PR tool call followups

llama_stack/providers/remote/inference/groq/groq_utils.py

@@ -6,7 +6,7 @@
 
 import json
 import warnings
-from typing import AsyncGenerator, Literal
+from typing import AsyncGenerator, Literal, Union
 
 from groq import Stream
 from groq.types.chat.chat_completion import ChatCompletion
@@ -30,6 +30,8 @@ from groq.types.shared.function_definition import FunctionDefinition
 
 from llama_models.llama3.api.datatypes import ToolParamDefinition
 
+from pydantic import BaseModel
+
 from llama_stack.apis.common.content_types import (
     TextDelta,
     ToolCallDelta,
@@ -150,15 +152,26 @@ def convert_chat_completion_response(
             _convert_groq_tool_call(tool_call)
             for tool_call in choice.message.tool_calls
         ]
-        return ChatCompletionResponse(
-            completion_message=CompletionMessage(
-                tool_calls=tool_calls,
-                stop_reason=StopReason.end_of_message,
-                # Content is not optional
-                content="",
-            ),
-            logprobs=None,
-        )
+        if any(isinstance(tool_call, UnparseableToolCall) for tool_call in tool_calls):
+            # If we couldn't parse a tool call, jsonify the tool calls and return them
+            return ChatCompletionResponse(
+                completion_message=CompletionMessage(
+                    stop_reason=StopReason.end_of_message,
+                    content=json.dumps(tool_calls, default=lambda x: x.model_dump()),
+                ),
+                logprobs=None,
+            )
+        else:
+            # Otherwise, return tool calls as normal
+            return ChatCompletionResponse(
+                completion_message=CompletionMessage(
+                    tool_calls=tool_calls,
+                    stop_reason=StopReason.end_of_message,
+                    # Content is not optional
+                    content="",
+                ),
+                logprobs=None,
+            )
     else:
         return ChatCompletionResponse(
             completion_message=CompletionMessage(
@@ -214,15 +227,27 @@ async def convert_chat_completion_response_stream(
 
             # We assume Groq produces fully formed tool calls for each chunk
             tool_call = _convert_groq_tool_call(choice.delta.tool_calls[0])
-            yield ChatCompletionResponseStreamChunk(
-                event=ChatCompletionResponseEvent(
-                    event_type=event_type,
-                    delta=ToolCallDelta(
-                        content=tool_call,
-                        parse_status=ToolCallParseStatus.succeeded,
-                    ),
+            if isinstance(tool_call, ToolCall):
+                yield ChatCompletionResponseStreamChunk(
+                    event=ChatCompletionResponseEvent(
+                        event_type=event_type,
+                        delta=ToolCallDelta(
+                            content=tool_call,
+                            parse_status=ToolCallParseStatus.succeeded,
+                        ),
+                    )
+                )
+            else:
+                # Otherwise it's an UnparseableToolCall - return the raw tool call
+                yield ChatCompletionResponseStreamChunk(
+                    event=ChatCompletionResponseEvent(
+                        event_type=event_type,
+                        delta=ToolCallDelta(
+                            content=tool_call.model_dump_json(),
+                            parse_status=ToolCallParseStatus.failed,
+                        ),
+                    )
                 )
-            )
         else:
             yield ChatCompletionResponseStreamChunk(
                 event=ChatCompletionResponseEvent(
@@ -234,12 +259,35 @@ async def convert_chat_completion_response_stream(
         event_type = ChatCompletionResponseEventType.progress
 
 
-def _convert_groq_tool_call(tool_call: ChatCompletionMessageToolCall) -> ToolCall:
+class UnparseableToolCall(BaseModel):
+    """
+    A ToolCall with arguments that are not valid JSON.
+    Mirrors the ToolCall schema, but with arguments as a string.
+    """
+
+    call_id: str
+    tool_name: str
+    arguments: str
+
+
+def _convert_groq_tool_call(
+    tool_call: ChatCompletionMessageToolCall,
+) -> Union[ToolCall, UnparseableToolCall]:
+    """
+    Convert a Groq tool call to a ToolCall.
+    Returns an UnparseableToolCall if the tool call is not valid JSON.
+    """
+    try:
+        arguments = json.loads(tool_call.function.arguments)
+    except Exception as e:
+        return UnparseableToolCall(
+            call_id=tool_call.id,
+            tool_name=tool_call.function.name,
+            arguments=tool_call.function.arguments,
+        )
+
     return ToolCall(
         call_id=tool_call.id,
         tool_name=tool_call.function.name,
-        # Note that Groq may return a string that is not valid JSON here
-        # So this may raise a 500 error. Going to leave this as is to see
-        # how big of an issue this is and what we can do about it.
-        arguments=json.loads(tool_call.function.arguments),
+        arguments=arguments,
     )

llama_stack/providers/tests/inference/groq/test_groq_utils.py

@@ -23,6 +23,7 @@ from groq.types.chat.chat_completion_message_tool_call import (
 from groq.types.shared.function_definition import FunctionDefinition
 from llama_models.datatypes import GreedySamplingStrategy, TopPSamplingStrategy
 from llama_models.llama3.api.datatypes import ToolParamDefinition
+from llama_stack.apis.common.content_types import ToolCallParseStatus
 from llama_stack.apis.inference import (
     ChatCompletionRequest,
     ChatCompletionResponseEventType,
@@ -347,6 +348,26 @@ class TestConvertNonStreamChatCompletionResponse:
             ),
         ]
 
+    def test_converts_unparseable_tool_calls(self):
+        response = self._dummy_chat_completion_response_with_tool_call()
+        response.choices[0].message.tool_calls = [
+            ChatCompletionMessageToolCall(
+                id="tool_call_id",
+                type="function",
+                function=Function(
+                    name="log",
+                    arguments="(number=10, base=2)",
+                ),
+            ),
+        ]
+
+        converted = convert_chat_completion_response(response)
+
+        assert (
+            converted.completion_message.content
+            == '[{"call_id": "tool_call_id", "tool_name": "log", "arguments": "(number=10, base=2)"}]'
+        )
+
     def _dummy_chat_completion_response(self):
         return ChatCompletion(
             id="chatcmpl-123",
@@ -478,6 +499,40 @@ class TestConvertStreamChatCompletionResponse:
             arguments={"origin": "AU", "destination": "LAX"},
         )
 
+    @pytest.mark.asyncio
+    async def test_returns_tool_calls_stream_with_unparseable_tool_calls(self):
+        def tool_call_stream():
+            chunk = self._dummy_chat_completion_chunk_with_tool_call()
+            chunk.choices[0].delta.tool_calls = [
+                ChoiceDeltaToolCall(
+                    index=0,
+                    type="function",
+                    id="tool_call_id",
+                    function=ChoiceDeltaToolCallFunction(
+                        name="get_flight_info",
+                        arguments="(origin=AU, destination=LAX)",
+                    ),
+                ),
+            ]
+            yield chunk
+
+            chunk = self._dummy_chat_completion_chunk_with_tool_call()
+            chunk.choices[0].delta.content = None
+            chunk.choices[0].finish_reason = "stop"
+            yield chunk
+
+        stream = tool_call_stream()
+        converted = convert_chat_completion_response_stream(stream)
+
+        iter = converted.__aiter__()
+        chunk = await iter.__anext__()
+        assert chunk.event.event_type == ChatCompletionResponseEventType.start
+        assert (
+            chunk.event.delta.content
+            == '{"call_id":"tool_call_id","tool_name":"get_flight_info","arguments":"(origin=AU, destination=LAX)"}'
+        )
+        assert chunk.event.delta.parse_status == ToolCallParseStatus.failed
+
     def _dummy_chat_completion_chunk(self):
         return ChatCompletionChunk(
             id="chatcmpl-123",

llama_stack/providers/tests/inference/test_text_inference.py

@@ -377,13 +377,6 @@ class TestInference:
         sample_tool_definition,
     ):
         inference_impl, _ = inference_stack
-        provider = inference_impl.routing_table.get_provider_impl(inference_model)
-        if (
-            provider.__provider_spec__.provider_type == "remote::groq"
-            and "Llama-3.2" in inference_model
-        ):
-            # TODO(aidand): Remove this skip once Groq's tool calling for Llama3.2 works better
-            pytest.skip("Groq's tool calling for Llama3.2 doesn't work very well")
 
         messages = sample_messages + [
             UserMessage(