groq: add support for accessing reasoning output from Groq models

libs/partners/groq/langchain_groq/chat_models.py

@@ -107,6 +107,12 @@ class ChatGroq(BaseChatModel):
             Sampling temperature. Ranges from 0.0 to 1.0.
         max_tokens: Optional[int]
             Max number of tokens to generate.
+        reasoning_format: Optional[Literal["parsed", "raw", "hidden]]
+            The format for reasoning output.
+
+            - ``parsed``: Separates reasoning into a dedicated field while keeping the response concise.
+            - ``raw``: Includes reasoning within think tags in the content.
+            - ``hidden``: Returns only the final answer.
         model_kwargs: Dict[str, Any]
             Holds any model parameters valid for create call not
             explicitly specified.
@@ -292,7 +298,7 @@ class Joke(BaseModel):
             'system_fingerprint': 'fp_c5f20b5bb1',
             'finish_reason': 'stop',
             'logprobs': None}
-    """
+    """  # noqa: E501
 
     client: Any = Field(default=None, exclude=True)  #: :meta private:
     async_client: Any = Field(default=None, exclude=True)  #: :meta private:
@@ -302,6 +308,13 @@ class Joke(BaseModel):
     """What sampling temperature to use."""
     stop: Optional[Union[list[str], str]] = Field(default=None, alias="stop_sequences")
     """Default stop sequences."""
+    reasoning_format: Optional[Literal["parsed", "raw", "hidden"]] = None
+    """The format for reasoning output.
+
+            - ``parsed``: Separates reasoning into a dedicated field while keeping the response concise.
+            - ``raw``: Includes reasoning within think tags in the content.
+            - ``hidden``: Returns only the final answer.
+    """  # noqa: E501
     model_kwargs: dict[str, Any] = Field(default_factory=dict)
     """Holds any model parameters valid for `create` call not explicitly specified."""
     groq_api_key: Optional[SecretStr] = Field(
@@ -606,6 +619,7 @@ def _default_params(self) -> dict[str, Any]:
             "n": self.n,
             "temperature": self.temperature,
             "stop": self.stop,
+            "reasoning_format": self.reasoning_format,
             **self.model_kwargs,
         }
         if self.max_tokens is not None:
@@ -1153,6 +1167,8 @@ def _convert_chunk_to_message_chunk(
     if role == "user" or default_class == HumanMessageChunk:
         return HumanMessageChunk(content=content)
     elif role == "assistant" or default_class == AIMessageChunk:
+        if reasoning := _dict.get("reasoning"):
+            additional_kwargs["reasoning_content"] = reasoning
         if usage := (chunk.get("x_groq") or {}).get("usage"):
             input_tokens = usage.get("prompt_tokens", 0)
             output_tokens = usage.get("completion_tokens", 0)
@@ -1196,6 +1212,8 @@ def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
     elif role == "assistant":
         content = _dict.get("content", "") or ""
         additional_kwargs: dict = {}
+        if reasoning := _dict.get("reasoning"):
+            additional_kwargs["reasoning_content"] = reasoning
         if function_call := _dict.get("function_call"):
             additional_kwargs["function_call"] = dict(function_call)
         tool_calls = []

libs/partners/groq/tests/integration_tests/test_chat_models.py

@@ -1,7 +1,7 @@
 """Test ChatGroq chat model."""
 
 import json
-from typing import Any, Optional
+from typing import Any, Optional, cast
 
 import pytest
 from langchain_core.messages import (
@@ -212,6 +212,58 @@ async def test_agenerate_streaming() -> None:
             assert generation.text == generation.message.content
 
 
+#
+# Test reasoning output
+#
+def test_reasoning_output_invoke() -> None:
+    """Test reasoning output from ChatGroq with invoke."""
+    chat = ChatGroq(
+        model="deepseek-r1-distill-llama-70b",
+        reasoning_format="parsed",
+    )
+    message = [
+        SystemMessage(
+            content="You are a helpful assistant that translates English to French."
+        ),
+        HumanMessage(content="I love programming."),
+    ]
+    response = chat.invoke(message)
+    assert isinstance(response, AIMessage)
+    assert "reasoning_content" in response.additional_kwargs
+    assert isinstance(response.additional_kwargs["reasoning_content"], str)
+    assert len(response.additional_kwargs["reasoning_content"]) > 0
+
+
+def test_reasoning_output_stream() -> None:
+    """Test reasoning output from ChatGroq with stream."""
+    chat = ChatGroq(
+        model="deepseek-r1-distill-llama-70b",
+        reasoning_format="parsed",
+    )
+    message = [
+        SystemMessage(
+            content="You are a helpful assistant that translates English to French."
+        ),
+        HumanMessage(content="I love programming."),
+    ]
+
+    full_response: Optional[AIMessageChunk] = None
+    for token in chat.stream(message):
+        assert isinstance(token, AIMessageChunk)
+
+        if full_response is None:
+            full_response = token
+        else:
+            # Casting since adding results in a type error
+            full_response = cast(AIMessageChunk, full_response + token)
+
+    assert full_response is not None
+    assert isinstance(full_response, AIMessageChunk)
+    assert "reasoning_content" in full_response.additional_kwargs
+    assert isinstance(full_response.additional_kwargs["reasoning_content"], str)
+    assert len(full_response.additional_kwargs["reasoning_content"]) > 0
+
+
 #
 # Misc tests
 #