# 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?
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?
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?
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.
# 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.
# What does this PR do?
This PR brings back the facility to not force registration of resources
onto the user. This is not just annoying but actually not feasible
sometimes. For example, you may have a Stack which boots up with private
providers for inference for models A and B. There is no way for the user
to actually know which model is being served by these providers now (to
be able to register it.)
How will this avoid the users needing to do registration? In a follow-up
diff, I will make sure I update the sample run.yaml files so they list
the models served by the distributions explicitly. So when users do
`llama stack build --template <...>` and run it, their distributions
come up with the right set of models they expect.
For self-hosted distributions, it also allows us to have a place to
explicit list the models that need to be served to make the "complete"
stack (including safety, e.g.)
## Test Plan
Started ollama locally with two lightweight models: Llama3.2-3B-Instruct
and Llama-Guard-3-1B.
Updated all the tests including agents. Here's the tests I ran so far:
```bash
pytest -s -v -m "fireworks and llama_3b" test_text_inference.py::TestInference \
--env FIREWORKS_API_KEY=...
pytest -s -v -m "ollama and llama_3b" test_text_inference.py::TestInference
pytest -s -v -m ollama test_safety.py
pytest -s -v -m faiss test_memory.py
pytest -s -v -m ollama test_agents.py \
--inference-model=Llama3.2-3B-Instruct --safety-model=Llama-Guard-3-1B
```
Found a few bugs here and there pre-existing that these test runs fixed.
* Significantly simpler and malleable test setup
* convert memory tests
* refactor fixtures and add support for composable fixtures
* Fix memory to use the newer fixture organization
* Get agents tests working
* Safety tests work
* yet another refactor to make this more general
now it accepts --inference-model, --safety-model options also
* get multiple providers working for meta-reference (for inference + safety)
* Add README.md
---------
Co-authored-by: Ashwin Bharambe <ashwin@meta.com>
PR #201 had made several changes while trying to fix issues with getting the stream=False branches of inference and agents API working. As part of this, it made a change which was slightly gratuitous. Namely, making chat_completion() and brethren "def" instead of "async def".
The rationale was that this allowed the user (within llama-stack) of this to use it as:
```
async for chunk in api.chat_completion(params)
```
However, it causes unnecessary confusion for several folks. Given that clients (e.g., llama-stack-apps) anyway use the SDK methods (which are completely isolated) this choice was not ideal. Let's revert back so the call now looks like:
```
async for chunk in await api.chat_completion(params)
```
Bonus: Added a completion() implementation for the meta-reference provider. Technically should have been another PR :)
This PR makes several core changes to the developer experience surrounding Llama Stack.
Background: PR #92 introduced the notion of "routing" to the Llama Stack. It introduces three object types: (1) models, (2) shields and (3) memory banks. Each of these objects can be associated with a distinct provider. So you can get model A to be inferenced locally while model B, C can be inference remotely (e.g.)
However, this had a few drawbacks:
you could not address the provider instances -- i.e., if you configured "meta-reference" with a given model, you could not assign an identifier to this instance which you could re-use later.
the above meant that you could not register a "routing_key" (e.g. model) dynamically and say "please use this existing provider I have already configured" for a new model.
the terms "routing_table" and "routing_key" were exposed directly to the user. in my view, this is way too much overhead for a new user (which almost everyone is.) people come to the stack wanting to do ML and encounter a completely unexpected term.
What this PR does: This PR structures the run config with only a single prominent key:
- providers
Providers are instances of configured provider types. Here's an example which shows two instances of the remote::tgi provider which are serving two different models.
providers:
inference:
- provider_id: foo
provider_type: remote::tgi
config: { ... }
- provider_id: bar
provider_type: remote::tgi
config: { ... }
Secondly, the PR adds dynamic registration of { models | shields | memory_banks } to the API surface. The distribution still acts like a "routing table" (as previously) except that it asks the backing providers for a listing of these objects. For example it asks a TGI or Ollama inference adapter what models it is serving. Only the models that are being actually served can be requested by the user for inference. Otherwise, the Stack server will throw an error.
When dynamically registering these objects, you can use the provider IDs shown above. Info about providers can be obtained using the Api.inspect set of endpoints (/providers, /routes, etc.)
The above examples shows the correspondence between inference providers and models registry items. Things work similarly for the safety <=> shields and memory <=> memory_banks pairs.
Registry: This PR also makes it so that Providers need to implement additional methods for registering and listing objects. For example, each Inference provider is now expected to implement the ModelsProtocolPrivate protocol (naming is not great!) which consists of two methods
register_model
list_models
The goal is to inform the provider that a certain model needs to be supported so the provider can make any relevant backend changes if needed (or throw an error if the model cannot be supported.)
There are many other cleanups included some of which are detailed in a follow-up comment.
