groq: add back streaming tool calls (#26391)

api no longer throws an error

https://console.groq.com/docs/tool-use#streaming

libs/partners/groq/langchain_groq/chat_models.py

@@ -52,7 +52,6 @@ from langchain_core.messages import (
     ToolMessage,
     ToolMessageChunk,
 )
-from langchain_core.messages.tool import tool_call_chunk as create_tool_call_chunk
 from langchain_core.output_parsers import (
     JsonOutputParser,
     PydanticOutputParser,
@@ -385,9 +384,11 @@ class ChatGroq(BaseChatModel):
             values["temperature"] = 1e-8
 
         client_params = {
-            "api_key": values["groq_api_key"].get_secret_value()
-            if values["groq_api_key"]
-            else None,
+            "api_key": (
+                values["groq_api_key"].get_secret_value()
+                if values["groq_api_key"]
+                else None
+            ),
             "base_url": values["groq_api_base"],
             "timeout": values["request_timeout"],
             "max_retries": values["max_retries"],
@@ -502,42 +503,6 @@ class ChatGroq(BaseChatModel):
     ) -> Iterator[ChatGenerationChunk]:
         message_dicts, params = self._create_message_dicts(messages, stop)
 
-        # groq api does not support streaming with tools yet
-        if "tools" in kwargs:
-            response = self.client.create(
-                messages=message_dicts, **{**params, **kwargs}
-            )
-            chat_result = self._create_chat_result(response)
-            generation = chat_result.generations[0]
-            message = cast(AIMessage, generation.message)
-            tool_call_chunks = [
-                create_tool_call_chunk(
-                    name=rtc["function"].get("name"),
-                    args=rtc["function"].get("arguments"),
-                    id=rtc.get("id"),
-                    index=rtc.get("index"),
-                )
-                for rtc in message.additional_kwargs.get("tool_calls", [])
-            ]
-            chunk_ = ChatGenerationChunk(
-                message=AIMessageChunk(
-                    content=message.content,
-                    additional_kwargs=message.additional_kwargs,
-                    tool_call_chunks=tool_call_chunks,
-                    usage_metadata=message.usage_metadata,
-                ),
-                generation_info=generation.generation_info,
-            )
-            if run_manager:
-                geninfo = chunk_.generation_info or {}
-                run_manager.on_llm_new_token(
-                    chunk_.text,
-                    chunk=chunk_,
-                    logprobs=geninfo.get("logprobs"),
-                )
-            yield chunk_
-            return
-
         params = {**params, **kwargs, "stream": True}
 
         default_chunk_class: Type[BaseMessageChunk] = AIMessageChunk
@@ -574,42 +539,6 @@ class ChatGroq(BaseChatModel):
     ) -> AsyncIterator[ChatGenerationChunk]:
         message_dicts, params = self._create_message_dicts(messages, stop)
 
-        # groq api does not support streaming with tools yet
-        if "tools" in kwargs:
-            response = await self.async_client.create(
-                messages=message_dicts, **{**params, **kwargs}
-            )
-            chat_result = self._create_chat_result(response)
-            generation = chat_result.generations[0]
-            message = cast(AIMessage, generation.message)
-            tool_call_chunks = [
-                {
-                    "name": rtc["function"].get("name"),
-                    "args": rtc["function"].get("arguments"),
-                    "id": rtc.get("id"),
-                    "index": rtc.get("index"),
-                }
-                for rtc in message.additional_kwargs.get("tool_calls", [])
-            ]
-            chunk_ = ChatGenerationChunk(
-                message=AIMessageChunk(
-                    content=message.content,
-                    additional_kwargs=message.additional_kwargs,
-                    tool_call_chunks=tool_call_chunks,  # type: ignore[arg-type]
-                    usage_metadata=message.usage_metadata,
-                ),
-                generation_info=generation.generation_info,
-            )
-            if run_manager:
-                geninfo = chunk_.generation_info or {}
-                await run_manager.on_llm_new_token(
-                    chunk_.text,
-                    chunk=chunk_,
-                    logprobs=geninfo.get("logprobs"),
-                )
-            yield chunk_
-            return
-
         params = {**params, **kwargs, "stream": True}
 
         default_chunk_class: Type[BaseMessageChunk] = AIMessageChunk

libs/partners/groq/tests/unit_tests/__snapshots__/test_standard.ambr

@@ -1,42 +1,6 @@
 # serializer version: 1
 # name: TestGroqStandard.test_serdes[serialized]
   dict({
-    'graph': dict({
-      'edges': list([
-        dict({
-          'source': 0,
-          'target': 1,
-        }),
-        dict({
-          'source': 1,
-          'target': 2,
-        }),
-      ]),
-      'nodes': list([
-        dict({
-          'data': 'ChatGroqInput',
-          'id': 0,
-          'type': 'schema',
-        }),
-        dict({
-          'data': dict({
-            'id': list([
-              'langchain_groq',
-              'chat_models',
-              'ChatGroq',
-            ]),
-            'name': 'ChatGroq',
-          }),
-          'id': 1,
-          'type': 'runnable',
-        }),
-        dict({
-          'data': 'ChatGroqOutput',
-          'id': 2,
-          'type': 'schema',
-        }),
-      ]),
-    }),
     'id': list([
       'langchain_groq',
       'chat_models',