# What does this PR do?
This PR proposes updates to the tools API in Inference and Agent.
Goals:
1. Agent's tool specification should be consistent with Inference's tool spec, but with add-ons.
2. Formal types should be defined for built in tools. Currently Agent tools args are untyped, e.g. how does one know that `builtin::rag_tool` takes a `vector_db_ids` param or even how to know 'builtin::rag_tool' is even available (in code, outside of docs)?
Inference:
1. BuiltinTool is to be removed and replaced by a formal `type` parameter.
2. 'brave_search' is replaced by 'web_search' to be more generic. It will still be translated back to brave_search when the prompt is constructed to be consistent with model training.
3. I'm not sure what `photogen` is. Maybe it can be removed?
Agent:
1. Uses the same format as in Inference for builtin tools.
2. New tools types are added, i.e. knowledge_sesarch (currently rag_tool), and MCP tool.
3. Toolgroup as a concept will be removed since it's really only used for MCP.
4. Instead MCPTool is its own type and available tools provided by the server will be expanded by default. Users can specify a subset of tool names if desired.
Example snippet:
```
agent = Agent(
client,
model=model_id,
instructions="You are a helpful assistant. Use the tools you have access to for providing relevant answers.",
tools=[
KnowledgeSearchTool(vector_store_id="1234"),
KnowledgeSearchTool(vector_store_id="5678", name="paper_search", description="Search research papers"),
KnowledgeSearchTool(vector_store_id="1357", name="wiki_search", description="Search wiki pages"),
# no need to register toolgroup, just pass in the server uri
# all available tools will be used
MCPTool(server_uri="http://localhost:8000/sse"),
# can specify a subset of available tools
MCPTool(server_uri="http://localhost:8000/sse", tool_names=["list_directory"]),
MCPTool(server_uri="http://localhost:8000/sse", tool_names=["list_directory"]),
# custom tool
my_custom_tool,
]
)
```
## Test Plan
# What does this PR do?
## Test Plan
# What does this PR do?
## Test Plan
# What does this PR do?
closes#1584
This should be a rather innocuous change.
## Test Plan
Verify that there's no more tool call parsing error for example in issue
<img width="1216" alt="image"
src="https://github.com/user-attachments/assets/a5a6f4e8-2093-4ca2-bc06-794b707a0429"
/>
LLAMA_STACK_CONFIG=fireworks pytest -s -v
tests/integration/agents/test_agents.py --safety-shield
meta-llama/Llama-Guard-3-8B --text-model
meta-llama/Llama-3.1-8B-Instruct
# What does this PR do?
Don't set type variables from register_schema().
`mypy` is not happy about it since type variables are calculated at
runtime and hence the typing hints are not available during static
analysis.
Good news is there is no good reason to set the variables from the
return type.
Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>
Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>
### What does this PR do?
Currently, `ToolCall.arguments` is a `Dict[str, RecursiveType]`.
However, on the client SDK side -- the `RecursiveType` gets deserialized
into a number ( both int and float get collapsed ) and hence when params
are `int` they get converted to float which might break client side
tools that might be doing type checking.
Closes: https://github.com/meta-llama/llama-stack/issues/1683
### Test Plan
Stainless changes --
https://github.com/meta-llama/llama-stack-client-python/pull/204
```
pytest -s -v --stack-config=fireworks tests/integration/agents/test_agents.py --text-model meta-llama/Llama-3.1-8B-Instruct
```
# What does this PR do?
Updated all instances of datetime.now() to use timezone.utc for
consistency in handling time across different systems. This ensures that
timestamps are always in Coordinated Universal Time (UTC), avoiding
issues with time zone discrepancies and promoting uniformity in
time-related data.
Signed-off-by: Sébastien Han <seb@redhat.com>
# Summary:
The current prompt doesn't work well and tend to overindex on tool
calling. This PR is not perfect, but should be an improvement over the
current prompt. We can keep iterating.
# Test Plan:
Ran on a (small) eval with 20 HotpotQA examples.
With current prompt:
https://gist.github.com/ehhuang/9f967e62751907165eb13781ea968f5c
{
│ 'basic::equality': {'accuracy': {'accuracy': 0.2, 'num_correct': 4.0,
'num_total': 20}},
│ 'F1ScoringFn': {
│ │ 'f1_average': 0.25333333333333335,
│ │ 'precision_average': 0.23301767676767676,
│ │ 'recall_average': 0.375
│ }
}
num_tool_calls=[5, 5, 5, 5, 5, 5, 2, 5, 5, 5, 5, 5, 2, 2, 1, 1, 2, 1, 2,
2]
num_examples_with_tool_call=20
num_examples_with_pythontag=0
#########################################################
With new prompt:
https://gist.github.com/ehhuang/6e4a8ecf54db68922c2be8700056f962
{
│ 'basic::equality': {'accuracy': {'accuracy': 0.25, 'num_correct': 5.0,
'num_total': 20}},
│ 'F1ScoringFn': {
│ │ 'f1_average': 0.35579260478321006,
│ │ 'precision_average': 0.32030238933180105,
│ │ 'recall_average': 0.6091666666666666
│ }
}
num_tool_calls=[2, 1, 1, 5, 5, 5, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 3,
2]
num_examples_with_tool_call=20
num_examples_with_pythontag=0
The answers have higher recall, and make fewer tool calls. Note that
these were run with max_infer_iter=5, so the current prompt hits this
limit more often, and without the limit, someitmes goes into infinite
tool calling loop.
The data here is with 3.3-70B. Results are equally poor with either
prompt with 3.2-3B ~30 recall.
llama-models should have extremely minimal cruft. Its sole purpose
should be didactic -- show the simplest implementation of the llama
models and document the prompt formats, etc.
This PR is the complement to
https://github.com/meta-llama/llama-models/pull/279
## Test Plan
Ensure all `llama` CLI `model` sub-commands work:
```bash
llama model list
llama model download --model-id ...
llama model prompt-format -m ...
```
Ran tests:
```bash
cd tests/client-sdk
LLAMA_STACK_CONFIG=fireworks pytest -s -v inference/
LLAMA_STACK_CONFIG=fireworks pytest -s -v vector_io/
LLAMA_STACK_CONFIG=fireworks pytest -s -v agents/
```
Create a fresh venv `uv venv && source .venv/bin/activate` and run
`llama stack build --template fireworks --image-type venv` followed by
`llama stack run together --image-type venv` <-- the server runs
Also checked that the OpenAPI generator can run and there is no change
in the generated files as a result.
```bash
cd docs/openapi_generator
sh run_openapi_generator.sh
```
