fix(fireworks): parse standard blocks in input (#33426)

libs/partners/fireworks/langchain_fireworks/_compat.py

@@ -0,0 +1,26 @@
+"""Converts between AIMessage output formats, governed by `output_version`."""
+
+from __future__ import annotations
+
+from langchain_core.messages import AIMessage
+
+
+def _convert_from_v1_to_chat_completions(message: AIMessage) -> AIMessage:
+    """Convert a v1 message to the Chat Completions format."""
+    if isinstance(message.content, list):
+        new_content: list = []
+        for block in message.content:
+            if isinstance(block, dict):
+                block_type = block.get("type")
+                if block_type == "text":
+                    # Strip annotations
+                    new_content.append({"type": "text", "text": block["text"]})
+                elif block_type in ("reasoning", "tool_call"):
+                    pass
+                else:
+                    new_content.append(block)
+            else:
+                new_content.append(block)
+        return message.model_copy(update={"content": new_content})
+
+    return message

libs/partners/fireworks/langchain_fireworks/chat_models.py

@@ -78,6 +78,8 @@ from pydantic import (
 )
 from typing_extensions import Self
 
+from langchain_fireworks._compat import _convert_from_v1_to_chat_completions
+
 logger = logging.getLogger(__name__)
 
 
@@ -152,6 +154,9 @@ def _convert_message_to_dict(message: BaseMessage) -> dict:
     elif isinstance(message, HumanMessage):
         message_dict = {"role": "user", "content": message.content}
     elif isinstance(message, AIMessage):
+        # Translate v1 content
+        if message.response_metadata.get("output_version") == "v1":
+            message = _convert_from_v1_to_chat_completions(message)
         message_dict = {"role": "assistant", "content": message.content}
         if "function_call" in message.additional_kwargs:
             message_dict["function_call"] = message.additional_kwargs["function_call"]
@@ -238,6 +243,7 @@ def _convert_chunk_to_message_chunk(
             additional_kwargs=additional_kwargs,
             tool_call_chunks=tool_call_chunks,
             usage_metadata=usage_metadata,  # type: ignore[arg-type]
+            response_metadata={"model_provider": "fireworks"},
         )
     if role == "system" or default_class == SystemMessageChunk:
         return SystemMessageChunk(content=content)
@@ -515,6 +521,8 @@ class ChatFireworks(BaseChatModel):
                     "output_tokens": token_usage.get("completion_tokens", 0),
                     "total_tokens": token_usage.get("total_tokens", 0),
                 }
+                message.response_metadata["model_provider"] = "fireworks"
+                message.response_metadata["model_name"] = self.model_name
             generation_info = {"finish_reason": res.get("finish_reason")}
             if "logprobs" in res:
                 generation_info["logprobs"] = res["logprobs"]
@@ -525,7 +533,6 @@ class ChatFireworks(BaseChatModel):
             generations.append(gen)
         llm_output = {
             "token_usage": token_usage,
-            "model_name": self.model_name,
             "system_fingerprint": response.get("system_fingerprint", ""),
         }
         return ChatResult(generations=generations, llm_output=llm_output)

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

@@ -57,7 +57,9 @@ async def test_astream() -> None:
         full = token if full is None else full + token
         if token.usage_metadata is not None:
             chunks_with_token_counts += 1
-        if token.response_metadata:
+        if token.response_metadata and not set(token.response_metadata.keys()).issubset(
+            {"model_provider", "output_version"}
+        ):
             chunks_with_response_metadata += 1
     if chunks_with_token_counts != 1 or chunks_with_response_metadata != 1:
         msg = (
@@ -76,6 +78,7 @@ async def test_astream() -> None:
     )
     assert isinstance(full.response_metadata["model_name"], str)
     assert full.response_metadata["model_name"]
+    assert full.response_metadata["model_provider"] == "fireworks"
 
 
 async def test_abatch_tags() -> None:
@@ -103,6 +106,7 @@ def test_invoke() -> None:
 
     result = llm.invoke("I'm Pickle Rick", config={"tags": ["foo"]})
     assert isinstance(result.content, str)
+    assert result.response_metadata["model_provider"] == "fireworks"
 
 
 def _get_joke_class(

libs/standard-tests/langchain_tests/integration_tests/chat_models.py

@@ -1498,8 +1498,8 @@ class ChatModelIntegrationTests(ChatModelTests):
         prompt = ChatPromptTemplate.from_messages(
             [("human", "Hello. Please respond in the style of {answer_style}.")]
         )
-        model = GenericFakeChatModel(messages=iter(["hello matey"]))
+        llm = GenericFakeChatModel(messages=iter(["hello matey"]))
-        chain = prompt | model | StrOutputParser()
+        chain = prompt | llm | StrOutputParser()
         tool_ = chain.as_tool(
             name="greeting_generator",
             description="Generate a greeting in a particular style of speaking.",