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feat(tools)!: substantial clean up of "Tool" related datatypes (#3627)

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This is a sweeping change to clean up some gunk around our "Tool"
definitions.

First, we had two types `Tool` and `ToolDef`. The first of these was a
"Resource" type for the registry but we had stopped registering tools
inside the Registry long back (and only registered ToolGroups.) The
latter was for specifying tools for the Agents API. This PR removes the
former and adds an optional `toolgroup_id` field to the latter.

Secondly, as pointed out by @bbrowning in
https://github.com/llamastack/llama-stack/pull/3003#issuecomment-3245270132,
we were doing a lossy conversion from a full JSON schema from the MCP
tool specification into our ToolDefinition to send it to the model.
There is no necessity to do this -- we ourselves aren't doing any
execution at all but merely passing it to the chat completions API which
supports this. By doing this (and by doing it poorly), we encountered
limitations like not supporting array items, or not resolving $refs,
etc.

To fix this, we replaced the `parameters` field by `{ input_schema,
output_schema }` which can be full blown JSON schemas.

Finally, there were some types in our llama-related chat format
conversion which needed some cleanup. We are taking this opportunity to
clean those up.

This PR is a substantial breaking change to the API. However, given our
window for introducing breaking changes, this suits us just fine. I will
be landing a concurrent `llama-stack-client` change as well since API
shapes are changing.
This commit is contained in:
Ashwin Bharambe 2025-10-02 15:12:03 -07:00 • committed by Raghotham Murthy
parent d933d354e4
commit 2e544ecd8a
179 changed files with 34186 additions and 9171 deletions
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93
llama_stack/providers/utils/inference/openai_compat.py
View file

@ -125,7 +125,6 @@ from llama_stack.models.llama.datatypes import (
StopReason,
ToolCall,
ToolDefinition,
ToolParamDefinition,
)
from llama_stack.providers.utils.inference.prompt_adapter import (
convert_image_content_to_url,
@ -537,18 +536,13 @@ async def convert_message_to_openai_dict(message: Message, download: bool = Fals
if isinstance(tool_name, BuiltinTool):
tool_name = tool_name.value
# arguments_json can be None, so attempt it first and fall back to arguments
if hasattr(tc, "arguments_json") and tc.arguments_json:
arguments = tc.arguments_json
else:
arguments = json.dumps(tc.arguments)
result["tool_calls"].append(
{
"id": tc.call_id,
"type": "function",
"function": {
"name": tool_name,
"arguments": arguments,
"arguments": tc.arguments,
},
}
)
@ -641,7 +635,7 @@ async def convert_message_to_openai_dict_new(
id=tool.call_id,
function=OpenAIFunction(
name=(tool.tool_name if not isinstance(tool.tool_name, BuiltinTool) else tool.tool_name.value),
arguments=json.dumps(tool.arguments),
arguments=tool.arguments, # Already a JSON string, don't double-encode
),
type="function",
)
@ -684,8 +678,7 @@ def convert_tool_call(
valid_tool_call = ToolCall(
call_id=tool_call.id,
tool_name=tool_call.function.name,
arguments=json.loads(tool_call.function.arguments),
arguments_json=tool_call.function.arguments,
arguments=tool_call.function.arguments,
)
except Exception:
return UnparseableToolCall(
@ -745,14 +738,8 @@ def convert_tooldef_to_openai_tool(tool: ToolDefinition) -> dict:
ToolDefinition:
tool_name: str | BuiltinTool
description: Optional[str]
parameters: Optional[Dict[str, ToolParamDefinition]]
ToolParamDefinition:
param_type: str
description: Optional[str]
required: Optional[bool]
default: Optional[Any]
input_schema: Optional[Dict[str, Any]] # JSON Schema
output_schema: Optional[Dict[str, Any]] # JSON Schema (not used by OpenAI)
OpenAI spec -
@ -761,20 +748,11 @@ def convert_tooldef_to_openai_tool(tool: ToolDefinition) -> dict:
"function": {
"name": tool_name,
"description": description,
"parameters": {
"type": "object",
"properties": {
param_name: {
"type": param_type,
"description": description,
"default": default,
},
...
},
"required": [param_name, ...],
},
"parameters": {<JSON Schema>},
},
}
NOTE: OpenAI does not support output_schema, so it is dropped here.
"""
out = {
"type": "function",
@ -783,37 +761,19 @@ def convert_tooldef_to_openai_tool(tool: ToolDefinition) -> dict:
function = out["function"]
if isinstance(tool.tool_name, BuiltinTool):
function.update(name=tool.tool_name.value) # TODO(mf): is this sufficient?
function["name"] = tool.tool_name.value
else:
function.update(name=tool.tool_name)
function["name"] = tool.tool_name
if tool.description:
function.update(description=tool.description)
function["description"] = tool.description
if tool.parameters:
parameters = {
"type": "object",
"properties": {},
}
properties = parameters["properties"]
required = []
for param_name, param in tool.parameters.items():
properties[param_name] = to_openai_param_type(param.param_type)
if param.description:
properties[param_name].update(description=param.description)
if param.default:
properties[param_name].update(default=param.default)
if param.items:
properties[param_name].update(items=param.items)
if param.title:
properties[param_name].update(title=param.title)
if param.required:
required.append(param_name)
if tool.input_schema:
# Pass through the entire JSON Schema as-is
function["parameters"] = tool.input_schema
if required:
parameters.update(required=required)
function.update(parameters=parameters)
# NOTE: OpenAI does not support output_schema, so we drop it here
# It's stored in LlamaStack for validation and other provider usage
return out
@ -874,22 +834,12 @@ def _convert_openai_request_tools(tools: list[dict[str, Any]] | None = None) ->
tool_fn = tool.get("function", {})
tool_name = tool_fn.get("name", None)
tool_desc = tool_fn.get("description", None)
tool_params = tool_fn.get("parameters", None)
lls_tool_params = {}
if tool_params is not None:
tool_param_properties = tool_params.get("properties", {})
for tool_param_key, tool_param_value in tool_param_properties.items():
tool_param_def = ToolParamDefinition(
param_type=str(tool_param_value.get("type", None)),
description=tool_param_value.get("description", None),
)
lls_tool_params[tool_param_key] = tool_param_def
lls_tool = ToolDefinition(
tool_name=tool_name,
description=tool_desc,
parameters=lls_tool_params,
input_schema=tool_params, # Pass through entire JSON Schema
)
lls_tools.append(lls_tool)
return lls_tools
@ -939,8 +889,7 @@ def _convert_openai_tool_calls(
ToolCall(
call_id=call.id,
tool_name=call.function.name,
arguments=json.loads(call.function.arguments),
arguments_json=call.function.arguments,
arguments=call.function.arguments,
)
for call in tool_calls
]
@ -1222,12 +1171,10 @@ async def convert_openai_chat_completion_stream(
)
try:
arguments = json.loads(buffer["arguments"])
tool_call = ToolCall(
call_id=buffer["call_id"],
tool_name=buffer["name"],
arguments=arguments,
arguments_json=buffer["arguments"],
arguments=buffer["arguments"],
)
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
@ -1390,7 +1337,7 @@ class OpenAIChatCompletionToLlamaStackMixin:
openai_tool_call = OpenAIChoiceDeltaToolCall(
index=0,
function=OpenAIChoiceDeltaToolCallFunction(
arguments=tool_call.arguments_json,
arguments=tool_call.arguments,
),
)
delta = OpenAIChoiceDelta(tool_calls=[openai_tool_call])