mistralai[patch]: add missing _combine_llm_outputs implementation in ChatMistralAI (#18603)

# Description
Implementing `_combine_llm_outputs` to `ChatMistralAI` to override the
default implementation in `BaseChatModel` returning `{}`. The
implementation is inspired by the one in `ChatOpenAI` from package
`langchain-openai`.
# Issue
None
# Dependencies
None
# Twitter handle
None

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>

libs/partners/mistralai/langchain_mistralai/chat_models.py

@@ -250,6 +250,22 @@ class ChatMistralAI(BaseChatModel):
         rtn = _completion_with_retry(**kwargs)
         return rtn
 
+    def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict:
+        overall_token_usage: dict = {}
+        for output in llm_outputs:
+            if output is None:
+                # Happens in streaming
+                continue
+            token_usage = output["token_usage"]
+            if token_usage is not None:
+                for k, v in token_usage.items():
+                    if k in overall_token_usage:
+                        overall_token_usage[k] += v
+                    else:
+                        overall_token_usage[k] = v
+        combined = {"token_usage": overall_token_usage, "model_name": self.model}
+        return combined
+
     @root_validator()
     def validate_environment(cls, values: Dict) -> Dict:
         """Validate api key, python package exists, temperature, and top_p."""

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

@@ -3,7 +3,7 @@
 import json
 from typing import Any
 
-from langchain_core.messages import AIMessageChunk
+from langchain_core.messages import AIMessageChunk, HumanMessage
 from langchain_core.pydantic_v1 import BaseModel
 
 from langchain_mistralai.chat_models import ChatMistralAI
@@ -70,6 +70,50 @@ def test_invoke() -> None:
     assert isinstance(result.content, str)
 
 
+def test_chat_mistralai_llm_output_contains_model_name() -> None:
+    """Test llm_output contains model_name."""
+    chat = ChatMistralAI(max_tokens=10)
+    message = HumanMessage(content="Hello")
+    llm_result = chat.generate([[message]])
+    assert llm_result.llm_output is not None
+    assert llm_result.llm_output["model_name"] == chat.model
+
+
+def test_chat_mistralai_streaming_llm_output_contains_model_name() -> None:
+    """Test llm_output contains model_name."""
+    chat = ChatMistralAI(max_tokens=10, streaming=True)
+    message = HumanMessage(content="Hello")
+    llm_result = chat.generate([[message]])
+    assert llm_result.llm_output is not None
+    assert llm_result.llm_output["model_name"] == chat.model
+
+
+def test_chat_mistralai_llm_output_contains_token_usage() -> None:
+    """Test llm_output contains model_name."""
+    chat = ChatMistralAI(max_tokens=10)
+    message = HumanMessage(content="Hello")
+    llm_result = chat.generate([[message]])
+    assert llm_result.llm_output is not None
+    assert "token_usage" in llm_result.llm_output
+    token_usage = llm_result.llm_output["token_usage"]
+    assert "prompt_tokens" in token_usage
+    assert "completion_tokens" in token_usage
+    assert "total_tokens" in token_usage
+
+
+def test_chat_mistralai_streaming_llm_output_contains_token_usage() -> None:
+    """Test llm_output contains model_name."""
+    chat = ChatMistralAI(max_tokens=10, streaming=True)
+    message = HumanMessage(content="Hello")
+    llm_result = chat.generate([[message]])
+    assert llm_result.llm_output is not None
+    assert "token_usage" in llm_result.llm_output
+    token_usage = llm_result.llm_output["token_usage"]
+    assert "prompt_tokens" in token_usage
+    assert "completion_tokens" in token_usage
+    assert "total_tokens" in token_usage
+
+
 def test_structured_output() -> None:
     llm = ChatMistralAI(model="mistral-large-latest", temperature=0)
     schema = {