ChatBedrock: add usage metadata

libs/aws/langchain_aws/chat_models/bedrock.py

@@ -31,6 +31,7 @@
     HumanMessage,
     SystemMessage,
 )
+from langchain_core.messages.ai import UsageMetadata
 from langchain_core.messages.tool import ToolCall, ToolMessage
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
 from langchain_core.pydantic_v1 import BaseModel, Extra
@@ -484,9 +485,15 @@ def _stream(
             **kwargs,
         ):
             delta = chunk.text
+            if generation_info := chunk.generation_info:
+                usage_metadata = generation_info.pop("usage_metadata", None)
+            else:
+                usage_metadata = None
             yield ChatGenerationChunk(
                 message=AIMessageChunk(
-                    content=delta, response_metadata=chunk.generation_info
+                    content=delta,
+                    response_metadata=chunk.generation_info,
+                    usage_metadata=usage_metadata,
                 )
                 if chunk.generation_info is not None
                 else AIMessageChunk(content=delta)
@@ -550,16 +557,31 @@ def _generate(
                 messages=formatted_messages,
                 **params,
             )
-
+        # usage metadata
+        if usage := llm_output.get("usage"):
+            input_tokens = usage.get("prompt_tokens", 0)
+            output_tokens = usage.get("completion_tokens", 0)
+            usage_metadata = UsageMetadata(
+                input_tokens=input_tokens,
+                output_tokens=output_tokens,
+                total_tokens=usage.get("total_tokens", input_tokens + output_tokens),
+            )
+        else:
+            usage_metadata = None
         llm_output["model_id"] = self.model_id
         if len(tool_calls) > 0:
             msg = AIMessage(
                 content=completion,
                 additional_kwargs=llm_output,
                 tool_calls=cast(List[ToolCall], tool_calls),
+                usage_metadata=usage_metadata,
             )
         else:
-            msg = AIMessage(content=completion, additional_kwargs=llm_output)
+            msg = AIMessage(
+                content=completion,
+                additional_kwargs=llm_output,
+                usage_metadata=usage_metadata,
+            )
         return ChatResult(
             generations=[
                 ChatGeneration(

libs/aws/langchain_aws/llms/bedrock.py

@@ -180,6 +180,19 @@ def _combine_generation_info_for_llm_result(
     return {"usage": total_usage_info, "stop_reason": stop_reason}
 
 
+def _get_invocation_metrics_chunk(chunk: Dict[str, Any]) -> GenerationChunk:
+    generation_info = {}
+    if metrics := chunk.get("amazon-bedrock-invocationMetrics"):
+        input_tokens = metrics.get("inputTokenCount", 0)
+        output_tokens = metrics.get("outputTokenCount", 0)
+        generation_info["usage_metadata"] = {
+            "input_tokens": input_tokens,
+            "output_tokens": output_tokens,
+            "total_tokens": input_tokens + output_tokens,
+        }
+    return GenerationChunk(text="", generation_info=generation_info)
+
+
 def extract_tool_calls(content: List[dict]) -> List[ToolCall]:
     tool_calls = []
     for block in content:
@@ -330,9 +343,11 @@ def prepare_output_stream(
                 provider == "mistral"
                 and chunk_obj.get(output_key, [{}])[0].get("stop_reason", "") == "stop"
             ):
+                yield _get_invocation_metrics_chunk(chunk_obj)
                 return
 
             elif messages_api and (chunk_obj.get("type") == "message_stop"):
+                yield _get_invocation_metrics_chunk(chunk_obj)
                 return
 
             generation_chunk = _stream_response_to_generation_chunk(

libs/aws/tests/integration_tests/chat_models/test_bedrock.py

@@ -1,6 +1,6 @@
 """Test Bedrock chat model."""
 import json
-from typing import Any, cast
+from typing import Any
 
 import pytest
 from langchain_core.messages import (
@@ -124,22 +124,54 @@ def on_llm_end(
 
 
 @pytest.mark.scheduled
-def test_bedrock_streaming(chat: ChatBedrock) -> None:
-    """Test streaming tokens from OpenAI."""
-
+@pytest.mark.parametrize(
+    "model_id",
+    [
+        "anthropic.claude-3-sonnet-20240229-v1:0",
+        "mistral.mistral-7b-instruct-v0:2",
+    ],
+)
+def test_bedrock_streaming(model_id: str) -> None:
+    chat = ChatBedrock(
+        model_id=model_id,
+        model_kwargs={"temperature": 0},
+    )  # type: ignore[call-arg]
     full = None
     for token in chat.stream("I'm Pickle Rick"):
         full = token if full is None else full + token  # type: ignore[operator]
         assert isinstance(token.content, str)
-    assert isinstance(cast(AIMessageChunk, full).content, str)
+    assert isinstance(full, AIMessageChunk)
+    assert isinstance(full.content, str)
+    assert full.usage_metadata is not None
+    assert full.usage_metadata["input_tokens"] > 0
+    assert full.usage_metadata["output_tokens"] > 0
+    assert full.usage_metadata["total_tokens"] > 0
 
 
 @pytest.mark.scheduled
-async def test_bedrock_astream(chat: ChatBedrock) -> None:
+@pytest.mark.parametrize(
+    "model_id",
+    [
+        "anthropic.claude-3-sonnet-20240229-v1:0",
+        "mistral.mistral-7b-instruct-v0:2",
+    ],
+)
+async def test_bedrock_astream(model_id: str) -> None:
     """Test streaming tokens from OpenAI."""
-
+    chat = ChatBedrock(
+        model_id=model_id,
+        model_kwargs={"temperature": 0},
+    )  # type: ignore[call-arg]
+    full = None
     async for token in chat.astream("I'm Pickle Rick"):
+        full = token if full is None else full + token  # type: ignore[operator]
         assert isinstance(token.content, str)
+    assert isinstance(full, AIMessageChunk)
+    assert isinstance(full.content, str)
+    assert full.usage_metadata is not None
+    assert full.usage_metadata["input_tokens"] > 0
+    assert full.usage_metadata["output_tokens"] > 0
+    assert full.usage_metadata["total_tokens"] > 0
 
 
 @pytest.mark.scheduled

libs/aws/tests/integration_tests/chat_models/test_standard.py

@@ -22,10 +22,6 @@ def chat_model_params(self) -> dict:
     def standard_chat_model_params(self) -> dict:
         return {}
 
-    @pytest.mark.xfail(reason="Not implemented.")
-    def test_usage_metadata(self, model: BaseChatModel) -> None:
-        super().test_usage_metadata(model)
-
     @pytest.mark.xfail(reason="Not implemented.")
     def test_stop_sequence(self, model: BaseChatModel) -> None:
         super().test_stop_sequence(model)