B-Instruct
Created session_id=b951b14f-a9d2-43a3-8b80-d80114d58322 for Agent(0687a251-6906-4081-8d4c-f52e19db9dd7)
memory_retrieval> Retrieved context from banks: ['test_bank'].
====
Here are the retrieved documents for relevant context:
=== START-RETRIEVED-CONTEXT ===
id:num-1; content:_
the template from Llama2 to better support multiturn conversations. The same text
in the Lla...
>
inference> Based on the retrieved documentation, the top 5 topics that were explained are:
...............
# Kill stack
# Bootup stack
llama stack run /Users/dineshyv/.llama/distributions/llamastack-together/together-run.yaml
# Run a RAG app with just the agent flow. it discovers the previously added documents
No available shields. Disable safety.
Using model: Llama3.1-8B-Instruct
Created session_id=7a30c1a7-c87e-4787-936c-d0306589fe5d for Agent(b30420f3-c928-498a-887b-d084f0f3806c)
memory_retrieval> Retrieved context from banks: ['test_bank'].
====
Here are the retrieved documents for relevant context:
=== START-RETRIEVED-CONTEXT ===
id:num-1; content:_
the template from Llama2 to better support multiturn conversations. The same text
in the Lla...
>
inference> Based on the provided documentation, the top 5 topics that were explained are:
.....
```
# 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>
# What does this PR do?
- Fix issue w/ llama stack build using together template
<img width="669" alt="image"
src="https://github.com/user-attachments/assets/1cbef052-d902-40b9-98f8-37efb494d117">
- For builds from templates, copy over the
`templates/<template-name>/run.yaml` file to the
`~/.llama/distributions/<name>/<name>-run.yaml` instead of re-building
run config.
## Test Plan
```
$ llama stack build --template together --image-type conda
..
Build spec configuration saved at /opt/anaconda3/envs/llamastack-together/together-build.yaml
Build Successful! Next steps:
1. Set the environment variables: LLAMASTACK_PORT, TOGETHER_API_KEY
2. `llama stack run /Users/xiyan/.llama/distributions/llamastack-together/together-run.yaml`
```
```
$ llama stack run /Users/xiyan/.llama/distributions/llamastack-together/together-run.yaml
```
```
$ llama-stack-client models list
$ pytest -v -s -m remote agents/test_agents.py --env REMOTE_STACK_URL=http://localhost:5000 --inference-model meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo
```
<img width="764" alt="image"
src="https://github.com/user-attachments/assets/b805b6c5-a316-4561-8fe3-24fc3b1f8b80">
## 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?
add more quantized model support for ollama.
- [ ] Addresses issue (#issue)
## Test Plan
Tested with ollama docker that run llama3.2 3b 4bit model.
```
root@docker-desktop:/# ollama ps
NAME ID SIZE PROCESSOR UNTIL
llama3.2:3b a80c4f17acd5 3.5 GB 100% CPU 3 minutes from now
```
## 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?
Automatically generates
- build.yaml
- run.yaml
- run-with-safety.yaml
- parts of markdown docs
for the distributions.
## Test Plan
At this point, this only updates the YAMLs and the docs. Some testing
(especially with ollama and vllm) has been performed but needs to be
much more tested.
# What does this PR do?
- move folder
## Test Plan
**Unit Test**
```
pytest -v -s -m "huggingface" datasetio/test_datasetio.py
```
**E2E**
```
llama stack run
```
```
llama-stack-client eval run_benchmark meta-reference-mmlu --num-examples 5 --output-dir ./ --eval-task-config ~/eval_task_config.json --visualize
```
<img width="657" alt="image"
src="https://github.com/user-attachments/assets/63d53f9d-6c7e-4667-af8c-9d16c91ae6e3">
## 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.
The semantics of an Update on resources is very tricky to reason about
especially for memory banks and models. The best way to go forward here
is for the user to unregister and register a new resource. We don't have
a compelling reason to support update APIs.
Tests:
pytest -v -s llama_stack/providers/tests/memory/test_memory.py -m
"chroma" --env CHROMA_HOST=localhost --env CHROMA_PORT=8000
pytest -v -s llama_stack/providers/tests/memory/test_memory.py -m
"pgvector" --env PGVECTOR_DB=postgres --env PGVECTOR_USER=postgres --env
PGVECTOR_PASSWORD=mysecretpassword --env PGVECTOR_HOST=0.0.0.0
$CONDA_PREFIX/bin/pytest -v -s -m "ollama"
llama_stack/providers/tests/inference/test_model_registration.py
---------
Co-authored-by: Dinesh Yeduguru <dineshyv@fb.com>
# What does this PR do?
This PR kills the notion of "pure passthrough" remote providers. You
cannot specify a single provider you must specify a whole distribution
(stack) as remote.
This PR also significantly fixes / upgrades testing infrastructure so
you can now test against a remotely hosted stack server by just doing
```bash
pytest -s -v -m remote test_agents.py \
--inference-model=Llama3.1-8B-Instruct --safety-shield=Llama-Guard-3-1B \
--env REMOTE_STACK_URL=http://localhost:5001
```
Also fixed `test_agents_persistence.py` (which was broken) and killed
some deprecated testing functions.
## Test Plan
All the tests.
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
# What does this PR do?
This PR kills the notion of "ShieldType". The impetus for this is the
realization:
> Why is keyword llama-guard appearing so many times everywhere,
sometimes with hyphens, sometimes with underscores?
Now that we have a notion of "provider specific resource identifiers"
and "user specific aliases" for those and the fact that this works with
models ("Llama3.1-8B-Instruct" <> "fireworks/llama-3pv1-..."), we can
follow the same rules for Shields.
So each Safety provider can make up a notion of identifiers it has
registered. This already happens with Bedrock correctly. We just
generalize it for Llama Guard, Prompt Guard, etc.
For Llama Guard, we further simplify by just adopting the underlying
model name itself as the identifier! No confusion necessary.
While doing this, I noticed a bug in our DistributionRegistry where we
weren't scoping identifiers by type. Fixed.
## Feature/Issue validation/testing/test plan
Ran (inference, safety, memory, agents) tests with ollama and fireworks
providers.
