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Author SHA1 Message Date
Ihar Hrachyshka
9e6561a1ec
chore: enable pyupgrade fixes (#1806)
# What does this PR do?

The goal of this PR is code base modernization.

Schema reflection code needed a minor adjustment to handle UnionTypes
and collections.abc.AsyncIterator. (Both are preferred for latest Python
releases.)

Note to reviewers: almost all changes here are automatically generated
by pyupgrade. Some additional unused imports were cleaned up. The only
change worth of note can be found under `docs/openapi_generator` and
`llama_stack/strong_typing/schema.py` where reflection code was updated
to deal with "newer" types.

Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>
2025-05-01 14:23:50 -07:00
Ashwin Bharambe
992f865b2e
chore: move embedding deps to RAG tool where they are needed (#1210)
`EMBEDDING_DEPS` were wrongly associated with `vector_io` providers.
They are needed by
https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/utils/memory/vector_store.py#L142
and related code and is used by the RAG tool and as such should only be
needed by the `inline::rag-runtime` provider.
2025-02-21 11:33:41 -08:00
Ashwin Bharambe
0bff6e1658 Move tool_runtime.memory -> tool_runtime.rag 2025-01-22 20:25:02 -08:00
Ashwin Bharambe
f3d8864c36 Rename builtin::memory -> builtin::rag 2025-01-22 20:22:51 -08:00
Ashwin Bharambe
1a7490470a
[memory refactor][3/n] Introduce RAGToolRuntime as a specialized sub-protocol (#832)
See https://github.com/meta-llama/llama-stack/issues/827 for the broader
design.

Third part:
- we need to make `tool_runtime.rag_tool.query_context()` and
`tool_runtime.rag_tool.insert_documents()` methods work smoothly with
complete type safety. To that end, we introduce a sub-resource path
`tool-runtime/rag-tool/` and make changes to the resolver to make things
work.
- the PR updates the agents implementation to directly call these typed
APIs for memory accesses rather than going through the complex, untyped
"invoke_tool" API. the code looks much nicer and simpler (expectedly.)
- there are a number of hacks in the server resolver implementation
still, we will live with some and fix some

Note that we must make sure the client SDKs are able to handle this
subresource complexity also. Stainless has support for subresources, so
this should be possible but beware.

## Test Plan

Our RAG test is sad (doesn't actually test for actual RAG output) but I
verified that the implementation works. I will work on fixing the RAG
test afterwards.

```bash
pytest -s -v tests/agents/test_agents.py -k "rag and together" --safety-shield=meta-llama/Llama-Guard-3-8B
```
2025-01-22 10:04:16 -08:00
Ashwin Bharambe
78a481bb22
[memory refactor][2/n] Update faiss and make it pass tests (#830)
See https://github.com/meta-llama/llama-stack/issues/827 for the broader
design.

Second part:

- updates routing table / router code 
- updates the faiss implementation


## Test Plan

```
pytest -s -v -k sentence test_vector_io.py --env EMBEDDING_DIMENSION=384
```
2025-01-22 10:02:15 -08:00
Dinesh Yeduguru
a5c57cd381
agents to use tools api (#673)
# 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
2025-01-08 19:01:00 -08:00
Xi Yan
3c72c034e6
[remove import *] clean up import *'s (#689)
# What does this PR do?

- as title, cleaning up `import *`'s
- upgrade tests to make them more robust to bad model outputs
- remove import *'s in llama_stack/apis/* (skip __init__ modules)
<img width="465" alt="image"
src="https://github.com/user-attachments/assets/d8339c13-3b40-4ba5-9c53-0d2329726ee2"
/>

- run `sh run_openapi_generator.sh`, no types gets affected

## Test Plan

### Providers Tests

**agents**
```
pytest -v -s llama_stack/providers/tests/agents/test_agents.py -m "together" --safety-shield meta-llama/Llama-Guard-3-8B --inference-model meta-llama/Llama-3.1-405B-Instruct-FP8
```

**inference**
```bash
# meta-reference
torchrun $CONDA_PREFIX/bin/pytest -v -s -k "meta_reference" --inference-model="meta-llama/Llama-3.1-8B-Instruct" ./llama_stack/providers/tests/inference/test_text_inference.py
torchrun $CONDA_PREFIX/bin/pytest -v -s -k "meta_reference" --inference-model="meta-llama/Llama-3.2-11B-Vision-Instruct" ./llama_stack/providers/tests/inference/test_vision_inference.py

# together
pytest -v -s -k "together" --inference-model="meta-llama/Llama-3.1-8B-Instruct" ./llama_stack/providers/tests/inference/test_text_inference.py
pytest -v -s -k "together" --inference-model="meta-llama/Llama-3.2-11B-Vision-Instruct" ./llama_stack/providers/tests/inference/test_vision_inference.py

pytest ./llama_stack/providers/tests/inference/test_prompt_adapter.py 
```

**safety**
```
pytest -v -s llama_stack/providers/tests/safety/test_safety.py -m together --safety-shield meta-llama/Llama-Guard-3-8B
```

**memory**
```
pytest -v -s llama_stack/providers/tests/memory/test_memory.py -m "sentence_transformers" --env EMBEDDING_DIMENSION=384
```

**scoring**
```
pytest -v -s -m llm_as_judge_scoring_together_inference llama_stack/providers/tests/scoring/test_scoring.py --judge-model meta-llama/Llama-3.2-3B-Instruct
pytest -v -s -m basic_scoring_together_inference llama_stack/providers/tests/scoring/test_scoring.py
pytest -v -s -m braintrust_scoring_together_inference llama_stack/providers/tests/scoring/test_scoring.py
```


**datasetio**
```
pytest -v -s -m localfs llama_stack/providers/tests/datasetio/test_datasetio.py
pytest -v -s -m huggingface llama_stack/providers/tests/datasetio/test_datasetio.py
```


**eval**
```
pytest -v -s -m meta_reference_eval_together_inference llama_stack/providers/tests/eval/test_eval.py
pytest -v -s -m meta_reference_eval_together_inference_huggingface_datasetio llama_stack/providers/tests/eval/test_eval.py
```

### Client-SDK Tests
```
LLAMA_STACK_BASE_URL=http://localhost:5000 pytest -v ./tests/client-sdk
```

### llama-stack-apps
```
PORT=5000
LOCALHOST=localhost

python -m examples.agents.hello $LOCALHOST $PORT
python -m examples.agents.inflation $LOCALHOST $PORT
python -m examples.agents.podcast_transcript $LOCALHOST $PORT
python -m examples.agents.rag_as_attachments $LOCALHOST $PORT
python -m examples.agents.rag_with_memory_bank $LOCALHOST $PORT
python -m examples.safety.llama_guard_demo_mm $LOCALHOST $PORT
python -m examples.agents.e2e_loop_with_custom_tools $LOCALHOST $PORT

# Vision model
python -m examples.interior_design_assistant.app
python -m examples.agent_store.app $LOCALHOST $PORT
```

### CLI
```
which llama
llama model prompt-format -m Llama3.2-11B-Vision-Instruct
llama model list
llama stack list-apis
llama stack list-providers inference

llama stack build --template ollama --image-type conda
```

### Distributions Tests
**ollama**
```
llama stack build --template ollama --image-type conda
ollama run llama3.2:1b-instruct-fp16
llama stack run ./llama_stack/templates/ollama/run.yaml --env INFERENCE_MODEL=meta-llama/Llama-3.2-1B-Instruct
```

**fireworks**
```
llama stack build --template fireworks --image-type conda
llama stack run ./llama_stack/templates/fireworks/run.yaml
```

**together**
```
llama stack build --template together --image-type conda
llama stack run ./llama_stack/templates/together/run.yaml
```

**tgi**
```
llama stack run ./llama_stack/templates/tgi/run.yaml --env TGI_URL=http://0.0.0.0:5009 --env INFERENCE_MODEL=meta-llama/Llama-3.1-8B-Instruct
```

## 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.
2024-12-27 15:45:44 -08:00
Dinesh Yeduguru
c8be0bf1c9
Tools API with brave and MCP providers (#639)
This PR adds a new Tools api and adds two tool runtime providers: brave
and MCP.

Test plan:
```
curl -X POST 'http://localhost:5000/alpha/toolgroups/register' \
-H 'Content-Type: application/json' \
-d '{ "tool_group_id": "simple_tool",
  "tool_group": {
    "type": "model_context_protocol",
    "endpoint": {"uri": "http://localhost:56000/sse"}
  },
  "provider_id": "model-context-protocol"
}'

 curl -X POST 'http://localhost:5000/alpha/toolgroups/register' \
-H 'Content-Type: application/json' \
-d '{
  "tool_group_id": "search", "provider_id": "brave-search",
  "tool_group": {
    "type": "user_defined",
    "tools": [
      {
        "name": "brave_search",
        "description": "A web search tool",
        "parameters": [
          {
            "name": "query",
            "parameter_type": "string",
            "description": "The query to search"
          }
        ],
        "metadata": {},
        "tool_prompt_format": "json"
      }
    ]
  }
}'

 curl -X GET http://localhost:5000/alpha/tools/list | jq .
  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100   662  100   662    0     0   333k      0 --:--:-- --:--:-- --:--:--  646k
[
  {
    "identifier": "brave_search",
    "provider_resource_id": "brave_search",
    "provider_id": "brave-search",
    "type": "tool",
    "tool_group": "search",
    "description": "A web search tool",
    "parameters": [
      {
        "name": "query",
        "parameter_type": "string",
        "description": "The query to search"
      }
    ],
    "metadata": {},
    "tool_prompt_format": "json"
  },
  {
    "identifier": "fetch",
    "provider_resource_id": "fetch",
    "provider_id": "model-context-protocol",
    "type": "tool",
    "tool_group": "simple_tool",
    "description": "Fetches a website and returns its content",
    "parameters": [
      {
        "name": "url",
        "parameter_type": "string",
        "description": "URL to fetch"
      }
    ],
    "metadata": {
      "endpoint": "http://localhost:56000/sse"
    },
    "tool_prompt_format": "json"
  }
]

curl -X POST 'http://localhost:5000/alpha/tool-runtime/invoke' \
-H 'Content-Type: application/json' \
-d '{
    "tool_name": "fetch",
    "args": {
        "url": "http://google.com/"
    }
}'

 curl -X POST 'http://localhost:5000/alpha/tool-runtime/invoke' \
-H 'Content-Type: application/json' -H 'X-LlamaStack-ProviderData: {"api_key": "<KEY>"}' \
-d '{
    "tool_name": "brave_search",
    "args": {
        "query": "who is meta ceo"
    }
}'
```
2024-12-19 21:25:17 -08:00