# What does this PR do?
Many of the URLs pointing to the Llama Stack's Read The Docs webpages
were broken, presumably due to recent refactor of the documentation.
This PR fixes all effected URLs throughout the repository.
# What does this PR do?
- Move Llama Stack Playground UI to llama-stack repo under
llama_stack/distribution
- Original PR in llama-stack-apps:
https://github.com/meta-llama/llama-stack-apps/pull/127
## Test Plan
```
cd llama-stack/llama_stack/distribution/ui
streamlit run app.py
```
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [ ] Ran pre-commit to handle lint / formatting issues.
- [ ] Read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
Pull Request section?
- [ ] Updated relevant documentation.
- [ ] Wrote necessary unit or integration tests.
# What does this PR do?
this allows setting an NVIDIA_BASE_URL variable to control the
NVIDIAConfig.url option
## Test Plan
`pytest -s -v --providers inference=nvidia
llama_stack/providers/tests/inference/ --env
NVIDIA_BASE_URL=http://localhost:8000`
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [x] Ran pre-commit to handle lint / formatting issues.
- [x] Read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
Pull Request section?
- [ ] Updated relevant documentation.
- [ ] Wrote necessary unit or integration tests.
# What does this PR do?
- braintrust scoring provider requires OPENAI_API_KEY env variable to be
set
- move this to be able to be set as request headers (e.g. like together
/ fireworks api keys)
- fixes pytest with agents dependency
## Test Plan
**E2E**
```
llama stack run
```
```yaml
scoring:
- provider_id: braintrust-0
provider_type: inline::braintrust
config: {}
```
**Client**
```python
self.client = LlamaStackClient(
base_url=os.environ.get("LLAMA_STACK_ENDPOINT", "http://localhost:5000"),
provider_data={
"openai_api_key": os.environ.get("OPENAI_API_KEY", ""),
},
)
```
- run `llama-stack-client eval run_scoring`
**Unit Test**
```
pytest -v -s -m meta_reference_eval_together_inference eval/test_eval.py
```
```
pytest -v -s -m braintrust_scoring_together_inference scoring/test_scoring.py --env OPENAI_API_KEY=$OPENAI_API_KEY
```
<img width="745" alt="image"
src="https://github.com/user-attachments/assets/68f5cdda-f6c8-496d-8b4f-1b3dabeca9c2">
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [ ] Ran pre-commit to handle lint / formatting issues.
- [ ] Read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
Pull Request section?
- [ ] Updated relevant documentation.
- [ ] Wrote necessary unit or integration tests.
# What does this PR do?
* Add a test fixture for tgi
* Fixes the logic to correctly pass the llama model for chat completion
Fixes#514
## Test Plan
pytest -k "tgi"
llama_stack/providers/tests/inference/test_text_inference.py --env
TGI_URL=http://localhost:$INFERENCE_PORT --env TGI_API_TOKEN=$HF_TOKEN
# What does this PR do?
this PR adds a basic inference adapter to NVIDIA NIMs
what it does -
- chat completion api
- tool calls
- streaming
- structured output
- logprobs
- support hosted NIM on integrate.api.nvidia.com
- support downloaded NIM containers
what it does not do -
- completion api
- embedding api
- vision models
- builtin tools
- have certainty that sampling strategies are correct
## Feature/Issue validation/testing/test plan
`pytest -s -v --providers inference=nvidia
llama_stack/providers/tests/inference/ --env NVIDIA_API_KEY=...`
all tests should pass. there are pydantic v1 warnings.
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [x] Did you read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes?
- [x] Did you write any new necessary tests?
Thanks for contributing 🎉!
# What does this PR do?
Update the llama model supported list for Ollama.
- [x] Addresses issue (#462)
Signed-off-by: Martin Hickey <martin.hickey@ie.ibm.com>
# What does this PR do?
This PR fixes some of the issues with our telemetry setup to enable logs
to be delivered to opentelemetry and jaeger. Main fixes
1) Updates the open telemetry provider to use the latest oltp exports
instead of deprected ones.
2) Adds a tracing middleware, which injects traces into each HTTP
request that the server recieves and this is going to be the root trace.
Previously, we did this in the create_dynamic_route method, which is
actually not the actual exectuion flow, but more of a config and this
causes the traces to end prematurely. Through middleware, we plugin the
trace start and end at the right location.
3) We manage our own methods to create traces and spans and this does
not fit well with Opentelemetry SDK since it does not support provide a
way to take in traces and spans that are already created. it expects us
to use the SDK to create them. For now, I have a hacky approach of just
maintaining a map from our internal telemetry objects to the open
telemetry specfic ones. This is not the ideal solution. I will explore
other ways to get around this issue. for now, to have something that
works, i am going to keep this as is.
Addresses: #509
# What does this PR do?
Safety provider `inline::meta-reference` is now deprecated. However, we
* aren't checking / printing the deprecation message in `llama stack
build`
* make the deprecated (unusable) provider
So I (1) added checking and (2) made `inline::llama-guard` the default
## Test Plan
Before
```
Traceback (most recent call last):
File "/home/dalton/.conda/envs/nov22/bin/llama", line 8, in <module>
sys.exit(main())
File "/home/dalton/all/llama-stack/llama_stack/cli/llama.py", line 46, in main
parser.run(args)
File "/home/dalton/all/llama-stack/llama_stack/cli/llama.py", line 40, in run
args.func(args)
File "/home/dalton/all/llama-stack/llama_stack/cli/stack/build.py", line 177, in _run_stack_build_command
self._run_stack_build_command_from_build_config(build_config)
File "/home/dalton/all/llama-stack/llama_stack/cli/stack/build.py", line 305, in _run_stack_build_command_from_build_config
self._generate_run_config(build_config, build_dir)
File "/home/dalton/all/llama-stack/llama_stack/cli/stack/build.py", line 226, in _generate_run_config
config_type = instantiate_class_type(
File "/home/dalton/all/llama-stack/llama_stack/distribution/utils/dynamic.py", line 12, in instantiate_class_type
module = importlib.import_module(module_name)
File "/home/dalton/.conda/envs/nov22/lib/python3.10/importlib/__init__.py", line 126, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "<frozen importlib._bootstrap>", line 1050, in _gcd_import
File "<frozen importlib._bootstrap>", line 1027, in _find_and_load
File "<frozen importlib._bootstrap>", line 1004, in _find_and_load_unlocked
ModuleNotFoundError: No module named 'llama_stack.providers.inline.safety.meta_reference'
```
After
```
Traceback (most recent call last):
File "/home/dalton/.conda/envs/nov22/bin/llama", line 8, in <module>
sys.exit(main())
File "/home/dalton/all/llama-stack/llama_stack/cli/llama.py", line 46, in main
parser.run(args)
File "/home/dalton/all/llama-stack/llama_stack/cli/llama.py", line 40, in run
args.func(args)
File "/home/dalton/all/llama-stack/llama_stack/cli/stack/build.py", line 177, in _run_stack_build_command
self._run_stack_build_command_from_build_config(build_config)
File "/home/dalton/all/llama-stack/llama_stack/cli/stack/build.py", line 309, in _run_stack_build_command_from_build_config
self._generate_run_config(build_config, build_dir)
File "/home/dalton/all/llama-stack/llama_stack/cli/stack/build.py", line 228, in _generate_run_config
raise InvalidProviderError(p.deprecation_error)
llama_stack.distribution.resolver.InvalidProviderError:
Provider `inline::meta-reference` for API `safety` does not work with the latest Llama Stack.
- if you are using Llama Guard v3, please use the `inline::llama-guard` provider instead.
- if you are using Prompt Guard, please use the `inline::prompt-guard` provider instead.
- if you are using Code Scanner, please use the `inline::code-scanner` provider instead.
```
<img width="469" alt="Screenshot 2024-11-22 at 4 10 24 PM"
src="https://github.com/user-attachments/assets/8c2e09fe-379a-4504-b246-7925f80a6ed6">
## Sources
Please link relevant resources if necessary.
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [x] Ran pre-commit to handle lint / formatting issues.
- [ ] Read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
Pull Request section?
- [ ] Updated relevant documentation.
- [ ] Wrote necessary unit or integration tests.
# 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?
Fix fp8 quantization script.
## Test Plan
```
sh run_quantize_checkpoint.sh localhost fp8 /home/yll/fp8_test/ /home/yll/fp8_test/quantized_2 /home/yll/fp8_test/tokenizer.model 1 1
```
## Sources
Please link relevant resources if necessary.
## 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.
- [x] Wrote necessary unit or integration tests.
Co-authored-by: Yunlu Li <yll@meta.com>
# 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
```
When running with dockers, the idea is that users be able to work purely
with the `llama stack` CLI. They should not need to know about the
existence of any YAMLs unless they need to. This PR enables it.
The docker command now doesn't need to volume mount a yaml and can
simply be:
```bash
docker run -v ~/.llama/:/root/.llama \
--env A=a --env B=b
```
## Test Plan
Check with conda first (no regressions):
```bash
LLAMA_STACK_DIR=. llama stack build --template ollama
llama stack run ollama --port 5001
# server starts up correctly
```
Check with docker
```bash
# build the docker
LLAMA_STACK_DIR=. llama stack build --template ollama --image-type docker
export INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct"
docker run -it -p 5001:5001 \
-v ~/.llama:/root/.llama \
-v $PWD:/app/llama-stack-source \
localhost/distribution-ollama:dev \
--port 5001 \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env OLLAMA_URL=http://host.docker.internal:11434
```
Note that volume mounting to `/app/llama-stack-source` is only needed
because we built the docker with uncommitted source code.
# What does this PR do?
Remove a check which skips provider registration if a resource is
already in stack registry. Since we do not reconcile state with
provider, register should always call into provider's register endpoint.
## Test Plan
```
# stack run
╰─❯ llama stack run /Users/dineshyv/.llama/distributions/llamastack-together/together-run.yaml
#register memory bank
❯ llama-stack-client memory_banks register your_memory_bank_name --type vector --provider-id inline::faiss-0
Memory Bank Configuration:
{
│ 'memory_bank_type': 'vector',
│ 'chunk_size_in_tokens': 512,
│ 'embedding_model': 'all-MiniLM-L6-v2',
│ 'overlap_size_in_tokens': 64
}
#register again
❯ llama-stack-client memory_banks register your_memory_bank_name --type vector --provider-id inline::faiss-0
Memory Bank Configuration:
{
│ 'memory_bank_type': 'vector',
│ 'chunk_size_in_tokens': 512,
│ 'embedding_model': 'all-MiniLM-L6-v2',
│ 'overlap_size_in_tokens': 64
}
```
# 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?
Add Tavily as a built-in search tool, in addition to Brave and Bing.
## Test Plan
It's tested using ollama remote, showing parity to the Brave search
tool.
- Install and run ollama with `ollama run llama3.1:8b-instruct-fp16`
- Build ollama distribution `llama stack build --template ollama
--image-type conda`
- Run ollama `stack run
/$USER/.llama/distributions/llamastack-ollama/ollama-run.yaml --port
5001`
- Client test command: `python - m
agents.test_agents.TestAgents.test_create_agent_turn_with_tavily_search`,
with enviroments:
MASTER_ADDR=0.0.0.0;MASTER_PORT=5001;RANK=0;REMOTE_STACK_HOST=0.0.0.0;REMOTE_STACK_PORT=5001;TAVILY_SEARCH_API_KEY=tvly-<YOUR-KEY>;WORLD_SIZE=1
Test passes on the specific case (ollama remote).
Server output:
```
Listening on ['::', '0.0.0.0']:5001
INFO: Started server process [7220]
INFO: Waiting for application startup.
INFO: Application startup complete.
INFO: Uvicorn running on http://['::', '0.0.0.0']:5001 (Press CTRL+C to quit)
INFO: 127.0.0.1:65209 - "POST /agents/create HTTP/1.1" 200 OK
INFO: 127.0.0.1:65210 - "POST /agents/session/create HTTP/1.1" 200 OK
INFO: 127.0.0.1:65211 - "POST /agents/turn/create HTTP/1.1" 200 OK
role='user' content='What are the latest developments in quantum computing?' context=None
role='assistant' content='' stop_reason=<StopReason.end_of_turn: 'end_of_turn'> tool_calls=[ToolCall(call_id='fc92ccb8-1039-4ce8-ba5e-8f2b0147661c', tool_name=<BuiltinTool.brave_search: 'brave_search'>, arguments={'query': 'latest developments in quantum computing'})]
role='ipython' call_id='fc92ccb8-1039-4ce8-ba5e-8f2b0147661c' tool_name=<BuiltinTool.brave_search: 'brave_search'> content='{"query": "latest developments in quantum computing", "top_k": [{"title": "IBM Unveils 400 Qubit-Plus Quantum Processor and Next-Generation IBM ...", "url": "https://newsroom.ibm.com/2022-11-09-IBM-Unveils-400-Qubit-Plus-Quantum-Processor-and-Next-Generation-IBM-Quantum-System-Two", "content": "This system is targeted to be online by the end of 2023 and will be a building b...<more>...onnect large-scale ...", "url": "https://news.mit.edu/2023/quantum-interconnects-photon-emission-0105", "content": "Quantum computers hold the promise of performing certain tasks that are intractable even on the world\'s most powerful supercomputers. In the future, scientists anticipate using quantum computing to emulate materials systems, simulate quantum chemistry, and optimize hard tasks, with impacts potentially spanning finance to pharmaceuticals.", "score": 0.71721, "raw_content": null}]}'
Assistant: The latest developments in quantum computing include:
* IBM unveiling its 400 qubit-plus quantum processor and next-generation IBM Quantum System Two, which will be a building block of quantum-centric supercomputing.
* The development of utility-scale quantum computing, which can serve as a scientific tool to explore utility-scale classes of problems in chemistry, physics, and materials beyond brute force classical simulation of quantum mechanics.
* The introduction of advanced hardware across IBM's global fleet of 100+ qubit systems, as well as easy-to-use software that users and computational scientists can now obtain reliable results from quantum systems as they map increasingly larger and more complex problems to quantum circuits.
* Research on quantum repeaters, which use defects in diamond to interconnect quantum systems and could provide the foundation for scalable quantum networking.
* The development of a new source of quantum light, which could be used to improve the efficiency of quantum computers.
* The creation of a new mathematical "blueprint" that is accelerating fusion device development using Dyson maps.
* Research on canceling noise to improve quantum devices, with MIT researchers developing a protocol to extend the life of quantum coherence.
```
Verified with tool response. The final model response is updated with
the search requests.
## Sources
## 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: Martin Yuan <myuan@meta.com>
# What does this PR do?
Adds description at the end of successful download the optionally run
the verify md5 checksums command.
## Test Plan
<img width="2004" alt="Screenshot 2024-11-19 at 12 11 37 PM"
src="https://github.com/user-attachments/assets/8d617aef-99f5-4c3b-b93c-eff3e68289ea">
## 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.
- [x] Wrote necessary unit or integration tests.
---------
Co-authored-by: varunfb <vontimitta@devgpu004.eag5.facebook.com>
